A method and system for obtaining a target oil product review data

Abstract

The application provides a kind of target oil product detailed evaluation data acquisition method and system, method as follows: according to the simple review data of target oil product, determine the oil product category to which the target oil product belongs, find whether there is an oil sample consistent with the target oil product in the oil product database of the oil product category;If not, find several oil sample similar to the target oil product in the oil product database of the oil product category to which the target oil product belongs;Determine the mixing ratio of several oil sample, and mix several oil sample into blending oil product according to the mixing ratio;Determine the molecular composition data and macroscopic physical property data of blending oil product based on the mixing ratio and the molecular composition data and macroscopic physical property data of several oil sample respectively;The molecular composition data and macroscopic physical property data of blending oil product are used as the detailed evaluation data of target oil product.The method of the application can quickly and accurately obtain the detailed evaluation data of target oil product according to the simple review data of target oil product, and provide technical support for subsequent production process.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of oil data processing, and particularly relates to a method and system for obtaining detailed evaluation data of a target oil product. BACKGROUND

[0002] In the era of big data, data plays a very important role in various industries, and also plays a very important role in the field of oil processing. In the precise control process of oil processing, related simulation is often needed according to the detailed evaluation data of the oil product to optimize the operating conditions of various oil product products. However, the difficulty often faced is that only the summary data of the oil product is available. As a result, due to the lack of data, errors occur in the simulation process, so that the product cannot meet the requirements.

[0003] To solve the above-mentioned difficulties, it is necessary to study how to obtain the detailed evaluation data of the oil product according to the summary data of the oil product. However, the existing data processing methods rarely have specific research on the data of the oil product. Therefore, it is urgent to provide a method for obtaining detailed evaluation data of a target oil product to overcome the above-mentioned difficulties. SUMMARY

[0004] In view of the above problems, one of the purposes of the present application is to provide a method for obtaining detailed evaluation data of a target oil product.

[0005] In order to achieve the above-mentioned purposes, the present application provides the following technical solutions:

[0006] A method for obtaining detailed evaluation data of a target oil product, comprising the following steps:

[0007] determining the oil product category to which the target oil product belongs according to the summary data of the target oil product, wherein the summary data of the target oil product includes macroscopic physical data of the target oil product;

[0008] checking whether there is an oil sample consistent with the target oil product in the oil product database of the oil product category to which the target oil product belongs;

[0009] if no oil sample consistent with the target oil product is found in the oil product database, then checking for a plurality of oil samples similar to the target oil product in the oil product database of the oil product category to which the target oil product belongs;

[0010] determining the blending ratio of the plurality of oil samples by using a mixed integer nonlinear programming method, and mixing the plurality of oil samples into a blended oil product according to the blending ratio;

[0011] determining the molecular composition data and the macroscopic physical data of the blended oil product based on the blending ratio and the molecular composition data and the macroscopic physical data of the plurality of oil samples respectively;

[0012] The molecular composition data and macroscopic physical property data of the blended oil product are taken as detailed evaluation data of the target oil product, wherein the macroscopic physical property data in the detailed evaluation data of the target oil product is more than that in the brief evaluation data of the target oil product.

[0013] Preferably, the method further comprises the following steps:

[0014] If an oil product sample consistent with the target oil product is found in the oil product database, the molecular composition data and macroscopic physical property data of the oil product sample are taken as detailed evaluation data of the target oil product.

[0015] Preferably, the macroscopic physical property data in the detailed evaluation data is any one or more of boiling point, density, octane number, aromatic hydrocarbon, olefin, benzene, flash point, refractive index, freezing point, cloud point, pour point, aniline point, freezing point, viscosity index, viscosity, API degree and wax content.

[0016] Preferably, the step of determining the molecular composition data and macroscopic physical property data of the blended oil product based on the blending ratio and the molecular composition data and macroscopic physical property data of the plurality of oil product samples specifically comprises:

[0017] The molecular composition data of the blended oil product is determined based on the blending ratio and the molecular composition data of the plurality of oil product samples;

[0018] For a linear macroscopic physical property, the data of the linear macroscopic physical property of the plurality of oil product samples is weighted and summed according to the blending ratio to obtain the data of the linear macroscopic physical property of the blended oil product;

[0019] For a nonlinear macroscopic physical property, the data of the nonlinear macroscopic physical property of the blended oil product is determined according to the molecular composition data of the blended oil product and a corresponding physical property calculation model.

[0020] To solve the above problems, a second object of the present application is to provide a target oil product detailed evaluation data acquisition system.

[0021] To achieve the above object, the present application provides the following technical scheme:

[0022] A target oil product detailed evaluation data acquisition system comprises:

[0023] A first analysis module is configured to determine the oil product category to which the target oil product belongs according to the brief evaluation data of the target oil product, wherein the brief evaluation data of the target oil product comprises macroscopic physical property data of the target oil product;

[0024] A first search module is configured to search an oil product database of the oil product category to which the target oil product belongs to find whether there is an oil product sample consistent with the target oil product;

[0025] The second searching module is configured to search for a plurality of oil sample products similar to the target oil product in the oil product database of the oil product category to which the target oil product belongs if the oil sample product consistent with the target oil product is not found in the oil product database.

[0026] The blending module is configured to determine a blending ratio of the plurality of oil sample products by using a mixed integer nonlinear programming method, and to blend the plurality of oil sample products into a blended oil product according to the blending ratio.

[0027] The second analyzing module is configured to determine molecular composition data and macroscopic physical property data of the blended oil product based on the blending ratio and the molecular composition data and the macroscopic physical property data of the plurality of oil sample products.

[0028] The first output module is configured to take the molecular composition data and the macroscopic physical property data of the blended oil product as detailed evaluation data of the target oil product, wherein the macroscopic physical property data in the detailed evaluation data of the target oil product is more than that in the brief evaluation data of the target oil product.

[0029] Preferably, the system further comprises a second output module configured to take the molecular composition data and the macroscopic physical property data of the oil sample product as detailed evaluation data of the target oil product if the oil sample product consistent with the target oil product is found in the oil product database.

[0030] Preferably, in the first output module, the macroscopic physical property data in the detailed evaluation data is any one or more of boiling point, density, octane number, aromatic hydrocarbon, olefin, benzene, flash point, refractive index, freezing point, cloud point, pour point, aniline point, freezing point, viscosity index, viscosity, API degree and wax content.

[0031] Preferably, the second analyzing module comprises:

[0032] The determining unit is configured to determine the molecular composition data of the blended oil product based on the blending ratio and the molecular composition data of the plurality of oil sample products.

[0033] The first calculating unit is configured to, for a linear macroscopic physical property, perform weighted summation on the data of the linear macroscopic physical property of the plurality of oil sample products according to the blending ratio, to obtain the data of the linear macroscopic physical property of the blended oil product.

[0034] The second calculating unit is configured to, for a nonlinear macroscopic physical property, determine the data of the nonlinear macroscopic physical property of the blended oil product according to the molecular composition data of the blended oil product and a corresponding physical property calculation model.

[0035] To solve the above problems, a third object of the present application is to provide an electronic device.

[0036] In order to achieve the above object, the present application provides the following technical solutions:

[0037] An electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus.

[0038] The memory is used for storing a computer program.

[0039] The processor is used for executing the program stored on the memory, and realizes the target oil product detailed evaluation data acquisition method.

[0040] Preferably, the memory is a disk memory, and the processor is one of a central processing unit, a network processor, a digital signal processor and a field programmable gate array.

[0041] In view of the above problems, a fourth object of the present application is to provide a computer readable storage medium.

[0042] In order to achieve the above object, the present application provides the following technical solutions:

[0043] A computer readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize the target oil product detailed evaluation data acquisition method.

[0044] The present application has the following beneficial effects:

[0045] 1. The method of the present application can quickly and accurately acquire the detailed evaluation data of the target oil product according to the simple evaluation data of the target oil product, and provides technical support for subsequent production processes.

[0046] 2. In the present application, linear macroscopic properties and nonlinear macroscopic properties are processed by two different methods, so that the method of the present application can accurately determine the molecular composition data and macroscopic property data of the blended oil product, and avoid economic losses caused by incorrect molecular composition in subsequent process flow.

[0047] Other features and advantages of the present application will be described in the following description, and some will become apparent from the description, or will be understood from the practice of the present application. The objects and other advantages of the present application can be realized and obtained by the structures indicated in the specification, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to make the technical solutions of the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0049] Figure 1 The working flow chart of the target oil product detailed evaluation data acquisition method of the embodiment one of the present application is shown;

[0050] Figure 2 The working flow chart of the target oil product detailed evaluation data acquisition method of the embodiment two of the present application is shown;

[0051] Figure 3 The schematic diagram of the target oil product detailed evaluation data acquisition system of the embodiment three of the present application is shown;

[0052] Figure 4 The schematic diagram of the second analysis module of the embodiment three of the present application is shown;

[0053] Figure 5 The schematic diagram of the electronic device of the embodiment four of the present application is shown. DETAILED DESCRIPTION

[0054] In order to make the technical solutions of the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on the embodiments in the present application.

[0055] The target oil product detailed evaluation data acquisition method, system, device and storage medium of the present application will be explained in detail in combination with specific embodiments.

[0056] Embodiment one

[0057] As shown in the figure, the present application aims to provide a target oil product detailed evaluation data acquisition method, which mainly includes the following steps: Figure 1

[0058] S10, determining the oil product category to which the target oil product belongs according to the brief evaluation data of the target oil product, wherein the brief evaluation data of the target oil product includes the macroscopic physical property data of the target oil product;

[0059] S20, searching the oil product database of the oil product category to which the target oil product belongs to find whether there is an oil product sample consistent with the target oil product; ​

[0060] S30, if no oil sample consistent with the target oil product is found in the oil product database, searching for several oil samples similar to the target oil product in the oil product database of the oil product category to which the target oil product belongs;

[0061] S40, determining the blending ratio of the several oil samples by using a mixed integer nonlinear programming method, and mixing the several oil samples into a blending oil product according to the blending ratio;

[0062] S50, determining the molecular composition data and macroscopic physical property data of the blending oil product based on the blending ratio and the molecular composition data and macroscopic physical property data of the several oil samples respectively;

[0063] S60, taking the molecular composition data and macroscopic physical property data of the blending oil product as the detailed evaluation data of the target oil product, wherein the macroscopic physical property data in the detailed evaluation data of the target oil product is more than that in the simple evaluation data of the target oil product.

[0064] Embodiment Two

[0065] Figure 2 is a workflow diagram of the method for obtaining the detailed evaluation data of the target oil product of Embodiment Two of the present application. As shown in the figure, the method for obtaining the detailed evaluation data of the target oil product of the present embodiment mainly includes the following steps: Figure 2

[0066] S10, determining the oil product category to which the target oil product belongs according to the simple evaluation data of the target oil product, wherein the simple evaluation data of the target oil product includes part of the macroscopic physical property data of the target oil product.

[0067] In the present embodiment, the simple evaluation data of the target oil product can be obtained by instrument measurement or by model calculation first; wherein the simple evaluation data of the target oil product can include, for example, macroscopic physical property data such as density, distillation range, sulfur content, octane number, cetane number, etc. In the present application, the simple evaluation data is relative to the detailed evaluation data. Generally, the macroscopic physical property data in the simple evaluation data is less than that in the detailed evaluation data.

[0068] Then, using the simple evaluation data of the target oil product, such as density and / or distillation range data, to perform target oil product collection screening in the pre-established crude oil database, gasoline database, diesel oil database, and wax oil database, to determine which oil product category the target oil product belongs to and the corresponding oil product database.

[0069] For example, using density, initial boiling point and final boiling point, 5% distillation temperature and 95% distillation temperature to determine which of the above oil product databases the target oil product belongs to.

[0070] ​First, the density interval of the minimum and maximum of each database density is determined, the first distillation interval of the minimum and maximum of the 5% distillation temperature, and the second distillation interval of the minimum and maximum of the 95% distillation temperature, then the density of the target oil product is matched with the density interval of each database, if single, select the database, if there is overlap, match the 5% distillation temperature of the target oil product with the first distillation interval of each database, determine whether there is overlap; or match the 95% distillation temperature of the target oil product with the second distillation interval of each database, determine whether there is overlap; if single, select the database, if there is overlap, continue to judge until the oil product database is determined.

[0071] After determining the oil product category to which the target oil product belongs, the following steps are performed:

[0072] S100, find an oil product sample consistent with the target oil product in the oil product database of the oil product category to which the target oil product belongs:

[0073] If an oil product sample consistent with the target oil product (target crude oil) is found in the oil product database, the molecular composition data of the oil product sample is taken as the molecular composition data of the target oil product;

[0074] If an oil product sample consistent with the target oil product is not found in the oil product database, step S200 is performed.

[0075] S200, the step of finding several oil product samples similar to the target oil product in the oil product database of the oil product category to which the target oil product belongs.

[0076] In this embodiment, this step S200 mainly analyzes the physical property similarity of each oil product sample in the oil product database with the target oil product according to the simple evaluation data of the target oil product, then sorts the oil product samples in the oil product database according to the physical property similarity, and finally selects several oil product samples closest to the physical property of the target oil product according to the sorting result.

[0077] For this, in specific application, the step S200 can include the following steps:

[0078] S210, for each oil product sample in the oil product database, calculate the physical property similarity of the oil product sample and the target oil product, wherein the physical property similarity is equal to the vector between the corresponding macroscopic physical property data of the oil product sample and the target oil product; wherein the weight can be determined in advance according to the importance of each macroscopic physical property;

[0079] S220, sort the oil product samples in the oil product database according to the physical property similarity of each oil product sample in the oil product database and the target oil product;

[0080] S230, selecting several oil sample products closest to the target oil product in terms of physical properties according to the ranking result.

[0081] For example, the oil sample products in the oil product database are ranked in order of physical property similarity from high to low, and then several oil sample products with high rankings are selected as the oil sample products closest to the target oil product in terms of physical properties. Here, the number of selected oil sample products is not limited, and is usually set according to the accuracy of the calculation result and the trade-off of time. The penalty function related to the number of oil sample products will prompt the number of selected oil products to be reduced.

[0082] S300, determining the blending ratio of the several oil sample products by using a mixed integer nonlinear programming method.

[0083] In this embodiment, the blending ratio of the several oil sample products is determined by using a mixed integer nonlinear programming method, mainly based on mixed integer nonlinear programming and penalty function to determine the blending ratio of the several oil sample products. The penalty function includes:

[0084] The penalty function of the number of oil product categories is used to limit the number of oil product categories selected from the ranked oil sample products for modeling;

[0085] The penalty function of the minimum blending ratio value is used to limit the minimum blending ratio value in the finally obtained blending ratio.

[0086] S400, mixing the several oil sample products into a blended oil product according to the blending ratio.

[0087] S500, determining the molecular composition data and macroscopic property data of the blended oil product based on the blending ratio and the molecular composition data and macroscopic property data of the several oil sample products.

[0088] In this embodiment, this step S500 can be subdivided into the following steps:

[0089] S510, determining the molecular composition data of the blended oil product based on the blending ratio and the molecular composition data of the several oil sample products;

[0090] S520, for a linear macroscopic property, weighting and summing the data of the linear macroscopic property of the several oil sample products according to the blending ratio to obtain the data of the linear macroscopic property of the blended oil product;

[0091] S530, for a nonlinear macroscopic property, determining the data of the nonlinear macroscopic property of the blended oil product according to the molecular composition data of the blended oil product and the corresponding property calculation model.

[0092] For linear properties, linear macroscopic property data of the blending oil product can be calculated by linear weighted summation of linear macroscopic property data of the several oil samples and the blending ratio.

[0093] For nonlinear properties, nonlinear macroscopic property data of the blending oil product can be calculated according to molecular composition data of the blending oil product and property calculation formula.

[0094] It should be noted that the execution order of steps S520 and S530 is not limited to this in actual application.

[0095] S600, analyze the difference between the macroscopic property data of the blending oil product and the macroscopic property data of the target oil product.

[0096] S700, if the difference does not meet the preset threshold condition, adjust the blending ratio, and return to execute step S400 to re-mix the several oil samples into a blending oil product according to the adjusted blending ratio, to re-analyze the difference between the macroscopic property data of the blending oil product and the macroscopic property data of the target oil product.

[0097] In the embodiment, the difference meeting the preset threshold condition means that the difference between the macroscopic property data of the blending oil product and the macroscopic property data of the target oil product can reach the minimum.

[0098] Therefore, the difference between the macroscopic property data of the blending oil product and the macroscopic property data of the target oil product can be preferably measured by means of a quality evaluation parameter. Specifically, the quality evaluation parameter is equal to the weighted distance between the vectors composed of the corresponding macroscopic property data of the blending oil product and the target oil product.

[0099] In the embodiment, when the value of the quality evaluation parameter reaches the minimum value, it is judged that the difference between the macroscopic property data of the blending oil product and the macroscopic property data of the target oil product reaches the minimum.

[0100] S800, if the difference meets the preset threshold condition, the molecular composition data and the macroscopic property data of the blending oil product are taken as the detailed evaluation data of the target oil product, wherein the macroscopic property data in the detailed evaluation data of the target oil product is more than the macroscopic property data in the brief evaluation data of the target oil product. The macroscopic property data of the detailed evaluation data is any one of boiling point, density, octane number, aromatic hydrocarbon, olefin, benzene, flash point, refractive index, freezing point, cloud point, pour point, aniline point, freezing point, viscosity index, viscosity, API degree and wax content.

[0101] The method of the embodiment can quickly and accurately determine the molecular composition of the oil product and the corresponding more comprehensive macroscopic physical property data (detailed evaluation data) through limited macroscopic physical property data, and can be used as a soft measurement tool for molecular composition and macroscopic physical property. Compared with the traditional analysis method, the detection speed is improved, the detection cost is reduced, and even beneficial guidance is provided for subsequent process operation.

[0102] Embodiment three

[0103] As Figure 3 shown, the embodiment of the present application provides a system for obtaining detailed evaluation data of a target oil product, comprising:

[0104] A first analysis module 1 is configured to determine the oil product category to which the target oil product belongs according to the simple evaluation data of the target oil product, wherein the simple evaluation data of the target oil product includes the macroscopic physical property data of the target oil product.

[0105] A first search module 2 is configured to search for an oil product sample consistent with the target oil product in an oil product database of the oil product category to which the target oil product belongs.

[0106] A second search module 3 is configured to search for a plurality of oil product samples similar to the target oil product in the oil product database of the oil product category to which the target oil product belongs if no oil product sample consistent with the target oil product is found in the oil product database.

[0107] A blending module 4 is configured to determine a blending ratio of the plurality of oil product samples by using a mixed integer nonlinear programming method, and to mix the plurality of oil product samples into a blended oil product according to the blending ratio.

[0108] A second analysis module 5 is configured to determine the molecular composition data and the macroscopic physical property data of the blended oil product based on the blending ratio and the molecular composition data and the macroscopic physical property data of each of the plurality of oil product samples.

[0109] A first output module 6 is configured to output the molecular composition data and the macroscopic physical property data of the blended oil product as the detailed evaluation data of the target oil product, wherein the macroscopic physical property data in the detailed evaluation data of the target oil product is more than the macroscopic physical property data in the simple evaluation data of the target oil product. In the first output module, the macroscopic physical property data of the detailed evaluation data is any one or more of boiling point, density, octane number, aromatic hydrocarbon, olefin, benzene, flash point, refractive index, freezing point, cloud point, pour point, aniline point, freezing point, viscosity index, viscosity, API degree and wax content.

[0110] In a possible implementation, the system further comprises a second output module,

[0111] The second output module is configured to output the molecular composition data and the macroscopic physical property data of the oil sample as the detailed evaluation data of the target oil product if the oil sample consistent with the target oil product is found in the oil database.

[0112] In a possible implementation, as shown in Figure 4 The second analysis module 5 includes:

[0113] The determining unit 501 is configured to determine the molecular composition data of the blended oil product based on the blending ratio and the molecular composition data of the oil samples.

[0114] The first calculating unit 502 is configured to, for a linear macroscopic physical property, weight and sum the data of the linear macroscopic physical property of the oil samples according to the blending ratio, to obtain the data of the linear macroscopic physical property of the blended oil product.

[0115] The second calculating unit 503 is configured to, for a nonlinear macroscopic physical property, determine the data of the nonlinear macroscopic physical property of the blended oil product according to the molecular composition data of the blended oil product and a corresponding physical property calculation model.

[0116] In a possible implementation, the second searching module 3 includes:

[0117] The sorting unit is configured to determine the physical property similarity between each oil sample in the oil database and the target oil product according to the brief evaluation data of the target oil product, and sort the oil samples in the oil database according to the physical property similarity.

[0118] The selecting unit is configured to select a plurality of oil samples closest to the physical property of the target oil product based on the sorting result.

[0119] Embodiment Four

[0120] Based on the same inventive concept, as shown in Figure 5 The electronic device provided by the embodiment of the present application includes a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120 and the memory 1130 complete the communication among each other through the communication bus 1140.

[0121] The memory 1130 is configured to store a computer program.

[0122] The processor 1110 is configured to execute the program stored in the memory 1130, and realize the method for obtaining the detailed evaluation data of the target oil product, which includes the following steps:

[0123] determine an oil variety to which the target oil belongs according to the brief data of the target oil, wherein the brief data of the target oil comprises macroscopic physical data of the target oil;

[0124] search for an oil sample consistent with the target oil in an oil database of the oil variety to which the target oil belongs;

[0125] if no oil sample consistent with the target oil is found in the oil database, search for several oil samples similar to the target oil in the oil database of the oil variety to which the target oil belongs;

[0126] determine a blending ratio of the several oil samples by using a mixed integer nonlinear programming method, and mix the several oil samples into a blending oil according to the blending ratio;

[0127] determine molecular composition data and macroscopic physical data of the blending oil based on the blending ratio and the molecular composition data and the macroscopic physical data of the several oil samples respectively;

[0128] use the molecular composition data and the macroscopic physical data of the blending oil as detailed data of the target oil, wherein the macroscopic physical data in the detailed data of the target oil is more than that in the brief data of the target oil.

[0129] The communication bus 1140 described above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0130] The communication interface 1120 is used for communication between the electronic device and other devices.

[0131] The memory 1130 can include a Random Access Memory (RAM) and can also include a non-volatile memory such as at least one disk memory. Optionally, the memory 1130 can also be at least one storage device located away from the aforementioned processor 1110.

[0132] The processor 1110 described above can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0133] Embodiment five

[0134] Based on the same inventive concept, the embodiments of the present application provide a computer readable storage medium storing one or more programs, which can be executed by one or more processors to implement the steps of the target oil product detailed review data acquisition method in any possible implementation manner described above.

[0135] Optionally, the storage medium can be a non-transitory computer readable storage medium, for example, the non-transitory computer readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0136] In the foregoing embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be a magnetic medium, (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)), etc.

[0137] Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some of the technical features can be replaced by equivalent features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for obtaining detailed evaluation data of a target oil product, characterized in that, Includes the following steps: The target oil product's category is determined based on its brief evaluation data, which includes the target oil product's macroscopic physical property data. Search the oil database for oil samples that match the target oil product; If no oil sample matching the target oil is found in the oil database, then several oil samples similar to the target oil will be searched in the oil database of the oil category to which the target oil belongs. Search the oil database for oil samples that are similar to the target oil, specifically including: Based on the brief evaluation data of the target oil, analyze the similarity of physical properties between each oil sample in the oil database and the target oil; The oil samples in the oil database are sorted according to the physical property similarity. Based on the ranking results, select several oil samples whose physical properties are closest to the target oil. The mixing ratio of the several oil samples is determined by the mixed integer nonlinear programming method, and the several oil samples are mixed into blended oil according to the mixing ratio. The mixing ratio of the several oil samples is determined using a mixed-integer nonlinear programming method, specifically including: The blending ratio of the several oil samples is determined based on mixed-integer nonlinear programming and penalty functions, wherein... The penalty function includes: The penalty function for the number of oil types is used to limit the oil types selected for modeling from the sorted oil samples. The number of classes; The penalty function for the minimum mixing ratio is used to limit the minimum mixing ratio in the final mixing ratio. Based on the mixing ratio and the molecular composition and macroscopic properties of the respective oil samples, the molecular composition and macroscopic properties of the blended oil are determined. The step of determining the molecular composition and macroscopic properties of the blended oil based on the mixing ratio and the molecular composition and macroscopic properties of the respective oil samples specifically includes: The molecular composition data of the blended oil is determined based on the mixing ratio and the molecular composition data of the several oil samples. For a linear macroscopic property, the data of the linear macroscopic property of the several oil samples are weighted and summed according to the mixing ratio to obtain the data of the linear macroscopic property of the blended oil. For a nonlinear macroscopic property, the data of the nonlinear macroscopic property of the blended oil are determined based on the molecular composition data of the blended oil and the corresponding property calculation model; The molecular composition data and macroscopic property data of the blended oil are used as detailed evaluation data of the target oil, wherein the macroscopic property data in the detailed evaluation data of the target oil is more than the macroscopic property data in the brief evaluation data of the target oil.

2. The method for obtaining detailed evaluation data of the target oil product according to claim 1, characterized in that, The method further includes the following steps: If an oil sample matching the target oil is found in the oil database, the molecular composition data and macroscopic physical property data of the oil sample will be used as detailed evaluation data of the target oil.

3. The method for obtaining detailed evaluation data of the target oil product according to claim 1, characterized in that, The macroscopic physical properties of the detailed evaluation data include any combination of boiling point, density, octane number, aromatics, olefins, benzene, flash point, refractive index, freezing point, cloud point, pour point, aniline point, freezing point, viscosity index, viscosity, API degree, and wax content.

4. A system for acquiring detailed evaluation data of a target oil product, characterized in that, include: The first analysis module is used to determine the type of oil to which the target oil belongs based on the brief evaluation data of the target oil, wherein the brief evaluation data of the target oil includes the macroscopic physical property data of the target oil. The first search module is used to search in the oil database of the oil type to which the target oil belongs to whether there is an oil sample that is consistent with the target oil. The second search module is used to search for several oil samples that are similar to the target oil in the oil database of the oil category to which the target oil belongs if no oil sample matching the target oil is found in the oil database. The second search module includes: The sorting unit is used to determine the physical property similarity between each oil sample in the oil database and the target oil based on the brief evaluation data of the target oil, and to sort the oil samples in the oil database according to the physical property similarity. The selection unit is used to select several oil samples whose physical properties are closest to the target oil based on the sorting results. The blending module is used to determine the blending ratio of the plurality of oil samples using a mixed integer nonlinear programming method, and to blend the plurality of oil samples into a blended oil according to the blending ratio. The mixing ratio of the several oil samples is determined using a mixed-integer nonlinear programming method, specifically including: The blending ratio of the several oil samples is determined based on mixed-integer nonlinear programming and penalty functions, wherein... The penalty function includes: The penalty function for the number of oil types is used to limit the number of oil types selected from the sorted oil samples for modeling. The penalty function for the minimum mixing ratio is used to limit the minimum mixing ratio in the final mixing ratio. The second analysis module is used to determine the molecular composition data and macroscopic property data of the blended oil based on the mixing ratio and the molecular composition data and macroscopic property data of the respective oil samples. The second analysis module includes: A determining unit is used to determine the molecular composition data of the blended oil based on the mixing ratio and the molecular composition data of the plurality of oil samples. The first calculation unit is used to perform a weighted summation of the data of the linear macroscopic property of the several oil samples according to the mixing ratio for a linear macroscopic property, so as to obtain the data of the linear macroscopic property of the blended oil. The second calculation unit is used to determine the data of the nonlinear macroscopic property of the blended oil based on the molecular composition data of the blended oil and the corresponding property calculation model for a nonlinear macroscopic property. The first output module is used to use the molecular composition data and macroscopic property data of the blended oil as detailed evaluation data of the target oil, wherein the macroscopic property data in the detailed evaluation data of the target oil is more than the macroscopic property data in the brief evaluation data of the target oil.

5. The system for acquiring detailed evaluation data of target oil products according to claim 4, characterized in that, It also includes a second output module, which is used to use the molecular composition data and macroscopic physical property data of the oil sample as detailed evaluation data of the target oil if an oil sample consistent with the target oil is found in the oil database.

6. The system for acquiring detailed evaluation data of target oil products according to claim 4, characterized in that, In the first output module, the macroscopic physical property data of the detailed evaluation data are any multiple of the following: boiling point, density, octane number, aromatics, olefins, benzene, flash point, refractive index, freezing point, cloud point, pour point, aniline point, freezing point, viscosity index, viscosity, API degree, and wax content.

7. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in a memory, implements the method for obtaining detailed evaluation data of the target oil product as described in any one of claims 1 to 3.

8. The electronic device according to claim 7, characterized in that, The memory is a disk storage device, and the processor is one of a central processing unit, a network processor, a digital signal processor, or a field-programmable gate array.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which can be executed by one or more processors to implement the method for obtaining detailed evaluation data of the target oil product as described in any one of claims 1 to 3.

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