A method and apparatus for stream directing recommendations for molecular refining

Abstract

The application discloses a kind of stream guidance recommendation method and device for molecular oil refining.The recommendation method determines a plurality of candidate downstream devices in response to the first selection instruction of user to upstream device, and the recommended score of each candidate downstream device, and these information is pushed to user;In response to the second selection instruction of downstream device selected by user, generate stream connection and show to user.The application solves the shortcomings of large operation amount, easy to make mistake operation and high time cost consumption caused in the process of manual stream connection operation to oil refining single device.

Description

Technical Field

[0001] This invention relates to the field of petroleum refining and chemical process simulation technology, and in particular to a logistics guidance recommendation method and apparatus for molecular petroleum refining. Background Technology

[0002] Molecular refining simulation can understand the petroleum processing process at the molecular level and accurately predict the properties of petroleum products. With the development and application of molecular refining technology, many related simulation tools and software systems have emerged in recent years.

[0003] Existing molecular refining simulation methods have resulted in various simulation software programs that provide interactive user interfaces, allowing users to build full-process models through graphical management. During the model building process, the software is often used with drag-and-drop interfaces to connect different refining units via material flow lines. However, due to the high complexity of petroleum refining processes, a single full-process model may involve dozens to hundreds of different processing units. Users of the simulation software must connect material flow lines between different units, resulting in a large amount of user interaction, a high probability of errors, and high time costs for building the full-process model. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide a logistics-guided recommendation method and apparatus for molecular refining.

[0005] In a first aspect, embodiments of the present invention provide a logistics-oriented recommendation method for molecular refining, comprising:

[0006] In response to the user's first selection instruction for the upstream unit in the created molecular refining process model, multiple candidate downstream units with logistics-oriented relationships with the upstream unit are identified, and a recommendation score for each candidate downstream unit is determined based on the unit correlation degree and fraction correlation degree of each candidate downstream unit; the unit correlation degree is used to characterize the probability that the candidate downstream unit has a logistics connection with the upstream unit, and the fraction correlation degree is used to characterize the similarity between the input fraction of the candidate downstream unit and the cut fraction output by the upstream unit;

[0007] Push information about multiple candidate downstream devices and their corresponding recommendation scores to the user;

[0008] In response to a second selection instruction from the user to select a downstream device from the plurality of candidate downstream devices, a logistics connection is generated between the upstream device and the selected downstream device and displayed to the user.

[0009] In one embodiment, in response to a user's first selection instruction for an upstream unit in a created molecular refining process model, multiple candidate downstream units with a logistics-oriented relationship to the upstream unit are identified, and a recommendation score for each candidate downstream unit is determined based on the unit correlation degree and fraction correlation degree of each candidate downstream unit; the unit correlation degree characterizes the probability that a candidate downstream unit has a logistics connection with an upstream unit, and the fraction correlation degree characterizes the similarity between the input fraction of the candidate downstream unit and the cut fraction output by the upstream unit, including:

[0010] The first selection instruction includes a user's selection instruction for a pre-configured upstream device, and a user's selection instruction for a cut fraction of the upstream device;

[0011] In response to the user's command to select the pre-configured upstream unit, the system determines multiple candidate downstream units corresponding to the upstream unit and the device correlation degree of each candidate downstream unit based on the pre-stored molecular refining process model database, and displays the multiple candidate downstream units and the device correlation degree of each candidate downstream unit.

[0012] In response to the user's setting command for the fraction cutting temperature of the upstream device, the system determines and displays multiple cut fractions output by the upstream device.

[0013] In response to a user's instruction to select a fraction from the upstream device, the fraction correlation of the plurality of candidate downstream devices is determined. Based on the device correlation and fraction correlation of the candidate downstream devices, a recommendation score is determined for each candidate downstream device, and a recommended list of candidate downstream devices with recommendation scores is generated and displayed.

[0014] In one embodiment, the molecular refining process model database includes: multiple historical molecular refining process models established based on historical data;

[0015] The molecular refining process history model includes: multiple refining units and the logistics connection information and fraction information of each refining unit.

[0016] In one embodiment, determining the device correlation degree of a plurality of candidate downstream devices corresponding to the upstream device and each candidate downstream device includes:

[0017] In the full-process model database, a single refining unit that is the same as the upstream unit is searched and identified. Multiple downstream refining units that have a material flow connection with the single unit are identified as multiple candidate downstream units.

[0018] The number of times a candidate downstream device corresponding to the upstream device appears in the model database is determined as the device correlation degree of the candidate downstream device.

[0019] In one embodiment, in response to a user's instruction to select a cut fraction from the upstream unit, the fraction correlation of the plurality of candidate downstream units is determined. Based on the unit correlation and fraction correlation of the candidate downstream units, a recommendation score is determined for each candidate downstream unit. A recommended list of candidate downstream units with recommendation scores is generated and displayed, including:

[0020] Based on the full-process model database, determine the input fractions of the candidate downstream units in different molecular refining full-process historical models;

[0021] Determine the fractional similarity between each input fraction of the candidate downstream device and the cut fraction;

[0022] The sum of the fraction similarities of the same candidate downstream device in different full-process historical models is determined as the fraction correlation degree of the same candidate downstream device.

[0023] The device correlation degree and the fraction correlation degree of each candidate downstream device are weighted and calculated to obtain the recommendation score of each candidate downstream device.

[0024] In one embodiment, the weighted calculation is performed using the following formula:

[0025] Score=P1×Count1+P2×Count2;

[0026] Wherein: Score is the recommended score, Count1 is the device correlation degree, Count2 is the fraction correlation degree; P1 and P2 are weights, P1+P2=1.

[0027] Secondly, embodiments of the present invention provide a logistics guidance and recommendation device for molecular refining, comprising:

[0028] The unit correlation and fraction correlation determination module is used to respond to the user's first selection command for upstream units in the created molecular refining process model, determine multiple candidate downstream units that have a logistics orientation relationship with the upstream units, and determine the recommendation score for each candidate downstream unit based on the unit correlation and fraction correlation of each candidate downstream unit; the unit correlation is used to characterize the probability that the candidate downstream unit has a logistics connection with the upstream unit, and the fraction correlation is used to characterize the similarity between the input fraction of the candidate downstream unit and the cut fraction output by the upstream unit;

[0029] The information push module is used to push information about multiple candidate downstream devices and their corresponding recommendation scores to the user;

[0030] The logistics connection module is used to generate a logistics connection between the upstream device and the selected downstream device and display it to the user in response to a second selection instruction from the user to select a downstream device from the plurality of candidate downstream devices.

[0031] Thirdly, embodiments of the present invention provide a computing device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned logistics-oriented recommendation method for molecular refining.

[0032] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned logistics-oriented recommendation method for molecular refining.

[0033] Fifthly, embodiments of the present invention provide a computer program product, comprising: a computer program that, when executed by a processor, implements the aforementioned logistics-oriented recommendation method for molecular refining.

[0034] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0035] This invention provides a logistics-oriented recommendation method for molecular refining. During the construction of a complete molecular refining process model, in response to a user's first selection command for an upstream unit, multiple candidate downstream units are identified, along with a recommendation score for each candidate downstream unit. This result information is then pushed to the user. Based on the pushed information, the user issues a second selection command for the downstream unit, generating and displaying a logistics connection between the upstream unit and the selected downstream unit. This invention addresses the drawbacks of manual logistics connection operations for individual refining units, including high workload, susceptibility to errors, and high time costs.

[0036] Furthermore, embodiments of the present invention provide a recommended list of multiple candidate downstream devices with recommendation scores, which is readily available to users and improves the efficiency of users in selecting downstream devices.

[0037] Furthermore, in this embodiment of the invention, the device correlation and fraction correlation of multiple candidate downstream devices are obtained based on multiple historical molecular refining process models in a pre-stored molecular refining process model database, as well as a preset algorithm. Since the historical molecular refining process models are built based on existing historical data, they have high reliability, thereby ensuring the reliability of the device correlation and fraction correlation.

[0038] Furthermore, considering both unit correlation and fraction correlation, the recommendation scores of multiple candidate downstream units are combined. Unit correlation reflects the basic facts of multiple candidate downstream units that have material flow connections with upstream units, while fraction correlation reflects the actual refining process between upstream units and multiple candidate downstream units. In this way, the final recommendation score can accurately reflect the recommendation results of downstream units.

[0039] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0041] Figure 1 This is a flowchart of a logistics-oriented recommendation method for molecular refining in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the entire process simulation of molecular refining in an embodiment of the present invention;

[0043] Figure 3 These are the various functional groups in the molecular structure-guided lumped SOL representation in the embodiments of this invention;

[0044] Figure 4 This is a schematic diagram of the material flow in the molecular refining process model in this embodiment of the invention;

[0045] Figure 5 This is a flowchart illustrating the recommended technical implementation of the molecular refining logistics connection in this invention.

[0046] Figure 6 This refers to the fraction cutting results of the upstream device in an embodiment of the present invention;

[0047] Figure 7 This refers to the recommendation results of multiple candidate downstream devices in the embodiments of the present invention;

[0048] Figure 8 This is a schematic diagram illustrating the results of generating logistics connections in an embodiment of the present invention;

[0049] Figure 9 This is a schematic diagram of the logistics guidance recommendation device for molecular oil refining in an embodiment of the present invention. Detailed Implementation

[0050] This invention provides a logistics-guided recommendation method and apparatus for molecular refining. While exemplary embodiments of this disclosure are shown in the accompanying drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this disclosure and to fully convey the scope of this disclosure to those skilled in the art.

[0051] This invention provides a logistics-oriented recommendation method for molecular refining, referring to... Figure 1 As shown, the method includes the following steps:

[0052] S11. In response to the user's first selection instruction for the upstream unit in the created molecular refining process model, determine multiple candidate downstream units that have a logistics-oriented relationship with the upstream unit, and determine the recommendation score for each candidate downstream unit based on the unit correlation degree and fraction correlation degree of each candidate downstream unit; the unit correlation degree is used to characterize the probability that the candidate downstream unit has a logistics connection with the upstream unit, and the fraction correlation degree is used to characterize the similarity between the input fraction of the candidate downstream unit and the cut fraction output by the upstream unit.

[0053] S12. Push information about multiple candidate downstream devices and their corresponding recommendation scores to the user.

[0054] S13. In response to a second selection instruction from the user to select a downstream device from the plurality of candidate downstream devices, a logistics connection is generated between the upstream device and the selected downstream device and displayed to the user.

[0055] Reference Figure 2 As shown, the typical molecular refining process simulation includes the following steps: the user inputs molecular-level raw material data into the molecular refining process model, the model calculates the process, and the refining product results are output.

[0056] The molecular refining process model is a model that describes the entire refining process of an oil refinery by understanding the input raw materials, refining process and output products at the molecular level. It realizes molecular management of the production process and is mainly composed of multiple refining units and the material flow links between the units.

[0057] Molecular-level raw material data refers to raw material information represented using structure-oriented lumping (SOL). The SOL method represents petroleum molecules based on specific structural features or functional groups, organizing these groups into a vector. The elements of the vector represent different petroleum molecule structures, and the number of elements indicates the quantity of a specific structural group within the molecule. (See reference...) Figure 3 As shown, the definitions of various groups in the SOL notation are as follows:

[0058] A6: A six-carbon aromatic ring, a structural unit that can exist independently;

[0059] A4 and A2: four-carbon and two-carbon aromatic rings, structural increments;

[0060] N6, N5: six-carbon and five-carbon cycloalkanes;

[0061] N4, N3, N2, N1: alicyclic structures with four, three, two, and one carbon atom;

[0062] R: The total number of carbon atoms contained in all alkyl structures attached to the ring structure, or the number of carbon atoms in an aliphatic molecule when no ring structure is present;

[0063] br: The number of branch nodes on a side-chain alkyl group, a straight-chain alkyl group, or an olefin;

[0064] me: The number of methyl groups in an alkyl structure that are directly attached to carbon atoms in an aromatic or aliphatic ring;

[0065] IH: Introduces hydrogen-related structural increments to describe the saturation of molecules (except aromatic molecules);

[0066] AA: A biphenyl bridge structure between any two unstructured incremental rings (A6, N6, or N5);

[0067] NS, NN, NO: Sulfur, nitrogen, and oxygen atoms located in an aliphatic ring or aliphatic chain and bonded to two carbon atoms (substituting for -CH2-);

[0068] RS, RN, RO: An S atom, -NH- group, or O atom is inserted between a carbon atom and a hydrogen atom;

[0069] AN: In aromatic rings, a nitrogen group is used to replace a carbon atom, such as in pyridine and quinoline;

[0070] Ko: Substitutes -CH2- or -CH3 to form a ketone or aldehyde group;

[0071] Ni and V: appear in porphyrin molecules.

[0072] Specifically, in molecular characterization, the SOL method arranges the number of groups in a molecule in the order from A6 to V to generate a 24-dimensional vector array. For example, the SOL formula for hydrogen sulfide (H2S) is: [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0].

[0073] The full-process model calculation is based on SOL structure raw material data and simulates the process of generating reaction products according to reaction rules and reaction rate parameters. Regarding the reaction rules, the simulated refining reaction rate varies with environmental changes; for example, the rate can be calculated using the Arrhenius formula.

[0074] The Arrhenius formula is as follows:

[0075] K = k * exp(-Ea / RT) * P Pk ;

[0076] In the formula: K is the reaction rate parameter for a certain reaction; k is a weight that can be obtained through regression calculation to find the optimal solution; Ea is the apparent activation energy, which is generally based on experimental measurements in actual use, and can be simulated by the carbon number of the SOL structure in simulation calculations; R is the molar gas constant, which can be taken as 8.314; T is the absolute temperature in K, obtained from process data; P is the pressure in MPa, obtained from process data; Pk is a parameter, which can be taken as 0.37. Adjusting the temperature T and pressure P, as well as adjusting the regression of the weight k, can change the reaction rate and affect the output of refined products. In the regression process, the yield and main physical properties of the simulated products are generally calculated and the difference is taken with the actual values. The minimum difference is taken as the regression target, and then a mathematical regression is constructed with k as the parameter to be regressed, and the value of k is iteratively calibrated.

[0077] Figure 2 The right side illustrates the process of establishing a complete molecular refining process model. First, reaction rules and parameters are designed for each individual refining unit. Then, the designed units are deployed into the graphical interface of the simulation software, and material flow lines are used to connect the individual units, ultimately forming the complete molecular refining process model. Simulation software typically provides functions for creating, editing, and managing complete molecular refining process models, used for designing refining units, editing material flow lines, and adjusting reaction rules and parameters.

[0078] In a molecular refining process model, the logistics connections are an essential element, as referenced... Figure 4 In the process of connecting the logistics links between individual units, the links are complex and intertwined, often involving many-to-many relationships. Users need to drag and drop the logistics links on the simulation software's interface to match the units according to the refining process. Since a complete molecular refining process model includes a large number of logistics links, this task is obviously extremely tedious. Moreover, manual operation is prone to causing interference and misjudgment for users, resulting in an unsatisfactory model.

[0079] The logistics connection recommendation method provided in this invention is applied to the operation of connecting various individual devices. In response to a user's first selection instruction for an upstream device in a pre-built model, multiple candidate downstream devices are identified, each with a recommendation score. This recommendation information is then pushed to the user for selection. In response to the user's specific selection of a candidate downstream device based on the recommendation score, a logistics connection is automatically generated between the upstream device and the selected downstream device and displayed to the user. Thus, during the logistics connection operation, a reliable and efficient recommendation mechanism for downstream devices is established for any upstream device, significantly improving the user's operational efficiency and accuracy.

[0080] Step S11 is to determine multiple candidate downstream devices and a recommended score for each candidate downstream device in response to the user's first selection instruction for the upstream device.

[0081] First, based on the logistics information of multiple historical molecular refining process models in the molecular refining process model database, candidate downstream units and the unit correlation degree of each candidate downstream unit are determined.

[0082] In one embodiment, the recommendation score for each candidate downstream device is determined based on the device correlation and fraction correlation of the candidate downstream device. Device correlation characterizes the probability that a candidate downstream device has a material flow connection with an upstream device; fraction correlation characterizes the similarity between the input fraction of the candidate downstream device and the cut fraction output by the upstream device.

[0083] In one embodiment, determining multiple candidate downstream devices for an upstream device, and the device correlation and fraction correlation of each candidate downstream device, can be achieved using the following method:

[0084] The first selection instruction includes a user's selection instruction for a pre-configured upstream unit and a user's selection instruction for a cut fraction output by that upstream unit. In response to the user's selection instruction for the pre-configured upstream unit, based on a pre-stored molecular refining process model database, multiple candidate downstream units corresponding to the upstream unit are determined, along with the unit correlation degree of each candidate downstream unit, and the information on the multiple candidate downstream units and their unit correlation degrees is displayed. In response to the user's setting instruction for the fraction cutting temperature of the upstream unit, multiple cut fractions output by the upstream unit are determined, and the information on the multiple cut fractions is displayed. In response to the user's selection instruction for a cut fraction of the upstream unit, the fraction correlation degree of multiple candidate downstream units is determined. Based on the unit correlation degree and fraction correlation degree of the candidate downstream units, a recommendation score is determined for each candidate downstream unit, and a recommended list of candidate downstream units with recommendation scores is generated and displayed.

[0085] The pre-configuration of upstream units includes reaction rules and reaction rate parameter settings, as well as user-defined fraction cut temperatures, used to separate the mixed products in the upstream units into different cut fractions. For example, in a catalytic cracking unit, the products are divided into different fractions according to the cut temperature after the reaction: dry gas with a boiling point of -300 to -50°C; liquefied petroleum gas (LPG) with a boiling point of -50 to 32°C; naphtha with a boiling point of 32 to 175°C; diesel oil with a boiling point of 175 to 340°C; and oil slurry coke with a boiling point of 340 to 2000°C.

[0086] In one embodiment, the molecular refining process model database stores multiple historical molecular refining process models. These are called historical models because they are built based on historical data. Each historical molecular refining process model includes multiple refining units, as well as the material flow information and fraction information between these units. Each historical molecular refining process model can be viewed as a directed graph.

[0087] In one embodiment, the device correlation degree of a plurality of candidate downstream devices corresponding to an upstream device and each candidate downstream device is determined according to the following method, including:

[0088] In the full-process model database, a single refining unit that is identical to the upstream unit is searched. Multiple downstream refining units that have a logistics connection with the same single unit are identified as multiple candidate downstream units. The number of times the same candidate downstream unit corresponding to the upstream unit appears in the model database is determined as the unit correlation degree of the candidate downstream unit.

[0089] For example, firstly, in the full-process model database, single units identical to the upstream unit selected by the user are retrieved. For instance, if the upstream unit is a catalytic cracking unit, then all catalytic cracking units are searched in the historical molecular refining full-process models in the database. For each found catalytic cracking unit, multiple downstream refining units with material flow connections to it are recorded and identified as multiple candidate downstream units. The number of times each candidate downstream unit appears is counted; this is the unit correlation degree. When determining the number of searches, if two single units are connected multiple times due to multiple fractions in a historical full-process model, only one result is counted. The units can also be sorted by correlation degree to obtain recommended results.

[0090] Next, based on the fraction information of the historical models of the entire refining process for each molecule in the model database, the fraction correlation of candidate downstream units is determined.

[0091] In one embodiment, the recommended score for each candidate downstream device is determined based on the device relevance and fraction relevance of the candidate downstream devices as follows:

[0092] Based on the full-process model database, the input fractions of candidate downstream units in different molecular refining full-process historical models are determined; the fraction similarity between each input fraction of the candidate downstream unit and the cut fraction is determined; the sum of the fraction similarity of the same candidate downstream unit in different full-process historical models is determined as the fraction correlation degree of the same candidate downstream unit; the unit correlation degree and fraction correlation degree of each candidate downstream unit are weighted and calculated to obtain the recommendation score of each candidate downstream unit.

[0093] Based on the aforementioned determination of the correlation between multiple devices, the fraction correlation of each candidate downstream device is determined. First, select the aforementioned multiple candidate downstream devices, or from multiple candidate downstream devices, select several candidate downstream devices that meet the preset correlation requirements based on their device correlation. This embodiment of the invention does not limit this. Then, for any candidate downstream device, search the full-process model database to find its fraction information in the molecular refining full-process historical model, and determine the corresponding input fractions of the candidate downstream device based on its fraction information. Next, compare each input fraction of the candidate downstream device with a cut fraction of the upstream device selected by the user to obtain the fraction similarity. The similarity can be calculated using methods such as Euclidean distance and cosine similarity, which is not limited in this embodiment of the invention. Finally, for a candidate downstream device, add the fraction similarities calculated in different molecular refining full-process historical models to obtain the fraction correlation of the candidate downstream device. For the same candidate downstream device with different input fractions, the fraction correlation should be calculated separately for each input fraction.

[0094] In one embodiment, after determining the device correlation and fraction correlation of multiple candidate downstream devices, a weighted average of the device correlation and fraction correlation of each candidate downstream device is performed to obtain a recommendation score. The formula for the recommendation score is as follows:

[0095] Score=P1×Count1+P2×Count2;

[0096] Where: Score is the recommended score, Count1 is the device correlation degree, Count2 is the fraction correlation degree; P1 and P2 are weights, P1+P2=1.

[0097] Step S12: Push the recommendation information obtained in step S11 to the user.

[0098] Step S13: Based on the device correlation degree and fraction correlation degree of each candidate downstream device, for the second selection instruction of the downstream device selected by the user, generate a logistics connection between the upstream device and the selected downstream device and display it to the user.

[0099] The following is a specific example illustrating the logistics-oriented recommendation method for molecular refining provided by this invention. The main steps are as follows: Figure 5 As shown, the device correlation degree of candidate downstream units is first determined, then the fraction correlation degree of candidate downstream units is determined, and finally, the two are weighted and calculated to obtain the final recommendation result. Specifically, for a user-selected configured upstream unit, assuming it is unit A, the pre-stored molecular refining process model database is scanned, and unit A is found to appear a total of 3 times. Multiple candidate downstream units with material flow connections are recorded, along with the number of times each candidate downstream unit appears. The statistical results are shown in Table 1 below. According to the definition of device correlation degree, the search count in Table 1 represents the device correlation degree corresponding to each candidate downstream unit. Therefore, the result after sorting by device correlation degree is: Unit B > Unit E = Unit F = Unit C > Unit D.

[0100] Table 1

[0101] The user performs fraction cutting on device A according to the set cutting temperature, as per [reference]. Figure 6 As shown, device A cuts into three fractions: the first, second, and third fractions. For the second fraction selected by the user, the input fractions of each candidate downstream device in different historical molecular refining processes are obtained based on the fraction information from the historical molecular refining process models of multiple candidate downstream devices. Then, each input fraction is compared with the second fraction, and their similarity is calculated. The calculated fraction similarity for each candidate downstream device is shown in Table 2, which displays some of the similarity data. The fraction similarity of each candidate downstream device is summed to obtain the fraction correlation score for each candidate downstream device. For example, the fraction correlation score for device B is 0.882 + 0.724 + 0.694 = 2.3, and the fraction correlation score for device E is 0.557 + 0.391 = 0.948.

[0102] Table 2

[0103]

[0104] For each candidate downstream unit, a weighted calculation is performed. The weight for the pre-set unit correlation is P1 = 0.4, and the weight for the pre-set fraction correlation is P2 = 0.6. Therefore, the recommended score for unit B is 3 × 0.4 + 2.3 × 0.6 = 2.58, and the recommended score for unit E is 2 × 0.4 + 0.948 × 0.6 = 1.3688. (Refer to...) Figure 7 As shown, multiple candidate downstream devices with recommendation scores will be obtained, sorted according to the recommendation scores, and then pushed to the user.

[0105] Reference Figure 8 Device B has the highest recommendation score. Based on the push information, the user selects a downstream device, and a logistics connection is generated and displayed between the upstream device A and the selected downstream device B.

[0106] Based on the same inventive concept, this embodiment of the invention also provides a logistics guidance recommendation device for molecular refining. Since the principle of solving the problem by these devices is similar to that of the aforementioned logistics guidance recommendation method for molecular refining, the implementation of this device can refer to the implementation of the aforementioned method, and the repeated parts will not be described again.

[0107] This invention provides a logistics guidance and recommendation device for molecular refining, referring to... Figure 9 As shown, it includes:

[0108] The unit correlation and fraction correlation determination module 91 is used to respond to the user's first selection instruction for the upstream unit in the created molecular refining process model, determine multiple candidate downstream units that have a logistics orientation relationship with the upstream unit, and determine the recommendation score of each candidate downstream unit based on the unit correlation and fraction correlation of each candidate downstream unit; the unit correlation is used to characterize the probability that the candidate downstream unit has a logistics connection with the upstream unit, and the fraction correlation is used to characterize the similarity between the input fraction of the candidate downstream unit and the cut fraction output by the upstream unit;

[0109] The information push module 92 is used to push information about multiple candidate downstream devices and their corresponding recommendation scores to the user;

[0110] The logistics connection module 93 is used to generate a logistics connection between the upstream device and the selected downstream device and display it to the user in response to a second selection instruction from the user to select a downstream device from the plurality of candidate downstream devices.

[0111] An embodiment of the present invention provides a computing device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the aforementioned logistics-oriented recommendation method for molecular refining.

[0112] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned logistics-oriented recommendation method for molecular refining.

[0113] The present invention provides a computer program product, characterized in that the computer program product includes a computer program, which, when executed by a processor, implements the aforementioned logistics-oriented recommendation method for molecular refining.

[0114] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0115] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0118] Obviously, those skilled in the art can make various modifications to this invention without departing from its spirit and scope. Therefore, if these modifications fall within the scope of the claims and their equivalents, this invention is also intended to include these modifications.

Claims

1. A logistics-oriented recommendation method for molecular refining, characterized in that, include: In response to the user's first selection instruction for the upstream unit in the created molecular refining process model, multiple candidate downstream units with logistics-oriented relationships with the upstream unit are identified, and a recommendation score for each candidate downstream unit is determined based on the unit correlation degree and fraction correlation degree of each candidate downstream unit. The device correlation degree is used to characterize the probability that a candidate downstream device and an upstream device have a material flow connection, and the fraction correlation degree is used to characterize the similarity between the input fraction of a candidate downstream device and the cut fraction output by the upstream device. Push information about multiple candidate downstream devices and their corresponding recommendation scores to the user; In response to a second selection instruction from the user to select a downstream device from the plurality of candidate downstream devices, a logistics connection is generated between the upstream device and the selected downstream device and displayed to the user.

2. The recommended method as described in claim 1, characterized in that, In response to the user's first selection instruction for the upstream unit in the created molecular refining process model, the system identifies multiple candidate downstream units that have a logistics-oriented relationship with the upstream unit, and determines the recommendation score for each candidate downstream unit based on the unit correlation degree and fraction correlation degree of each candidate downstream unit. The device correlation degree is used to characterize the probability that a candidate downstream device and an upstream device have a material flow connection, and the fraction correlation degree is used to characterize the similarity between the input fraction of a candidate downstream device and the cut fraction output by the upstream device, including: The first selection instruction includes a user's selection instruction for a pre-configured upstream device, and a user's selection instruction for a cut fraction of the upstream device; In response to the user's command to select the pre-configured upstream unit, the system determines multiple candidate downstream units corresponding to the upstream unit and the device correlation degree of each candidate downstream unit based on the pre-stored molecular refining process model database, and displays the multiple candidate downstream units and the device correlation degree of each candidate downstream unit. In response to the user's setting command for the fraction cutting temperature of the upstream device, the system determines and displays multiple cut fractions output by the upstream device. In response to a user's instruction to select a fraction from the upstream device, the fraction correlation of the plurality of candidate downstream devices is determined. Based on the device correlation and fraction correlation of the candidate downstream devices, a recommendation score is determined for each candidate downstream device, and a recommended list of candidate downstream devices with recommendation scores is generated and displayed.

3. The recommended method as described in claim 2, characterized in that, The molecular refining process model database includes: multiple historical molecular refining process models established based on historical data; The molecular refining process history model includes: multiple refining units and the logistics connection information and fraction information of each refining unit.

4. The recommended method as described in claim 3, characterized in that, Determining the device correlation degree of multiple candidate downstream devices corresponding to the upstream device and each candidate downstream device, including: In the full-process model database, a single refining unit that is the same as the upstream unit is searched and identified. Multiple downstream refining units that have a material flow connection with the single unit are identified as multiple candidate downstream units. The number of times a candidate downstream device corresponding to the upstream device appears in the model database is determined as the device correlation degree of the candidate downstream device.

5. The recommended method as described in claim 3, characterized in that, In response to a user's command to select a fraction from the upstream unit, the fraction correlation of the plurality of candidate downstream units is determined. Based on the unit correlation and fraction correlation of the candidate downstream units, a recommendation score is determined for each candidate downstream unit. A recommended list of candidate downstream units with recommendation scores is generated and displayed, including: Based on the full-process model database, determine the input fractions of the candidate downstream units in different molecular refining full-process historical models; Determine the fractional similarity between each input fraction of the candidate downstream device and the cut fraction; The sum of the fraction similarities of the same candidate downstream device in different full-process historical models is determined as the fraction correlation degree of the same candidate downstream device. The device correlation degree and the fraction correlation degree of each candidate downstream device are weighted and calculated to obtain the recommendation score of each candidate downstream device.

6. The recommended method as described in claim 5, characterized in that, The weighted calculation uses the following formula: Score=P1×Count1+P2×Count2; Wherein: Score is the recommended score, Count1 is the device correlation degree, Count2 is the fraction correlation degree; P1 and P2 are weights, P1+P2=1.

7. A logistics guidance and recommendation device for molecular refining, characterized in that, include: The unit correlation and fraction correlation determination module is used to respond to the user's first selection instruction for the upstream unit in the created molecular refining process model, determine multiple candidate downstream units that have a logistics orientation relationship with the upstream unit, and determine the recommended score of each candidate downstream unit based on the unit correlation and fraction correlation of each candidate downstream unit. The device correlation degree is used to characterize the probability that a candidate downstream device and an upstream device have a material flow connection, and the fraction correlation degree is used to characterize the similarity between the input fraction of a candidate downstream device and the cut fraction output by the upstream device. The information push module is used to push information about multiple candidate downstream devices and their corresponding recommendation scores to the user; The logistics connection module is used to generate a logistics connection between the upstream device and the selected downstream device and display it to the user in response to a second selection instruction from the user to select a downstream device from the plurality of candidate downstream devices.

8. A computing device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the logistics-guided recommendation method for molecular refining as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the logistics-guided recommendation method for molecular refining as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the logistics-guided recommendation method for molecular refining as described in any one of claims 1-6.

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