CN113934943A - Formula module collocation recommendation method and device based on FP-growth correlation analysis rule - Google Patents
Formula module collocation recommendation method and device based on FP-growth correlation analysis rule Download PDFInfo
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Abstract
The invention discloses a formula module collocation recommendation method and device based on FP-growth correlation analysis rules, wherein the method comprises the steps of responding to a formula collocation recommendation instruction when receiving the formula collocation recommendation instruction, and acquiring a selected first leaf group module; inquiring matching formula data matched with the first leaf group module from the historical formula data based on an FP-growth algorithm model; and generating recommendation information based on each second leaf group module except the first leaf group module in each matching formula data, and displaying the recommendation information. The invention realizes that the formulator can carry out fast and efficient matching selection of the leaf group, and reduces a large amount of suction processes when the formulator carries out matching selection.
Description
Technical Field
The application relates to the technical field of tobacco formula collocation processing, in particular to a formula module collocation recommendation method and device based on FP-growth correlation analysis rules.
Background
Association analysis refers to looking up frequent patterns, associations, correlations, or causal effects that exist between sets of items or objects in transactional data, relational data, or other information carriers. Correlation analysis is commonly used in consumer shopping basket analysis, bioinformatics, medical diagnostics, web page mining, scientific data analysis, and the like, where the frequency with which an event occurs when another event occurs can be discovered. The purchasing habits of the customer are analyzed, for example, through shopping basket analysis, to discover the association between different items placed in the shopping basket by the customer.
The style of cigarette products is the basis of brand development, and ensuring the style of cigarette products is an important means for maintaining customers. There are many factors influencing the sensory quality of cigarettes, but the cigarette formula is always the most important of the cigarette products, and the stability of the product style can be ensured only by the stability of the formula style. In actual production, more than one formula module with the same fragrance, the same region and the same location of an industrial enterprise is often contained. At present, in the process of preparing a novel cigarette formula, after the price and style of a product are positioned, a formula worker usually performs blending of a leaf group matching preparation formula on the basis of mastering factors such as sensory quality, physicochemical characteristics and formula positioning of a raw material module according to self ability. However, the formulas of different formula personnel have different capacities, and the formula cannot be allocated and researched only by fixed people in actual production, so that enterprises usually spend a large amount of manpower, physics and financial resources to develop formula experiments to determine the final formula structure, and the formula output efficiency is low.
Disclosure of Invention
In order to solve the above problem, an embodiment of the present application provides a formula module collocation recommendation method and apparatus based on FP-growth association analysis rules.
In a first aspect, an embodiment of the present application provides a formula module collocation recommendation method based on FP-growth association analysis rules, where the method includes:
when a formula collocation recommending instruction is received, responding to the formula collocation recommending instruction, and acquiring a selected first leaf group module;
searching matching formula data matched with the first leaf group module from historical formula data based on an FP-growth algorithm model;
and generating recommendation information based on each second leaf group module except the first leaf group module in each matching formula data, and displaying the recommendation information.
Preferably, the generating recommendation information based on each second leaf group module except for the first leaf group module in each matching recipe data, and displaying the recommendation information, includes:
determining each second leaf group module except the first leaf group module in each matching formula data;
labeling the second leaf set modules present in at least two of the matching recipe data as common leaf set modules, and labeling each of the second leaf set modules other than the common leaf set modules as difference leaf set modules;
acquiring first leaf group module information corresponding to the common leaf group module and second leaf group module information corresponding to the difference leaf group module, generating recommendation information based on the common leaf group module, the difference leaf group module, the first leaf group module information and the second leaf group module information, and displaying the recommendation information.
Preferably, before presenting the recommendation information, the method further includes:
and optimizing the arrangement sequence of the common leaf group modules in the recommendation information according to the sequence from large to small based on the occurrence frequency of each common leaf group module in each matching formula data.
Preferably, after the presenting the recommendation information, the method further includes:
receiving a differential leaf group module query instruction, responding to the differential leaf group module query instruction, and acquiring a differential leaf group module to be queried corresponding to the differential leaf group module query instruction;
and acquiring a common collocation leaf group module and a common collocation formula of the to-be-queried difference leaf group module from the historical formula data.
Preferably, the method further comprises:
and when a screening instruction is received, screening the recommended information based on screening conditions corresponding to the screening instruction to obtain screening information.
Preferably, after the screening the recommendation information based on the screening condition corresponding to the screening instruction, the method further includes:
if the recommended information can not meet the screening condition, screening the historical formula data based on the screening condition to obtain screened formula data;
determining a screening leaf group module meeting the screening conditions in the screening formula data, and acquiring third leaf group module information corresponding to the screening leaf group module;
and judging whether adverse reactions exist between the screening leaf group module and the first leaf group module or not based on the information of the third leaf group module.
Preferably, the method further comprises:
receiving a feedback query instruction, responding to the feedback query instruction and determining matched formula data to be queried corresponding to the feedback query instruction;
and acquiring user feedback information corresponding to each matched formula data to be inquired, and displaying the user feedback information.
In a second aspect, an embodiment of the present application provides a formula module collocation recommendation device based on FP-growth correlation analysis rules, where the device includes:
the receiving module is used for responding to the formula collocation recommending instruction and acquiring the selected first leaf group module when the formula collocation recommending instruction is received;
the query module is used for querying matching formula data matched with the first leaf group module from historical formula data based on an FP-growth algorithm model;
and the recommending module is used for generating recommending information based on each second leaf group module except the first leaf group module in the matching formula data and displaying the recommending information.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method as provided in the first aspect or any one of the possible implementation manners of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method as provided in the first aspect or any one of the possible implementations of the first aspect.
The invention has the beneficial effects that: 1. the module matching experience in the historical formula is fully mined, and the experience is provided for the formula manufacturing of new products. 2. The information provided by the data model is more objective, and the loss of manpower, material resources and financial resources caused by repeated tests due to insufficient capability of a formulator is avoided. 3. The method has the advantages that the method is faster and more efficient when the formulator carries out the matching selection of the leaf group, and a large amount of suction processes are reduced when the formulator carries out the matching selection.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a formula module collocation recommendation method based on FP-growth association analysis rules according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a formula module collocation recommendation device based on FP-growth correlation analysis rules according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
In the following description, the terms "first" and "second" are used for descriptive purposes only and are not intended to indicate or imply relative importance. The following description provides embodiments of the present application, where different embodiments may be substituted or combined, and thus the present application is intended to include all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then this application should also be considered to include an embodiment that includes one or more of all other possible combinations of A, B, C, D, even though this embodiment may not be explicitly recited in text below.
The following description provides examples, and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For example, the described methods may be performed in an order different than the order described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
Referring to fig. 1, fig. 1 is a schematic flow chart of a formula module collocation recommendation method based on FP-growth association analysis rules according to an embodiment of the present application. In an embodiment of the present application, the method includes:
s101, when a formula collocation recommending instruction is received, responding to the formula collocation recommending instruction, and acquiring the selected first leaf group module.
The execution main body of the application can be a cloud server.
The leaf group module can be understood as a selection module corresponding to the tobacco leaf composition in the system in the embodiment of the application. The first leaf group module in the embodiment of the present application may be understood as a leaf group module corresponding to a formula principal component pre-selected by a user for formula collocation.
In the embodiment of the application, when a user wants to recommend formula matching, the user sends a formula matching recommendation instruction to the cloud server. Because the user wants to recommend the formula collocation, the main components of the formula need to be selected first, so that the server can recommend the collocation based on the main components. Therefore, when the server receives the formula collocation recommendation instruction, the server responds to the instruction and firstly confirms the main components of the formula selected by the user at the moment, namely the first leaf group module.
S102, searching matching formula data matched with the first leaf group module from historical formula data based on an FP-growth algorithm model.
The historical recipe data may be understood as data corresponding to all historical recipes once prepared by each recipe preparing person, which is stored in the server in the embodiment of the present application.
In the embodiment of the application, after the first leaf group module is determined, the historical formula data can be subjected to traversal query according to the FP-growth algorithm model, and the matching formula data which can be matched with the first leaf group module, namely the formula components include the first leaf group module, is obtained from the historical formula data.
The FP-growth algorithm model only needs to scan the data set twice, so that the operation efficiency of the algorithm is improved, and the method is suitable for the condition that the historical formula data volume is very large. Specifically, the FP-growth algorithm model is an algorithm model known to those skilled in the art, and may be performed by 1) scanning the data to obtain counts of all frequent item sets. Then deleting the items with the support degree lower than the threshold value, putting the 1 item frequent set into the item head table, and arranging the items according to the support degree in a descending order. 2) Scanning data, removing the non-frequent 1 item set from the read original data, and arranging the original data in descending order according to the support degree. 3) Reading the sorted data set, inserting the FP tree, and inserting the FP tree into the FP tree according to the sorted order during insertion, wherein the node in the front of the order is an ancestor node, and the node in the back of the order is a descendant node. If there is a common ancestor, the corresponding common ancestor node count is incremented by 1. After insertion, if a new node appears, the node corresponding to the entry head table is linked with the new node through the node linked list. And completing the building of the FP tree until all data are inserted into the FP tree. 4) And finding the condition mode base corresponding to the item head table items from the bottom item of the item head table upwards in sequence. A frequent item set of item head table items is derived from the conditional schema base recursive mining. 5) And if the number of the items of the frequent item set is not limited, returning to all the frequent item sets in the step 4, otherwise, only returning to the frequent item set meeting the requirement of the number of the items.
S103, generating recommendation information based on each second leaf group module except the first leaf group module in each matching formula data, and displaying the recommendation information.
In the embodiment of the application, each formula data is formed by matching a plurality of leaf group modules, and as the first leaf group module serving as the main component is determined, the rest of the second leaf group modules can be regarded as leaf group modules corresponding to the auxiliary material components, and the recommendation information can be generated and displayed based on the second leaf group modules, so that the user can be helped to select and recommend the matching of the auxiliary materials in the formula.
In one possible embodiment, step S103 includes:
determining each second leaf group module except the first leaf group module in each matching formula data;
labeling the second leaf set modules present in at least two of the matching recipe data as common leaf set modules, and labeling each of the second leaf set modules other than the common leaf set modules as difference leaf set modules;
acquiring first leaf group module information corresponding to the common leaf group module and second leaf group module information corresponding to the difference leaf group module, generating recommendation information based on the common leaf group module, the difference leaf group module, the first leaf group module information and the second leaf group module information, and displaying the recommendation information.
In the embodiment of the application, in the second leaf group module, some components are main components corresponding to the first leaf group module and are often used for matching, in order to enable a user to visually distinguish various components from the disordered second leaf group module, the second leaf group module at least appearing in two matching formula data is marked as a common leaf group module, the rest of the second leaf group modules are marked as difference leaf group modules, and module information corresponding to each leaf group module is respectively obtained, so that the user can inquire in recommendation information according to own requirements.
The formula data can include product specification name, wholesale price, product style positioning, leaf group formula module composition and proportion information, and relevant information of each module in the leaf group formula. The leaf group module information can include specific information such as definition name, producing area, industrial grade, year, purchase price, chemical index, sensory index, module positioning in the product and the like of each module.
In an implementation manner, before presenting the recommendation information, the method further includes:
and optimizing the arrangement sequence of the common leaf group modules in the recommendation information according to the sequence from large to small based on the occurrence frequency of each common leaf group module in each matching formula data.
In the embodiment of the application, in order to facilitate a user to find needed information from recommended information more conveniently and quickly, before the recommended information is displayed, the information is sorted according to the occurrence times of the common leaf group modules, so that the user can visually find out the formula matching frequency between each component and the main component selected by the user, and the formula can be referred to the user for research and development of the formula.
In an implementation manner, after presenting the recommendation information, the method further includes:
receiving a differential leaf group module query instruction, responding to the differential leaf group module query instruction, and acquiring a differential leaf group module to be queried corresponding to the differential leaf group module query instruction;
and acquiring a common collocation leaf group module and a common collocation formula of the to-be-queried difference leaf group module from the historical formula data.
In the embodiment of the application, in order to develop a new formula, a user may need to refer to the action and effect of various unusual ingredients added in a formula in a research history formula consistent with the main effect of the formula to be developed by the user. Therefore, after the recommendation information is displayed, a user may click on a certain interested difference leaf group module to generate a difference leaf group module query instruction, so that the server controls and calls the commonly used collocation and the commonly used formula of the difference leaf group module to be queried, and the user can know the effect of the component more intuitively and clearly.
In one embodiment, the method further comprises:
and when a screening instruction is received, screening the recommended information based on screening conditions corresponding to the screening instruction to obtain screening information.
In the embodiment of the present application, the information included in the recommendation information may still be too complicated for the user who already has some specific recipe matching ideas, so that the user cannot find the information needed by himself. At this time, the user can generate a screening instruction according to the selection of the content, such as chemical indexes and sensory indexes, which needs to be further refined, so that the recommendation information is further screened based on the screening condition selected by the user, and the screened screening information meets the requirements of the user.
In an implementation manner, after the filtering the recommendation information based on the filtering condition corresponding to the filtering instruction, the method further includes:
if the recommended information can not meet the screening condition, screening the historical formula data based on the screening condition to obtain screened formula data;
determining a screening leaf group module meeting the screening conditions in the screening formula data, and acquiring third leaf group module information corresponding to the screening leaf group module;
and judging whether adverse reactions exist between the screening leaf group module and the first leaf group module or not based on the information of the third leaf group module.
In the embodiment of the application, as a user needs to develop a new formula, the sensory effect and the like which the user wants to increase may be none of the existing formulas corresponding to the main component, and at this time, after finding that none of the recommended information can meet the screening condition, the server directly screens from the historical formula data to obtain data, and then obtains the screening leaf group module meeting the condition. The leaf group module screened out in this way is not matched with the main component, possibly because the two are matched to cause adverse chemical reactions and the like, so that the components with adverse reactions in the screened leaf group module are determined according to the information of the corresponding third leaf group module, and are compared with the first leaf group module.
In one embodiment, the method further comprises:
receiving a feedback query instruction, responding to the feedback query instruction and determining matched formula data to be queried corresponding to the feedback query instruction;
and acquiring user feedback information corresponding to each matched formula data to be inquired, and displaying the user feedback information.
The user feedback information can be understood as the feedback of the use experience of the consumer user collected after the cigarette product is actually produced based on the formula in the embodiment of the application.
In the embodiment of the present application, when a user is interested in some matching recipe data and needs to compare their differences further, a feedback query instruction is generated. The server can inquire out the user feedback information of the matched formula data to be inquired based on the feedback inquiry instruction. Since most of the components in the general formula data for comparison are the same, based on the feedback information of each user, the user can be helped to confirm how the differences among some components bring experience changes to the user, and further, the user can be helped to select the leaf group module meeting the needs of the user.
The following describes in detail a formula module collocation recommendation device based on FP-growth correlation analysis rules according to an embodiment of the present application with reference to fig. 2. It should be noted that, the recipe module collocation recommendation device based on the FP-growth correlation analysis rule shown in fig. 2 is used for executing the method of the embodiment shown in fig. 1 of the present application, and for convenience of description, only the part related to the embodiment of the present application is shown, and details of the specific technology are not disclosed, please refer to the embodiment shown in fig. 1 of the present application.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a formula module collocation recommendation device based on FP-growth correlation analysis rules according to an embodiment of the present disclosure. As shown in fig. 2, the apparatus includes:
the receiving module 201 is configured to, when a formula collocation recommendation instruction is received, respond to the formula collocation recommendation instruction and obtain a selected first leaf group module;
the query module 202 is configured to query matching formula data matched with the first leaf group module from historical formula data based on an FP-growth algorithm model;
and the recommending module 203 is configured to generate recommending information based on each second leaf group module except the first leaf group module in the matching formula data, and display the recommending information.
In one possible implementation, the recommendation module 203 includes:
a first determining unit, configured to determine each second leaf group module except the first leaf group module in each matching recipe data;
a marking unit, configured to mark the second leaf set module at least existing in the two matching recipe data as a common leaf set module, and mark each of the second leaf set modules except the common leaf set module as a difference leaf set module;
the first obtaining unit is used for obtaining first leaf group module information corresponding to the common leaf group module and second leaf group module information corresponding to the difference leaf group module, generating recommendation information based on the common leaf group module, the difference leaf group module, the first leaf group module information and the second leaf group module information, and displaying the recommendation information.
In one embodiment, the first obtaining unit includes:
and the matching element is used for optimizing the arrangement sequence of the common leaf group modules in the recommendation information according to the sequence from large to small based on the occurrence frequency of each common leaf group module in each matching formula data.
In one embodiment, the first obtaining unit further includes:
the receiving element is used for receiving the query instruction of the difference leaf group module, responding to the query instruction of the difference leaf group module and acquiring the difference leaf group module to be queried corresponding to the query instruction of the difference leaf group module;
and the query element is used for acquiring the commonly used collocation leaf group module and the commonly used collocation formula of the to-be-queried difference leaf group module from the historical formula data.
In one embodiment, the apparatus further comprises:
and the screening module is used for screening the recommendation information based on the screening condition corresponding to the screening instruction to obtain screening information when the screening instruction is received.
In one embodiment, the apparatus further comprises:
the first judgment module is used for screening the historical formula data based on the screening condition to obtain screened formula data if the recommended information cannot meet the screening condition;
the first acquisition module is used for determining a screening leaf group module meeting the screening conditions in the screening formula data and acquiring third leaf group module information corresponding to the screening leaf group module;
and the second judgment module is used for judging whether adverse reactions exist between the screening leaf group module and the first leaf group module or not based on the information of the third leaf group module.
In one embodiment, the apparatus further comprises:
the response module is used for receiving a feedback query instruction, responding to the feedback query instruction and determining matched formula data to be queried corresponding to the feedback query instruction;
and the display module is used for acquiring the user feedback information corresponding to each matched formula data to be inquired and displaying the user feedback information.
It is clear to a person skilled in the art that the solution according to the embodiments of the present application can be implemented by means of software and/or hardware. The "unit" and "module" in this specification refer to software and/or hardware that can perform a specific function independently or in cooperation with other components, where the hardware may be, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), or the like.
Each processing unit and/or module in the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.
Referring to fig. 3, a schematic structural diagram of an electronic device according to an embodiment of the present application is shown, where the electronic device may be used to implement the method in the embodiment shown in fig. 1. As shown in fig. 3, the electronic device 300 may include: at least one central processor 301, at least one network interface 304, a user interface 303, a memory 305, at least one communication bus 302.
Wherein a communication bus 302 is used to enable the connection communication between these components.
The user interface 303 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 303 may further include a standard wired interface and a wireless interface.
The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The central processor 301 may include one or more processing cores. The central processor 301 connects various parts within the entire electronic device 300 using various interfaces and lines, and performs various functions of the terminal 300 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 305 and calling data stored in the memory 305. Alternatively, the central Processing unit 301 may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The CPU 301 may integrate one or a combination of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the cpu 301, but may be implemented by a single chip.
The Memory 305 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 may be used to store instructions, programs, code sets, or instruction sets. The memory 305 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 305 may alternatively be at least one storage device located remotely from the central processor 301. As shown in fig. 3, memory 305, which is a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.
In the electronic device 300 shown in fig. 3, the user interface 303 is mainly used for providing an input interface for a user to obtain data input by the user; the central processing unit 301 may be configured to invoke the recipe module collocation recommendation application program based on the FP-growth correlation analysis rule stored in the memory 305, and specifically execute the following operations:
when a formula collocation recommending instruction is received, responding to the formula collocation recommending instruction, and acquiring a selected first leaf group module;
searching matching formula data matched with the first leaf group module from historical formula data based on an FP-growth algorithm model;
and generating recommendation information based on each second leaf group module except the first leaf group module in each matching formula data, and displaying the recommendation information.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk including floppy disks, optical disks, DVD, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some service interfaces, devices or units, and may be an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program, which is stored in a computer-readable memory, and the memory may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The above description is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure should not be limited thereby. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure are intended to be included within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A formula module collocation recommendation method based on FP-growth correlation analysis rules is characterized by comprising the following steps:
when a formula collocation recommending instruction is received, responding to the formula collocation recommending instruction, and acquiring a selected first leaf group module;
searching matching formula data matched with the first leaf group module from historical formula data based on an FP-growth algorithm model;
and generating recommendation information based on each second leaf group module except the first leaf group module in each matching formula data, and displaying the recommendation information.
2. The method of claim 1, wherein generating recommendation information based on each second leaf cluster module in each matching recipe data except the first leaf cluster module, and presenting the recommendation information comprises:
determining each second leaf group module except the first leaf group module in each matching formula data;
labeling the second leaf set modules present in at least two of the matching recipe data as common leaf set modules, and labeling each of the second leaf set modules other than the common leaf set modules as difference leaf set modules;
acquiring first leaf group module information corresponding to the common leaf group module and second leaf group module information corresponding to the difference leaf group module, generating recommendation information based on the common leaf group module, the difference leaf group module, the first leaf group module information and the second leaf group module information, and displaying the recommendation information.
3. The method of claim 2, wherein before presenting the recommendation information, further comprising:
and optimizing the arrangement sequence of the common leaf group modules in the recommendation information according to the sequence from large to small based on the occurrence frequency of each common leaf group module in each matching formula data.
4. The method of claim 2, wherein after presenting the recommendation information, further comprising:
receiving a differential leaf group module query instruction, responding to the differential leaf group module query instruction, and acquiring a differential leaf group module to be queried corresponding to the differential leaf group module query instruction;
and acquiring a common collocation leaf group module and a common collocation formula of the to-be-queried difference leaf group module from the historical formula data.
5. The method of claim 1, further comprising:
and when a screening instruction is received, screening the recommended information based on screening conditions corresponding to the screening instruction to obtain screening information.
6. The method according to claim 5, wherein after the filtering the recommendation information based on the filtering condition corresponding to the filtering instruction, further comprising:
if the recommended information can not meet the screening condition, screening the historical formula data based on the screening condition to obtain screened formula data;
determining a screening leaf group module meeting the screening conditions in the screening formula data, and acquiring third leaf group module information corresponding to the screening leaf group module;
and judging whether adverse reactions exist between the screening leaf group module and the first leaf group module or not based on the information of the third leaf group module.
7. The method of claim 1, further comprising:
receiving a feedback query instruction, responding to the feedback query instruction and determining matched formula data to be queried corresponding to the feedback query instruction;
and acquiring user feedback information corresponding to each matched formula data to be inquired, and displaying the user feedback information.
8. A formula module collocation recommendation device based on FP-growth correlation analysis rules is characterized by comprising:
the receiving module is used for responding to the formula collocation recommending instruction and acquiring the selected first leaf group module when the formula collocation recommending instruction is received;
the query module is used for querying matching formula data matched with the first leaf group module from historical formula data based on an FP-growth algorithm model;
and the recommending module is used for generating recommending information based on each second leaf group module except the first leaf group module in the matching formula data and displaying the recommending information.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-7 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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