CN112287190A - Replacement material recommendation method, device and equipment - Google Patents
Replacement material recommendation method, device and equipment Download PDFInfo
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- CN112287190A CN112287190A CN202011213253.9A CN202011213253A CN112287190A CN 112287190 A CN112287190 A CN 112287190A CN 202011213253 A CN202011213253 A CN 202011213253A CN 112287190 A CN112287190 A CN 112287190A
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- G06F16/90—Details of database functions independent of the retrieved data types
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The application relates to a replacement material recommendation method, a replacement material recommendation device and replacement material recommendation equipment, wherein the method comprises the following steps: the method comprises the steps of firstly obtaining recommendation demand data, then determining a target recommendation material list according to the recommendation demand data, wherein the target recommendation material list comprises a plurality of replacement materials corresponding to the recommendation demand data, and finally displaying the target recommendation material list so as to be convenient for selecting the required target replacement materials from the plurality of replacement materials. Because the scheme of the application can recommend the target recommended material list according to the recommended demand data, a user only needs to determine the target replacement materials from the list, and the plurality of replacement materials in the list meet the recommended demand data, so that the user can select the replacement materials meeting the recommended demand data without having very rich experience, the dependence on manual experience is reduced to a certain extent, and the project process can be accelerated.
Description
Technical Field
The application relates to the technical field of material management, in particular to a replacement material recommendation method, device and equipment.
Background
With the rise of big data technology and machine learning technology, millions of useless data are gathered together, and after data processing of a series of machine learning algorithms after optimization, similar data can be accurately obtained according to the data, and even new rules can be obtained according to the data.
For the manufacturing industry, when a certain project is implemented, a lot of materials are involved, and the materials may be replaced, but when the materials are searched for and replaced, engineers often select and match according to own experiences, and the mode can seriously depend on the engineers with abundant experiences, so that the project is too dependent on manpower in the implementation process, and when the personnel are insufficient, the progress of the project can be blocked.
Disclosure of Invention
In order to overcome the problems in the related art at least to a certain extent, the application provides a replacement material recommendation method, a replacement material recommendation device and replacement material recommendation equipment.
According to a first aspect of the present application, there is provided a replacement material recommendation method, including:
acquiring recommendation demand data;
determining a target recommended material list according to the recommended demand data, wherein the target recommended material list comprises a plurality of replacement materials which are consistent with the recommended demand data;
and displaying the target recommended material list so as to facilitate a user to select a required target replacement material from the plurality of replacement materials.
Optionally, the determining a target recommended material list according to the recommended requirement data includes:
analyzing the recommendation demand data to obtain a material recommendation condition and a material to be replaced;
and determining the target recommended material list from a plurality of preset recommended material lists according to the material to be replaced and the material recommendation condition.
Optionally, the determining the target recommended material list from a plurality of preset recommended material lists according to the material to be replaced and the material recommendation condition includes:
determining a target category of the material to be replaced, and determining a first recommended material list from a plurality of preset recommended material lists according to the target category, wherein the replacing material in the first recommended material list belongs to the target category;
and removing the replacement materials which do not meet the material recommendation condition in the first recommended material list to obtain the target recommended material list.
Optionally, the process of determining the preset recommended material list includes:
acquiring replacement material data, wherein the replacement material data comprises material attributes of each replacement material and material combination data of each replacement material;
determining similarity among the replacement materials according to the material combination data;
classifying all the replacement materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain a plurality of preset recommended material lists.
Optionally, classifying all the replacement materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain a plurality of preset recommended material lists, including:
classifying all the materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain the replacement materials of various categories and the relevance weight of each replacement material relative to the category;
and sequencing the replacement materials in the same category according to the relevance weight of each replacement material to obtain a preset recommended material list of each category.
Optionally, the method further includes:
acquiring the target replacement material determined by the user based on the target recommended material list;
increasing the relevance weight of the target replacement material by a preset incremental value;
and reordering all the replacement materials in the target recommended material list according to the current relevance weight.
According to a second aspect of the present application, there is provided a replacement material recommendation device comprising:
the acquisition module is used for acquiring recommendation demand data;
the determining module is used for determining a target recommended material list according to the recommended demand data, wherein the target recommended material list comprises a plurality of replacement materials which are consistent with the recommended demand data;
and the display module is used for displaying the target recommended material list so as to facilitate a user to select a required target replacement material from the multiple replacement materials.
Optionally, the determining module includes:
the analysis unit is used for analyzing the recommendation demand data to obtain a material recommendation condition and a material to be replaced;
and the determining unit is used for determining the target recommended material list from a plurality of preset recommended material lists according to the material to be replaced and the material recommendation condition.
Optionally, the determining unit includes:
the determining subunit is configured to determine a target category of the material to be replaced, and determine a first recommended material list from a plurality of preset recommended material lists according to the target category, where a replacement material in the first recommended material list belongs to the target category;
and the removing subunit is used for removing the replacement materials which do not meet the material recommendation condition in the first recommended material list to obtain the target recommended material list.
According to a third aspect of the present application, there is provided a replacement material recommendation apparatus comprising: at least one processor and memory;
the processor is configured to execute the replacement material recommendation program stored in the memory, so as to implement the replacement material recommendation method according to the first aspect of the present application.
The technical scheme provided by the application can comprise the following beneficial effects: according to the method and the device, recommendation demand data are firstly obtained, a target recommended material list is determined according to the recommendation demand data, the target recommended material list comprises a plurality of replacement materials corresponding to the recommendation demand data, and the target recommended material list is finally displayed so as to be conveniently used for selecting the needed target replacement materials from the plurality of replacement materials. Because the scheme of the application can recommend the target recommended material list according to the recommended demand data, a user only needs to determine the target replacement materials from the list, and the plurality of replacement materials in the list meet the recommended demand data, so that the user can select the replacement materials meeting the recommended demand data without having very rich experience, the dependence on manual experience is reduced to a certain extent, and the project process can be accelerated.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic flow chart diagram illustrating a method for alternative material recommendation provided by an embodiment of the present application;
FIG. 2 is a flow diagram illustrating one manner in which a list of target recommended materials may be determined in the present application;
FIG. 3 is a flow diagram illustrating another manner in which a list of target recommended materials may be determined according to the present application;
FIG. 4 is a schematic diagram illustrating a process for determining a plurality of preset recommended material lists in the present application;
FIG. 5 is a schematic flow chart illustrating a process of obtaining a plurality of preset recommended material lists according to the present application;
FIG. 6 is a schematic flow chart illustrating the update of a target recommended material list according to the present application;
FIG. 7 is a schematic structural diagram of an alternative material recommendation device according to another embodiment of the present application;
fig. 8 is a schematic structural diagram of an alternative material recommendation device according to another embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a method for recommending alternative materials according to an embodiment of the present application.
As shown in fig. 1, the method for recommending alternative materials provided in this embodiment may include:
and step S101, acquiring recommendation requirement data.
In this step, the recommended requirement data includes a recommended material replacement requirement, which may specifically include a material recommendation condition and a material to be replaced, where the material recommendation condition may refer to a condition that the material to be replaced needs to meet, such as a working temperature range, a working humidity range, a strength range that should be provided, and the material to be replaced refers to the material that needs to be replaced.
In a specific example, for example, in a mobile phone production project, a material of a current mobile phone rear shell is stainless steel, at this time, in order to meet a project requirement, a material capable of replacing the stainless steel needs to be found, at this time, a material to be replaced is the stainless steel, and the material recommendation condition may be a condition that a hardness is greater than that of a replacement material, which needs to be met.
It should be noted that the content included in the recommendation demand data may be changed according to the manner of determining the target recommended material list in step S102, for example, the manner of determining the target recommended material list in step S102 is a method M, and the data required by the method M is only the material to be replaced, at this time, the recommendation demand data only includes the material to be replaced, and for example, a method N is employed, and the data required by the method N requires the material recommendation condition in addition to the material to be replaced, and in this case, the recommendation demand data in this step needs to include the material to be replaced and the material recommendation condition.
Step S102, determining a target recommended material list according to the recommended requirement data, wherein the target recommended material list comprises a plurality of replacement materials which are consistent with the recommended requirement data.
In this step, the target recommended material list is a list determined according to the recommended requirement data, and a plurality of replacement materials corresponding to the recommended requirement data are recorded in the list.
It should be noted that there are many ways to determine the target recommended material list, for example, refer to fig. 2, and fig. 2 is a flowchart illustrating one way to determine the target recommended material list in this application.
As shown in fig. 2, the process of determining the target recommended material list may include:
step S201, determining material recommendation conditions and materials to be replaced according to recommendation demand data.
In this step, the recommended requirement data may be analyzed to obtain an identifier of the material to be replaced, where the identifier may be a predefined number symbol identifier, a name of the material to be replaced, or another identifier capable of distinguishing the material from other materials.
And S202, acquiring replacement material data, wherein the replacement material data comprises material attributes of each replacement material.
In this step, the material replacement data refers to the related data of all currently stored materials, such as material attributes, which may refer to name, specification, basic usage, applicable scenario, and the like.
And S203, determining the similarity between each replaced material and the material to be replaced according to the material data.
It should be noted that there are many methods for calculating the similarity between individuals based on data, and most commonly, the similarity may be represented by a distance, such as a euclidean distance, a manhattan distance, a minkowski distance, or may be calculated by other similarity calculation methods, such as a cosine similarity.
And S204, sequencing the replacement materials according to the similarity to obtain an initial recommended material list.
After the similarity between each replacement material and the material to be replaced is obtained through calculation, the replacement materials can be sorted according to the similarity, for example, the replacement materials are sorted according to the maximum similarity to the minimum similarity (the larger the similarity is, the higher the similarity is), and after the sorting is completed, the initial recommended material list is obtained.
In addition, before sorting, the replacement materials can be selected, part of the replacement materials with the similarity greater than or equal to a certain preset value are selected to appear in the initial recommended material list, and the similarity of the description of the replacement materials corresponding to the other replacement materials with the similarity less than the preset value is too low, so that recommendation is not needed. Therefore, the number of the replacement materials in the initial recommended material list can be reduced, and further the calculated amount and the memory occupied by the execution process are reduced.
And S205, removing the replacement materials which do not meet the material recommendation condition in the initial recommended material list to obtain a target recommended material list.
However, in the above process, each time the target recommended material list is obtained, similarity calculation is performed, and the similarity calculation is for all currently stored alternative materials, so that the calculation amount in the above process is large, and a certain influence may be generated on the recommended speed, and therefore, in order to avoid the influence on the speed, the present application also provides another way of determining the target recommended material list, and refer to fig. 3, where fig. 3 is a flowchart illustrating another way of determining the target recommended material list in the present application.
As shown in fig. 3, the process of determining the target recommended material list may include:
and S301, analyzing the recommendation requirement data to obtain a material recommendation condition and a material to be replaced.
The step is similar to step S201, and the description in step S201 may be specifically referred to, and is not repeated here.
Step S302, determining the target recommended material list from a plurality of preset recommended material lists according to the material to be replaced and the material recommendation condition.
In this step, the specific process of determining the target recommended material list may be to first determine a target category of the material to be replaced, determine a first recommended material list from a plurality of preset recommended material lists according to the target category, and then remove the replacement material that does not satisfy the material recommendation condition in the first recommended material list to obtain the target recommended material list.
Wherein the object class refers to the class to which the material to be replaced belongs, such as metal, hard material, elastic material, etc. In addition, all the replacement materials in the first recommended material list belong to the target category.
It should be noted that, reference may be made to fig. 4 for a process of determining a preset recommended material list, and fig. 4 is a schematic diagram of a process of determining a plurality of preset recommended material lists in the present application.
As shown in fig. 4, the process of determining the preset recommended material list may include:
s401, replacing material data are obtained, wherein the replacing material data comprise material attributes of replacing materials and material combination data of the replacing materials.
For the replacement material data, reference may be made to the detailed description of step S202, which is not described herein again. In addition, the material combination data refers to a combination of all materials in a certain item or a certain product in which the replacement material is used.
It should be noted that, in order to ensure that the data has higher validity, the step may further perform cleaning on the data, find out illegal and non-compliant data, perform corresponding processing, perform deduplication on duplicate data, and perform isomorphism processing on similar data. The source of illegal data may be errors in manual entry or errors caused by bugs in previous program operations, for example, in the case of data entry, the system does not limit the price field, resulting in some entry personnel entering 10000 yuan and some entering ten thousand yuan, which requires recalibration and conversion of the data.
And S402, determining the similarity between the replaced materials according to the material combination data.
The material combination data can reflect the scene of material use, and the similarity of use among various replacement materials is calculated based on the scene.
And S403, classifying all the replacement materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain a plurality of preset recommended material lists.
Specifically, in this step, reference may be made to fig. 5, and fig. 5 is a schematic flow chart of obtaining a plurality of preset recommended material lists according to the present application.
As shown in fig. 5, the process of obtaining a plurality of preset recommended material lists may include:
s501, classifying all materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain the replacement materials of various types and the relevance weight of each replacement material relative to the type.
In this step, a proximity algorithm, such as knn (k Near neighbor), may be used to determine the category of the replacement material according to k replacement materials that are close to the replacement material, that is, k "neighbors" (k replacement materials with similarity smaller than a certain value) of a certain replacement material to be classified are determined according to the calculated similarity, and when most of the k "neighbors" belong to a certain category, the replacement material also belongs to the category.
In a specific example, the replacement material to be classified is a, and the replacement materials with the similarity smaller than the preset value X are b, c, d, e and f in sequence. If b, c, d, e belong to the first category, then a should also belong to the first category.
Of course, in the classification process, the correlation weight corresponding to the classification is obtained, and the calculation manner of the correlation weight is common, which is not described herein again.
And S502, sequencing the replacement materials in the same category according to the relevance weight of each replacement material to obtain a preset recommended material list of each category.
For the specific sorting process, reference may be made to the foregoing related descriptions, which are not described herein again.
And S103, displaying the target recommended material list so that a user can select a required target replacement material from the multiple replacement materials conveniently.
According to the embodiment, recommendation demand data are firstly obtained, a target recommended material list is determined according to the recommendation demand data, wherein the target recommended material list comprises a plurality of replacement materials corresponding to the recommendation demand data, and the target recommended material list is finally displayed so as to be conveniently used for selecting the required target replacement materials from the plurality of replacement materials. Because the scheme of the application can recommend the target recommended material list according to the recommended demand data, a user only needs to determine the target replacement materials from the list, and the plurality of replacement materials in the list meet the recommended demand data, so that the user can select the replacement materials meeting the recommended demand data without having very rich experience, the dependence on manual experience is reduced to a certain extent, and the project process can be accelerated.
In addition, in order to continuously improve the recommendation capability of the target recommended material list in the scheme, after the user determines the target replacement material, the scheme also updates the target recommended material list, and a specific updating process can refer to fig. 6, where fig. 6 is a schematic flow diagram for updating the target recommended material list in the application.
As shown in fig. 6, the process of updating the target recommended material list may include:
and S601, acquiring the target replacement material determined by the user based on the target recommended material list.
Step S602, increasing the relevance weight of the target replacement material by a preset increment value.
In the present application, the preset increment value may be adjusted according to the magnitude of the correlation weight, for example, in an example, the magnitude of the correlation weight is 1 power of 10, such as the ones or tens of 5, 7, 15, 21, 30, and the preset increment value may be "1".
And S603, reordering all the replacement materials in the target recommended material list according to the current relevance weight.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an alternative material recommending apparatus according to another embodiment of the present application.
As shown in fig. 7, the replacement material recommending apparatus of this embodiment may include:
an obtaining module 701, configured to obtain recommendation demand data;
a determining module 702, configured to determine a target recommended material list according to the recommendation demand data, where the target recommended material list includes a plurality of replacement materials that are consistent with the recommendation demand data;
a display module 703, configured to display the target recommended material list, so that a user can select a required target replacement material from the multiple replacement materials.
In this embodiment, the obtaining module obtains recommendation demand data first, then the determining module determines a target recommended material list according to the recommendation demand data, where the target recommended material list includes a plurality of replacement materials corresponding to the recommendation demand data, and finally the display module displays the target recommended material list so as to be used for selecting a required target replacement material from the plurality of replacement materials. Because the scheme of the application can recommend the target recommended material list according to the recommended demand data, a user only needs to determine the target replacement materials from the list, and the plurality of replacement materials in the list meet the recommended demand data, so that the user can select the replacement materials meeting the recommended demand data without having very rich experience, the dependence on manual experience is reduced to a certain extent, and the project process can be accelerated.
Further, the determining module includes:
the analysis unit is used for analyzing the recommendation demand data to obtain a material recommendation condition and a material to be replaced;
and the determining unit is used for determining the target recommended material list from a plurality of preset recommended material lists according to the material to be replaced and the material recommendation condition.
Further, the determining unit includes:
the determining subunit is configured to determine a target category of the material to be replaced, and determine a first recommended material list from a plurality of preset recommended material lists according to the target category, where a replacement material in the first recommended material list belongs to the target category;
and the removing subunit is used for removing the replacement materials which do not meet the material recommendation condition in the first recommended material list to obtain the target recommended material list.
In addition, the apparatus of this embodiment further comprises:
the replacement material data acquisition module is used for acquiring replacement material data, and the replacement material data comprises material attributes of each replacement material and material combination data of each replacement material;
the similarity calculation module is used for determining the similarity between the replacement materials according to the material combination data;
and the classification module is used for classifying all the replacement materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain a plurality of preset recommended material lists.
Further, the classification module includes:
the classification unit is used for classifying all materials according to the similarity and the material attributes of the various replacement materials based on a proximity algorithm to obtain various types of replacement materials and the relevance weight of each replacement material relative to the type of the replacement material;
and the sorting unit is used for sorting the replacement materials in the same category according to the relevance weight of each replacement material to obtain a preset recommended material list of each category.
Further, the apparatus of this embodiment further includes:
the target replacement material acquisition module is used for acquiring the target replacement material determined by the user based on the target recommended material list;
an increasing module for increasing the relevance weight of the target replacement material by a preset incremental value;
and the reordering module is used for reordering all the replacement materials in the target recommended material list according to the current relevance weight.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an alternative material recommendation device according to another embodiment of the present application.
As shown in fig. 8, the replacement material recommending apparatus 800 provided in this embodiment includes: at least one processor 801, memory 802, at least one network interface 803, and other user interfaces 804. Production node management the various components in production node management system 800 are coupled together by a bus system 805. It is understood that the bus system 805 is used to enable communications among the components connected. The bus system 805 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 805 in fig. 8.
The user interface 804 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, trackball, touch pad, or touch screen, among others.
It will be appreciated that the memory 802 in embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (ddr Data Rate SDRAM, ddr SDRAM), Enhanced Synchronous SDRAM (ESDRAM), synchlronous SDRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 802 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some embodiments, memory 802 stores elements, executable units or data structures, or a subset thereof, or an expanded set thereof as follows: an operating system 8021 and second application programs 8022.
The operating system 8021 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The second application 8022 includes various second applications, such as a Media Player (Media Player), a Browser (Browser), and the like, for implementing various application services. A program implementing a method according to an embodiment of the present invention may be included in second application program 8022.
In the embodiment of the present invention, the processor 801 is configured to execute the method steps provided by each method embodiment by calling the program or instruction stored in the memory 802, specifically, the program or instruction stored in the second application program 8022, for example, including:
acquiring recommendation demand data;
determining a target recommended material list according to the recommended demand data, wherein the target recommended material list comprises a plurality of replacement materials which are consistent with the recommended demand data;
and displaying the target recommended material list so as to facilitate a user to select a required target replacement material from the plurality of replacement materials.
Optionally, the determining a target recommended material list according to the recommended requirement data includes:
analyzing the recommendation demand data to obtain a material recommendation condition and a material to be replaced;
and determining the target recommended material list from a plurality of preset recommended material lists according to the material to be replaced and the material recommendation condition.
Optionally, the determining the target recommended material list from a plurality of preset recommended material lists according to the material to be replaced and the material recommendation condition includes:
determining a target category of the material to be replaced, and determining a first recommended material list from a plurality of preset recommended material lists according to the target category, wherein the replacing material in the first recommended material list belongs to the target category;
and removing the replacement materials which do not meet the material recommendation condition in the first recommended material list to obtain the target recommended material list.
Optionally, the process of determining the preset recommended material list includes:
acquiring replacement material data, wherein the replacement material data comprises material attributes of each replacement material and material combination data of each replacement material;
determining similarity among the replacement materials according to the material combination data;
classifying all the replacement materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain a plurality of preset recommended material lists.
Optionally, classifying all the replacement materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain a plurality of preset recommended material lists, including:
classifying all the materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain the replacement materials of various categories and the relevance weight of each replacement material relative to the category;
and sequencing the replacement materials in the same category according to the relevance weight of each replacement material to obtain a preset recommended material list of each category.
Optionally, the method further includes:
acquiring the target replacement material determined by the user based on the target recommended material list;
increasing the relevance weight of the target replacement material by a preset incremental value;
and reordering all the replacement materials in the target recommended material list according to the current relevance weight.
The methods disclosed in the embodiments of the present invention described above may be implemented in the processor 801 or implemented by the processor 801. The processor 801 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 801. The Processor 801 may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software elements in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in the memory 802, and the processor 801 reads the information in the memory 802, and combines the hardware to complete the steps of the method.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the Processing units may be implemented in one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions of the present Application, or a combination thereof.
For a software implementation, the techniques herein may be implemented by means of units performing the functions herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A replacement material recommendation method, the method comprising:
acquiring recommendation demand data;
determining a target recommended material list according to the recommended demand data, wherein the target recommended material list comprises a plurality of replacement materials which are consistent with the recommended demand data;
and displaying the target recommended material list so as to facilitate a user to select a required target replacement material from the plurality of replacement materials.
2. The replacement material recommendation method according to claim 1, wherein the determining a target recommended material list according to the recommendation demand data comprises:
analyzing the recommendation demand data to obtain a material recommendation condition and a material to be replaced;
and determining the target recommended material list from a plurality of preset recommended material lists according to the material to be replaced and the material recommendation condition.
3. The replacement material recommendation method according to claim 2, wherein the determining the target recommended material list from a plurality of preset recommended material lists according to the material to be replaced and the material recommendation condition includes:
determining a target category of the material to be replaced, and determining a first recommended material list from a plurality of preset recommended material lists according to the target category, wherein the replacing material in the first recommended material list belongs to the target category;
and removing the replacement materials which do not meet the material recommendation condition in the first recommended material list to obtain the target recommended material list.
4. The replacement material recommendation method according to claim 2, wherein the process of determining the preset recommended material list comprises:
acquiring replacement material data, wherein the replacement material data comprises material attributes of each replacement material and material combination data of each replacement material;
determining similarity among the replacement materials according to the material combination data;
classifying all the replacement materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain a plurality of preset recommended material lists.
5. The replacement material recommendation method according to claim 4, wherein the classifying all replacement materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain a plurality of preset recommendation material lists comprises:
classifying all the materials according to the similarity and the material attributes of the replacement materials based on a proximity algorithm to obtain the replacement materials of various categories and the relevance weight of each replacement material relative to the category;
and sequencing the replacement materials in the same category according to the relevance weight of each replacement material to obtain a preset recommended material list of each category.
6. The replacement material recommendation method of claim 5, further comprising:
acquiring the target replacement material determined by the user based on the target recommended material list;
increasing the relevance weight of the target replacement material by a preset incremental value;
and reordering all the replacement materials in the target recommended material list according to the current relevance weight.
7. A replacement material recommendation device, the device comprising:
the acquisition module is used for acquiring recommendation demand data;
the determining module is used for determining a target recommended material list according to the recommended demand data, wherein the target recommended material list comprises a plurality of replacement materials which are consistent with the recommended demand data;
and the display module is used for displaying the target recommended material list so as to facilitate a user to select a required target replacement material from the multiple replacement materials.
8. The replacement material recommendation device of claim 7, wherein the determining module comprises:
the analysis unit is used for analyzing the recommendation demand data to obtain a material recommendation condition and a material to be replaced;
and the determining unit is used for determining the target recommended material list from a plurality of preset recommended material lists according to the material to be replaced and the material recommendation condition.
9. The replacement material recommendation device according to claim 8, wherein the determining unit comprises:
the determining subunit is configured to determine a target category of the material to be replaced, and determine a first recommended material list from a plurality of preset recommended material lists according to the target category, where a replacement material in the first recommended material list belongs to the target category;
and the removing subunit is used for removing the replacement materials which do not meet the material recommendation condition in the first recommended material list to obtain the target recommended material list.
10. A replacement material recommendation apparatus, comprising: at least one processor and memory;
the processor is used for executing the replacement material recommendation program stored in the memory to realize the replacement material recommendation method of any one of claims 1-6.
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