CN117495508A - Multi-data collaborative purchase screening method, device, equipment and storage medium - Google Patents
Multi-data collaborative purchase screening method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a multi-data collaborative purchasing screening method device, equipment and a storage medium, wherein the method comprises the following steps: obtaining the preference classification of the purchasing person according to the attribute information of the purchasing person, obtaining the purchasing demand information, determining the commodity to be purchased and the supplier to be purchased for supplying the commodity to be purchased according to the purchasing demand information, analyzing the market change trend, analyzing the commodity to be purchased and the supplier to be purchased, obtaining the ranking list of the commodity to be purchased and the ranking list of the supplier to be purchased, and selecting and confirming the purchased commodity and supplier in the ranking list according to the preference classification of the purchasing person and the purchasing demand information, thereby improving the accuracy and efficiency of commodity purchasing screening.
Description
Technical Field
The embodiment of the invention relates to a data processing technology, in particular to a multi-data collaborative purchasing screening method device, equipment and a storage medium.
Background
With the development of technology, network technology, big data technology and cloud computing technology have been widely used in various fields. For enterprises, purchasing is an important ring affecting the development of the enterprises, and current enterprise purchasing mainly depends on subjective judgment of purchasing staff to determine suppliers and products of products to be purchased, but the suppliers and products determined by manual judgment may not be capable of accurately matching with actual demands of the enterprises, and the efficiency is low.
Therefore, how to apply various new technologies to the purchasing field, to improve the efficiency and the specification of purchasing links is an important means for improving the competitiveness of enterprises.
Disclosure of Invention
The invention provides a multi-data collaborative purchasing screening method device, equipment and a storage medium, which improve the accuracy and efficiency of commodity purchasing screening.
In a first aspect, an embodiment of the present invention provides a multiple data collaborative purchase screening method, including:
obtaining the preference classification of the purchasing person according to the attribute information of the purchasing person;
acquiring purchasing demand information, determining commodities to be purchased and suppliers to be purchased for supplying the commodities to be purchased according to the purchasing demand information, and analyzing market change trend;
analyzing and processing the commodity to be purchased and the provider to be purchased to obtain a ranking list of the commodity to be purchased and a ranking list of the provider to be purchased;
and selecting the purchased goods and suppliers from the ranking list according to the purchasing person preference classification and purchasing demand information.
In one possible implementation manner of the first aspect, obtaining the buyer preference classification according to the buyer attribute information includes:
and inputting the attribute information of the buyer into a preference classification model to obtain the preference classification label of the buyer.
In a possible implementation manner of the first aspect, the buyer attribute information includes at least one of a buyer's purchase history, a business attribute, and a custom attribute.
In one possible implementation manner of the first aspect, obtaining purchase demand information, determining a commodity to be purchased and a provider to be purchased for supplying the commodity to be purchased according to the purchase demand information, and analyzing a market change trend, including:
acquiring purchasing demand information, and identifying commodities to be purchased in the purchasing demand information;
inquiring and supplying a to-be-purchased supplier of the to-be-purchased commodity according to the to-be-purchased commodity;
and analyzing market change trend of the commodity to be purchased based on the commodity to be purchased and the provider to be purchased.
In one possible implementation manner of the first aspect, the analyzing the to-be-purchased goods and the to-be-purchased suppliers to obtain a ranking list of the to-be-purchased goods and a ranking list of the to-be-purchased suppliers includes:
obtaining a ranking list of the commodities to be purchased according to the marketing data of the commodities to be purchased;
and obtaining a ranking list of the suppliers to be purchased according to the market evaluation data of the suppliers to be purchased.
In one possible implementation manner of the first aspect, obtaining a ranking list of the to-be-purchased goods according to market sales data of the to-be-purchased goods includes:
classifying the commodities to be purchased, and obtaining ranking lists of various commodities to be purchased according to the marketing data of the commodities to be purchased;
obtaining a ranking list of suppliers to be purchased according to market evaluation data of the suppliers to be purchased, wherein the ranking list comprises the following steps:
and classifying the suppliers according to the market evaluation data of the suppliers to be purchased, and obtaining a ranking list of each classified supplier to be purchased according to the market evaluation data.
In a possible implementation manner of the first aspect, the marketing data includes at least one of sales volume, rating, cost performance, price deviation;
the market rating data includes at least one of response, expiration, complaint, rejection, zone, bid rate.
In one possible implementation manner of the first aspect, selecting and confirming purchased goods and suppliers in the ranking list according to the buyer preference classification and the purchasing demand information includes:
and according to the purchasing screening rules, cross analysis is carried out on the purchasing preference classification, the market change trend, the ranking list of the commodities to be purchased and the ranking list of the suppliers to be purchased, and the purchased commodities and suppliers are confirmed.
In a second aspect, an embodiment of the present invention provides a multiple data collaborative purchase screening apparatus, including:
the purchasing person classifying module is used for obtaining purchasing person preference classification according to the purchasing person attribute information;
the demand analysis module is used for acquiring purchasing demand information, determining commodities to be purchased and suppliers to be purchased for supplying the commodities to be purchased according to the purchasing demand information, and analyzing market change trend;
the ranking module is used for analyzing and processing the commodities to be purchased and the commodities to be purchased to obtain a ranking list of the commodities to be purchased and a ranking list of the suppliers to be purchased;
and the screening module is used for selecting and confirming purchased goods and suppliers in the ranking list according to the preference classification of the buyers and the purchase demand information.
In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement a multiple data collaborative purchase screening method as in any of the implementations of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, where computer executable instructions are stored, where the computer executable instructions, when executed by a processor, are configured to implement a multiple data collaborative purchase screening method according to any of the implementations of the first aspect.
According to the multi-data collaborative purchasing screening method device, equipment and storage medium, firstly, purchasing preference classification is obtained according to purchasing attribute information, then purchasing demand information is obtained, to-be-purchased goods and to-be-purchased suppliers for supplying to-be-purchased goods are determined according to the purchasing demand information, market change trend is analyzed, to-be-purchased goods and to-be-purchased suppliers are analyzed and processed, a ranking list of to-be-purchased goods and a ranking list of to-be-purchased suppliers are obtained, finally, purchased goods and suppliers are selected and confirmed in the ranking list according to the purchasing preference classification and the purchasing demand information, ideal goods sources can be intelligently, efficiently and accurately recommended to the purchasing person, purchasing efficiency and flow standardization are improved, and cost benefit of the purchasing person is maximized.
Drawings
FIG. 1 is a flowchart of a multi-data collaborative purchasing screening method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a multiple data collaborative purchasing screening device 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 invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
The purchasing has very important significance for the production and operation of enterprises, the number of products with the same or similar functions on the market is numerous, but the quality and price are huge, the service quality and market evaluation of different suppliers for providing the commodities are also irregular, and the commodity with high quality and low price is the common requirement of each enterprise in order to reduce the enterprise cost and improve the enterprise competitiveness. However, the current purchasing work mainly relies on personnel responsible for purchasing work in enterprises to analyze enterprise demands to determine commodities to be purchased, then research the commodities in markets to determine the commodities meeting the enterprise demands, and finally examine aspects of qualification, reputation and the like of suppliers producing the commodities to finally determine the commodities to be purchased. However, because massive information is difficult to screen manually, the most preferable commodity is difficult to find and screen by the traditional purchasing screening method, the accuracy is insufficient, the manual screening workload is huge, and the purchasing efficiency is low.
Fig. 1 is a flowchart of a multi-data collaborative purchasing screening method according to an embodiment of the present application, as shown in fig. 1, the multi-data collaborative purchasing screening method according to the embodiment includes:
step S110, obtaining the preference classification of the buyer according to the attribute information of the buyer.
The multi-data collaborative purchasing screening method provided by the embodiment of the application is applied to a computer or a server with data acquisition and processing capabilities, and the computer or the server can be arranged on a purchasing person local to a team purchasing screening requirement or on a cloud. The multi-data collaborative purchasing screening method provided by the embodiment of the application is used for screening the commodity to be purchased by the buyer and the suppliers of the commodity, so that the problem that the manually selected commodity and the suppliers are inaccurate is avoided, and the screening efficiency is improved.
Considering the screening result of the purchased goods, the influence of the factors of the purchased goods, the influence of the purchasing demands, the influence of different goods and the influence of different suppliers are considered, the multi-data collaborative purchasing screening method provided by the embodiment of the application performs common analysis from the angles, screens the goods purchased by the purchased goods, so as to realize automatic purchasing screening of the goods, and can accurately screen the goods and suppliers with the highest matching degree with the purchased goods. In the embodiment of the present application, the buyer refers to an enterprise, an organization, a person, etc. that needs to purchase, and does not refer to a person responsible for purchasing.
Different buyers have different personalized requirements for purchasing of the commodity, so in this embodiment, attribute information of the buyers is obtained, and the preference classification of the buyers is obtained according to the attribute information of the buyers. The attribute information of the purchasing person is related information for representing the purchasing characteristics of the purchasing person, and different purchasing persons have different personalized requirements on commodities to be purchased due to different business attributes, using habits and other attributes of the purchasing person. Therefore, in this embodiment, the preference of the buyer is classified, and the type of demand of the buyer for the commodity is distinguished. The buyer preference categorization may be one or more and may be directed to the type of merchandise required by the buyer at one or more angles. For example, when purchasing is a school or a market, both have the requirement of using paper, wherein the paper purchased by the school is mostly used for printing exercises and test papers, and has higher requirements on the quality and the writing easiness of the paper, but only ordinary white paper is generally needed; the paper products in the market are mostly used for printing notices and publicity sheets, the quality requirements for common paper products are not high, but the paper products have the use requirements for other special paper products besides white paper. The preference classifications of the two buyers are different, the preference classification of the school is high-quality white paper, and the preference classification of the market is common white paper and special paper.
The buyer attribute information may include at least one of a buyer's purchase history, business attributes, custom attributes. The purchasing history of the purchasing person represents the commodity which the purchasing person has purchased and the information of the corresponding provider, the business attribute represents the business type which the purchasing person engages in, and the information can represent the purchasing characteristics and purchasing preference of the purchasing person to the commodity. For example, the purchase history of the buyer is all the commodity purchase records within a preset time period, and the business attribute of the buyer is the business type engaged in by the buyer. In addition, the attribute information of the buyer can also be the custom attribute of the buyer, and the buyer can configure the attribute information of the buyer according to the custom requirement, so that the personalized requirement of the buyer on the purchased goods is further embodied. For example, the buyer may configure buyer attribute information to favor a particular product at a particular location or a particular provider.
Specifically, obtaining the buyer preference classification according to the buyer attribute information includes: and inputting the attribute information of the buyer into a preference classification model to obtain the preference classification label of the buyer. The preference training model can be trained in advance, and the obtained attribute information of the purchasing person is input into the preference training model, so that the preference classification label of the purchasing person can be output. The preference training model can be trained by a deep learning algorithm. Or classifying the buyer preferences may employ any of the existing clustering algorithms or classification algorithms.
Step S120, obtaining purchasing demand information, determining the commodity to be purchased and the supplier to be purchased for supplying the commodity to be purchased according to the purchasing demand information, and analyzing the market change trend.
Each time of purchase of the buyer has different purchase demands, and each time of purchase demands need to be specified according to the actual production and operation conditions of the buyer. After the purchasing demand information is acquired, the purchasing demand information is analyzed, so that the commodity to be purchased of the purchasing person can be determined, and the supplier to be purchased for supplying the commodity to be purchased is determined based on the determined commodity to be purchased. The purchasing demand information may be specific information such as name, type, model, etc. of the commodity to be purchased, or may be descriptive information of the commodity to be purchased, such as commodity, purchasing budget, etc. with working parameters within a certain threshold range.
In addition, market change trend can be analyzed according to the purchasing demand information, and the market change trend is used for representing market change conditions of commodities meeting the purchasing demand information in a certain time range, such as supply and demand relation, price change, market prospect and the like. The market change trend may be obtained by statistical analysis of data before purchase occurs, or may be data for predicting future markets according to existing data. The market change trend can analyze and display the determined commodity to be purchased, and can analyze and display the supplier to be purchased.
Specifically, the purchasing demand information can be obtained first, the commodity to be purchased in the purchasing demand information is identified, then the supplier to be purchased for supplying the commodity to be purchased is inquired according to the commodity to be purchased, and finally the market change trend of the commodity to be purchased is analyzed based on the commodity to be purchased and the supplier to be purchased.
And step S130, analyzing and processing the commodities to be purchased and the suppliers to be purchased to obtain a ranking list of the commodities to be purchased and a ranking list of the suppliers to be purchased.
The number of the commodities to be purchased and the suppliers to be purchased determined in step S120 is generally plural, and for better screening and display to the buyer, the commodities to be purchased and the suppliers to be purchased may be ranked, that is, the purchase priorities of the commodities to be purchased and the suppliers to be purchased are determined. Therefore, in the step, the to-be-purchased goods and the to-be-purchased suppliers are analyzed and processed, and the to-be-purchased goods and the to-be-purchased suppliers are ordered according to the acquired related information of the to-be-purchased goods and the to-be-purchased suppliers, so that a ranking list of the to-be-purchased goods and the to-be-purchased suppliers is obtained.
The ranking list of the commodities to be purchased can be obtained according to market sales data of the commodities to be purchased, and the ranking list of the suppliers to be purchased can be obtained according to market evaluation data of the suppliers to be purchased. The marketing data of the goods to be purchased is various data generated when each goods to be purchased is sold on the market, including data of sales statistics and data of service statistics, for example, the marketing data includes at least one of sales volume, evaluation, cost performance and price deviation. The evaluation data of the suppliers to be purchased are mainly obtained through feedback data of market service ends of the suppliers, and the market evaluation data comprises at least one of response, overdue, complaint, rejection, area and bid rate.
In one embodiment, the commodity classification can be performed on the commodities to be purchased, and then a ranking list of various commodities to be purchased is obtained according to the marketing data of the commodities to be purchased. The commodities to be purchased are determined according to the purchasing demand information, but on one hand, multiple different types of commodities may be required in the purchasing demand information, and on the other hand, the commodities meeting the requirement of the agreeing type may also have different attributes, so that the commodities to be purchased may be classified first, so that the commodities in the same classification are ordered. Similarly, the supplier qualification classification can be performed according to the market evaluation data of the suppliers to be purchased, and a ranking list of the suppliers to be purchased in each classification can be obtained according to the market evaluation data. Suppliers may be classified into different qualifications according to their different characteristics, for example, suppliers may be classified as available, qualified, preferred, and then the different classified suppliers may be ranked. In addition, the supply and demand relationship of the suppliers to be purchased can be further determined.
And step 140, selecting the purchased goods and suppliers from the ranking list according to the preference classification of the buyer and the purchasing demand information.
Finally, the purchased goods and suppliers can be selected and confirmed from the ranking list according to the buyer preference classification and the purchasing demand information determined in the steps. In short, the goods and suppliers meeting the purchasing preference classification and purchasing demand information are selected from the goods ranking list and the supplier ranking list according to the order of priority from top to bottom. The method has the advantages that the determined purchasing goods and suppliers are confirmed, the preference of the purchasing person is considered, the purchasing demand can be met, the goods and suppliers with higher priority are selected according to the marketing condition and the market evaluation data, and the goods and suppliers which are most suitable for the purchasing person can be accurately selected. On one hand, the screening efficiency of purchased goods is improved, on the other hand, the purchasing person can acquire the goods and suppliers which are most matched with the self requirements, the cost benefit of the purchasing person is maximized, and the competitiveness of the purchasing person is improved.
Specifically, cross analysis is performed on the preference classification of the buyer, the market change trend, the ranking list of the commodities to be purchased and the ranking list of the suppliers to be purchased according to the purchase screening rule, and the purchased commodities and suppliers are confirmed. The purchasing screening rule can allocate different weights to the purchasing person preference classification, the market change trend, the ranking list of the commodity to be purchased and the ranking list of the supplier to be purchased, the weights can be adjusted according to actual use requirements, scores of different influencing factors are obtained through calculation, and finally the selection of the purchased commodity and the supplier is confirmed according to the calculated scores.
According to the multi-data collaborative purchasing screening method, firstly, purchasing preference classification is obtained according to purchasing person attribute information, then purchasing demand information is obtained, to-be-purchased goods and to-be-purchased suppliers for supplying the to-be-purchased goods are determined according to the purchasing demand information, market change trend is analyzed, to-be-purchased goods and to-be-purchased suppliers are analyzed and processed, to-be-purchased goods ranking list and to-be-purchased suppliers ranking list are obtained, finally, purchased goods and suppliers are selected and confirmed in the ranking list according to the purchasing person preference classification and the purchasing demand information, ideal goods sources can be intelligently, efficiently and accurately recommended to the purchasing person, purchasing efficiency and flow normalization are improved, and cost benefit of the purchasing person is maximized.
Fig. 2 is a schematic structural diagram of a multiple data collaborative purchasing screening device according to an embodiment of the present application, as shown in fig. 2, the multiple data collaborative purchasing screening device provided in this embodiment includes:
the buyer classification module 21 is configured to obtain buyer preference classification according to the buyer attribute information.
The demand analysis module 22 is configured to obtain purchasing demand information, determine a commodity to be purchased and a provider to be purchased that supplies the commodity to be purchased according to the purchasing demand information, and analyze market variation trend.
The ranking module 23 is configured to analyze and process the to-be-purchased goods and the to-be-purchased goods, and obtain a ranking list of the to-be-purchased goods and a ranking list of the to-be-purchased suppliers.
And the screening module 24 is used for selecting and confirming purchased goods and suppliers in the ranking list according to the purchasing preference classification and purchasing demand information.
The multi-data collaborative purchase screening device provided in this embodiment is used to implement the multi-data collaborative purchase screening method shown in fig. 1, and its implementation principle and technical effects are similar, and will not be described here again.
In the embodiment shown in fig. 2, the buyer classification module 21 is specifically configured to input buyer attribute information into the preference classification model to obtain a buyer preference classification label.
In the embodiment shown in fig. 2, the buyer attribute information includes at least one of a buyer's purchase history, business attributes, custom attributes.
In the embodiment shown in fig. 2, the demand analysis module 22 is specifically configured to obtain purchasing demand information, and identify a commodity to be purchased in the purchasing demand information; inquiring and supplying a to-be-purchased supplier of the to-be-purchased commodity according to the to-be-purchased commodity; and analyzing market change trend of the commodity to be purchased based on the commodity to be purchased and the provider to be purchased.
In the embodiment shown in fig. 2, the ranking module 23 is specifically configured to obtain a ranking list of the to-be-purchased goods according to the marketing data of the to-be-purchased goods; and obtaining a ranking list of the suppliers to be purchased according to the market evaluation data of the suppliers to be purchased.
Further, the ranking module 23 is specifically configured to sort the commodities to be purchased, and obtain ranking lists of various commodities to be purchased according to market sales data of the commodities to be purchased; and classifying the suppliers according to the market evaluation data of the suppliers to be purchased, and obtaining a ranking list of each classified supplier to be purchased according to the market evaluation data. The marketing data includes at least one of sales volume, rating, cost performance, price bias; the market rating data includes at least one of response, expiration, complaint, rejection, zone, bid rate.
In the embodiment shown in fig. 2, the screening module 24 is specifically configured to perform cross analysis on the buyer preference classification, the market variation trend, the ranking list of the to-be-purchased goods and the ranking list of the to-be-purchased suppliers according to the purchase screening rule, and confirm the purchased goods and suppliers.
It should be noted that, it should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the buyer classification module 21, the demand analysis module 22, the ranking module 23, and the screening module 24 may be individually set up processing elements, or may be integrated into one of the chips of the above-mentioned device, or may be stored in the memory of the above-mentioned device in the form of program codes, and the functions of the buyer classification module 21, the demand analysis module 22, the ranking module 23, and the screening module 24 may be called and executed by one of the processing elements of the above-mentioned device. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element here may be an integrated circuit with signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 3, the electronic device may include: a transceiver 31, a processor 32, a memory 33.
Processor 32 executes the computer-executable instructions stored in memory, causing processor 32 to perform the aspects of the embodiments described above. The processor 32 may be a general-purpose processor including a central processing unit CPU, a network processor (network processor, NP), etc.; but may also be a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.
The memory 33 is connected to the processor 32 via a system bus and communicates with each other, the memory 33 being arranged to store computer program instructions.
The transceiver 31 may be used to obtain a task to be run and configuration information of the task to be run.
The system bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The system bus may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus. The transceiver is used to enable communication between the database access device and other computers (e.g., clients, read-write libraries, and read-only libraries). The memory may include random access memory (random access memory, RAM) and may also include non-volatile memory (non-volatile memory).
The embodiment of the application also provides a chip for running the instruction, which is used for executing the technical scheme of the multi-data collaborative purchasing screening method in the embodiment.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer instructions, and when the computer instructions run on a computer, the computer is caused to execute the technical scheme of the data collaborative purchase screening method in the embodiment.
The embodiment of the application also provides a computer program product, which comprises a computer program stored in a computer readable storage medium, wherein at least one processor can read the computer program from the computer readable storage medium, and the technical scheme of the multi-data collaborative purchase screening method in the embodiment can be realized when the at least one processor executes the computer program.
In general, the various embodiments of the application may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the application is not limited thereto.
Embodiments of the present application may be implemented by a data processor of a computer device executing computer program instructions, e.g. in a processor entity, either in hardware, or in a combination of software and hardware. The computer program instructions may be assembly instructions, instruction set architecture (Instruction Set Architecture, ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages.
The block diagrams of any logic flow in the figures of this application may represent program steps, or may represent interconnected logic circuits, modules, and functions, or may represent a combination of program steps and logic circuits, modules, and functions. The computer program may be stored on a memory. The Memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as, but not limited to, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), optical Memory devices and systems (digital versatile disks (Digital Video Disc, DVD) or Compact Discs (CDs)), etc., the computer readable medium may comprise a non-transitory storage medium.
Claims (11)
1. The multi-data collaborative purchase screening method is characterized by comprising the following steps of:
obtaining the preference classification of the purchasing person according to the attribute information of the purchasing person;
acquiring purchasing demand information, determining a commodity to be purchased and a provider to be purchased for supplying the commodity to be purchased according to the purchasing demand information, and analyzing market change trend;
analyzing and processing the commodity to be purchased and the provider to be purchased to obtain a ranking list of the commodity to be purchased and a ranking list of the provider to be purchased;
and selecting the purchased goods and suppliers from the ranking list according to the purchasing preference classification and the purchasing demand information.
2. The method of claim 1, wherein the deriving the buyer preference classification based on the buyer attribute information comprises:
and inputting the attribute information of the purchasing person into a preference classification model to obtain a preference classification label of the purchasing person.
3. The method of claim 2, wherein the buyer attribute information comprises at least one of a buyer's purchase history, business attributes, custom attributes.
4. The method of claim 1, wherein the obtaining purchasing demand information, determining a commodity to be purchased and a provider to be purchased that supplies the commodity to be purchased according to the purchasing demand information, and analyzing market change trend, comprises:
acquiring purchasing demand information, and identifying commodities to be purchased in the purchasing demand information;
inquiring a to-be-purchased supplier for supplying the to-be-purchased commodity according to the to-be-purchased commodity;
and analyzing market change trend of the commodity to be purchased based on the commodity to be purchased and the provider to be purchased.
5. The method of claim 1, wherein the analyzing the to-be-purchased goods and to-be-purchased suppliers to obtain the ranked list of to-be-purchased goods and the ranked list of to-be-purchased suppliers comprises:
obtaining a ranking list of the commodities to be purchased according to the marketing data of the commodities to be purchased;
and obtaining a ranking list of the suppliers to be purchased according to the market evaluation data of the suppliers to be purchased.
6. The method of claim 5, wherein the obtaining the ranked list of items to be purchased based on the marketing data of the items to be purchased comprises:
classifying the commodities to be purchased, and obtaining ranking lists of various commodities to be purchased according to the marketing data of the commodities to be purchased;
obtaining a ranking list of the suppliers to be purchased according to the market evaluation data of the suppliers to be purchased, wherein the ranking list comprises the following steps:
and classifying the suppliers according to the market evaluation data of the suppliers to be purchased, and obtaining a ranking list of each classified supplier to be purchased according to the market evaluation data.
7. The method of claim 5 or 6, wherein the marketing data comprises at least one of sales volume, ratings, cost performance, price bias;
the market rating data includes at least one of response, expiration, complaint, rejection, zone, bid rate.
8. The method of claim 1, wherein the selecting the purchased goods and suppliers in the ranked list based on the buyer preference classification and the purchasing demand information comprises:
and according to purchase screening rules, cross analysis is carried out on the preference classification of the purchasing person, the market change trend, the ranking list of the commodities to be purchased and the ranking list of the suppliers to be purchased, and the purchased commodities and suppliers are confirmed.
9. A multiple data collaborative purchase screening apparatus, comprising:
the purchasing person classifying module is used for obtaining purchasing person preference classification according to the purchasing person attribute information;
the demand analysis module is used for acquiring purchasing demand information, determining commodities to be purchased and suppliers to be purchased for supplying the commodities to be purchased according to the purchasing demand information, and analyzing market change trend;
the ranking module is used for analyzing and processing the commodity to be purchased and the commodity to be purchased to obtain a ranking list of the commodity to be purchased and a ranking list of a provider to be purchased;
and the screening module is used for selecting and confirming purchased goods and suppliers in the ranking list according to the preference classification of the purchasing person and the purchasing demand information.
10. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-8.
11. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-8.
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