An enterprise advertising promotion ranking method, device, system and storage medium

Abstract

The present invention discloses a method for sorting enterprise advertising promotions. The method is applied to an enterprise advertising promotion sorting system, and the method includes: obtaining data information of enterprise advertising promotion objects in multiple parameter dimensions, and integrating the data parameter information to obtain a customer big data set; obtaining supply data of enterprise advertising promoters in multiple parameter dimensions, and integrating the supply data parameter information to obtain a supply data set; sorting and classifying the customer big data set to obtain a first promotion label set, and sorting and classifying the supply data set to obtain a second promotion label set; generating an interest data set according to the first promotion label set and the second promotion label set; optimizing and arranging the interest data set according to the customer big data set to generate a promotion data set; according to the sorting result of the interest data in the promotion data set.

Description

Technical Field

[0001] The present invention relates to the field of e-commerce platforms, and specifically provides a method, device, system, and storage medium for sorting enterprise advertising promotions. Background Art

[0002] The B2B platform is a model of e-commerce, which is the abbreviation of English Business-to-Business, that is, business-to-business, or e-commerce between enterprises. That is, enterprises exchange products, services, and information through the Internet. It combines the enterprise internal network with customers through the B2B website, and through the rapid response of the network, provides better services for customers, thereby promoting the business development of enterprises. The intermediary-controlled market strategy is established by a third party outside the buyer and seller to match the needs and prices of the buyer and seller. Through the B2B e-commerce method, the transaction and circulation efficiency of traditional industries is improved, and then the traditional industry pattern is optimized and reshaped.

[0003] Currently, the resources of both buyers and sellers on the B2B platform are becoming increasingly large. To meet the search needs of both enterprises, keyword search is generally used. Keywords, also known as reserved words, refer to the terms for the names of products, services, or companies that visitors hope to understand in the search engine industry. Simply put, keywords are what users input when using them, which can most comprehensively summarize the information content that users want to search for. The needs or products of both buyers and sellers can be summarized into one or more keyword terms, and by matching the keyword terms of both sides, the desired information can be presented more accurately.

[0004] Although existing platforms have added optional auxiliary search items to improve the accuracy in search, the promotion accuracy is still relatively low. The products of sellers often have situations such as new brands or brands, etc., resulting in parameters such as sales volume not being able to truly reflect the product quality. At this time, the search settings of buyers may exclude potential suitable sellers, and thus the buyer and seller may miss each other. Summary of the Invention

[0005] This application provides a method, device, system, and storage medium for sorting enterprise advertising promotions to solve the technical problem of relatively low promotion accuracy in the prior art.

[0006] In view of the above problems, this application provides a method, device, system, and storage medium for sorting enterprise advertising promotions.

[0007] In the first aspect of the present application, a method for sorting enterprise advertising promotions is provided. The method is applied to an enterprise advertising promotion sorting system, and the method includes: obtaining data information of enterprise advertising promotion objects in multiple parameter dimensions, and integrating the data parameter information to obtain a customer big data set; obtaining supply data of enterprise advertising promoters in multiple parameter dimensions, and integrating the supply data parameter information to obtain a supply data set; sorting and classifying the customer big data set to obtain a first promotion label set, and sorting and classifying the supply data set to obtain a second promotion label set; generating an interest data set according to the first promotion label set and the second promotion label set; optimizing and arranging the interest data set according to the customer big data set to generate a promotion data set; and according to the sorting result of the interest data in the promotion data set, sequentially retrieving the corresponding supply data set, and generating a promotion page for the enterprise advertising promotion object according to the retrieved supply data set.

[0008] In the second aspect of the present application, an apparatus for sorting enterprise advertising promotions is provided. The apparatus includes: a first obtaining unit for obtaining data information of enterprise advertising promotion objects in multiple parameter dimensions, and integrating the data parameter information to obtain a customer big data set; a second obtaining unit for obtaining supply data of enterprise advertising promoters in multiple parameter dimensions, and integrating the supply data parameter information to obtain a supply data set; a first processing unit for sorting and classifying the customer big data set to obtain a first promotion label set, and sorting and classifying the supply data set to obtain a second promotion label set; a second processing unit for generating an interest data set according to the first promotion label set and the second promotion label set; a third processing unit for optimizing and arranging the interest data set according to the customer big data set to generate a promotion data set; and a fourth processing unit for sequentially retrieving the corresponding supply data set according to the sorting result of the interest data in the promotion data set, and generating a promotion page for the enterprise advertising promotion object according to the retrieved supply data set.

[0009] In the third aspect of the present application, an enterprise advertising promotion sorting system is provided, including: a processor, the processor is coupled with a memory, and the memory is used for storing a program, when the program is executed by the processor, the system is enabled to execute the functions of the method described in the first aspect.

[0010] In the fourth aspect of the present application, a computer-readable storage medium is provided. A computer program is stored on the storage medium, and when the computer program is executed by a processor, the functions of the method described in the first aspect are realized.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] In an embodiment of this application, by obtaining data information of an enterprise advertising promotion object in multiple parameter dimensions and integrating the data parameter information, a customer big data set is obtained; by obtaining supply data of an enterprise advertising promoter in multiple parameter dimensions and integrating the supply data parameter information, a supply data set is obtained; by sorting and classifying the customer big data set, a first promotion label set is obtained, and by sorting and classifying the supply data set, a second promotion label set is obtained; according to the first promotion label set and the second promotion label set, an interest data set is generated; according to the customer big data set, the interest data set is optimized and arranged to generate a promotion data set; according to the sorting result of the interest data in the promotion data set, the corresponding supply data set is retrieved in sequence, and according to the retrieved supply data set, a promotion page for the enterprise advertising promotion object is generated, thereby solving the technical problem of low promotion accuracy in the prior art.

[0013] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 It is a flowchart showing the process of a method for sorting enterprise advertising promotions provided by this application;

[0016] Figure 2 It is a flowchart showing the process of generating a promotion data set in a method for sorting enterprise advertising promotions provided by this application;

[0017] Figure 3 It is a flowchart showing the process of optimizing the initial arrangement set in a method for sorting enterprise advertising promotions provided by this application;

[0018] Figure 4 It is a schematic diagram of the device structure of a method for sorting enterprise advertising promotions provided by this application;

[0019] Figure 5This is a schematic structural diagram of an exemplary electronic device of the present application.

[0020] In the figure: 11, the first acquisition unit; 12, the second acquisition unit; 13, the first processing unit; 14, the second processing unit; 15, the third processing unit; 16, the fourth processing unit; 300, the electronic device; 301, the memory; 302, the processor; 303, the communication interface; 304, the bus architecture. Detailed implementation manners

[0021] The present application provides an enterprise advertising promotion ranking method and system to solve the technical problem of low promotion accuracy in the prior art.

[0022] Application overview

[0023] Although existing platforms have added optional auxiliary search items such as price and sales volume in search to improve accuracy, the promotion accuracy is still low. The products of sellers often have parameters such as price and sales volume that cannot truly reflect their product quality due to new brands or brand premiums, etc. At this time, the search settings of buyers may exclude potential suitable sellers, and thus the buyers and sellers may miss each other.

[0024] In view of the above technical problems, the general idea of the technical solution provided by the present application is as follows:

[0025] In an embodiment of the present application, data information of an enterprise advertising promotion object in multiple parameter dimensions is acquired, and the data parameter information is integrated to obtain a customer big data set; supply data of an enterprise advertising promoter in multiple parameter dimensions is acquired, and the supply data parameter information is integrated to obtain a supply data set; the customer big data set is sorted and classified to obtain a first promotion label set, and the supply data set is sorted and classified to obtain a second promotion label set; an interest data set is generated according to the first promotion label set and the second promotion label set; the interest data set is optimized and arranged according to the customer big data set to generate a promotion data set; according to the sorting result of the interest data in the promotion data set, the corresponding supply data set is retrieved in sequence, and a promotion page for the enterprise advertising promotion object is generated according to the retrieved supply data set, thereby solving the technical problem of low promotion accuracy in the prior art.

[0026] After introducing the basic principle of the present application, hereinafter, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application. Additionally, it should be noted that for the sake of convenience of description, only the parts related to the present application rather than all are shown in the accompanying drawings.

[0027] Embodiment 1

[0028] As Figure 1 shown, the present application provides an enterprise advertising promotion ranking method, which is applied to an enterprise advertising promotion ranking system. The method includes:

[0029] S100: Obtain the data information of the enterprise advertising promotion object in multiple parameter dimensions, and integrate the data parameter information to obtain a customer big data set;

[0030] In the embodiment of the present application, the enterprise advertising promotion object refers to the customer who uses the system to search for suppliers. Specifically, by retrieving the data of the buyer customers using the system and through public big data and past cooperation information in the system, the supplier information desired by the customers can be more accurately located and pushed.

[0031] The step S100 in the method provided by the embodiment of the present application includes:

[0032] S110: Collect and obtain the search parameters of the enterprise advertising promotion object to obtain the first customer big data;

[0033] S120: Collect and obtain the past purchase parameters of the enterprise advertising promotion object to obtain the second customer big data;

[0034] S130: Collect and obtain the past feedback parameters of the enterprise advertising promotion object to obtain the third customer big data;

[0035] S140: Use the first customer big data, the second customer big data, and the third customer big data as the customer big data set.

[0036] In the embodiments of the present application, the search parameter refers to the search keyword input by the customer during this search. The past purchase parameter refers to the procurement information publicly disclosed by the customer in the system or other source big data in the past. The past feedback parameter refers to the evaluation feedback made by the customer on past purchases. Specifically, the search keyword can be used to roughly locate the customer's needs. Combining with the past purchase parameter and the past feedback parameter can more accurately locate the customer's demand direction, and then match more suitable suppliers in the subsequent calculation and analysis. Exemplarily, the enterprise buyer representative inputs a certain keyword in the platform search. At this time, the purchase records matching the keyword are synchronously called in the company's past purchase records, and the purchase records and their subsequent evaluation feedback are packaged together for subsequent calls.

[0037] S200: Obtain the supply data of the enterprise advertising promoter in multiple parameter dimensions, and integrate the parameter information of the supply data to obtain a supply data set;

[0038] The enterprise advertising promoter refers to the supply enterprise that provides supply information to customer enterprises on this platform. By collecting the supply characteristics of the supply enterprise and collecting its past supply data on this platform, the evaluation of customers on it, and other publicly available big data information, the supply characteristics can be corrected in subsequent steps, so as to more accurately match the buyer and the seller.

[0039] The steps in the method provided by the embodiments of the present application include:

[0040] S210: Obtain the data parameter information of the enterprise advertising promoter in the keyword parameter dimension to obtain the first supply data. The keyword parameter refers to the brand name of the supplier, supply information, and parameter keywords;

[0041] S220: Obtain the data parameter information of the enterprise advertising promoter in the supply feedback parameter dimension to obtain the second supply data. The supply feedback parameter refers to the past supply volume of the supplier and the evaluation made by the customer enterprise on its product characteristics after procurement;

[0042] S230: Obtain the data parameter information of the enterprise advertising promoter in the product characteristic parameter dimension to obtain the third supply data. The product characteristic parameter refers to the selling points of the product in its related field;

[0043] S240: Integrate the first supply data, the second supply data, and the third supply data to obtain a supply data set.

[0044] In the embodiments of the present application, keyword parameters refer to keyword terms such as the brand name of the supplier, supply information, and parameters. Supply feedback parameters refer to the past supply volume of the supplier and the evaluations made by the customer enterprise on the product characteristics after procurement. Product characteristic parameters refer to the selling points and characteristics of the product in its relevant field. Keyword parameters can be used to locate the supply items of the supply enterprise. Product characteristic parameters locate the nature level of the products supplied by the supply enterprise in the industry and are corrected in conjunction with supply feedback parameters. Specifically, when the supplier displays its supplied products on the platform, it analyzes the product name and the selling points mentioned in the product introduction, then defines keyword terms for its products and defines their product characteristics. At the same time, it synchronously calls the evaluations of the buyer enterprises that match the definition from the company's past supply records, and packages the purchase records and their subsequent evaluation feedback for subsequent calls.

[0045] For example, a company displays a certain electronic product it supplies on the platform and mentions selling point information such as the light weight of the product in its advertising introduction. At the same time, it calls the evaluation information of the product, such as information that does not match the described weight, and packages all the evaluation information and selling point information and sends it to the subsequent steps.

[0046] S300: Sort and classify the customer big data set to obtain a first set of promotion labels, and sort and classify the supply data set to obtain a second set of promotion labels;

[0047] In the embodiments of the present application, the first supply data is analyzed to obtain a first set of promotion labels, and the first customer big data is analyzed to obtain a second set of promotion labels. Specifically, the keywords input by the buyer enterprise representative are analyzed to adjust the label range to be searched, and the keyword terms defined by the seller enterprise are integrated and labeled.

[0048] Step S300 in the method provided by the embodiments of the present application includes:

[0049] S310: Collect and obtain the key label information in the second customer big data to obtain a first initial set of promotion labels;

[0050] S320: Adjust the first initial set of promotion labels according to the third customer big data to obtain a first set of promotion labels;

[0051] S330: Collect and obtain the key label information in the third supply data to obtain a second initial set of promotion labels;

[0052] S340: Adjust the second initial set of promotion labels according to the second supply data to obtain a second set of promotion labels.

[0053] When the buyer enterprise representative enters keywords, since the words entered are from within their own knowledge scope, within different industries or product circles, the same product may have multiple different names. In many cases, the keywords entered by the buyer enterprise representative cannot directly correspond to the product 100%. Therefore, it is crucial to identify synonyms and product aliases for the entered keywords and generate professional noun tags based on them. The same applies to the products provided by the seller enterprise. Due to its own characteristics or certain local language factors, there will be a large difference in the name. Professional adjustment is made to the self-defined words of the seller enterprise's products to form professional noun tags.

[0054] Optionally, in order to reflect the professionalism of the platform and enable the buyer and seller to better recognize the platform, after forming the professional noun tags, a suggested replacement function can be directly provided at the input to make the information of the buyer and seller more standardized and professional.

[0055] S400: Generate an interest data set according to the first promotion tag set and the second promotion tag set;

[0056] In the embodiments of the present application, tags that meet the needs of the buyer enterprise are screened out from the second promotion tag set, so as to optimize the promotion arrangement according to the screening results later, arrange the merchants and commodities that meet the needs of the buyer enterprise in the priority position, and improve the retrieval efficiency of the buyer enterprise.

[0057] The steps in the method provided by the embodiments of the present application for step S400 include:

[0058] S410: Use the second supply data as a screening condition to screen the second promotion tag set to obtain a first interest result set;

[0059] S420: Use the first promotion tag set as a screening condition to screen the first interest result set to obtain a preferred interest result;

[0060] S430: Use the preferred interest result and the first interest result set as the interest data set.

[0061] Screen the second promotion tags according to the information fed back by the second supply data. Specifically, since the evaluation information in the second supply data can objectively reflect whether the tags identified in the second promotion tags are true and valid, screen the tag validity according to the evaluation and eliminate the invalid part of the tags. Exemplarily, when a certain company's tag for its product is extremely light in weight, but after the joint feedback of multiple enterprises that its product promotion is false, it should be determined that this tag is invalid.

[0062] Optionally, swap steps S410 and S420 and make adaptive adjustments as:

[0063] S410: Use the first promoted label set as a screening condition to screen the first interest result set to obtain the first interest result set;

[0064] S420: Use the second supply data as a screening condition to screen the first interest result set to obtain the preferred interest result.

[0065] In the original step sequence, S410 can be executed regularly to adjust the network-wide data. However, as the platform data volume increases, it will lead to a long search time and affect the user experience. After making an adaptive adjustment at this time, only a small-scale label validity determination is performed during retrieval. As the data volume further increases, the execution of S420 can be set to be executed at regular intervals, and the non-execution steps skip S420. At this time, S430 is adaptively adjusted to use the first interest result set as the interest data set.

[0066] S500: Optimally arrange the interest data set according to the customer big data set to generate a promoted data set;

[0067] In the embodiments of the present application, by analyzing and calculating the specific needs of the customer enterprise and inferring the preferences based on the big data of the customer enterprise, the accuracy of the push can be improved, so that the customer enterprise can better find the most suitable supply enterprise for itself on the platform.

[0068] See Figure 2 As shown in, step S500 in the method provided by the embodiments of the present application includes:

[0069] S510: Construct a matching optimization model according to the customer big data set and the supply data set. Among them, multiple calculation channels in the matching optimization model extract parameters from each other according to the initial values;

[0070] S520: Generate a weight parameter set according to the customer big data set, the supply data set, and the weight evaluation of the first promoted label set and the second promoted label set;

[0071] S530: Correlate the weight parameter set with the interest data set in a corresponding manner, and arrange the interest data set according to the weight parameter set to generate an initial arrangement set;

[0072] S540: Sort and adjust the initial arrangement set according to the customer big data set and the supply data set to generate a promoted data set.

[0073] As Figure 2, enterprise customers generally have regularities in the selection of suppliers. They will follow certain focuses for selection. By analyzing the past purchase records of customer enterprises, it is possible to better analyze the types of suppliers preferred by customer enterprises, thereby improving the effectiveness of promotion. Based on the past purchase records of customers, classify and summarize all the tags of the cooperation partners of customer enterprises, divide the weight coefficients according to the proportion of the total number of tags, and bind the divided weight coefficients to the tags, thereby judging the selection preferences of customer enterprises. On this basis, sort according to the settlement value to obtain the optimal promotion data set.

[0074] Among them, the weight coefficient is a specific value calculated based on the number of purchases and the proportion in past purchases. This value to a certain extent reflects the threshold requirements of customer enterprises for the characteristics represented by certain tags of suppliers. And based on this specific value and the tags owned by the potential suppliers obtained after this search, a digital settlement value can be obtained, and then direct numerical arrangement can be carried out in the subsequent arrangement.

[0075] For example, when an enterprise purchases a certain steel structure part, it has a relatively high requirement for small errors. Therefore, all the suppliers it has purchased from in the past have tags with small errors certified as valid. At this time, the weight coefficient of this tag will be taken as the maximum value when calculating and used in subsequent numerical calculations.

[0076] S600: According to the sorting result of the interest data in the promotion data set, sequentially retrieve the corresponding supply data set, and generate a promotion page for the enterprise advertising promotion object according to the retrieved supply data set.

[0077] In the embodiment of the present application, the suppliers and their related products are arranged according to the obtained promotion ranking, and a promotion page belonging to the customer's current search is generated under the web page algorithm.

[0078] Among them, as Figure 3 , step S540 in the method provided by the embodiment of the present application includes:

[0079] S541: Generate a set of screening conditions according to the customer big data set;

[0080] S542: Generate a set of parameters to be screened according to the supply data set;

[0081] S543: Use the set of screening conditions as the screening criterion to screen the set of parameters to be screened to obtain a set of screening rankings;

[0082] S544: Sort and adjust the initial arrangement set according to the set of screening rankings to generate a promotion data set.

[0083] Most of the time, the inspection standards of each enterprise are not exactly the same. For the same characteristic of the same commodity, there are also differences in the evaluations of each purchasing enterprise. Therefore, it is more important to retrieve the past evaluation parameters of the customer enterprise and participate in the evaluation. For a certain label, when other enterprises have already evaluated the label as false, but due to the insufficient total proportion, the global determination of the label is true. After the customer enterprise purchases the products of this supplier and evaluates them as true, before the next evaluation of the products as false, it is still regarded as a true label for promotion. And even for a label with a global determination of true, after the customer enterprise evaluates it as false, during subsequent retrievals, this label is false for this customer enterprise.

[0084] Exemplarily, after enterprise A purchases a certain standard part from this supplier and evaluates that its error is large, but at this time, the proportion of negative reviews globally is small, and the promotion label of this commodity still shows that the error is small. Then enterprise B purchases this commodity again and gives a good review. Then during subsequent promotions, even if the label with a small error disappears due to the global proportion, when enterprise B searches again, the label with a small error for this commodity is still regarded as valid and participates in the calculation of the weight value. This shows that for enterprise B, the label with a small error for this commodity is sufficient to meet its acceptance standards and conforms to the procurement standards of enterprise B. Most of the time, for enterprise customers, the subjective standards of good quality and low price are generated accordingly.

[0085] Embodiment 2

[0086] Based on the same inventive concept as an enterprise advertisement promotion ranking method in the foregoing embodiment, as Figure 4 shown, the present application provides one of the enterprise advertisement promotion ranking devices, wherein, the enterprise advertisement promotion ranking device includes:

[0087] The first obtaining unit 11 is configured to obtain the data information of the enterprise advertisement promotion object on multiple parameter dimensions, and integrate the data parameter information to obtain a customer big data set;

[0088] The second obtaining unit 12 is configured to obtain the supply data of the enterprise advertisement promoter on multiple parameter dimensions, and integrate the supply data parameter information to obtain a supply data set;

[0089] The first processing unit 13 is configured to sort and classify the customer big data set to obtain a first promotion label set, and sort and classify the supply data set to obtain a second promotion label set;

[0090] The second processing unit 14 is configured to generate an interest data set according to the first promotion label set and the second promotion label set;

[0091] The third processing unit 15 is configured to optimize and arrange the interest data set according to the customer big data set to generate a promotion data set;

[0092] The fourth processing unit 16 is configured to sequentially retrieve the corresponding supply data set according to the sorting result of the interest data in the promotion data set, and generate a promotion page for the enterprise advertisement promotion object according to the retrieved supply data set.

[0093] Furthermore, the system further includes:

[0094] The third acquisition unit is configured to collect and obtain search parameters of the enterprise advertisement promotion object to obtain the first customer big data;

[0095] The fourth acquisition unit is configured to collect and obtain past purchase parameters of the enterprise advertisement promotion object to obtain the second customer big data;

[0096] The fifth acquisition unit is configured to collect and obtain past feedback parameters of the enterprise advertisement promotion object to obtain the third customer big data;

[0097] The fifth processing unit is configured to use the first customer big data, the second customer big data, and the third customer big data as the customer big data set.

[0098] Furthermore, the system further includes:

[0099] The sixth acquisition unit is configured to obtain data parameter information of the enterprise advertisement promoter in the dimension of keyword parameters to obtain the first supply data, where the keyword parameters refer to the brand name of the supplier, the supply information, and the parameter keyword;

[0100] The seventh acquisition unit is configured to obtain data parameter information of the enterprise advertisement promoter in the dimension of supply feedback parameters to obtain the second supply data, where the supply feedback parameters refer to the past supply volume of the supplier and the evaluation of the product characteristics made by the customer enterprise after procurement;

[0101] The eighth acquisition unit is configured to obtain data parameter information of the enterprise advertisement promoter in the dimension of product characteristic parameters to obtain the third supply data, where the product characteristic parameters refer to the selling points of the product in its related field;

[0102] The sixth processing unit is configured to integrate the first supply data, the second supply data, and the third supply data to obtain a supply data set.

[0103] Furthermore, the system further includes:

[0104] The ninth acquisition unit is configured to collect and obtain key label information in the second customer big data to obtain a first initial promotion label set;

[0105] A seventh processing unit, configured to adjust the first initial promotion tag set according to the third customer big data to obtain a first promotion tag set;

[0106] A tenth obtaining unit, configured to collect and obtain key tag information in the third supply data to obtain a second initial promotion tag set;

[0107] An eighth processing unit, configured to adjust the second initial promotion tag set according to the second supply data to obtain a second promotion tag set.

[0108] Further, the system further includes:

[0109] A ninth processing unit, configured to use the second supply data as a screening condition to screen the second promotion tag set to obtain a first interest result set;

[0110] A tenth processing unit, configured to use the first promotion tag set as a screening condition to screen the first interest result set to obtain a preferred interest result;

[0111] An eleventh processing unit, configured to use the preferred interest result and the first interest result set as an interest data set.

[0112] Further, the system further includes:

[0113] A first construction unit, configured to construct a matching optimization model according to the customer big data set and the supply data set, wherein multiple calculation channels in the matching optimization model extract parameters from each other according to initial values;

[0114] A twelfth processing unit, configured to generate a weight parameter set according to the customer big data set, the supply data set, and weight evaluation of the first promotion tag set and the second promotion tag set;

[0115] A thirteenth processing unit, configured to correspondingly associate the weight parameter set with the interest data set and arrange the interest data set according to the weight parameter set to generate an initial arrangement set;

[0116] A fourteenth processing unit, configured to sort and adjust the initial arrangement set according to the customer big data set and the supply data set to generate a promotion data set.

[0117] Further, the system further includes:

[0118] A fifteenth processing unit, configured to generate a screening condition set according to the customer big data set;

[0119] A sixteenth processing unit, configured to generate a screened parameter set according to the supply data set;

[0120] The seventeenth processing unit is configured to screen the set of parameters to be screened with the set of screening conditions as the screening criterion, and obtain a screened and sorted set;

[0121] The eighteenth processing unit is configured to sort and adjust the initial permutation set according to the screened and sorted set to generate a promoted data set.

[0122] Embodiment III

[0123] Based on the same inventive concept as an enterprise advertising promotion sorting method in the foregoing embodiments, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method in Embodiment I is implemented.

[0124] Exemplary electronic device

[0125] Based on the same inventive concept as an enterprise advertising promotion sorting method in the foregoing embodiments, the present application provides an electronic device, where the electronic device can be configured as an enterprise advertising promotion sorting system. The following will refer to Figure 5 to describe the electronic device of the present application. The electronic device 300 includes: a processor 302, and the processor 302 is coupled to a memory 301. The memory 301 is used to store a program. When the program is executed by the processor, the electronic device 300 is caused to execute the steps of the method described in Embodiment I.

[0126] The electronic device 300 further includes: a communication interface 303 and a bus architecture 304. Among them, the communication interface 303, the processor 302, and the memory 301 can be interconnected through the bus architecture 304; the bus architecture 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus architecture 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0127] The processor 302 can be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the solution of the present application.

[0128] A communication interface 303, using any device such as a transceiver, for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), wired access network, etc.

[0129] The memory 301 can be a ROM or other types of static storage devices that can store static information and instructions, a RAM or other types of dynamic storage devices that can store information and instructions, or it can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but not limited to this. The memory can exist independently and be connected to the processor through the bus architecture 304. The memory can also be integrated with the processor.

[0130] Among them, the memory 301 is used to store computer execution instructions for implementing the solution of this application, and is controlled by the processor 302 for execution. The processor 302 is used to execute the computer execution instructions stored in the memory 301, so as to implement an enterprise advertising promotion sorting method provided by the above embodiments of this application.

[0131] Those of ordinary skill in the art can understand that: the various digital numbers such as the first and second involved in this application are only for the convenience of description and are not used to limit the scope of this application, nor do they represent the order of precedence. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one" means one or more. At least two means two or more. "At least one", "any one" or their similar expressions refer to any combination of these items, including any combination of single items (pieces) or plural items (pieces). For example, at least one (piece, type) of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple.

[0132] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in this application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer refers to

[0133] The instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0134] The various illustrative logical units and circuits described in this application can be implemented or operated to perform the described functions by a design of a general-purpose processor, a digital signal processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of the above. The general-purpose processor can be a microprocessor. Optionally, the general-purpose processor can also be any conventional processor, controller, microcontroller, or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0135] The steps of the methods or algorithms described in this application can be directly embedded in hardware, software units executed by a processor, or a combination of both. The software units can be stored in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be provided in an ASIC, and the ASIC can be provided in a terminal. Optionally, the processor and the storage medium can also be provided in different components of the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the steps in the process Figure 1 in one process or multiple processes and / or blocks Figure 1 or steps for the functions specified in multiple blocks.

[0136] Although this application has been described in conjunction with specific features and their embodiments, it is obvious that various modifications and combinations can be made without departing from the spirit and scope of this application. Accordingly, this specification and the drawings are merely exemplary descriptions of this application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art can make various changes and modifications to this application without departing from the scope of this application. Thus, if these modifications and variations of this application fall within the scope of this application and its equivalent technologies, this application is intended to include these changes and modifications.

Claims

1. An enterprise advertising promotion ranking method, characterized in that, the method is applied to an enterprise advertising promotion ranking system, and the method includes: Obtain the data information of enterprise advertising promotion objects on multiple parameter dimensions, and integrate the data parameter information to obtain a customer big data set; Obtain the supply data of enterprise advertising promoters on multiple parameter dimensions, and integrate the supply data parameter information to obtain a supply data set, including: obtaining the data parameter information of the enterprise advertising promoter on the keyword parameter dimension to obtain the first supply data, where the keyword parameter refers to the brand name of the supplier, supply information and parameter keyword words, and the keyword parameter refers to the brand name of the supplier, supply information and parameter keyword words; obtaining the data parameter information of the enterprise advertising promoter on the supply feedback parameter dimension to obtain the second supply data, where the supply feedback parameter refers to the past supply volume of the supplier and the evaluation made by the customer enterprise on its product characteristics after purchase, and the supply feedback parameter refers to the past supply volume of the supplier and the evaluation made by the customer enterprise on its product characteristics after purchase; obtaining the data parameter information of the enterprise advertising promoter on the product characteristic parameter dimension to obtain the third supply data, where the product characteristic parameter refers to the selling point characteristics of the product in its related field, and the product characteristic parameter refers to the selling point characteristics of the product in its related field; integrating the first supply data, the second supply data and the third supply data to obtain a supply data set; Sort and classify the customer big data set to obtain a first promotion label set, and sort and classify the supply data set to obtain a second promotion label set, including: collecting and obtaining the key label information in the second customer big data to obtain a first initial promotion label set; adjusting the first initial promotion label set according to the third customer big data to obtain a first promotion label set; collecting and obtaining the key label information in the third supply data to obtain a second initial promotion label set; adjusting the second initial promotion label set according to the second supply data to obtain a second promotion label set; Generate an interest data set according to the first promotion label set and the second promotion label set; Optimize and arrange the interest data set according to the customer big data set to generate a promotion data set; According to the sorting result of the interest data in the promotion data set, sequentially retrieve the corresponding supply data set, and generate a promotion page for the enterprise advertising promotion object according to the retrieved supply data set.

2. The method according to claim 1, characterized in that, the supply data on the multiple parameter dimensions includes at least two of: a search parameter dimension, a past purchase parameter dimension, and a past feedback parameter; wherein, the search parameter refers to the search keyword words input by the customer during the current search, the past purchase parameter refers to the purchase information publicly disclosed by the customer in this system or other source big data in the past, and the past feedback parameter refers to the evaluation feedback made by the customer on past purchases.

3. The method according to claim 1, wherein, generating an interest data set according to the first promotion tag set and the second promotion tag set includes: using the second supply data as a screening condition to screen the second promotion tag set to obtain a first interest result set; using the first promotion tag set as a screening condition to screen the first interest result set to obtain a preferred interest result; using the preferred interest result and the first interest result set as the interest data set.

4. The method according to any one of claims 1-3, wherein, optimally arranging the interest data set according to the customer big data set to generate a promotion data set includes: constructing a matching optimization model according to the customer big data set and the supply data set, wherein multiple calculation channels in the matching optimization model are constructed by mutually extracting parameters according to initial values; generating a weight parameter for each calculation channel according to the customer big data set, the supply data set, and weight evaluation of the first promotion tag set and the second promotion tag set, and generating a weight parameter set according to the weight parameter of each calculation channel; correspondingly associating the weight parameter set with the interest data set, and arranging the interest data set according to the weight parameter set to generate an initial arrangement set; sorting and adjusting the initial arrangement set according to the customer big data set and the supply data set to generate a promotion data set.

5. The method according to any one of claim 4, wherein, sorting and adjusting the initial arrangement set according to the customer big data set and the supply data set to generate a promotion data set includes: generating a screening condition set according to the customer big data set; generating a screened parameter set according to the supply data set; using the screening condition set as a screening criterion to screen the screened parameter set to obtain a screening and sorting set; sorting and adjusting the initial arrangement set according to the screening and sorting set to generate the promotion data set.

6. An enterprise advertising promotion sorting device, wherein, configured to implement an enterprise advertising promotion sorting method according to any one of claims 1 to 5, the device includes: a first obtaining unit (11) configured to obtain data information of an enterprise advertising promotion object in multiple parameter dimensions, and integrate the data parameter information to obtain a customer big data set; a second obtaining unit (12) configured to obtain supply data of an enterprise advertising promoter in multiple parameter dimensions, and integrate the supply data parameter information to obtain a supply data set; a first processing unit (13) configured to sort and classify the customer big data set to obtain a first promotion tag set, and sort and classify the supply data set to obtain a second promotion tag set; a second processing unit (14) configured to generate an interest data set according to the first promotion tag set and the second promotion tag set; A third processing unit (15) for optimizing and arranging the interest data set according to the customer big data set to generate a promotion data set; A fourth processing unit (16) for sequentially retrieving the corresponding supply data set according to the sorting result of the interest data in the promotion data set, and generating a promotion page for the enterprise advertising promotion object according to the retrieved supply data set.

7. An enterprise advertising promotion sorting system, characterized in that, it includes: A processor, the processor is coupled to a memory, and the memory is used to store a program. When the program is executed by the processor, the system is enabled to execute the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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