An online business opportunity matching management system based on SaaS cloud platform

Abstract

The present invention provides an online business opportunity matching management system based on a SaaS cloud platform, comprising: a seller business opportunity mining end, used to mine corresponding seller business opportunities based on a product list provided by the seller end; a buyer business opportunity mining end, used to mine corresponding buyer business opportunities based on usage information of the buyer end; a SaaS cloud platform, used to aggregate all seller business opportunities and all seller business opportunities to obtain a corresponding latest business opportunity pool, match business opportunities based on the latest business opportunity pool, and obtain corresponding business opportunity matching results; used to seize all sales opportunities based on the SaaS cloud platform and understand all sales channels with the greatest potential opportunities, so as to achieve more transactions; increase the potential possibility of business opportunity matching, and reduce the cost of personalized definition of mining mechanisms.

Description

Technical Field

[0001] The present invention relates to the technical field of business opportunity matching, and in particular to an online business opportunity matching management system based on a SaaS cloud platform. Background Art

[0002] SaaS is the abbreviation of Software-as-a-service. It is a model that has emerged with the development of Internet technology and the maturity of application software. It is a model of providing software through the Internet. Manufacturers deploy application software uniformly on their own servers. Customers can order the required application software services from manufacturers through the Internet according to their actual needs, and obtain the services provided by manufacturers through the Internet. Users use web-based software to manage business operations. For many small businesses, SaaS is the best way to adopt advanced technology. It eliminates the need for enterprises to purchase, build and maintain infrastructure and applications.

[0003] At present, in the process of business opportunity matching, business opportunities are gradually screened and the number is getting smaller and smaller, and only a small part of them are finally sold. Therefore, how to seize all sales opportunities and reach more transactions is the main pursuit in the field of business opportunity matching. The SaaS cloud platform increases the potential for business opportunity matching and reduces the cost of personalized definition of mining mechanisms.

[0004] Therefore, the present invention proposes an online business opportunity matching management system based on a SaaS cloud platform. Summary of the invention

[0005] The present invention provides an online business opportunity matching management system based on a SaaS cloud platform, which is used to seize all sales opportunities based on the SaaS cloud platform and understand all sales channels with the greatest potential opportunities, thereby achieving more transactions; the potential possibility of business opportunity matching is increased, and the cost of personalized definition of mining mechanisms can be reduced.

[0006] The present invention provides a business opportunity online matching management system based on a SaaS cloud platform, comprising:

[0007] The seller business opportunity mining end is used to mine corresponding seller business opportunities based on the product list provided by the seller end;

[0008] The buyer's business opportunity mining end is used to mine corresponding buyer's business opportunities based on the buyer's usage information;

[0009] The SaaS cloud platform is used to aggregate all seller business opportunities and obtain the corresponding latest business opportunity pool for all seller business opportunities, match business opportunities based on the latest business opportunity pool, and obtain corresponding business opportunity matching results.

[0010] Preferably, the seller business opportunity mining terminal includes:

[0011] A commodity receiving module, used for receiving a commodity list input by the seller;

[0012] A rough extraction module, used to roughly extract business opportunities from the product list to obtain a corresponding first business opportunity keyword bag;

[0013] A business opportunity search module, configured to search the current business opportunity pool for a corresponding related hot business opportunity word bag based on the first business opportunity keyword contained in the first business opportunity keyword bag;

[0014] A fine extraction module is used to perform fine extraction on the product list based on the relevant hot business opportunity word bag to obtain the seller business opportunities corresponding to the seller side.

[0015] Preferably, the fine extraction module comprises:

[0016] A bag-of-words extraction unit, used to extract a bag of keywords for commodity attributes and a bag of keywords for commodity types that can be provided by the seller based on the commodity list;

[0017] A first matching unit, configured to match each relevant hot business opportunity keyword included in the relevant hot business opportunity word bag with each product attribute keyword included in the product attribute keyword bag, and calculate a corresponding first matching degree;

[0018] A first screening unit, configured to use the related hot business opportunity keyword with the first matching degree greater than a first matching degree threshold in the related hot business opportunity word bag as a corresponding first business opportunity keyword;

[0019] A second matching unit, configured to match each relevant hot business opportunity keyword included in the relevant hot business opportunity word bag with each commodity type keyword included in the commodity type keyword bag, and calculate a corresponding second matching degree;

[0020] A second screening unit, configured to use the relevant hot business opportunity keyword in the relevant hot business opportunity word bag whose second matching degree is greater than a second matching degree threshold as a corresponding second business opportunity keyword;

[0021] A unique screening unit, used to determine the unique business opportunity keywords of the seller side based on the product list;

[0022] The first aggregation unit is used to aggregate the first business opportunity keyword, the second business opportunity keyword and the unique business opportunity keyword to obtain the seller business opportunity corresponding to the seller side.

[0023] Preferably, the unique screening unit comprises:

[0024] A feature extraction subunit, configured to extract features of the commodities included in the commodity list to obtain a first commodity feature of each commodity;

[0025] A first screening subunit, configured to screen out corresponding second product features in the current business opportunity pool based on product attributes corresponding to the product;

[0026] A second screening subunit is used to screen out corresponding third commodity features in the current business opportunity pool based on the commodity type corresponding to the commodity;

[0027] A feature aggregation subunit, used for aggregating all second product features and all third product features to obtain existing product features corresponding to the product;

[0028] A frequency counting subunit, used to count the first occurrence frequency of the existing commodity feature in the current business opportunity pool;

[0029] a feature matching subunit, configured to match the first product feature with the existing product feature to obtain a corresponding matching result, and determine a second occurrence frequency corresponding to the first product feature based on the matching result and the first occurrence frequency;

[0030] an exclusive judgment subunit, configured to judge whether there is an exclusive product feature with a second occurrence frequency of 0 in the first product features, and if so, construct a unique business opportunity keyword corresponding to the seller side based on the exclusive product feature;

[0031] a uniqueness determination subunit, configured to determine the uniqueness corresponding to the first product feature based on the appearance frequency when there is no unique product feature with a second appearance frequency of 0 in the first product features;

[0032] A uniqueness sorting subunit, used to sort the first product features in descending order of the uniqueness, and obtain a uniqueness sorting sequence of product features corresponding to the seller side;

[0033] The business opportunity determination subunit is used to determine the unique product features corresponding to the seller side based on the product feature uniqueness ranking sequence, and construct the unique business opportunity keywords corresponding to the seller side based on the unique product features.

[0034] Preferably, the buyer-side business opportunity mining terminal includes:

[0035] An information acquisition module, used to acquire the transaction records and browsing records of the buyer and user information;

[0036] A first mining module, configured to mine a first buyer business opportunity corresponding to the buyer side based on the transaction record;

[0037] A second mining module, used for mining a second buyer business opportunity corresponding to the buyer end based on the browsing record;

[0038] A third mining module is used to mine potential buyer business opportunities corresponding to the buyer side based on the user information;

[0039] The business opportunity aggregation module is used to aggregate the first buyer business opportunity, the second buyer business opportunity and the potential buyer business opportunity to obtain the buyer business opportunity corresponding to the buyer side.

[0040] Preferably, the first mining module includes:

[0041] A first classification unit is used to determine a corresponding transaction commodity list based on the transaction record, classify the transaction commodities included in the transaction commodity list based on commodity types, and obtain a transaction commodity set corresponding to each commodity type;

[0042] A first determining unit, configured to determine a corresponding first commodity feature type list based on the commodity type;

[0043] A first judgment unit is used to judge whether there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodity set, and if so, extract the first common feature corresponding to the corresponding commodity feature type from the transaction commodity set, and construct a first buyer sub-opportunity corresponding to the buyer end based on the first common feature;

[0044] A first clustering unit is configured to perform cluster analysis on the commodity names corresponding to the transaction commodities contained in the transaction commodity set to obtain a plurality of commodity name classification clusters corresponding to the transaction commodity set when there is no common feature corresponding to the commodity feature type contained in the first commodity feature type list in the transaction commodity set;

[0045] A second judgment unit is used to judge whether there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the commodity name classification cluster, and if so, extract a second common feature corresponding to the corresponding commodity feature type from the transaction commodities corresponding to the commodity names included in the commodity name classification cluster, and construct a first buyer sub-opportunity corresponding to the buyer end based on the second common feature;

[0046] a second clustering unit, configured to, when there is no common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the commodity name classification cluster, continue to perform cluster analysis on the commodity name classification cluster to obtain a plurality of subclusters corresponding to the commodity name classification cluster;

[0047] A third judgment unit is used to judge whether there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the sub-cluster, and if so, extract the third common feature corresponding to the corresponding commodity feature type from the transaction commodities corresponding to the commodity names included in the sub-cluster, and construct the first buyer sub-business opportunity corresponding to the buyer end based on the third common feature;

[0048] A third clustering unit is used to, when there is no common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the sub-cluster, continue clustering analysis on the sub-cluster until the total number of commodity names included in the latest determined sub-cluster is less than a preset threshold or there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the latest determined sub-cluster, stop clustering analysis, and use the extracted common feature as the corresponding fourth common feature, and construct the first buyer sub-business opportunity corresponding to the buyer side based on the fourth common feature;

[0049] The second aggregation unit is used to aggregate all the first buyer sub-business opportunities to obtain the first buyer business opportunity corresponding to the buyer side.

[0050] Preferably, the second mining module includes:

[0051] A product determination unit, configured to divide the browsing record into time periods to obtain sub-browsing records corresponding to different time periods, and determine browsing product sets corresponding to different time periods based on the sub-browsing records;

[0052] A second determining unit, configured to classify the browsed product set to obtain corresponding browsed product types, and determine a corresponding second product feature type list based on the browsed product types;

[0053] The business opportunity determination unit is used to extract common features of the browsed product set based on the product feature types included in the second product feature type list, obtain the fifth common features corresponding to the corresponding time period, and construct the second buyer business opportunities corresponding to the buyer side in different time periods based on the fifth common features.

[0054] Preferably, the third mining module includes:

[0055] A buyer screening unit, configured to extract identity features from the user information, obtain corresponding identity features, and screen out other buyers with the same identity features as the buyer in the current business opportunity pool;

[0056] The commonality extraction unit is used to retrieve the business opportunity set corresponding to the other buyer ends, extract the commonality of the business opportunity set, obtain the corresponding sixth commonality feature, and construct the potential buyer business opportunity corresponding to the corresponding buyer end based on the sixth commonality feature.

[0057] Preferably, the SaaS cloud platform includes:

[0058] A business opportunity pool update module, used to update all seller business opportunities and all seller business opportunities to the current business opportunity pool to obtain the corresponding latest business opportunity pool;

[0059] A two-way matching module, used for performing two-way matching between the seller business opportunities and the buyer business opportunities contained in the latest business opportunity pool to obtain corresponding two-way matching results;

[0060] A two-way push module, used to generate a corresponding two-way push mechanism based on the two-way matching result;

[0061] A feedback receiving module, configured to receive first feedback information from a corresponding buyer and second feedback information from a corresponding seller based on the two-way push mechanism;

[0062] A business opportunity matching module is used to obtain a corresponding business opportunity matching result based on the first feedback information and the second feedback information.

[0063] Preferably, the business opportunity matching module includes:

[0064] a relationship establishing unit, configured to establish a transaction relationship based on the first feedback information and the second feedback information;

[0065] The business opportunity matching unit is used to generate a corresponding electronic transaction contract based on the transaction relationship and use the electronic transaction contract as the corresponding business opportunity matching result.

[0066] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0067] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0069] Figure 1Schematic diagram of an online business opportunity matching management system based on a SaaS cloud platform in an embodiment of the present invention;

[0070] Figure 2 This is a schematic diagram of a seller's business opportunity mining terminal in an embodiment of the present invention;

[0071] Figure 3 This is a schematic diagram of a fine extraction module in an embodiment of the present invention;

[0072] Figure 4 A schematic diagram of a unique screening unit in an embodiment of the present invention;

[0073] Figure 5 This is a schematic diagram of a buyer-side business opportunity mining terminal in an embodiment of the present invention;

[0074] Figure 6 This is a schematic diagram of a first mining module in an embodiment of the present invention;

[0075] Figure 7 This is a schematic diagram of a second mining module in an embodiment of the present invention;

[0076] Figure 8 This is a schematic diagram of a third mining module in an embodiment of the present invention;

[0077] Fig. 9 This is a schematic diagram of a SaaS cloud platform in an embodiment of the present invention;

[0078] Fig.10 Schematic diagram of a business opportunity matching module in an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0080] Embodiment 1:

[0081] The present invention provides a business opportunity online matching management system based on the SaaS cloud platform. Figure 1 ,include:

[0082] The seller business opportunity mining end is used to mine corresponding seller business opportunities based on the product list provided by the seller end;

[0083] The buyer's business opportunity mining end is used to mine corresponding buyer's business opportunities based on the buyer's usage information;

[0084] The SaaS cloud platform is used to aggregate all seller business opportunities and obtain the corresponding latest business opportunity pool for all seller business opportunities, match business opportunities based on the latest business opportunity pool, and obtain corresponding business opportunity matching results.

[0085] In this embodiment, the seller side is the seller user in the business opportunity matching process.

[0086] In this embodiment, the commodity list is a list of salable commodities provided by the seller.

[0087] In this embodiment, the seller business opportunity is the seller's business opportunity mined based on the product list provided by the seller.

[0088] In this embodiment, the buyer side refers to the buyer user in the business opportunity matching process.

[0089] In this embodiment, the usage information is the relevant information when the buyer uses the business opportunity online matching management system based on the SaaS cloud platform.

[0090] In this embodiment, the buyer's business opportunity is the buyer's business opportunity mined based on the usage information of the buyer.

[0091] In this embodiment, the latest business opportunity pool is a pool obtained by aggregating all seller business opportunities and all buyer business opportunities.

[0092] In this embodiment, the business opportunity matching result is the result obtained after matching business opportunities in the latest business opportunity pool.

[0093] The beneficial effects of the above technologies are: based on the SaaS cloud platform, all sales opportunities are captured and all sales channels with the greatest potential opportunities are understood, resulting in more transactions; the potential for business opportunity matchmaking is increased, and the cost of personalized definition of mining mechanisms can be reduced.

[0094] Embodiment 2:

[0095] Based on Example 1, the seller's business opportunity mining end refers to Figure 2 ,include:

[0096] A commodity receiving module, used for receiving a commodity list input by the seller;

[0097] A rough extraction module, used to roughly extract business opportunities from the product list to obtain a corresponding first business opportunity keyword bag;

[0098] A business opportunity search module, configured to search the current business opportunity pool for a corresponding related hot business opportunity word bag based on the first business opportunity keyword contained in the first business opportunity keyword bag;

[0099] A fine extraction module is used to perform fine extraction on the product list based on the relevant hot business opportunity word bag to obtain the seller business opportunities corresponding to the seller side.

[0100] In this embodiment, the rough extraction of business opportunities is to extract the major categories of the commodity list, such as automobiles and automobile accessories.

[0101] In this embodiment, the first business opportunity keyword bag is a word bag consisting of business opportunity keywords obtained after roughly extracting business opportunities from the product list.

[0102] In this embodiment, the relevant hot business opportunity word bag is a word bag consisting of hot business opportunity words related to the first business opportunity keyword retrieved from the current business opportunity pool.

[0103] In this embodiment, the first business opportunity keyword is the keyword included in the first business opportunity keyword bag.

[0104] In this embodiment, the product list is finely extracted based on the relevant hot business opportunity word bag to obtain the seller business opportunities corresponding to the seller end, that is: a fine search is made in the product list to determine whether there are relevant hot business opportunity words included in the relevant hot business opportunity word bag, and the existing relevant hot business opportunity words are taken as the seller business opportunities corresponding to the seller end.

[0105] The beneficial effects of the above technology are: by roughly extracting the products provided by the seller, the first business opportunity keyword bag is obtained, and based on the first business opportunity keyword bag, relevant popular business opportunity words are retrieved in the current business opportunity pool, and then the product list is finely searched based on the popular business opportunity words, so as to accurately and comprehensively mine the popular business opportunities in the products provided by the seller, thereby increasing the probability of successful business opportunity matching.

[0106] Embodiment 3:

[0107] On the basis of Example 2, the fine extraction module, referring to Figure 3 ,include:

[0108] A bag-of-words extraction unit, used to extract a bag of keywords for commodity attributes and a bag of keywords for commodity types that can be provided by the seller based on the commodity list;

[0109] A first matching unit, configured to match each relevant hot business opportunity keyword included in the relevant hot business opportunity word bag with each product attribute keyword included in the product attribute keyword bag, and calculate a corresponding first matching degree;

[0110] A first screening unit, configured to use the related hot business opportunity keyword with the first matching degree greater than a first matching degree threshold in the related hot business opportunity word bag as a corresponding first business opportunity keyword;

[0111] A second matching unit, configured to match each relevant hot business opportunity keyword included in the relevant hot business opportunity word bag with each commodity type keyword included in the commodity type keyword bag, and calculate a corresponding second matching degree;

[0112] A second screening unit, configured to use the relevant hot business opportunity keyword in the relevant hot business opportunity word bag whose second matching degree is greater than a second matching degree threshold as a corresponding second business opportunity keyword;

[0113] A unique screening unit, used to determine the unique business opportunity keywords of the seller side based on the product list;

[0114] The first aggregation unit is used to aggregate the first business opportunity keyword, the second business opportunity keyword and the unique business opportunity keyword to obtain the seller business opportunity corresponding to the seller side.

[0115] In this embodiment, the bag of product attribute keywords is a bag of words consisting of product attribute keywords extracted based on the product list.

[0116] In this embodiment, the commodity type keyword bag is a word bag consisting of commodity type keywords extracted based on the commodity list.

[0117] In this embodiment, each relevant hot business opportunity keyword included in the relevant hot business opportunity word bag is matched with each product attribute keyword included in the product attribute keyword bag, and the corresponding first matching degree is calculated, including:

[0118]

[0119] Wherein, α1 is the first matching degree between the currently calculated relevant hot business opportunity keywords contained in the relevant hot business opportunity word bag and the currently calculated product attribute keywords contained in the product attribute keyword bag, i is the currently calculated first difference character ordinal number contained in the currently calculated relevant hot business opportunity keywords (the character ordinal number corresponding to the character contained in the currently calculated relevant hot business opportunity keywords that is inconsistent with the character ordinal number corresponding to the currently calculated product attribute keywords), n is the total number of first difference characters contained in the currently calculated relevant hot business opportunity keywords, and a is the first difference character ordinal number contained in the currently calculated relevant hot business opportunity keywords. i is the i-th first difference character ordinal number contained in the currently calculated relevant hot business opportunity keyword, A is the total number of characters contained in the currently calculated relevant hot business opportunity keyword, j is the currently calculated second difference character ordinal number contained in the currently calculated product attribute keyword (the character ordinal number corresponding to the character contained in the currently calculated product attribute keyword that is inconsistent with the character corresponding to the currently calculated relevant hot business opportunity keyword), m is the total number of second difference characters contained in the currently calculated, b i is the ordinal number of the jth second difference character contained in the currently calculated product attribute keyword, and B is the total number of characters contained in the currently calculated product attribute keyword;

[0120] For example, if the currently calculated related hot business opportunity keyword is winter clothing, and the currently calculated product attribute keyword is summer clothing, then α1 is 0.75.

[0121] In this embodiment, the first business opportunity keyword is a related hot business opportunity keyword in the related hot business opportunity word bag whose first matching degree is greater than a first matching degree threshold.

[0122] In this embodiment, the first matching degree threshold is the minimum first matching degree that should be satisfied when the relevant hot business opportunity keyword is determined to be the first business opportunity keyword.

[0123] In this embodiment, each relevant hot business opportunity keyword included in the relevant hot business opportunity word bag is matched with each commodity type keyword included in the commodity type keyword bag, and a corresponding second matching degree is calculated, including:

[0124]

[0125] Wherein, α2 is the second matching degree between the currently calculated relevant hot business opportunity keywords contained in the relevant hot business opportunity word bag and the currently calculated product type keywords contained in the product attribute keyword bag, p is the currently calculated third difference character ordinal number contained in the currently calculated relevant hot business opportunity keywords (the character ordinal number corresponding to the character contained in the currently calculated relevant hot business opportunity keywords that is inconsistent with the character ordinal number corresponding to the currently calculated product type keyword), x is the total number of third difference characters contained in the currently calculated relevant hot business opportunity keywords, c p is the pth third difference character ordinal number contained in the currently calculated relevant hot business opportunity keywords, C is the total number of characters contained in the currently calculated relevant hot business opportunity keywords, q is the currently calculated fourth difference character ordinal number contained in the currently calculated product type keywords (the character ordinal number corresponding to the character contained in the currently calculated product type keywords that is inconsistent with the character ordinal number corresponding to the currently calculated relevant hot business opportunity keywords), z is the total number of fourth difference characters contained in the currently calculated, d q is the ordinal number of the qth fourth difference character contained in the currently calculated product type keyword, and D is the total number of characters contained in the currently calculated product type keyword;

[0126] For example, if the currently calculated related hot business opportunity keyword is winter clothing, and the currently calculated product type keyword is summer clothing, then α1 is 0.75.

[0127] In this embodiment, the second business opportunity keyword is a related hot business opportunity keyword in the related hot business opportunity word bag whose second matching degree is greater than a second matching degree threshold.

[0128] In this embodiment, the second matching degree threshold is the minimum second matching degree that should be satisfied when the relevant hot business opportunity keyword is determined to be the second business opportunity keyword.

[0129] In this embodiment, the unique business opportunity keyword is a business opportunity keyword determined on the seller side based on the product list and having a low frequency of occurrence in the current business opportunity pool.

[0130] The beneficial effects of the above technology are: based on the keywords contained in the corresponding product attribute keyword bag and product type keyword bag extracted from the product list, they are matched with the keywords contained in the relevant popular business opportunity keyword bag extracted from the current business opportunity pool, and the corresponding business opportunity keywords are screened out. At the same time, business opportunity keywords with high uniqueness on the seller side are mined in the product list, realizing the dual mining of popular business opportunities and unique business opportunities on the seller side, making the results of business opportunity mining more comprehensive and increasing the possibility of business opportunity matchmaking.

[0131] Embodiment 4:

[0132] Based on Example 3, the unique screening unit, reference Figure 4 ,include:

[0133] A feature extraction subunit, configured to extract features of the commodities included in the commodity list to obtain a first commodity feature of each commodity;

[0134] A first screening subunit, configured to screen out corresponding second product features in the current business opportunity pool based on product attributes corresponding to the product;

[0135] A second screening subunit is used to screen out corresponding third commodity features in the current business opportunity pool based on the commodity type corresponding to the commodity;

[0136] A feature aggregation subunit, used for aggregating all second product features and all third product features to obtain existing product features corresponding to the product;

[0137] A frequency counting subunit, used to count the first occurrence frequency of the existing commodity feature in the current business opportunity pool;

[0138] a feature matching subunit, configured to match the first product feature with the existing product feature to obtain a corresponding matching result, and determine a second occurrence frequency corresponding to the first product feature based on the matching result and the first occurrence frequency;

[0139] an exclusive judgment subunit, configured to judge whether there is an exclusive product feature with a second occurrence frequency of 0 in the first product features, and if so, construct a unique business opportunity keyword corresponding to the seller side based on the exclusive product feature;

[0140] a uniqueness determination subunit, configured to determine the uniqueness corresponding to the first product feature based on the appearance frequency when there is no unique product feature with a second appearance frequency of 0 in the first product features;

[0141] A uniqueness sorting subunit, used to sort the first product features in descending order of the uniqueness, and obtain a uniqueness sorting sequence of product features corresponding to the seller side;

[0142] The business opportunity determination subunit is used to determine the unique product features corresponding to the seller side based on the product feature uniqueness ranking sequence, and construct the unique business opportunity keywords corresponding to the seller side based on the unique product features.

[0143] In this embodiment, the first product feature is the product feature of each product obtained by extracting features from the products included in the product list, such as size features, color features, appearance features, style features, usage features, etc.

[0144] In this embodiment, the second product feature is a product feature selected from the current business opportunity pool based on the product attributes corresponding to the product.

[0145] In this embodiment, the third product feature is a product feature screened out from the current business opportunity pool based on the product type corresponding to the product.

[0146] In this embodiment, the existing product features are product features of the product obtained by summarizing all the second product features and all the third product features.

[0147] In this embodiment, the first occurrence frequency is the occurrence frequency of the existing product feature in the current business opportunity pool, which is also the ratio of the number of times the existing product feature appears in the current business opportunity pool to the total number of times all product features appear in the current business opportunity pool.

[0148] In this embodiment, the matching result is the result obtained after matching the first product feature with the existing product features.

[0149] In this embodiment, determining the second occurrence frequency corresponding to the first product feature based on the matching result and the first occurrence frequency includes:

[0150] The first outgoing line frequency corresponding to the existing product feature matching the first product feature is used as the second occurrence frequency corresponding to the first product feature.

[0151] In this embodiment, the second occurrence frequency is the occurrence frequency corresponding to a product feature determined based on the matching result and the first occurrence frequency.

[0152] In this embodiment, determining the uniqueness corresponding to the first product feature based on the occurrence frequency includes:

[0153] ε=1-γ

[0154] In the formula, ε is the uniqueness and γ is the frequency of occurrence;

[0155] For example, if γ is 0.5, then ε is 0.5.

[0156] In this embodiment, the uniqueness ranking sequence of product features is a ranking sequence of product features corresponding to the seller side obtained by ranking the first product features in descending order of uniqueness.

[0157] In this embodiment, the unique product features corresponding to the seller are determined based on the uniqueness ranking sequence of the product features, namely:

[0158] That is, a preset number (specifically set according to the strength of business opportunity screening) of product features ranked first in the product feature uniqueness sorting sequence are used as the corresponding unique product features on the seller side.

[0159] The beneficial effects of the above technology are: feature extraction is performed on the product list on the seller's side to obtain the corresponding first product feature, corresponding existing product features are determined in the existing business opportunity pool based on product attributes and product types, the existing product features are matched with the first product features to obtain matching results, the uniqueness of the first product features is determined based on the matching results and the frequency of occurrence of the existing product features in the business opportunity pool, the first product features are sorted and screened based on the uniqueness, and potential unique business opportunities on the seller's side are comprehensively and accurately screened, which also increases the transaction probability of business opportunity matchmaking.

[0160] Embodiment 5:

[0161] Based on Example 4, the buyer-side business opportunity mining end refers to Figure 5 ,include:

[0162] An information acquisition module, used to acquire the transaction records and browsing records of the buyer and user information;

[0163] A first mining module, configured to mine a first buyer business opportunity corresponding to the buyer side based on the transaction record;

[0164] A second mining module, used for mining a second buyer business opportunity corresponding to the buyer end based on the browsing record;

[0165] A third mining module is used to mine potential buyer business opportunities corresponding to the buyer side based on the user information;

[0166] The business opportunity aggregation module is used to aggregate the first buyer business opportunity, the second buyer business opportunity and the potential buyer business opportunity to obtain the buyer business opportunity corresponding to the buyer side.

[0167] In this embodiment, the transaction record is the transaction record generated when the buyer uses the online business opportunity matching management system based on the SaaS cloud platform.

[0168] In this embodiment, the browsing history is the browsing history generated when the buyer uses the business opportunity online matching management system based on the SaaS cloud platform.

[0169] In this embodiment, the user information is identity information related to the buyer, such as age, occupation, etc.

[0170] In this embodiment, the first buyer business opportunity is a buyer-side business opportunity mined based on the transaction record.

[0171] In this embodiment, the second buyer business opportunity is a buyer-side business opportunity mined based on the browsing history.

[0172] In this embodiment, the potential buyer business opportunity is the buyer-side business opportunity mined based on user information.

[0173] The beneficial effects of the above technology are: mining the buyer's business opportunities based on the buyer's transaction records, browsing records, and user information, and then realizing comprehensive and accurate mining of the buyer's direct and potential business opportunities.

[0174] Embodiment 6:

[0175] Based on Example 5, the first mining module refers to Figure 6 ,include:

[0176] A first classification unit is used to determine a corresponding transaction commodity list based on the transaction record, classify the transaction commodities included in the transaction commodity list based on commodity types, and obtain a transaction commodity set corresponding to each commodity type;

[0177] A first determining unit, configured to determine a corresponding first commodity feature type list based on the commodity type;

[0178] A first judgment unit is used to judge whether there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodity set, and if so, extract the first common feature corresponding to the corresponding commodity feature type from the transaction commodity set, and construct a first buyer sub-opportunity corresponding to the buyer end based on the first common feature;

[0179] A first clustering unit is configured to perform cluster analysis on the commodity names corresponding to the transaction commodities contained in the transaction commodity set when there is no common feature corresponding to the commodity feature type contained in the first commodity feature type list in the transaction commodity set, so as to obtain a plurality of commodity name classification clusters corresponding to the transaction commodity set;

[0180] A second judgment unit is used to judge whether there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the commodity name classification cluster, and if so, extract a second common feature corresponding to the corresponding commodity feature type from the transaction commodities corresponding to the commodity names included in the commodity name classification cluster, and construct a first buyer sub-opportunity corresponding to the buyer end based on the second common feature;

[0181] a second clustering unit, configured to, when there is no common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the commodity name classification cluster, continue to perform cluster analysis on the commodity name classification cluster to obtain a plurality of subclusters corresponding to the commodity name classification cluster;

[0182] A third judgment unit is used to judge whether there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the sub-cluster, and if so, extract the third common feature corresponding to the corresponding commodity feature type from the transaction commodities corresponding to the commodity names included in the sub-cluster, and construct the first buyer sub-business opportunity corresponding to the buyer end based on the third common feature;

[0183] A third clustering unit is used to, when there is no common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the sub-cluster, continue clustering analysis on the sub-cluster until the total number of commodity names included in the latest determined sub-cluster is less than a preset threshold or there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the latest determined sub-cluster, stop clustering analysis, and use the extracted common feature as the corresponding fourth common feature, and construct the first buyer sub-business opportunity corresponding to the buyer side based on the fourth common feature;

[0184] The second aggregation unit is used to aggregate all the first buyer sub-business opportunities to obtain the first buyer business opportunity corresponding to the buyer side.

[0185] In this embodiment, the transaction commodity is the commodity included in the transaction commodity list.

[0186] In this embodiment, the transaction commodity list is a list of commodities included in the transaction record determined based on the transaction record.

[0187] In this embodiment, the transaction commodity set is a set of transaction commodities corresponding to each commodity type obtained by classifying the transaction commodities included in the transaction commodity list based on commodity type.

[0188] In this embodiment, the first commodity feature type list is a list including commodity type features determined based on commodity types. For example, commodity type features corresponding to clothing commodities include winter clothing and summer clothing.

[0189] In this embodiment, the first common feature is the common feature corresponding to the feature type of the corresponding commodity extracted from the transaction commodity set.

[0190] In this embodiment, the first buyer sub-business opportunity is a business opportunity corresponding to the buyer side constructed based on the first common feature, the second common feature, the third common feature, or the fourth common feature.

[0191] In this embodiment, the commodity name classification cluster is a cluster obtained by performing cluster analysis on the commodity names corresponding to the transaction commodities included in the transaction commodity set.

[0192] In this embodiment, the second common feature is to extract the common feature corresponding to the corresponding commodity feature type from the transaction commodities corresponding to the commodity names included in the commodity name classification cluster.

[0193] In this embodiment, the sub-cluster is a sub-cluster obtained by further performing cluster analysis on the commodity name classification cluster.

[0194] In this embodiment, the third common feature is a common feature corresponding to a corresponding commodity feature type extracted from the transaction commodities corresponding to the commodity names included in the sub-cluster.

[0195] In this embodiment, the preset threshold is the maximum total number of product names included in the most recently determined sub-cluster corresponding to when it is determined that there are too few product names and the cluster analysis needs to be stopped.

[0196] In this embodiment, the fourth common feature is the common feature extracted when the cluster analysis is stopped.

[0197] The beneficial effects of the above technology are: based on the transaction commodity list contained in the transaction record, a corresponding commodity feature type list is determined, based on the commodity type, the transaction commodity list is classified to obtain a corresponding transaction commodity set, based on the commodity feature type list, commonality extraction and cluster analysis are performed on the transaction commodity set until common features that meet the requirements are extracted, and the extracted common features are summarized to construct the buyer's side corresponding to the transaction record to mine the buyer's side business opportunities, thereby mining all business opportunities contained in the buyer's transaction record and increasing the probability of successful business opportunity matching.

[0198] Embodiment 7:

[0199] Based on Example 6, the second mining module refers to Figure 7 ,include:

[0200] A product determination unit, configured to divide the browsing record into time periods to obtain sub-browsing records corresponding to different time periods, and determine browsing product sets corresponding to different time periods based on the sub-browsing records;

[0201] A second determining unit, configured to classify the browsed product set to obtain corresponding browsed product types, and determine a corresponding second product feature type list based on the browsed product types;

[0202] The business opportunity determination unit is used to extract common features of the browsed product set based on the product feature types included in the second product feature type list, obtain the fifth common features corresponding to the corresponding time period, and construct the second buyer business opportunities corresponding to the buyer side in different time periods based on the fifth common features.

[0203] In this embodiment, the sub-browsing record is a portion of the browsing record corresponding to different time periods obtained by dividing the browsing record into time periods.

[0204] In this embodiment, the browsed product set is a combination of browsed products corresponding to different time periods determined based on the sub-browsing records.

[0205] In this embodiment, the browsed product type is the browsed product type in the browsed product set.

[0206] In this embodiment, the second commodity feature type list is a list including commodity type features determined based on the browsed commodity type, for example, the commodity type features corresponding to clothing commodities include winter clothing and summer clothing, etc.

[0207] In this embodiment, the fifth common feature is the common feature corresponding to the corresponding time period obtained by extracting common features of the browsed product set based on the product feature types included in the second product feature type list.

[0208] The beneficial effects of the above technology are: based on the extraction of common features of the commodity sets in different time periods contained in the browsing records, all business opportunities contained in the buyer's browsing records can be mined based on the browsing records, thereby increasing the probability of successful business opportunity matching.

[0209] Embodiment 8:

[0210] Based on Example 7, the third mining module refers to Figure 8 ,include:

[0211] A buyer screening unit, configured to extract identity features from the user information, obtain corresponding identity features, and screen out other buyers with the same identity features as the buyer in the current business opportunity pool;

[0212] The commonality extraction unit is used to retrieve the business opportunity set corresponding to the other buyer ends, extract the commonality of the business opportunity set, obtain the corresponding sixth commonality feature, and construct the potential buyer business opportunity corresponding to the corresponding buyer end based on the sixth commonality feature.

[0213] In this embodiment, the identity feature is a feature obtained by extracting the identity feature from the user information.

[0214] In this embodiment, other buyers are buyer users selected from the current business opportunity pool and having the same identity characteristics as the buyer.

[0215] In this embodiment, the business opportunity set is a set consisting of buyer businesses corresponding to other buyer ends.

[0216] In this embodiment, the sixth common feature is the common feature obtained after common features are extracted from the business opportunity set corresponding to all buyers.

[0217] The beneficial effects of the above technology are: by screening out other buyers with the same identity characteristics as the buyer in the current business opportunity pool, and determining the business opportunity sets corresponding to other buyers, and extracting common features from all business opportunity sets, potential business opportunities on the buyer side can be mined, thus achieving comprehensive mining of buyer-side business opportunities.

[0218] Embodiment 9:

[0219] Based on Example 8, the SaaS cloud platform, reference Fig. 9 ,include:

[0220] A business opportunity pool update module, used to update all seller business opportunities and all seller business opportunities to the current business opportunity pool to obtain the corresponding latest business opportunity pool;

[0221] A two-way matching module, used for performing two-way matching between the seller business opportunities and the buyer business opportunities contained in the latest business opportunity pool to obtain corresponding two-way matching results;

[0222] A two-way push module, used to generate a corresponding two-way push mechanism based on the two-way matching result;

[0223] A feedback receiving module, configured to receive first feedback information from a corresponding buyer and second feedback information from a corresponding seller based on the two-way push mechanism;

[0224] A business opportunity matching module is used to obtain a corresponding business opportunity matching result based on the first feedback information and the second feedback information.

[0225] In this embodiment, the latest business opportunity pool is the latest business opportunity pool obtained after all seller business opportunities and all seller business opportunities are updated to the current business opportunity pool.

[0226] In this embodiment, the two-way matching result is the result obtained by two-way matching the seller business opportunities and the buyer business opportunities included in the latest business opportunity pool.

[0227] In this embodiment, the two-way push mechanism is a mechanism for performing two-way push to the buyer and the seller based on the two-way matching result.

[0228] In this embodiment, the first feedback information is feedback information received from the corresponding buyer end based on the two-way push mechanism.

[0229] In this embodiment, the second feedback information is the feedback information from the corresponding seller side received based on the two-way push mechanism.

[0230] The beneficial effects of the above technology are: all the buyer-side business opportunities and seller-side business opportunities that were previously fully explored are updated to the current business opportunity pool, the latest business opportunity pool is obtained, business opportunities are matched in the latest business opportunity pool, and pushed to both parties based on the matching results, and feedback information from the buyer and seller sides regarding the push mechanism is received, and corresponding business opportunity matching results are reached based on the feedback information, completing the business opportunity matching process.

[0231] Embodiment 10:

[0232] Based on Example 9, the business opportunity matching module refers to Fig.10 ,include:

[0233] a relationship establishing unit, configured to establish a transaction relationship based on the first feedback information and the second feedback information;

[0234] The business opportunity matching unit is used to generate a corresponding electronic transaction contract based on the transaction relationship and use the electronic transaction contract as the corresponding business opportunity matching result.

[0235] In this embodiment, the transaction relationship is a commodity transaction relationship established based on the first feedback information and the second feedback information.

[0236] In this embodiment, the electronic transaction contract is an electronic contract generated based on the transaction relationship and representing the transaction reached between the two parties.

[0237] The beneficial effects of the above technology are: establishing a transaction relationship based on feedback information, generating a corresponding electronic transaction contract based on the transaction relationship, and realizing a complete business opportunity matching process.

[0238] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A business opportunity online matching management system based on SaaS cloud platform, characterized in that: include: The seller business opportunity mining end is used to mine corresponding seller business opportunities based on the product list provided by the seller end; The buyer's business opportunity mining end is used to mine corresponding buyer's business opportunities based on the buyer's usage information; The SaaS cloud platform is used to aggregate all seller business opportunities and obtain the corresponding latest business opportunity pool for all seller business opportunities, match business opportunities based on the latest business opportunity pool, and obtain corresponding business opportunity matching results; The seller's business opportunity mining end includes: A commodity receiving module, used for receiving a commodity list input by the seller; A rough extraction module, used to roughly extract business opportunities from the product list to obtain a corresponding first business opportunity keyword bag; A business opportunity search module, configured to search the current business opportunity pool for a corresponding related hot business opportunity word bag based on the first business opportunity keyword contained in the first business opportunity keyword bag; A fine extraction module, used for finely extracting the product list based on the relevant hot business opportunity word bag to obtain the seller business opportunities corresponding to the seller side; Wherein, the fine extraction module includes: A bag-of-words extraction unit, used to extract a bag of keywords for commodity attributes and a bag of keywords for commodity types that can be provided by the seller based on the commodity list; A first matching unit, configured to match each relevant hot business opportunity keyword included in the relevant hot business opportunity word bag with each product attribute keyword included in the product attribute keyword bag, and calculate a corresponding first matching degree; A first screening unit, configured to use the related hot business opportunity keyword with the first matching degree greater than a first matching degree threshold in the related hot business opportunity word bag as a corresponding first business opportunity keyword; A second matching unit, configured to match each relevant hot business opportunity keyword included in the relevant hot business opportunity word bag with each commodity type keyword included in the commodity type keyword bag, and calculate a corresponding second matching degree; A second screening unit, configured to use the relevant hot business opportunity keyword in the relevant hot business opportunity word bag whose second matching degree is greater than a second matching degree threshold as a corresponding second business opportunity keyword; A unique screening unit, used to determine the unique business opportunity keywords of the seller side based on the product list; The first aggregation unit is used to aggregate the first business opportunity keyword, the second business opportunity keyword and the unique business opportunity keyword to obtain the seller business opportunity corresponding to the seller side.

2. According to claim 1, a business opportunity online matching management system based on a SaaS cloud platform is characterized in that: The unique screening unit comprises: A feature extraction subunit, configured to extract features of the commodities included in the commodity list to obtain a first commodity feature of each commodity; A first screening subunit, configured to screen out corresponding second product features in the current business opportunity pool based on product attributes corresponding to the product; A second screening subunit is used to screen out corresponding third commodity features in the current business opportunity pool based on the commodity type corresponding to the commodity; A feature aggregation subunit, used for aggregating all second product features and all third product features to obtain existing product features corresponding to the product; A frequency counting subunit, used to count the first occurrence frequency of the existing commodity feature in the current business opportunity pool; a feature matching subunit, configured to match the first product feature with the existing product feature to obtain a corresponding matching result, and determine a second occurrence frequency corresponding to the first product feature based on the matching result and the first occurrence frequency; an exclusive judgment subunit, configured to judge whether there is an exclusive product feature with a second occurrence frequency of 0 in the first product feature, and if so, construct a unique business opportunity keyword corresponding to the seller side based on the exclusive product feature; a uniqueness determination subunit, configured to determine the uniqueness corresponding to the first product feature based on the appearance frequency when there is no unique product feature with a second appearance frequency of 0 in the first product features; A uniqueness sorting subunit, used to sort the first product features in descending order of the uniqueness, and obtain a uniqueness sorting sequence of product features corresponding to the seller side; The business opportunity determination subunit is used to determine the unique product features corresponding to the seller side based on the product feature uniqueness ranking sequence, and construct the unique business opportunity keywords corresponding to the seller side based on the unique product features.

3. According to claim 2, a business opportunity online matching management system based on a SaaS cloud platform is characterized in that: The buyer's business opportunity mining end includes: An information acquisition module, used to acquire the transaction records and browsing records of the buyer and user information; A first mining module, configured to mine a first buyer business opportunity corresponding to the buyer side based on the transaction record; A second mining module, used for mining a second buyer business opportunity corresponding to the buyer end based on the browsing record; A third mining module is used to mine potential buyer business opportunities corresponding to the buyer side based on the user information; The business opportunity aggregation module is used to aggregate the first buyer business opportunity, the second buyer business opportunity and the potential buyer business opportunity to obtain the buyer business opportunity corresponding to the buyer side.

4. According to claim 3, a business opportunity online matching management system based on a SaaS cloud platform is characterized in that: The first mining module comprises: A first classification unit is used to determine a corresponding transaction commodity list based on the transaction record, classify the transaction commodities included in the transaction commodity list based on commodity types, and obtain a transaction commodity set corresponding to each commodity type; A first determining unit, configured to determine a corresponding first commodity feature type list based on the commodity type; A first judgment unit is used to judge whether there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodity set, and if so, extract the first common feature corresponding to the corresponding commodity feature type from the transaction commodity set, and construct a first buyer sub-opportunity corresponding to the buyer end based on the first common feature; A first clustering unit is configured to perform cluster analysis on the commodity names corresponding to the transaction commodities contained in the transaction commodity set to obtain a plurality of commodity name classification clusters corresponding to the transaction commodity set when there is no common feature corresponding to the commodity feature type contained in the first commodity feature type list in the transaction commodity set; A second judgment unit is used to judge whether there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the commodity name classification cluster, and if so, extract a second common feature corresponding to the corresponding commodity feature type from the transaction commodities corresponding to the commodity names included in the commodity name classification cluster, and construct a first buyer sub-opportunity corresponding to the buyer end based on the second common feature; a second clustering unit, configured to, when there is no common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the commodity name classification cluster, continue to perform cluster analysis on the commodity name classification cluster to obtain a plurality of subclusters corresponding to the commodity name classification cluster; A third judgment unit is used to judge whether there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the sub-cluster, and if so, extract the third common feature corresponding to the corresponding commodity feature type from the transaction commodities corresponding to the commodity names included in the sub-cluster, and construct the first buyer sub-business opportunity corresponding to the buyer end based on the third common feature; A third clustering unit is used to, when there is no common feature corresponding to the commodity feature type included in the first commodity feature type list in the transaction commodities corresponding to the commodity names included in the sub-cluster, continue clustering analysis on the sub-cluster until the total number of commodity names included in the latest determined sub-cluster is less than a preset threshold or there is a common feature corresponding to the commodity feature type included in the first commodity feature type list in the latest determined sub-cluster, stop clustering analysis, and use the extracted common feature as the corresponding fourth common feature, and construct the first buyer sub-business opportunity corresponding to the buyer side based on the fourth common feature; The second aggregation unit is used to aggregate all the first buyer sub-business opportunities to obtain the first buyer business opportunity corresponding to the buyer side.

5. According to claim 4, a business opportunity online matching management system based on a SaaS cloud platform is characterized in that: The second mining module comprises: A product determination unit, configured to divide the browsing record into time periods to obtain sub-browsing records corresponding to different time periods, and determine browsing product sets corresponding to different time periods based on the sub-browsing records; A second determining unit, configured to classify the browsed product set to obtain corresponding browsed product types, and determine a corresponding second product feature type list based on the browsed product types; The business opportunity determination unit is used to extract common features of the browsed product set based on the product feature types included in the second product feature type list, obtain the fifth common features corresponding to the corresponding time period, and construct the second buyer business opportunities corresponding to the buyer side in different time periods based on the fifth common features.

6. The online business opportunity matching management system based on the SaaS cloud platform according to claim 5 is characterized in that: The third mining module comprises: A buyer screening unit, configured to extract identity features from the user information, obtain corresponding identity features, and screen out other buyers with the same identity features as the buyer in the current business opportunity pool; The commonality extraction unit is used to retrieve the business opportunity set corresponding to the other buyer ends, extract the commonality of the business opportunity set, obtain the corresponding sixth commonality feature, and construct the potential buyer business opportunity corresponding to the corresponding buyer end based on the sixth commonality feature.

7. The online business opportunity matching management system based on the SaaS cloud platform according to claim 6 is characterized in that: The SaaS cloud platform includes: A business opportunity pool update module, used to update all seller business opportunities and all seller business opportunities to the current business opportunity pool to obtain the corresponding latest business opportunity pool; A two-way matching module, used for performing two-way matching between the seller business opportunities and the buyer business opportunities contained in the latest business opportunity pool to obtain corresponding two-way matching results; A two-way push module, used to generate a corresponding two-way push mechanism based on the two-way matching result; A feedback receiving module, configured to receive first feedback information from a corresponding buyer and second feedback information from a corresponding seller based on the two-way push mechanism; A business opportunity matching module is used to obtain a corresponding business opportunity matching result based on the first feedback information and the second feedback information.

8. According to claim 7, a business opportunity online matching management system based on a SaaS cloud platform is characterized in that: The business opportunity matching module includes: a relationship establishing unit, configured to establish a transaction relationship based on the first feedback information and the second feedback information; The business opportunity matching unit is used to generate a corresponding electronic transaction contract based on the transaction relationship and use the electronic transaction contract as the corresponding business opportunity matching result.

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