JP4237312B2 - Commodity transaction processing device - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention is applicable to various products whose product value is lost or reduced after a certain period of time, such as fresh products with a short life cycle such as flowers and fruits and service products such as air tickets with expiration dates. The present invention relates to a merchandise transaction processing apparatus that is effective in transactions between a plurality of buyers and a plurality of sellers.
[0002]
[Prior art]
Conventionally, for example, transactions for selling and buying fresh flowers are performed in a wholesale market. That is, the buyer trader goes to the site, observes the actual product actually sold by the seller trader, and grasps what kind of fresh flowers are sold and how much. Then, if the desired fresh flower is sold, a transaction including the price and the like is performed with the seller.
[0003]
[Problems to be solved by the invention]
By the way, the thing which systematized electronically the sales transaction of the above goods is proposed. In this system (commodity trading system), purchase information (such as attributes or items such as the item or varieties, color, class, etc. of the fresh flowers desired to be sold, desired sales price, minimum sales price, etc.) input from the terminal device of the buyer ) And sales information input from the seller's terminal device (information on the desired flower items or varieties, color, class, etc., information on desired purchase price, etc.) and purchase information and sales Information is allocated and the transaction of goods is decided (contracted). Thereby, each trader can perform a buying and selling transaction of goods on its own terminal device without going to the wholesale market.
At this time, when a plurality of buyers and sellers are participating in the system, and purchase information and sales information are issued from each supplier, the purchase information for each sales information presented by the buyer is purchased. In accordance with fixed rules such as intentions and conditions, purchase information input order, and purchase price included in purchase information in descending order, provision with sales information is performed to determine a transaction.
[0004]
However, there are a wide variety of conditions for transactions between buyers and sellers, and the purchase information and sales information issued by each are usually different. In such a situation, if a transaction is simply determined according to a fixed rule, the transaction opportunity is limited, and the opportunity and rate of transaction determination are also reduced.
Specifically, for example, when purchase information and sales information are allocated in the order of higher purchase desired price, it is difficult to establish a transaction for a buyer with a lower purchase desired price. This is disadvantageous for a seller who wants to reduce the unit price of a product and judge a large amount of products, or a buyer that wants to purchase a large amount of products that have a low unit price.
[0005]
The present invention has been made in view of the above points, and provides a merchandise transaction processing apparatus capable of efficiently performing a sales transaction between a plurality of buyers and a plurality of sellers and increasing the rate of establishment of the sales transaction. The purpose is to provide.
[0006]
[Means for Solving the Problems]
Therefore, in order to solve the above problem, the present invention is connected to the seller side terminal and the purchaser side terminal via the network, and sales information issued from the seller side terminal and issued from the purchaser side terminal. A merchandise transaction processing apparatus that performs merchandise sales transaction processing based on purchase information, the plurality of sales information received from the seller-side terminal, the value of which is lost or decreased after a certain period, and the sales information Storage means for storing a plurality of purchase information relating to a product whose value is lost or decreased after a certain period of time received from a terminal on the purchaser side, and first clustering for clustering the sales information by product attributes included in the sales information Means, second clustering means for clustering purchase information according to desired purchase product attributes included in the purchase information, For each of the clustered sales information and the clustered purchase information, the individual sales information and the purchase information included in the set of sales information and the set of purchase information that are clustered under the same conditions. Using the first and second clustering means, in order from the set having a large number of conditions when searching, sales information satisfying each other and purchase information are searched, sales information satisfying the conditions, Transaction processing means for associating purchase information with the purchase information.
[0021]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
[0022]
(First embodiment)
The present invention is applied to, for example, a fresh flower trading system 100 as shown in FIG.
[0023]
In this fresh flower trading system 100, as shown in FIG. 1, a server-side terminal device 140 that manages the fresh flower market and a plurality of buyers A1, A2,. _A1 , 120 _A2 , ..., 120 _An And a plurality of sellers B1, B2,... _B1 , 130 _B2 , ..., 130 _Bn Are configured to be communicably connected to each other via the WAN 110.
Each terminal device on the server side, seller's B1-Bn side, and buyer's A1-An side stores a central processing unit (CPU), a keyboard, a mouse, a display, a communication device, and a processing program for this system in advance. A personal computer (personal computer) provided with a memory or the like, and the CPU reads and executes a processing program in the memory, whereby various processes described later are performed.
[0024]
Further, in the fresh flower trading system 100, each of the buyers A1, A2,. _A1 , 120 _A2 , ..., 120 _An , (A1), (A2),..., (An) of the product (fresh flowers) to be purchased (purchasing information, hereinafter also referred to as “order information”), Similarly, B2,... _B1 , 130 _B2 , ..., 130 _Bn (B1), (B2),..., (Bn) of the product (fresh flowers) to be sold (sales information, hereinafter also referred to as “shipment information”) By referring to the screen of the apparatus, it is possible to proceed with the arrangement of fresh flowers in various transaction forms.
The “product information” here includes, for example, quantity, unit price, information input time, and attribute data, and the “attribute data” includes date, deadline, product type, color, and the like. Further, the “seller” here includes a producer of an actual product, a sales agent entrusted with sales from the producer, a sales wholesaler, a sales agent, or an importer.
[0025]
Therefore, the server-side terminal device 140 stores an information reception / delivery function 141 for receiving and transmitting (distributing) various information, an application function (APP function) 142, and data necessary for various processes. Storage function (database memory: DBM) 143.
[0026]
The APP function 142 includes various transactions such as transactions based on order information (A1), (A2),..., (An) and sales information (B1), (B2),. A transaction processing function for executing transactions in the form and a system operation management function for operating and managing the terminal device 140 on the server side to control and manage the entire system and a clustering function are provided. ing.
The clustering function is a function that is the most characteristic of the present system, and will be described in detail later. Each of the clustering functions includes a plurality of purchase information (A1), (A2),..., (An) and sales information (B1), This is a function for linking (B2),..., (Bn) according to a predetermined algorithm to determine the establishment of a transaction.
[0027]
On the other hand, the terminal devices 120 on the buyer side A1, A2,. _A1 , 120 _A2 , ..., 120 _An , And terminal devices 130 on the side of sellers B1, B2,..., Bn _B1 , 130 _B2 , ..., 130 _Bn Each of them has the same configuration, and is necessary for various processes such as an information reception / distribution function 121 for receiving and distributing various information, a user interface (I / F) function 122 using a WWW browser, an application function 123, and the like. And a DBM 124 for storing data.
[0028]
Terminal device 120 _A1 , 120 _A2 , ..., 120 _An And 130 _B1 , 130 _B2 , ..., 130 _Bn Then, the interface function 122 (WWW browser, etc.) displays various data (order information, sales information, etc.) sent from the server side, and the user refers to the screen and uses the mouse, keyboard, etc. By performing the predetermined operation used, processing in various transaction forms is advanced.
[0029]
Therefore, in the fresh flower trading system 100 as described above, for example, buyers A1, A2, ..., An and sellers B1, B2, ..., Bn participate in this system, and from each terminal device, When purchase information (A1), (A2), ..., (An) and sales information (B1), (B2), ..., (Bn) are issued, these purchase information (A1), ( A2),..., (An) and sales information (B1), (B2),..., (Bn) are stored in the DBM 143 in the terminal device 140 on the server side via the WAN 110.
[0030]
Here, the purchase information (A1), (A2),..., (An) and the sales information (B1), (B2),..., (Bn) stored in the DBM 143 are shown in FIGS. Information as shown in
As shown in FIG. 2 above, the purchase information here includes “item”, “variety”, “color”, “equal class”, “origin”, “number of purchased items”, And “Purchase Price”. As for the sales information, as shown in FIG. 3 above, the “item”, “variety”, “color”, “class”, “origin”, “number of items sold”, Includes “Selling Price” and “Minimum Price”.
[0031]
Purchase information (A1), (A2),..., (An) and sales information (B1), (B2),..., (Stored in the DBM 143 as shown in FIGS. In Bn), the clustering function of the terminal device 140 on the server side is linked according to the algorithm described below, and the transaction is determined to be established.
Here, [algorithm 1] and [algorithm 2] as an example of the above algorithm will be described.
[0032]
[Algorithm 1]
This algorithm is shown by the flowchart of FIG. 4, for example.
[0033]
Step S201: Clustering processing is performed on the purchase information (A1), (A2),..., (An). The clustering process at this time is shown by the flowchart of FIG.
Note that “clustering” is a set of all order information (A1), (A2),..., (An) (hereinafter “cluster” or “clustering field”) based on a preset clustering attribute. Also called).
[0034]
First, the first purchase information (A1) of the purchase information (A1), (A2),..., (An) stored in the DBM 143 is read (step S211).
[0035]
Next, the clustering attribute of the purchase information (A1) read in step S211 is read (step S212). As the clustering attribute, an important item among items of the product attribute is set as an element for determining the price of the product by the market operator. Here, as an example, it is assumed that “item” and “color” are set.
Therefore, in this step S212, “item—“ sunflower ”” and “color—“ orange ”” included in the purchase information (A1) are read.
[0036]
Next, the clustering attribute read in step S212 is determined (step S213), and purchase information (A1) is assigned to the corresponding cluster according to the determination result (step S214). As a result, the purchase information (A1) is assigned to the “sunflower-orange” cluster b as shown in FIG.
[0037]
The subsequent purchase information (A2),..., (An) are similarly processed in steps S211 to S214, and all of the purchase information (A1) to (An) are processed in steps S211 to S214. When the process ends (step S215), the process ends.
[0038]
Therefore, the purchase information (A1), (A2),..., (An) is as shown in FIG.
"Kiku Daiwa-White" cluster a:
Purchase information (A2), purchase information (A3), purchase information (A7)
“Sunflower-Orange” cluster b:
Purchase information (A1), Purchase information (A5), Purchase information (A8), Purchase information (A9)
Cluster "Curtis-purple" c:
Purchase information (A4), purchase information (A6)
Are clustered.
[0039]
Step S202: The order of purchase information (allocation order) is determined in each of the clusters a to c generated by the clustering process described above.
For example, focusing on the cluster a, the cluster a includes purchase information (A2), purchase information (A3), and purchase information (A7). Therefore, the processing order of the purchase information (A2), (A3), and (A7) is determined in descending order of the purchase desired price. Accordingly, the processing order in this case is purchase information (A3), purchase information (A7), and purchase information (A2). By the way, the purchase desired price of the purchase information (A7) is higher than the purchase desired price of the purchase information (A2), but the specified number (number of specified items) of the purchase condition is smaller than the purchase information (A2). That is, in the purchase information (A7), “item”, “color”, “number of purchases”, and “preferred purchase price” are specified, but on the other hand, the purchase information (A2) In addition to the items specified in A7), “variety”, “class of class”, and “origin” are also specified. In this way, when the fresh flower desired to be purchased is specified more specifically, the purchase information (A2) is given priority. Therefore, the processing order in the cluster a is determined as purchase information (A3), purchase information (A2), and purchase information (A7) as shown in FIG.
The processing order of purchase information is similarly determined for the other clusters b and c.
[0040]
Step S203: In accordance with the clustering process shown in FIG. 5, the clustering process is also performed on the sales information (B1), (B2),..., (Bn).
As a result, the sales information (B1), (B2),..., (Bn) is as shown in FIG.
“Kiku Daiwa-White” cluster a ′:
Sales information (B1), Sales information (B5), Sales information (B6), Sales information (B9)
Cluster “b” of “sunflower-orange”:
Sales information (B2), Sales information (B3), Sales information (B7)
Cluster "C 'of" Statis-Purple ":
Sales information (B4), Sales information (B8)
Are clustered.
[0041]
Step S204: In the purchase information clusters a, b, and c and the corresponding sales information clusters a ′, b ′, and c ′, the transaction is established by associating the purchase information with the sales information for each cluster. To decide. The linking process here is shown by the flowchart in FIG.
[0042]
For example, focus on the “Kiku Daiwa-White” cluster. In this case, as shown in FIGS. 6 (a) and (b) above,
Purchase information cluster a:
Purchase information (A3), purchase information (A2), purchase information (A7)
Sales information cluster a ':
Sales information (B1), Sales information (B5), Sales information (B6), Sales information (B9)
It becomes.
Therefore, the processing shown in FIG. 7 is executed in order from the purchase information (A3) according to the processing order determined in step S202.
[0043]
The first purchase information (A3) has a desired sales price closest to the desired purchase price “130 yen” in the purchase information (A3), and the desired purchase price “130 yen” does not fall below the minimum sales price. Information is searched (step S221). Here, sales information (B9) is searched.
[0044]
Next, whether or not the sales number of the sales information (B9) searched in step S221 is not “0”, that is, whether or not all the sales numbers of the sales information (B9) have ended (determination of the transaction is determined). It is determined whether or not (step S222). Here, since the transaction for the sales information (B9) has not been performed yet, the processing from the next step S223 is executed.
[0045]
In step S223, the sales number of the sales information (B9) is allocated to the purchase information (A3). At this time, the purchase number of the purchase information (A3) is “5000”, and the sales number of the sales information (B9) is “4000”. Therefore, as shown in FIG. Of the number of purchased “5000”, it is assigned to “4000” (shaded portion in the figure). As a result, a transaction is established between “4000” of the purchase number “5000” in the purchase information (A3) and “4000” in the sales information (B9).
[0046]
When the transaction between the purchase information (A3) and the sales information (B9) is established in step S223, the number of sales in the sales information (B9) is updated (step S224). Here, since all the sales numbers “4000” of the sales information (B9) have been established, the sales number is updated to “0”.
[0047]
And it is discriminate | determined whether there is a remaining number in the purchase number of purchase information (A3) (step S225). Here, out of the number of purchases “5000” in the purchase information (A3), “4000” is a transaction with the sales information (B9), and “1000” is not yet a transaction. It is determined that there is. Accordingly, in this case, the purchased number is updated to “1000” in the next step S226. Thereafter, the process returns to step S221.
[0048]
Returning to step S221, the sales information (B1) that satisfies the condition next to the sales information (B9) is searched by the step S221 and the next step S222. Then, in step S223, as shown in FIG. 8, the remaining purchase number “1000” in the purchase information (A3) is set to “1000” out of the “10000” sales in the sales information (B1). ”Is allocated (black part in the figure), the transaction between the remaining purchase number“ 1000 ”in the purchase information (A3) and“ 1000 ”of the sales number“ 10000 ”in the sales information (B1) To establish. As a result, all the purchase numbers “5000” of the purchase information (A3) are terminated.
[0049]
Then, after the number of sales information (B1) is updated to “9000” in step S224, it is determined in step S225 that there is no remaining number of purchase information (A3), and the book for the purchase information (A3). The process ends.
[0050]
As described above, when the process for the purchase information (A3) is completed, the process for the next purchase information (A2) is performed. In this case as well, in the same way as the purchase information (A3), first, the desired purchase price “85 yen” closest to the desired purchase price “85 yen” in the purchase information (A2) is obtained, and the desired purchase price “85 yen” is the lowest sale. Sales information that is not lower than the price is searched (step S221). Here, sales information (B1) is searched.
[0051]
Next, whether or not the sales number of the sales information (B1) searched in step S221 is not “0”, that is, whether or not all the sales numbers of the sales information (B1) have ended (determination of the transaction). It is determined whether or not (step S222). Here, since the number of sales information (B1) transactions that have not been established is "9000", the processing from the next step S223 is executed.
[0052]
In step S223, the sales number of the sales information (B1) is allocated to the purchase information (A2). At this time, the purchase number of the purchase information (A2) is “12,000” and the sales number of the sales information (B1) is “9000”, so the sales number “9000” is the purchase number “12000”. Try to allocate to “9000” of them. As a result, a transaction is established between “9000” of the purchase number “12000” of the purchase information (A2) and “9000” of the sales information (B1).
[0053]
When the transaction between the purchase information (A2) and the sales information (B1) is established in step S223, the number of sales in the sales information (B1) is updated (step S224). Here, since all the sales numbers “9000” of the sales information (B1) are successful, the number of sales is updated to “0”.
And it is discriminate | determined whether there is a remaining number in the purchase number of purchase information (A2) (step S225). Here, out of the number of purchases “12000” in the purchase information (A2), “9000” has been traded with the sales information (B1), and “3000” has not yet been traded. It is determined that there is. Therefore, in this case, the purchased number is updated to “3000” in the next step S226.
[0054]
Thereafter, the process returns to step S221, and the subsequent processing proceeds as described above. When the process for the purchase information (A2) is completed, the process for the next purchase information (A7) is performed in the same manner.
[0055]
Step S205: Each buyer and seller are notified of the transaction completion determined in step S204 as described above.
[0056]
As described above, in [Algorithm 1], the purchase information (A1), (A2),..., (An) output from each buyer's merchant is used as a clustering attribute ( Clustering according to “item”, “color”) generates clusters a, b, c,. At this time, the processing order of purchase information is also determined within each cluster. Further, the sales information (B1), (B2),..., (Bn) output from each seller trader side is also clustered in the same manner, so that the clusters a ′, b ′, c ′,. Generate Then, in each of the purchase information cluster a and the sales information cluster a ′, the purchase information cluster b and the sales information cluster b ′,... Link information. As a result, a plurality of pieces of purchase information and a plurality of pieces of sales information can be associated with each other in accordance with various buying and selling requests, and the rate of transaction establishment can be increased efficiently in a short time.
[0057]
[Algorithm 2]
This algorithm is shown by the flowchart of FIG. 9, for example.
[0058]
Step S301: In all the purchase information (A1), (A2),..., (An) as shown in FIG. 2, the processing order for executing the processing from the next step S302 is determined. The determination of the processing order is, for example, in the order of the desired purchase price as in step S202 in [Algorithm 1]. In addition, priority is given to purchase information with many designated items.
Therefore, the processing order of the purchase information (A1), (A2),..., (An) is determined in the order as shown in FIG.
[0059]
Step S302: First, purchase information (A5) is read in accordance with the processing order determined in step S303 (see FIG. 10 above).
[0060]
Step S303: The clustering attribute of the purchase information (A5) read in step S302 is read. The clustering attribute here is also “item” and “color” as in [Algorithm 1].
Therefore, in this step S303, “item—“ sunflower ”” and “color—“ orange ”” included in the purchase information (A5) are read.
[0061]
Step S304: According to the clustering attributes (“item—“ sunflower ”” and “color—“ orange ””) read in step S303, the sales information (B1), (B2),. .., (Bn) is subjected to clustering processing.
Therefore, from the sales information (B1), (B2),..., (Bn), as shown in FIG.
Cluster “b” of “sunflower-orange”:
Sales information (B2), Sales information (B3), Sales information (B7)
Will be obtained.
[0062]
Step S305: The purchase information (A5) and the sales information (B2), sales information (B3), and sales information (B7) in the cluster b ′ are linked in the same manner as in step S204 in [Algorithm 1]. To make a deal.
At this time, if items other than “item” and “color” are specified in the purchase information, the sales information that matches the specification is given priority. As a result, in the purchase information (A5), items other than “item” and “color” are designated (such as “origin”), so that the sales information (B3) matching the designation is linked. It will be. Therefore, the sales number “11000” of the sales information (B3) is allocated to “11000” of the purchase number “12000” of the purchase information (A5). For the remaining “1000”, “1000” of the sales number “8000” of the sales information (B2) that meets the next condition is allocated.
[0063]
Step S306: Each buyer and seller are notified of the transaction determined in step S205.
[0064]
Step S307: When the process for the purchase information (A5) is completed as described above, the process returns to step S302 to execute the process for the next purchase information (A3). Accordingly, the processing of steps S302 to S306 is similarly performed on the purchase information (A3). In addition, processing is performed in the same manner for subsequent purchase information (A6), (A4),.
[0065]
As described above, in [Algorithm 2], the purchase information (A1), (A2),..., (An) output from each buyer trader is sorted according to the order of the desired purchase price, etc. In that order, the sales information is clustered according to the clustering attributes (“item”, “color”) of the purchase information, and the association is performed in the cluster generated. As in [Algorithm 1], it is possible to link a plurality of pieces of purchase information and a plurality of pieces of sales information in accordance with various buying and selling requests as in [Algorithm 1]. Can be increased.
[0066]
In the above [Algorithm 1] and [Algorithm 2], the purchase information is processed in the descending order of purchase price, but the present invention is not limited thereto. For example, the order of purchase number may be the order of entry or the order of entry.
Further, the clustering attribute is not limited to “item” and “color”.
[0067]
(Second Embodiment)
In the first embodiment described above, the market operator predetermines clustering attribute items (such as “item” and “color”). On the other hand, in the present embodiment, it is assumed that a buyer contractor can arbitrarily set the items of clustering attributes and the contents (conditions) thereof.
As an example of this algorithm, the following [Algorithm 3] and [Algorithm 4] will be described here.
[0068]
[Algorithm 3]
FIG. 12 illustrates the present algorithm.
As shown in FIG. 12, level 1 (“A” in the figure) with “product type” and “color” as clustering attributes, and level 2 (with “cluster” attributes of “product”, “color”, and “standard”). Level 3 ((C) in the figure) having “cluster” attributes “product type”, “color”, “standard”, and “producer” is defined in advance. That is, level 2 is more detailed than level 1 and level 3 is more detailed than level 2. For example, the market operator prepares these levels (hereinafter also referred to as “cluster levels”) in advance and systematically prepares them.
Here, three levels are defined for simplicity of explanation, but the present invention is not limited to this, and a plurality of other levels may be prepared.
[0069]
The buyer trader designates at which level of the levels 1 to 3 the transaction is performed, and sets the contents in each item of the clustering attribute of the designated level.
Specifically, for example, a buyer trader designates a transaction at level 1 and, for the items “variety” and “color” of the level 1 clustering attribute, he / she desires “seimo” and “white”. Set. Also, a certain buyer trader designates a transaction at level 2 and, for the level 2 clustering attribute items “variety”, “color”, and “standard”, “desired cloud” and “white” are desired. And “Hide L”.
Accordingly, purchase information in which the contents are set for the clustering attribute item according to each designated level is issued from the buyer. Here, purchase information A- (1) is designated as purchase information with level 1 designation, purchase information B- (1) is designated as purchase information with level 2 designation, and purchase information C- (1) is designated as purchase information with level 3 designation. And
[0070]
Therefore, first, the purchase information A- <3> with the finest condition designation is clustered by "cluster", "color", "standard", and "producer" which are items of the clustering attribute. Further, the sales information from the seller is clustered according to the above clustering attribute. Then, in the same cluster, the purchase information and the sales information are linked and the contract is determined (hereinafter, the contract determination process is referred to as “contract determination process 1”).
[0071]
Next, the purchase information A- <2>, which has the next finest specification of conditions, is clustered according to "cluster", "color", and "standard" which are items of the clustering attribute. Further, the sales information from the seller (the sales information remaining in the contract determination process 1) is clustered in accordance with the clustering attribute. Then, in the same cluster, the purchase information and the sales information are linked and the contract is determined (hereinafter, the contract determination process is referred to as “contract determination process 2”).
[0072]
Finally, the purchase information A- (1) is clustered with “product type” and “color” which are items of the clustering attribute. Further, the sales information from the seller (the sales information remaining in the contract determination processes 1 and 2) is clustered according to the clustering attribute. Then, within the same cluster, the purchase information and the sales information are linked and the contract is determined.
[0073]
Here, in the clustering according to the algorithm as described above, the contract is determined in a predetermined processing order (here, level 3 → level 2 → level 1) for each cluster corresponding to purchase information and sales information. I made it. This is because, for example, the “cloud” and “white” clusters at level 1 are included in the “cloud”, “white”, and “excellent L” clusters at level 2. This is because the cluster may partially overlap with other clusters.
Therefore, the level of demand is usually high for sellers and the order of processing is generally level 3 → level 2 → level 1 where the price increases. Therefore, here, level 3 → level 2 → level 1 In addition, processing was performed in the order of detailed specification of conditions. However, the processing order is not limited, and the market operator or the like may arbitrarily prescribe the processing order.
The details of the tying when the contract is determined will be described later.
[0074]
[Algorithm 4]
In [Algorithm 2] in the first embodiment described above, as shown in FIG. 13A, the order of processing of purchase information from the buyer's side (in order of high purchase price, in descending order of quantity, information) The processing order defined in advance, such as the order of input time, is determined, and the sales information is clustered according to the conditions of the purchase information in the determined order.
On the other hand, in this algorithm, as shown in FIG. 13 (b), the order of processing of sales information from the seller side (in order of high or low desired sales price, high order of quantity, and early information input time) The processing order defined in advance, such as order, is determined, and the purchase information is clustered in accordance with the conditions of the sales information in the determined order.
[0075]
FIG. 14 shows an example of the association of purchase information and sales information in the corresponding cluster in each algorithm as described above.
Here, it is assumed that purchase information (1) to (4) exists in a cluster of certain purchase information, and sales information (a) to (b) exists in a corresponding cluster of sales information. In addition, the price, quantity, and information input time are used as the reference for linking in the cluster.
The purchase information (1) and the purchase information (4) specify the same upper limit price, and the quantity also specifies the same range. However, it is assumed that purchase information (1) is input earlier than purchase information (4). The purchase information (2) and the purchase information (3) specify the upper limit price, the quantity, and the range different from the others.
On the other hand, sales information (b) designates the lowest lower limit price, and sales information (c) designates the highest lower limit price. The sales information (a) specifies the price between the lower limit prices of the sales information (b) and the sales information (c) as the lower limit price.
[0076]
Therefore, the association between the clusters of purchase information (1) to (4) and the clusters of sales information (a) to (b) as described above is performed in the following order.
[0077]
1. The purchase information linking process order is determined in descending order of the upper limit price. At this time, when purchase information having the same upper limit price exists, priority is given to the case where a larger quantity is specified, and priority is given to the earlier information input time when the quantity is also specified.
Here, the purchase information specifying the highest upper limit price is two, purchase information (1) and purchase information (4). Of these purchase information, the information input time is earlier than the purchase information (1). Therefore, priority is given to this purchase information (1). Therefore, the purchase information linking process order is purchase information (1) → purchase information (4) → purchase information (3) → purchase information (2).
[0078]
2. The sales information linking process order is determined in descending order of the lower limit price. Alternatively, from the viewpoint of prompting the buyer to input a high price for the upper limit price and prompting the seller to enter a low price for the lower price, the order of the sales information linking process is determined in ascending order of the lower price. Also in this case, when sales information having the same lower limit price exists, priority is given to the one with a larger quantity specified, and when the same quantity is specified, priority is given to the earlier information input time.
Therefore, in the order of the lowest price, the sales information linking processing order is sales information (c) → sales information (a) → sales information (b). The order of attachment processing is sales information (b) → sales information (a) → sales information (c).
[0079]
3. In the case where the sales information linking processing order is the order in which the lower limit price is high, the purchase information (1) is linked to this if the first sales information (c) satisfies the condition, and the contract is decided. .
At this time, when the condition of the sales information (c) is not satisfied, it is associated with the next sales information (a). When the sales information (a) is not satisfied, it is linked with the last sales information (b). Further, for example, when purchase information (1) and sales information (c) are linked, and the desired quantity (necessary quantity) of purchase information (1) is not satisfied with the quantity specified in sales information (c), Until the required quantity is satisfied, the association with the next sales information (here, sales information (a), sales information (b)) is repeated.
Similarly, in the case where the sales information linking processing order is the lowest price, the purchase information (1) is linked in the order of sales information (b), sales information (a), and sales information (c). The contract is decided. If the processing proceeds in the order of the linking process in this case, the product of the lower limit price (lowest price) of the sales information that is lower than the upper limit price of the purchase information (1) is linked to the purchase information (1). There is an effect that the probability of closing is increased and the transaction is more activated.
Thereafter, the next purchase information (4) is processed in the same manner, and subsequently, the purchase information (3) and the purchase information (2) are sequentially processed.
[0080]
2. Processing described above Generally, the price at the time of closing contract is determined based on the upper limit price (@ 1) specified in the purchase information, but not limited to this, it is specified in the sales information. It may be determined based on the lower limit price (@ 2). Or you may make it determine based on the price designated by each of purchase information and sales information. For example, the price is determined based on the inner dividing point ((@ 1 + @ 2) / 2) of the upper limit price (@ 1) of the purchase information and the lower limit price (@ 2) of the sales information.
[0081]
Note that the above-described linking process and price determination process may be NON-ruled according to the characteristics of the target content or the evaluation function of the trading participant, or may be made to use GA.
[0082]
By the way, in this Embodiment, although object goods are made into a fresh flower, it is not restricted to this. For example, it may be a product whose expiration date is determined, such as an air ticket, a ticket for other transportation, or a concert ticket.
[0083]
As an example, in FIG. 15, when the target product is an air ticket, the buyer performs various conditions on the seller's sales information (limitation of contents of clustering attribute items at various cluster levels). This shows how the abstraction level of the linking process order corresponds to the case of fresh flowers described above.
As shown in FIG. 15, in the case of an air ticket, in addition to “price” and “quantity” which are common items, “flight name”, “airline”, “time zone”, “flight date”, The more designated items, such as “OD (Departure point, Arrival point)”, the higher the level of abstraction and the earlier processing.
In the case of air tickets, producers correspond to airlines, major sellers correspond to sales departments such as airline branches or sales agents such as major travel agencies, and small sellers act as travel agents. It corresponds to stores such as stores and convenience stores that handle air tickets.
[0084]
Specifically, for example, in FIG. 16, cluster levels 1 to 4 are determined in advance by an intermediary, and the buyer side arbitrarily selects the cluster level clustering attribute items determined from the cluster levels 1 to 4. This shows the linking process order of buyers 1 to 3 when setting the contents.
As shown in FIG. 16, the details are set at cluster level 4 (all designations of “flight date”, “morning or afternoon designation”, “airline”, and “flight name”) with the finest condition designation. Buyer 3 who has set the details at cluster level 3 (designation of “flight date”, “morning or afternoon designation”, and “airline”), which is the first processing target, then the detailed designation of conditions Side 2 is the next processing target, and the last buyer side 1 is the processing target.
[0085]
FIG. 17 shows the linking process order of the buyers 1 to 4 when the buyers 1 to 4 arbitrarily set the items of clustering attributes and their contents (conditions).
As shown in FIG. 17, the buyer side 4 with the finest condition designation is the first processing target, and then the buyer side 3, the buyer side 2, and the buyer side 1 are sequentially processed.
First, for the first buyer side 4, the corresponding sales information is only one of the sales information (5), so this is linked. For the next buyer 3, the corresponding sales information is sales information (5) and (9), so the sales information (5) with the higher sales amount is given priority. To do. Similarly, for the next buyer side 2, the number of seats is the same if the sales amount is the same among the four corresponding sales information (8), (2), (9), and (5), in descending order of the sales amount. Priority is given to the person with the largest number of sales information. For the last buyer side 1 as well, in the corresponding sales information (1) to (9) (all sales information), if the sales price is the same in descending order of sales price, If the number of seats is the same, priority is given to the earlier information input time, and the corresponding sales information is linked.
[0086]
From the above description, the item designation and processing order of the clustering attribute in the present embodiment can be summarized as follows.
1. Clustering attribute items and their contents
(1) Market operators and brokers determine in advance.
(2) The buyer decides arbitrarily.
2. Processing order between clusters
{Circle around (1)} The conditions are specified in detail (the one with many specified items, the one with the limited content), and processing is performed in order from the cluster.
(2) Market operators and brokers decide in advance.
3. Processing order within the cluster
(1) After determining the processing order in the cluster of purchase information, the sales information is clustered for each purchase information.
(2) After determining the processing order within the sales information cluster, the purchase information is clustered for each sales information.
[0087]
It goes without saying that the processing order between clusters and within clusters described in the second embodiment can also be applied to the first embodiment. Also, in the first embodiment, the target product is a fresh flower, but the product is not limited to this, and a product whose expiration date is determined, such as an air ticket, a ticket of other transportation, or a concert ticket. It is also applicable to.
[0088]
Further, the present invention is not limited to the fresh flower trading system in each of the above-described embodiments. The present invention is applicable to any product transaction system in which a plurality of buyers and a plurality of sellers participate.
[0089]
Another object of the present invention is to supply a storage medium storing software program codes for realizing the functions of the server and terminal according to each of the above-described embodiments to a system or apparatus, and the computer (or CPU) of the system or apparatus. Needless to say, this can also be achieved by reading and executing the program code stored in the storage medium.
In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the storage medium storing the program code constitutes the present invention.
As a storage medium for supplying the program code, ROM, flexible disk, hard disk, optical disk, magneto-optical disk, CD-ROM, CD-R, magnetic tape, nonvolatile memory card, and the like can be used.
Further, by executing the program code read by the computer, not only the functions of the above-described embodiments are realized, but an OS or the like running on the computer is actually executed based on an instruction of the program code. It goes without saying that a case where the function of each embodiment is realized by performing a part or all of the process and the process is included.
Further, after the program code read from the storage medium is written to the memory provided in the extension function board inserted in the computer or the function extension unit connected to the computer, the function extension is performed based on the instruction of the program code. It goes without saying that the CPU or the like provided in the board or the function expansion unit performs part or all of the actual processing, and the functions of the above-described embodiments are realized by the processing.
As described above, in this embodiment, a set (cluster) according to purchase conditions (attributes such as “type” and “color” of products) is formed from a plurality of pieces of purchase information output from a plurality of buyers. (Clustering process). This purchase condition is set in advance by, for example, a market operator (broker) or arbitrarily set by the buyer. On the other hand, a plurality of pieces of sales information output from a plurality of sellers are also formed in accordance with the above purchase conditions. Thus, a set of purchase information and a set of sales information are formed for each purchase condition. Then, the purchase information and the sales information are linked between the purchase information set and the sales information set under the same purchase conditions. At this time, the above association may be performed in the order determined in accordance with a predetermined condition (in order of highest purchase desired price, etc.) in the purchase information constituting the collection of purchase information.
In addition, in a plurality of pieces of purchase information output from a plurality of buyers, the order of associating with sales information is determined in accordance with a predetermined condition (in order of the desired purchase price, etc.) In accordance with preset purchase conditions (attributes such as “type” and “color” of the product) included in the purchase information to be processed according to this order, a set of a plurality of pieces of sales information output from a plurality of sellers ( Cluster). Thereby, a set of sales information corresponding to the purchase conditions of the processing target purchase information is formed. Then, in the sales information that constitutes the collection of sales information, the processing target purchase information is linked.
Alternatively, in a plurality of pieces of sales information output from a plurality of sellers, the order of associating with purchase information is determined in accordance with a predetermined condition (such as the order in which the desired sales price is high or low). In accordance with preset sales conditions (attributes such as “type” and “color” of the product) included in the sales information to be processed according to this order, a set of a plurality of purchase information output from a plurality of buyers ( Cluster). Thereby, a set of purchase information corresponding to the sales conditions of the processing target sales information is formed. Then, the sales information to be processed is associated with the purchase information constituting the collection of purchase information.
By adopting the configuration as described above, it is possible to link a plurality of purchase information and a plurality of sales information in accordance with various sales requests, and efficiently increase the rate of transaction establishment in a short time. be able to.
[0090]
【The invention's effect】
ADVANTAGE OF THE INVENTION According to this invention, the merchandise transaction processing apparatus which can perform the sales transaction between several buyer traders and several seller traders efficiently, and can raise the ratio of establishment of this sales deal can be provided.
[Brief description of the drawings]
FIG. 1 is a block diagram showing a configuration of a fresh flower trading system to which the present invention is applied in the first embodiment.
FIG. 2 is a diagram for explaining purchase information output from each buyer trader in the fresh flower trading system.
FIG. 3 is a diagram for explaining sales information output from each seller trader side in the fresh flower trading system.
FIG. 4 is a flowchart for explaining an algorithm 1 for establishing a transaction by linking a plurality of the purchase information and a plurality of the sales information.
FIG. 5 is a flowchart for explaining clustering processing in the algorithm 1;
FIG. 6 is a diagram for explaining a cluster of purchase information and a cluster of sales information generated by the clustering process.
FIG. 7 is a flowchart for explaining the linking process in the purchase information cluster and the sales information cluster;
FIG. 8 is a diagram for specifically explaining the linking process.
FIG. 9 is a flowchart for explaining an algorithm 2 for establishing a transaction by associating a plurality of the purchase information and a plurality of the sales information.
FIG. 10 is a diagram for explaining the processing order determination of purchase information in the algorithm 2;
FIG. 11 is a diagram for explaining a process for associating purchase information and sales information in a cluster in accordance with the processing order determination of the purchase information.
FIG. 12 is a diagram for explaining an algorithm 3 for establishing a transaction by linking a plurality of the purchase information and a plurality of the sales information in the second embodiment.
FIG. 13 is a diagram for explaining an algorithm 4 for establishing a transaction by linking a plurality of the purchase information and a plurality of the sales information.
FIG. 14 is a diagram for explaining an example of association between purchase information and sales information in a corresponding cluster in each of the algorithms described above.
FIG. 15 is a diagram for explaining the abstraction level of the linking process order when the buyer performs various conditions on the seller's sales information when the target product is an air ticket.
FIG. 16 is a diagram for explaining the above linking processing order in a more specific manner (when a buyer specifies a condition level);
FIG. 17 is a diagram for explaining the above linking processing order in a more specific manner (when a buyer arbitrarily specifies a condition).
[Explanation of symbols]
100 Flower trading system
110 WAN
120 _A1 , 120 _A2 , ..., 120 _An Buyer side terminal device
120 _B1 , 120 _B2 , ..., 120 _Bn Vendor side terminal device
121 Information reception and delivery function
122 Interface (I / F) function
123 Application functions
124 Database memory
140 Server-side terminal device
141 Information reception and delivery function
142 Application functions
143 Database memory

Claims (2)

It is connected to the seller side terminal and the purchaser side terminal via a network, and based on the sales information issued from the seller side terminal and the purchase information issued from the purchaser side terminal, the sales transaction processing of the product is performed. A commodity transaction processing apparatus to perform,
Plural items of sales information received from the seller-side terminal that loses or decreases its value after a certain period, and a plurality of items received from the buyer-side terminal that have a value that decreases or decreases after a certain period Storage means for storing the purchase information of
First clustering means for clustering the sales information according to product attributes included in the sales information;
Second clustering means for clustering purchase information according to purchase desired product attributes included in the purchase information;
For each of the clustered sales information and the clustered purchase information, the individual sales information and the purchase information included in the set of sales information and the set of purchase information clustered under the same conditions; Using the first and second clustering means, in order from the set having a large number of conditions when searching, sales information that satisfies each other and purchase information are searched, and the sales information that satisfies the conditions , Transaction processing means for linking purchase information,
A merchandise transaction processing apparatus comprising:   The transaction processing means performs the linking process for a set of sales information and a set of purchase information that are clustered under the same conditions, and when there is a remaining number in the purchase information, The merchandise transaction processing apparatus according to claim 1, wherein the associating process with the sales information included in the set is repeatedly performed.

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