CN103489108A - Large-scale order form matching method in community commerce cloud - Google Patents
Large-scale order form matching method in community commerce cloud Download PDFInfo
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Abstract
The invention discloses a large-scale order form matching method in community commerce cloud. The method includes the steps of firstly, achieving positioning of a user based on a base station and WIFI; secondly, confirming the non-directional requirement of the user; thirdly, determining the matching range through a cloud search algorithm; fourthly, conducting order form matching by combining the amount which can be reached with the set amount; fifthly, selecting a plurality of merchants according to a merchant optimization module; sixthly, calculating the secondary logistics cost according to user region information; seventhly, obtaining a commerce purchasing and logistics scheme of a matched largest-scale order form through the adoption of a MonteCarlo simulation dynamic programming method; eighthly, completing the matching of the large-scale order form. At present, the method is mainly applied to the electronic commerce field so that commercial demand order forms which are of the same type and in adjacent regions can be matched into a large-scale order form, therefore, the business negotiation efficiency and the transaction efficiency can be improved, a lower negotiated price can be found, better cost performance can be obtained, the shopping experience that the goods are attractive in price and quality is brought to purchasers, and the circulation of the goods and the capital of a high-quality seller can be accelerated.
Description
Technical field
The present invention relates to a kind of order and bring method together, specifically in a kind of community's commercial affairs cloud, extensive order is brought method together.
Background technology
In ecommerce at present, the mode that the consumer makes a profit mainly contains two kinds:
Rate of exchange network: the buyer can, by online various rate of exchange networks, obtain the selling price of different businessmans in similar commodity, and then therefrom make a choice relatively.Although rate of exchange network can provide the minimum retail price information of Related product to the user, but can not guarantee the commercial quality optimum of minimum selling price, and because the unique user demand is limited, thereby also just can't obtain from businessman the more favourable price of pass on very substantial orders there.
Purchase by group: the buyer can specify the at the appointed time interior specific favourable price of section of commodity by forming a team online to obtain specific merchant.But the marketing purchased by group recommends tendentiousness obvious, basically be all each large electric business's recommendation of websites businessman and product separately, and purchasing by group basically of each website initiated by businessman, more mostly be the raising popularity, removed overstocked discounting canvasser method, many restrictions are arranged on time, type of merchandize, thereby deprived to a certain extent user's right to choose.In addition, the quality that purchases by group commodity also can't guarantee, some wicked business is about to overdue product tissue by some and opens group, adulterates, and to reduce loss, in fact loss transferred to it the consumer.
Extensive order is brought technology together and is mainly used at present e-commerce field, bring into a large order together in order to will be close to the same kind business needs order of region, to improve the efficiency of commercial negotiation and transaction, seek lower negotiated prices, obtain better cost performance, thereby bring inexpensive shopping experience to the buyer, also can accelerate high-quality seller's goods and the turnover of fund.In addition, present technique also can be applied to the fields such as logistics field, international trade.
Summary of the invention
In order to solve the above-mentioned technical matters existed in prior art, the invention provides extensive order in a kind of community's commercial affairs cloud and bring method together, comprise the steps:
(1) realize user's location based on wireless signals such as base station, WIFI;
(2) determine user's non-directional demand;
(3), by the cloud searching algorithm, determine the scope of brining together;
(4) carrying out order in conjunction with the amount that can reach with the amount set brings together;
(5) according to the selected a number of businessmans of businessman's preferred module:
At first, relationship trading, comment and the news information of each businessman of oriented acquisition on all kinds of E-commerce transaction platforms, generate the instant objective credit matrix of businessman through pre-service and quantification; Then the preference function to commodity adeditive attributes such as commercial quality, price, sales volume, service and recommendation degree in conjunction with the user, generate businessman's evaluation number matrix of supporting this ownership goal; Finally, consider inventory information, selected corresponding high praise index businessman is according to the selected a number of businessmans of businessman's preferred module;
(6) calculate the second logistic cost according to user's regional information;
(7) adopt Monte Carlo simulation dynamic programming method, obtain mm Suppliers and the logistics programme of brining on a large scale order together;
(8) extensive order has been brought together.
Further, described cloud searching algorithm is to be mixed and formed by K-means clustering algorithm and improved K-nearest neighbor algorithm.
Further, first utilize the K-means clustering algorithm to carry out Local Search at the cloud node at user place, search the order that same requirements is arranged, the K-means clustering algorithm is specific as follows:
Input: bunch number k and the database that comprises n object;
Output: k bunch, make square error criterion minimum;
Algorithm steps:
1. determine an initial cluster center for each cluster, K initial cluster center so just arranged;
2. the sample in sample set is assigned to the most contiguous cluster according to minimal distance principle;
3. use sample average in each cluster as new cluster centre;
Repeating step 2.3 until cluster centre no longer change;
5. finish, obtain K cluster.
Further, the contiguous cloud node of described improved K-neighbour Spatial Sphere algorithm search, to reach the required corresponding reserve quota of negotiated prices within the set time till, step is as follows:
1. centered by the cloud node o of user place, the distance between nearest cloud node i and cloud node o of take from o is radius, and in this spatial dimension, search has the order of close demand;
2., if can reach the required amount of negotiated prices, stop search;
Otherwise, get larger radius and searched for;
4. repetition said process.
Further, described step (7) is specific as follows:
At first, construct one and using businessman, purchase quantity, second logistic cost etc. as stochastic variable, the minimum probability model as target of the total price of usining; Secondly, according to the distribution character of characteristics and the stochastic variable of model, and each stochastic variable is sampled; Again, carry out l-G simulation test, calculating on set up model, obtain the RANDOM SOLUTION of total price; Finally, by simulation repeatedly, provide the probabilistic solutions of minimum total price and the precision of solution and estimate, and then determine concrete mm Suppliers and logistics programme according to probabilistic solutions.
Extensive order of the present invention is brought method together and is mainly used at present e-commerce field, bring into a large order together in order to will be close to the same kind business needs order of region, to improve the efficiency of commercial negotiation and transaction, seek lower negotiated prices, obtain better cost performance, thereby bring inexpensive shopping experience to the buyer, also can accelerate high-quality seller's goods and the turnover of fund.In addition, this method also can be applied to the fields such as logistics field, international trade.
Extensive order is brought method together can use the householder dynamically and group, and then can obtain the high-quality commodity with favorable rates lattice more.Meanwhile, businessman, by the tendency information of this platform issue, can design their product more targetedly, and the expansion of order scale allows them to community's commercial affairs cloud, to promise to undertake more economical negotiated prices.Continuous optimum mutual by customer group and businessman, user, businessman, e-commerce platform, community's commercial affairs cloud etc. may be realized win-win situation, e-commerce environment also will more healthy, orderly.
The current pattern that purchases by group is often initiated by businessman, do not have at any time, and the user does not have right to choose substantially on commodity to be purchased by group.In community's commercial affairs cloud, extensive order is brought the scale effect that technology has well been utilized community's cloud together, actively facilitates certain scale commercial affairs, and then obtains lower negotiated prices, obtains better cost performance, to the buyer, brings inexpensive shopping experience; Simultaneously, the goods that extensive order also can be accelerated high-quality seller is deposited the turnaround speed of pin and fund; In addition, extensive order has also been created the chance of logistic optmum, and then has effectively reduced the logistics cost in commercial negotiation and cost of marketing and ecommerce.
The accompanying drawing explanation
Fig. 1 is the schematic flow sheet that in commercial affairs cloud in community's of the present invention, extensive order is brought method together.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described.
In commercial affairs cloud in community's of the present invention, extensive order is brought the consideration of method based on the operation performance together, the object of brining together only limits to contiguous region and the more similar order of order list, the commercial affairs of the similar business needs by contiguous region are merged, realize that in community's commercial affairs cloud, extensive order is brought together, on the basis of guarantee user right to choose in full, realize that the active of the business needs of network-wide basis dynamically purchases by group pattern.Concrete steps are as follows:
1, realize user's location based on wireless signals such as base station, WIFI;
2, determine user's non-directional demand;
3,, by specific cloud searching algorithm, determine the scope of brining together:
Cloud searching algorithm used is to be mixed and formed by K-means clustering algorithm and improved K-nearest neighbor algorithm, first utilizes the K-means clustering algorithm to carry out Local Search at the cloud node at user place, searches the order that same requirements is arranged.The K-means clustering algorithm is specific as follows:
Input: bunch number k and the database that comprises n object.
Output: k bunch, make square error criterion minimum.
Algorithm steps:
1. determine an initial cluster center for each cluster, K initial cluster center so just arranged;
2. the sample in sample set is assigned to the most contiguous cluster according to minimal distance principle;
3. use sample average in each cluster as new cluster centre;
Repeating step 2.3 until cluster centre no longer change;
5. finish, obtain K cluster.
Sample is distributed to the center vector nearest apart from them, and target function value is reduced:
Upgrade bunch mean value:
Calculation criterion function E:
If the quantity on order of same requirements does not reach the required reserve quota of negotiated prices, recycle the contiguous cloud node of improved K-neighbour Spatial Sphere algorithm search, to reach the required corresponding reserve quota of negotiated prices within the set time till.The specific algorithm step is as follows:
5. centered by the cloud node o of user place, the distance between nearest cloud node i and cloud node o of take from o is radius, and in this spatial dimension, search has the order of close demand;
6., if can reach the required amount of negotiated prices, stop search;
Otherwise, get larger radius and searched for;
8. repetition said process;
9. carrying out order in conjunction with the amount that can reach with the amount set brings together;
10. according to the selected a number of businessmans of businessman's preferred module:
At first, relationship trading, comment and the news information of each businessman of oriented acquisition on all kinds of E-commerce transaction platforms, generate the instant objective credit matrix of businessman through pre-service and quantification; Then the preference function to commodity adeditive attributes such as commercial quality, price, sales volume, service and recommendation degree in conjunction with the user, generate businessman's evaluation number matrix of supporting this ownership goal; Finally, consider inventory information, selected corresponding high praise index businessman is according to the selected a number of businessmans of businessman's preferred module
11. calculate the second logistic cost according to user's regional information
The second logistic cost is exactly that the cloud node is dispensed into the required logistics cost of particular user after receiving commodity
12. adopt Monte Carlo simulation dynamic programming method, obtain mm Suppliers and the logistics programme of brining on a large scale order together
At first, construct one and using businessman, purchase quantity, second logistic cost etc. as stochastic variable, the minimum probability model as target of the total price of usining; Secondly, according to the distribution character of characteristics and the stochastic variable of model, and each stochastic variable is sampled; Again, carry out l-G simulation test, calculating on set up model, obtain the RANDOM SOLUTION of total price; Finally, by simulation repeatedly, provide the probabilistic solutions of minimum total price and the precision of solution and estimate, and then determine concrete mm Suppliers and logistics programme according to probabilistic solutions.
13. extensive order has been brought together.
Claims (5)
1. in community commercial affairs cloud, extensive order is brought method together, comprises the steps:
(1) realize user's location based on wireless signals such as base station, WIFI;
(2) determine user's non-directional demand;
(3), by the cloud searching algorithm, determine the scope of brining together;
(4) carrying out order in conjunction with the amount that can reach with the amount set brings together;
(5) according to the selected a number of businessmans of businessman's preferred module:
At first, relationship trading, comment and the news information of each businessman of oriented acquisition on all kinds of E-commerce transaction platforms, generate the instant objective credit matrix of businessman through pre-service and quantification; Then the preference function to commodity adeditive attributes such as commercial quality, price, sales volume, service and recommendation degree in conjunction with the user, generate businessman's evaluation number matrix of supporting this ownership goal; Finally, consider inventory information, selected corresponding high praise index businessman is according to the selected a number of businessmans of businessman's preferred module;
(6) calculate the second logistic cost according to user's regional information;
(7) adopt Monte Carlo simulation dynamic programming method, obtain mm Suppliers and the logistics programme of brining on a large scale order together;
(8) extensive order has been brought together.
2. in commercial affairs cloud in community's as claimed in claim 1, extensive order is brought method together, and it is characterized in that: described cloud searching algorithm is to be mixed and formed by K-means clustering algorithm and improved K-nearest neighbor algorithm.
3. in commercial affairs cloud in community's as claimed in claim 2, extensive order is brought method together, and it is characterized in that: first utilize the K-means clustering algorithm to carry out Local Search at the cloud node at user place, search the order that same requirements is arranged, the K-means clustering algorithm is specific as follows:
Input: bunch number k and the database that comprises n object;
Output: k bunch, make square error criterion minimum;
Algorithm steps:
(1) determine an initial cluster center for each cluster, K initial cluster center so just arranged;
(2) sample in sample set is assigned to the most contiguous cluster according to minimal distance principle;
(3) use sample average in each cluster as new cluster centre;
(4) repeating step 2.3 until cluster centre no longer change;
(5) finish, obtain K cluster.
4. in commercial affairs cloud in community's as claimed in claim 2, extensive order is brought method together, it is characterized in that: the contiguous cloud node of described improved K-neighbour Spatial Sphere algorithm search, to reach the required corresponding reserve quota of negotiated prices within the set time till, step is as follows:
Centered by the cloud node o of user place, the distance between nearest cloud node i and cloud node o of take from o is radius, and in this spatial dimension, search has the order of close demand;
If can reach the required amount of negotiated prices, stop search;
Otherwise, get larger radius and searched for;
Repeat said process.
5. in commercial affairs cloud in community's as claimed in claim 1, extensive order is brought method together, and it is characterized in that: described step (7) is specific as follows:
At first, construct one and using businessman, purchase quantity, second logistic cost etc. as stochastic variable, the minimum probability model as target of the total price of usining; Secondly, according to the distribution character of characteristics and the stochastic variable of model, and each stochastic variable is sampled; Again, carry out l-G simulation test, calculating on set up model, obtain the RANDOM SOLUTION of total price; Finally, by simulation repeatedly, provide the probabilistic solutions of minimum total price and the precision of solution and estimate, and then determine concrete mm Suppliers and logistics programme according to probabilistic solutions.
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| CN107026885A (en) * | 2016-02-02 | 2017-08-08 | 阿里巴巴集团控股有限公司 | Information-pushing method and device |
| CN107392374A (en) * | 2017-07-21 | 2017-11-24 | 顺丰科技有限公司 | A kind of task parcel optimization method, system, equipment |
| CN107808314A (en) * | 2016-09-09 | 2018-03-16 | 腾讯科技（深圳）有限公司 | User recommends method and device |
| CN108491377A (en) * | 2018-03-06 | 2018-09-04 | 中国计量大学 | A kind of electric business product comprehensive score method based on multi-dimension information fusion |
| CN109214884A (en) * | 2018-08-02 | 2019-01-15 | 阿里巴巴集团控股有限公司 | Demand match method and device, electronic equipment |
| CN109597858A (en) * | 2018-12-14 | 2019-04-09 | 拉扎斯网络科技（上海）有限公司 | A kind of classification method of trade company and its recommended method and its device of device and trade company |
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| CN111028049A (en) * | 2019-11-18 | 2020-04-17 | 政采云有限公司 | Commodity purchasing method, commodity purchasing platform and commodity purchasing system |
| CN112183799A (en) * | 2019-07-01 | 2021-01-05 | 北京京东振世信息技术有限公司 | Task allocation method and device for synthesizing task list |
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| WO2017059789A1 (en) * | 2015-10-09 | 2017-04-13 | 阿里巴巴集团控股有限公司 | Logistics performance mode information processing method and device |
| CN107026885A (en) * | 2016-02-02 | 2017-08-08 | 阿里巴巴集团控股有限公司 | Information-pushing method and device |
| CN107026885B (en) * | 2016-02-02 | 2020-09-29 | 阿里巴巴集团控股有限公司 | Information pushing method and device |
| CN107808314B (en) * | 2016-09-09 | 2020-04-21 | 腾讯科技（深圳）有限公司 | User recommendation method and device |
| CN107808314A (en) * | 2016-09-09 | 2018-03-16 | 腾讯科技（深圳）有限公司 | User recommends method and device |
| CN107392374A (en) * | 2017-07-21 | 2017-11-24 | 顺丰科技有限公司 | A kind of task parcel optimization method, system, equipment |
| CN108491377A (en) * | 2018-03-06 | 2018-09-04 | 中国计量大学 | A kind of electric business product comprehensive score method based on multi-dimension information fusion |
| CN108491377B (en) * | 2018-03-06 | 2021-10-08 | 中国计量大学 | E-commerce product comprehensive scoring method based on multi-dimensional information fusion |
| CN109214884A (en) * | 2018-08-02 | 2019-01-15 | 阿里巴巴集团控股有限公司 | Demand match method and device, electronic equipment |
| CN109214884B (en) * | 2018-08-02 | 2022-04-15 | 创新先进技术有限公司 | Demand matching method and device and electronic equipment |
| CN109597858A (en) * | 2018-12-14 | 2019-04-09 | 拉扎斯网络科技（上海）有限公司 | A kind of classification method of trade company and its recommended method and its device of device and trade company |
| CN110348889A (en) * | 2019-06-21 | 2019-10-18 | 腾讯科技（深圳）有限公司 | Method and device for business processing and storage medium |
| CN110348889B (en) * | 2019-06-21 | 2022-12-30 | 腾讯科技（深圳）有限公司 | Service processing method and device and storage medium |
| CN112183799A (en) * | 2019-07-01 | 2021-01-05 | 北京京东振世信息技术有限公司 | Task allocation method and device for synthesizing task list |
| CN111028049A (en) * | 2019-11-18 | 2020-04-17 | 政采云有限公司 | Commodity purchasing method, commodity purchasing platform and commodity purchasing system |
| CN113222632A (en) * | 2020-02-04 | 2021-08-06 | 北京京东振世信息技术有限公司 | Object mining method and device |
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