CN115994664B - Intelligent recommendation method, device and equipment for shared refrigeration house mode - Google Patents
Intelligent recommendation method, device and equipment for shared refrigeration house mode Download PDFInfo
- Publication number
- CN115994664B CN115994664B CN202310008399.7A CN202310008399A CN115994664B CN 115994664 B CN115994664 B CN 115994664B CN 202310008399 A CN202310008399 A CN 202310008399A CN 115994664 B CN115994664 B CN 115994664B
- Authority
- CN
- China
- Prior art keywords
- refrigeration house
- leasing
- storage
- refrigeration
- refrigerator
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/14—Thermal energy storage
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application relates to the technical field of big data, in particular to an intelligent recommendation method, device and equipment for a shared refrigeration house mode. Comprising the following steps: measuring and calculating the inventory capacity of the refrigeration house in real time to obtain the residual inventory capacity data of the refrigeration house; meanwhile, lease demand information of a refrigerator leaser is acquired, and the lease demand information comprises: the storage capacity requirement size, the leasing days, the storage type and the product type; constructing a shared refrigeration house mode by utilizing the residual refrigeration house capacity data and the lease demand information of the refrigeration house; and selecting a corresponding shared refrigeration house mode by utilizing the storage type and the product type, and performing intelligent recommendation for the renter. The agricultural product management system and the agricultural product management method have the advantages that agricultural products are stored locally conveniently, the problem that rural refrigerator leasing user experience is poor is solved, rural shared refrigerator accurate operation is achieved, the farmers are guaranteed to fully utilize refrigerator resources, and the effect of preserving and adding value of the agricultural products is achieved.
Description
Technical Field
The application relates to the technical field of big data, in particular to an intelligent recommendation method, device and equipment for a shared refrigeration house mode.
Background
Currently, most of cold storage suppliers in a 'shared cloud cold storage' mode are in cities, are not in field cold storages and are a certain distance away from agricultural product production places, so that transportation cost is increased for farmers, and meanwhile, prices, renting scales and renting are too high and too long for the farmers, so that the farmers cannot afford to carry out the process.
At present, a 'rural shared refrigeration house' is put on line in certain areas where fruits are contained and areas where vegetables are contained, and the basic requirements of 'loss reduction, cost reduction, supply protection' and the like are mainly met, but when agricultural products are concentrated and marketed, fresh fruits and vegetables cannot be guaranteed to enter the refrigeration house in time for refrigeration and preservation; the lack of basic information sharing of the refrigeration house brings inconvenience to farmers in quickly searching the refrigeration house, and the experience of the refrigeration house leasing users is poor.
In the prior art, when agricultural products are concentrated and marketed, fresh fruits and vegetables cannot be guaranteed to enter the refrigerator in time for refrigeration and preservation, and farmers quickly find the refrigerator to bring inconvenience, so that the experience of a refrigerator renting user is poor.
Disclosure of Invention
In order to overcome the problems that fresh fruits and vegetables can not be guaranteed to enter a refrigerator in time for refrigeration and preservation and farmers can quickly find the refrigerator to bring inconvenience to a certain extent when agricultural products are concentrated and marketed in the related technology at least, and the experience of a refrigerator leasing user is poor, the application provides an intelligent recommendation method, device and equipment for sharing a refrigerator mode.
The scheme of the application is as follows:
in a first aspect, the present application provides a method for intelligent recommendation of a shared refrigeration house mode, the method comprising:
measuring and calculating the inventory capacity of the refrigeration house in real time to obtain the residual inventory capacity data of the refrigeration house;
the method comprises the steps of obtaining lease demand information of a refrigerator leaser, wherein the lease demand information comprises the following steps: the storage capacity requirement size, the leasing days, the storage type and the product type;
constructing a shared refrigeration house mode by utilizing the residual refrigeration house capacity data and the lease demand information of the refrigeration house;
and selecting a corresponding shared refrigeration house mode by utilizing the storage type and the product type, and performing intelligent recommendation for the renter.
Further, the real-time measurement and calculation of the storage capacity of the refrigeration house to obtain the residual storage capacity data of the refrigeration house comprises the following steps:
and (3) measuring and calculating the inventory capacity of the refrigeration house in real time by adopting a 3D vision technology to obtain the residual inventory capacity data of the refrigeration house.
Further, the constructing a shared refrigeration house mode by using the residual refrigeration house capacity data and the lease demand information includes:
and respectively constructing a first shared cold storage intelligent mode, a second shared cold storage intelligent mode and a third shared cold storage intelligent mode by utilizing the leasing demand information.
Further, the constructing the first shared refrigeration house intelligent mode includes:
respectively determining a first leasing refrigeration house set and a second leasing refrigeration house set;
obtaining the lease demand information of the store capacity demand size, lease days, storage type and product type, searching a freezer set matched with the lease demand information, and setting the freezer set as a first lease freezer set;
acquiring a data set of the refrigerator with the linear distance from the renter being less than or equal to a set mileage, searching the data information with the same storage type in the data set, and setting the data information as a second renting refrigerator set;
calculating an intersection of the first leasing refrigeration house set and the second leasing refrigeration house set to obtain a third leasing refrigeration house set;
if the third leasing refrigeration house set is an empty set, sequencing the first leasing refrigeration house set according to the distance from the leaser from the near to the far;
otherwise, sorting the refrigerators in the third leasing refrigerator set according to the score from top to bottom.
Further, the constructing a second shared refrigeration house intelligent mode includes:
dividing according to a certain area to obtain the information of the renting requirements, such as the size of the storage capacity, the renting days, the storage type and the product type submitted by renters in the same area, and searching a freezer set matched with the information, namely a fourth renting freezer set;
if the storage capacity requirement of any one of the refrigeration houses in the fourth leasing refrigeration house set is smaller than the storage capacity requirement of the leaser, the leaser does not participate in uniform allocation, and the demand of the leaser is allocated to the latest refrigeration house meeting the storage capacity requirement;
otherwise, establishing a 0-1 type integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area;
the establishing a 0-1 integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area comprises the following steps:
constructing an objective function of the total storage cost of all renters in the same area by taking the minimum overall storage cost of the renters in the same area as an optimization target;
constructing constraint conditions of the objective function according to the principle that the renters are distributed to the refrigeration houses in the same area and the total storage capacity of the refrigeration houses in the same area is smaller than or equal to the total storage capacity of the refrigeration houses and each renter can be distributed to one refrigeration house at most;
constructing and building a 0-1 type integer programming model according to the objective function and the constraint condition of the objective function;
and solving the 0-1 type integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area.
Further, the constructing a third shared refrigeration house intelligent mode includes:
obtaining the renting demand information of the size, the renting days, the storage type and the product type of the library capacity demand submitted by the renters, and searching a freezer set matched with the renters, namely a fifth renting freezer set;
searching the refrigerator which is leased by the leaseholder to obtain a sixth leasehold set;
calculating the similarity between the fifth leasing refrigeration house set and the sixth leasing refrigeration house set to obtain a first refrigeration house similarity set;
and obtaining the recommended degree of the refrigerators in the first refrigerator similarity set by using the first refrigerator similarity set, and sequencing the recommended degree according to the sequence from top to bottom.
Further, the calculating the similarity between the fifth rental refrigerator set and the sixth rental refrigerator set to obtain a first refrigerator similarity set includes:
calculating Euclidean distance between the fifth leasing refrigeration house set and the sixth leasing refrigeration house set;
calculating the similarity between the fifth refrigeration house set and the sixth refrigeration house set by using the Euclidean distance between the fifth refrigeration house set and the sixth refrigeration house set to obtain a first refrigeration house similarity set;
the calculation formula of the Euclidean distance is as follows:
wherein D represents Euclidean distance between a fifth leasing refrigeration house set and the sixth leasing refrigeration house set;
distance a represents the distance from the renter's body operating address to any one of the fifth set of rental refrigerators;
distance B represents the distance from the renter's body operating address to any one of the sixth set of rental refrigerators;
lease A represents lease of any refrigerator in the fifth lease refrigerator set;
lease B represents lease of any refrigerator in the sixth leasing refrigerator set;
the evaluation score A represents the evaluation score of any refrigerator in the fifth leasing refrigerator set;
the evaluation score B represents the evaluation score of any refrigerator in the sixth leasing refrigerator set;
the calculation formula of the similarity is as follows: s=1/(1+d), where D represents the euclidean distance between the fifth and sixth rental refrigerator sets; s represents the similarity.
Further, selecting a corresponding shared refrigeration house mode by using the storage type and the product type to intelligently recommend to the renter, including:
if the storage type is a refrigeration fresh-keeping warehouse and the product type is common agricultural products, selecting a first shared refrigeration warehouse intelligent mode to intelligently recommend for the renter;
otherwise, if the storage type is a refrigeration fresh-keeping warehouse and the product type is a season-to-be-sold agricultural product, selecting a second shared refrigeration warehouse intelligent mode to intelligently recommend for the renter;
otherwise, if the product type is non-agricultural products, selecting a third shared refrigeration house intelligent mode to intelligently recommend to the renter. In a second aspect, the present application provides an apparatus for intelligent recommendation of a shared freezer mode, the apparatus comprising:
the measuring and calculating module is used for measuring and calculating the inventory capacity of the refrigeration house in real time to obtain the residual inventory capacity data of the refrigeration house;
the system comprises an acquisition module, a storage management module and a storage management module, wherein the acquisition module is used for acquiring lease demand information of a refrigerator leaser, and the lease demand information comprises: the storage capacity requirement size, the leasing days, the storage type and the product type; the recommending module is used for intelligently recommending the shared refrigeration house mode by utilizing the lease demand information;
and the execution module is used for carrying out corresponding operation of the shared refrigeration house mode by utilizing the intelligent recommending result.
In a third aspect, the present application provides an apparatus for intelligent recommendation of a shared freezer mode, the apparatus comprising:
a memory having an executable program stored thereon;
a processor for executing the executable program in the memory to implement the steps of any of the methods described above. The technical scheme that this application provided can include following beneficial effect:
according to the method, the residual storage capacity data of the refrigeration house are obtained through real-time measurement and calculation of the storage capacity of the refrigeration house; meanwhile, lease demand information of a refrigerator leaser is acquired, and the lease demand information comprises: the storage capacity requirement size, the leasing days, the storage type and the product type; constructing a shared refrigeration house mode by utilizing the residual refrigeration house capacity data and the lease demand information of the refrigeration house; and selecting a corresponding shared refrigeration house mode by utilizing the storage type and the product type, and performing intelligent recommendation for the renter. The agricultural product management system and the agricultural product management method have the advantages that agricultural products are stored locally conveniently, the problem that rural refrigerator leasing user experience is poor is solved, rural shared refrigerator accurate operation is achieved, the farmers are guaranteed to fully utilize refrigerator resources, and the effect of preserving and adding value of the agricultural products is achieved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic flow chart of a method for intelligent recommendation of a shared refrigeration house mode according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an apparatus for intelligent recommendation of a shared refrigeration house mode according to another embodiment of the present application;
FIG. 3 is a schematic diagram of an apparatus composition for intelligent recommendation of a shared freezer mode according to yet another embodiment of the present application;
FIG. 4 is a schematic diagram of a first method for intelligent mode of a shared refrigeration house according to one embodiment of the present application;
FIG. 5 is a schematic diagram of a second method for intelligent mode of a shared refrigeration store according to one embodiment of the present application;
fig. 6 is a schematic diagram of a third method for intelligent mode of a shared refrigeration house according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
Currently, most of cold storage suppliers in a 'shared cloud cold storage' mode are in cities, are not in field cold storages and are a certain distance away from agricultural product production places, so that transportation cost is increased for farmers, and meanwhile, prices, renting scales and renting are too high and too long for the farmers, so that the farmers cannot afford to carry out the process.
At present, the rural shared refrigeration house is on line in southwest region where fruits are contained and Shandong region where vegetables are contained, and the basic requirements of loss reduction, cost reduction, supply protection and the like are mainly surrounded, but a big data algorithm model technology is not embedded, so that the operation of the refrigeration house is rough, and the method mainly comprises the following steps that 1. The real-time data sharing of the residual storage capacity is lacked, so that fresh fruits and vegetables can not be guaranteed to enter the refrigeration house in time for refrigeration and fresh preservation when agricultural products are concentrated and marketed; 2. the lack of basic information sharing of the refrigeration house brings inconvenience to farmers in quickly searching the refrigeration house, and the experience of the refrigeration house leasing users is poor.
Aiming at the problems, the application provides the intelligent recommending method, the intelligent recommending device and the intelligent recommending equipment for the shared refrigeration house mode, which not only facilitate the local storage of agricultural products, but also solve the problem that the experience of rural refrigeration house renting users is poor, realize the accurate operation of rural shared refrigeration houses, ensure that farmers fully utilize refrigeration house resources and achieve the effect of guaranteeing and adding value of agricultural products.
Example 1
Referring to fig. 1, fig. 1 is a flowchart of a method for intelligent recommendation of a shared refrigeration house mode according to an embodiment of the present application, where the method includes:
s1, measuring and calculating the storage capacity of a refrigeration house in real time to obtain the residual storage capacity data of the refrigeration house;
s2, lease demand information of a refrigerator leaser is obtained, wherein the lease demand information comprises the following components: the storage capacity requirement size, the leasing days, the storage type and the product type;
s3, constructing a shared refrigeration house mode by utilizing the residual refrigeration house capacity data and the leasing demand information of the refrigeration house;
s4, selecting a corresponding shared refrigeration house mode by utilizing the storage type and the product type, and performing intelligent recommendation for the renters.
In one embodiment, as described in step S1, the measuring and calculating the inventory capacity of the refrigerator in real time to obtain the remaining inventory capacity data of the refrigerator includes:
the intelligent camera is arranged at the entrance and exit position of the large door of the refrigeration house, the volume of the agricultural products and goods entering and exiting is read in real time by adopting a 3D vision technology, the real-time sharing of the residual storage capacity data of the refrigeration house is realized, and the data preparation is prepared for intelligent recommendation of the refrigeration house.
In one embodiment, 3D vision is a multidisciplinary fusion technique that can be summarized as: computational graphics + computer vision + artificial intelligence = 3D vision. The 3D vision technology is that three-dimensional coordinate information of each point in the visual field space is acquired through the 3D camera, three-dimensional imaging is acquired through algorithm restoration, the influence of external environment and complex light cannot be easily received, and compared with the 2D imaging technology, the three-dimensional imaging technology is more stable, the experience sense is stronger, and the safety is higher.
In one embodiment, as described in step S3, the constructing a shared refrigeration house mode by using the refrigeration house remaining storage capacity data and the rental demand information includes:
and respectively constructing a first shared cold storage intelligent mode, a second shared cold storage intelligent mode and a third shared cold storage intelligent mode by utilizing the leasing demand information.
In this embodiment of the present application, the constructing a first shared refrigeration house intelligent mode includes:
respectively determining a first leasing refrigeration house set and a second leasing refrigeration house set;
obtaining the lease demand information of the store capacity demand size, lease days, storage type and product type, searching a freezer set matched with the lease demand information, and setting the freezer set as a first lease freezer set;
acquiring a data set of the refrigerator with the linear distance from the renter being less than or equal to a set mileage, searching the data information with the same storage type in the data set, and setting the data information as a second renting refrigerator set;
calculating an intersection of the first leasing refrigeration house set and the second leasing refrigeration house set to obtain a third leasing refrigeration house set;
if the third leasing refrigeration house set is an empty set, sequencing the first leasing refrigeration house set according to the distance from the leaser from the near to the far;
otherwise, sorting the refrigerators in the third leasing refrigerator set according to the score from top to bottom.
In specific implementation, as shown in fig. 4, a large amount of common agricultural products need to be rented into a refrigerator (a large amount of agricultural products need to be marketed into a market and pre-cooled by the refrigerator to ensure fresh transportation to the hands of consumers). And judging according to the experience value of the farmer on the agricultural product marketing period, setting a marketing period of the agricultural product in the background in advance, and indicating that the agricultural product centralized marketing period begins when a lease order is generated in the background in the marketing period. In the period of marketing, after a first renting user inputs the size of a storage capacity requirement, the number of renting days and the storage type of the storage capacity requirement on a platform, recommending a rentable refrigerator closest to the management address of a user main body for the first renting user, and grading the refrigerator after the renting is finished; if the freezer set B leased by the user with the same storage type is empty (i.e. no similar leasing group is found nearby) within 5 km from the user, the freezer pool A is ordered from near to far according to the distance from the user main body operation address to the freezer.
Dividing according to a certain area to obtain the information of the renting requirements, such as the size of the storage capacity, the renting days, the storage type and the product type submitted by renters in the same area, and searching a freezer set matched with the information, namely a fourth renting freezer set;
if the storage capacity requirement of any one of the refrigeration houses in the fourth leasing refrigeration house set is smaller than the storage capacity requirement of the leaser, the leaser does not participate in uniform allocation, and the demand of the leaser is allocated to the latest refrigeration house meeting the storage capacity requirement;
otherwise, establishing a 0-1 type integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area;
the establishing a 0-1 integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area comprises the following steps:
constructing an objective function of the total storage cost of all renters in the same area by taking the minimum overall storage cost of the renters in the same area as an optimization target;
constructing constraint conditions of the objective function according to the principle that the renters are distributed to the refrigeration houses in the same area and the total storage capacity of the refrigeration houses in the same area is smaller than or equal to the total storage capacity of the refrigeration houses and each renter can be distributed to one refrigeration house at most;
constructing and building a 0-1 type integer programming model according to the objective function and the constraint condition of the objective function;
and solving the 0-1 type integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area.
In the specific implementation, as shown in fig. 5, fruits with summer characteristics such as waxberry, juicy peach and grape are collected and sold in the market (the fruits are pre-sold and shipped), the fruits need to be refrigerated and stored before entering the market, and the fruits enter the market to be transported to the consumer after reaching the expected refrigeration effect, so that the same fruits are refrigerated for the same time and are set as T; fruit planting has certain regionality, so that unified distribution recommendation is carried out by taking town or street as a unit; during the fruit centralized marketing period, a user uniformly issues the demand of the next day of refrigerator renting warehouse before 24 points, the platform receives the demand of the next day of refrigerator renting of all farmers in the town or street, and issues a distribution result according to the current rentable residual warehouse after 24 points; if the storage capacity requirement of a certain user is larger than the residual storage capacity of any one of the refrigerators, the farmer does not participate in unified allocation, the requirement of the user is allocated to the latest refrigerator meeting the storage capacity, and then the requirement is uniformly allocated according to the principle of 'centralized storage as much as possible and storage cost reduction for the user': if an unallocated user appears, it is allocated to the most recent refrigerator meeting its storage capacity requirement, wherein, as shown in FIG. 5, m i For the renting inventory requirement of a certain user, M is the total inventory requirement number issued by the user, F 1 ，F 2 ，…，F N The total storage capacity of the refrigeration house which can provide leasing at present is V 1 ，V 2 ，…，V N Refrigerator capable of providing leasing at presentIs a residual storage capacity size of (c). An integer programming model of type 0-1 is built with an objective function that minimizes the overall storage cost of users who issue rental requirements for the town or street.
Objective function:
constraint conditions:
in this embodiment of the present application, where C is the total cost, T is the storage time, k is the storage cost coefficient of unit storage capacity (under the same refrigeration house, the more the stored goods are, the larger the occupied storage capacity is, the higher the utilization rate of the storage capacity is, the smaller the cost of the electric charge spread on average), and m i For the renting inventory requirement of a certain user, M is the total inventory requirement number issued by the user, F 1 ，F 2 ，…，F N The total storage capacity of the refrigeration house which can provide leasing at present is V 1 ，V 2 ，…，V N The residual storage capacity of the refrigeration houses which can provide leases at present are respectively, N is the total number of the refrigeration houses which can provide leases at present, a i1 ，a i2 ，…，a iN For decision variables, 0 or 1 is taken to indicate whether the storage capacity requirement of a certain user is allocated to a certain refrigerator (0: unassigned, 1: allocated), and i is taken from 1 to N and is an integer.
Each term in the formula (1) represents the total storage cost corresponding to the storage capacity of the farmer's demand storage capacity allocated to each refrigeration house, and each term is added to the total storage cost of the refrigeration houses reserved by the farmers in the town or street of the day.
Each inequality in the formula (2) indicates that the total required storage capacity of the refrigerator allocated by the user should be less than or equal to the remaining storage capacity of the refrigerator.
Equation (3) shows that each user can be allocated to at most one refrigerator.
In this embodiment of the present application, the constructing a third shared refrigeration house intelligent mode includes:
obtaining the renting demand information of the size, the renting days, the storage type and the product type of the library capacity demand submitted by the renters, and searching a freezer set matched with the renters, namely a fifth renting freezer set;
searching the refrigerator which is leased by the leaseholder to obtain a sixth leasehold set;
calculating the similarity between the fifth leasing refrigeration house set and the sixth leasing refrigeration house set to obtain a first refrigeration house similarity set;
and obtaining the recommended degree of the refrigerators in the first refrigerator similarity set by using the first refrigerator similarity set, and sequencing the recommended degree according to the sequence from top to bottom.
Specifically, the calculating the similarity between the fifth leasing refrigeration house set and the sixth leasing refrigeration house set to obtain a first refrigeration house similarity set includes:
calculating Euclidean distance between the fifth leasing refrigeration house set and the sixth leasing refrigeration house set;
calculating the similarity between the fifth refrigeration house set and the sixth refrigeration house set by using the Euclidean distance between the fifth refrigeration house set and the sixth refrigeration house set to obtain a first refrigeration house similarity set;
the calculation formula of the Euclidean distance is as follows:
wherein, as shown in formula 4, D represents the euclidean distance between the fifth rental refrigerator set and the sixth rental refrigerator set;
distance a represents the distance from the renter's body operating address to any one of the fifth set of rental refrigerators;
distance B represents the distance from the renter's body operating address to any one of the sixth set of rental refrigerators;
lease A represents lease of any refrigerator in the fifth lease refrigerator set;
lease B represents lease of any refrigerator in the sixth leasing refrigerator set;
the evaluation score A represents the evaluation score of any refrigerator in the fifth leasing refrigerator set;
the evaluation score B represents the evaluation score of any refrigerator in the sixth leasing refrigerator set;
the calculation formula of the similarity is as follows: s=1/(1+d), where D represents the euclidean distance between the fifth and sixth rental refrigerator sets and S represents the similarity.
Specifically, the selecting a corresponding shared refrigeration house mode by using the storage type and the product type to intelligently recommend to the renter includes:
if the storage type is a refrigeration fresh-keeping warehouse and the product type is common agricultural products, selecting a first shared refrigeration warehouse intelligent mode to intelligently recommend for the renter;
otherwise, if the storage type is a refrigeration fresh-keeping warehouse and the product type is a season-to-be-sold agricultural product, selecting a second shared refrigeration warehouse intelligent mode to intelligently recommend for the renter;
otherwise, if the product type is non-agricultural products, selecting a third shared refrigeration house intelligent mode to intelligently recommend to the renter. In this application embodiment, divide agricultural products into ordinary agricultural product piece and should season hot-selling agricultural products, should season hot-selling be should season hot-selling fruit agricultural products, generally have regionalism, include: summer-featured smooth-selling fruits such as grapes and peaches.
In particular implementations, as shown in FIG. 6, non-agricultural products are heavily concentrated on the market for a normal rental period. For new users, sorting and recommending the distance from the user main body operation address to the refrigerator from near to far, and grading the refrigerator after the renting is finished; regarding old users, considering two directions of basic demands of freezer renting and freezer renting preference, wherein the dimension considered by the basic demands of freezer renting is a size demand of a freezer, a renting day and a storage type, when the users send out basic renting demand applications, a freezer pool A which can be rented by the users, namely a fifth renting freezer set, is calculated, the freezer which is rented by the users is a freezer set B, namely the fifth renting freezer set, the similarity S between the freezer pool A and the freezer set B is calculated, a first freezer similarity set is constructed based on the obtained similarity S, the similarity S is defined by Euclidean distance D, the dimension calculated by the Euclidean distance D is the renting distance (the distance from a user main body to the freezer), the renting price and the freezer evaluation score, the formula is specifically shown as (4), and the similarity is calculated according to the obtained Euclidean distance D, and is specifically shown as formula (5).
S(A,B)＝1/1+D (5)
It should be noted that, the higher the degree of similarity between the freezer in freezer pool a and the freezer in freezer set B, the more suitable it is recommended to the rental user, but considering that the number of rentals of the freezer in freezer set B is different, the number of rentals of the freezer in freezer set B is multiplied by the degree of similarity between the freezer in freezer set B and the freezer in pool a, so the degree of similarity between each freezer in freezer pool a is calculated, and then the user is recommended in order according to the degree of recommendation, and the freezer is scored after the renting is finished.
In one embodiment, as described in step S4, the selecting the shared refrigerator mode to intelligently recommend to the renter by using the shared refrigerator mode and the storage type includes:
if the storage type is an agricultural product and the product type of the agricultural product is a common agricultural product, selecting a first shared cold storage intelligent mode to intelligently recommend for the renter;
otherwise, if the storage type is an agricultural product and the product type of the agricultural product is a season-to-be-sold agricultural product, selecting a second shared cold storage intelligent mode, and performing intelligent recommendation for the renter;
otherwise, if the storage type is non-agricultural products, selecting a third shared refrigeration house intelligent mode to intelligently recommend to the renter.
The method and the system for calculating the rural area share refrigeration house based on the agricultural Internet big data accurately adopt accurate operation, thereby facilitating the local storage of agricultural products and solving the problem of poor experience of renting users of the rural area refrigeration house; 2. aiming at the summer special smooth selling fruits of waxberries, juicy peaches and grapes, the fruits are required to be sent to a storage for refrigeration and fresh-keeping at the first time, the demands of the storage capacity are issued on a platform one day before a user, the distributed refrigeration is obtained before picking the fruits the next day, and the distribution principle is that the fruits in the same town or street are refrigerated in a concentrated manner as much as possible, so that the storage cost of the user is reduced as much as possible; 3. in the period of non-agricultural product marketing, the lease preference of the user is mined according to the historical order of the user, and the accurate recommending effect of the user is achieved by combining the basic lease requirement of the user, so that the rural shared refrigeration house is accurately operated, the refrigeration house resources are fully utilized by farmers, and the effect of preserving and adding value of agricultural products is achieved.
Example two
Referring to fig. 2, fig. 2 is a schematic diagram illustrating an apparatus for intelligent recommendation of a shared refrigeration house mode according to another embodiment of the present application, where the apparatus includes:
the measuring and calculating module 101 is used for measuring and calculating the inventory capacity of the refrigeration house in real time to obtain the residual inventory capacity data of the refrigeration house;
an obtaining module 102, configured to obtain rental requirement information of a refrigerator renter, where the rental requirement information includes: the storage capacity requirement size, the leasing days, the storage type and the product type;
the recommending module 103 is configured to intelligently recommend a shared refrigeration house mode by using the lease requirement information;
and the execution module 104 is used for carrying out corresponding operation of the shared refrigeration house mode by utilizing the intelligent recommended result.
Example III
Referring to fig. 3, fig. 3 is a schematic diagram illustrating an apparatus for intelligent recommendation of a shared refrigeration house mode according to another embodiment of the present application, where the apparatus includes:
a memory 31 on which an executable program is stored;
a processor 32 for executing the executable program in the memory 31 to implement the steps of the method as described in any one of the above.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (8)
1. An intelligent recommendation method for a shared refrigeration house mode, which is characterized by comprising the following steps:
measuring and calculating the inventory capacity of the refrigeration house in real time to obtain the residual inventory capacity data of the refrigeration house;
the method comprises the steps of obtaining lease demand information of a refrigerator leaser, wherein the lease demand information comprises the following steps: the storage capacity requirement size, the leasing days, the storage type and the product type;
utilizing the residual storage capacity data of the refrigeration storage and the lease demand information to construct a shared refrigeration storage mode, comprising: respectively constructing a first shared cold storage intelligent mode, a second shared cold storage intelligent mode and a third shared cold storage intelligent mode by utilizing the leasing requirement information;
wherein, construct the intelligent mode of second shared freezer, include: dividing according to a certain area to obtain the information of the renting requirements, such as the size of the storage capacity, the renting days, the storage type and the product type submitted by renters in the same area, and searching a freezer set matched with the information, namely a fourth renting freezer set; if the storage capacity requirement of any one of the refrigeration houses in the fourth leasing refrigeration house set is smaller than the storage capacity requirement of the leaser, the leaser does not participate in uniform allocation, and the demand of the leaser is allocated to the latest refrigeration house meeting the storage capacity requirement; otherwise, establishing a 0-1 type integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area;
the establishing a 0-1 integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area comprises the following steps: constructing an objective function of the total storage cost of all renters in the same area by taking the minimum overall storage cost of the renters in the same area as an optimization target; constructing constraint conditions of the objective function according to the principle that the renters are distributed to the refrigeration houses in the same area and the total storage capacity of the refrigeration houses in the same area is smaller than or equal to the total storage capacity of the refrigeration houses and each renter can be distributed to one refrigeration house at most; constructing and building a 0-1 type integer programming model according to the objective function and the constraint condition of the objective function; solving the 0-1 type integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area;
and selecting a corresponding shared refrigeration house mode by utilizing the storage type and the product type, and performing intelligent recommendation for the renter.
2. The method of claim 1, wherein the real-time measuring and calculating the inventory capacity of the freezer to obtain the remaining inventory capacity data of the freezer comprises:
and (3) measuring and calculating the inventory capacity of the refrigeration house in real time by adopting a 3D vision technology to obtain the residual inventory capacity data of the refrigeration house.
3. The method of claim 1, wherein said constructing a first shared freezer intelligent mode comprises:
respectively determining a first leasing refrigeration house set and a second leasing refrigeration house set;
obtaining the lease demand information of the store capacity demand size, lease days, storage type and product type, searching a freezer set matched with the lease demand information, and setting the freezer set as a first lease freezer set;
acquiring a data set of the refrigerator with the linear distance from the renter being less than or equal to a set mileage, searching the data information with the same storage type in the data set, and setting the data information as a second renting refrigerator set;
calculating an intersection of the first leasing refrigeration house set and the second leasing refrigeration house set to obtain a third leasing refrigeration house set;
if the third leasing refrigeration house set is an empty set, sequencing the first leasing refrigeration house set according to the distance from the leaser from the near to the far;
otherwise, sorting the refrigerators in the third leasing refrigerator set according to the score from top to bottom.
4. The method of claim 1, wherein said constructing a third shared freezer intelligent mode comprises:
obtaining the renting demand information of the size, the renting days, the storage type and the product type of the library capacity demand submitted by the renters, and searching a freezer set matched with the renters, namely a fifth renting freezer set;
searching the refrigerator which is leased by the leaseholder to obtain a sixth leasehold set;
calculating the similarity between the fifth leasing refrigeration house set and the sixth leasing refrigeration house set to obtain a first refrigeration house similarity set;
and obtaining the recommended degree of the refrigerators in the first refrigerator similarity set by using the first refrigerator similarity set, and sequencing the recommended degree according to the sequence from top to bottom.
5. The method of claim 4, wherein the calculating the similarity between the fifth and sixth rental refrigerator sets to obtain a first refrigerator similarity set comprises:
calculating Euclidean distance between the fifth leasing refrigeration house set and the sixth leasing refrigeration house set;
calculating the similarity between the fifth leasing refrigeration house set and the sixth leasing refrigeration house set by using the Euclidean distance between the fifth leasing refrigeration house set and the sixth leasing refrigeration house set to obtain a first refrigeration house similarity set;
the calculation formula of the Euclidean distance is as follows:
wherein D represents Euclidean distance between a fifth leasing refrigeration house set and the sixth leasing refrigeration house set;
distance a represents the distance from the renter's body operating address to any one of the fifth set of rental refrigerators;
distance B represents the distance from the renter's body operating address to any one of the sixth set of rental refrigerators;
lease A represents lease of any refrigerator in the fifth lease refrigerator set;
lease B represents lease of any refrigerator in the sixth leasing refrigerator set;
the evaluation score A represents the evaluation score of any refrigerator in the fifth leasing refrigerator set;
the evaluation score B represents the evaluation score of any refrigerator in the sixth leasing refrigerator set;
the calculation formula of the similarity is as follows: s=1/(1+d), where D represents the euclidean distance between the fifth and sixth rental refrigerator sets; s represents the similarity.
6. The method of claim 1, wherein selecting a corresponding shared freezer mode for the renter using the storage type and the product type comprises:
if the storage type is a refrigeration fresh-keeping warehouse and the product type is common agricultural products, selecting a first shared refrigeration warehouse intelligent mode to intelligently recommend for the renter;
otherwise, if the storage type is a refrigeration fresh-keeping warehouse and the product type is a season-to-be-sold agricultural product, selecting a second shared refrigeration warehouse intelligent mode to intelligently recommend for the renter;
otherwise, if the product type is non-agricultural products, selecting a third shared refrigeration house intelligent mode to intelligently recommend to the renter.
7. An apparatus for intelligent recommendation of a shared freezer mode, the apparatus comprising:
the measuring and calculating module is used for measuring and calculating the inventory capacity of the refrigeration house in real time to obtain the residual inventory capacity data of the refrigeration house;
the system comprises an acquisition module, a storage management module and a storage management module, wherein the acquisition module is used for acquiring lease demand information of a refrigerator leaser, and the lease demand information comprises: the storage capacity requirement size, the leasing days, the storage type and the product type; the recommending module is used for intelligently recommending the shared refrigeration house mode by utilizing the lease demand information; the recommending module is specifically used for respectively constructing a first shared cold storage intelligent mode, a second shared cold storage intelligent mode and a third shared cold storage intelligent mode by utilizing the leasing requirement information; the recommending module is specifically configured to divide the second shared refrigeration house according to a certain area when constructing the second shared refrigeration house intelligent mode, obtain the information of the refrigeration house capacity requirement, the number of rentals, the storage type and the product type submitted by renters in the same area, and search a refrigeration house set matched with the information, namely a fourth refrigeration house set; if the storage capacity requirement of any one of the refrigeration houses in the fourth leasing refrigeration house set is smaller than the storage capacity requirement of the leaser, the leaser does not participate in uniform allocation, and the demand of the leaser is allocated to the latest refrigeration house meeting the storage capacity requirement; otherwise, establishing a 0-1 type integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area; the establishing a 0-1 integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area comprises the following steps: constructing an objective function of the total storage cost of all renters in the same area by taking the minimum overall storage cost of the renters in the same area as an optimization target; constructing constraint conditions of the objective function according to the principle that the renters are distributed to the refrigeration houses in the same area and the total storage capacity of the refrigeration houses in the same area is smaller than or equal to the total storage capacity of the refrigeration houses and each renter can be distributed to one refrigeration house at most; constructing and building a 0-1 type integer programming model according to the objective function and the constraint condition of the objective function; solving the 0-1 type integer programming model to obtain the minimum value of the overall storage cost of the renters in the same area;
and the execution module is used for carrying out corresponding operation of the shared refrigeration house mode by utilizing the intelligent recommending result.
8. An apparatus for intelligent recommendation of a shared freezer mode, the apparatus comprising:
a memory having an executable program stored thereon;
a processor for executing the executable program in the memory to implement the steps of the method of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310008399.7A CN115994664B (en) | 2023-01-04 | 2023-01-04 | Intelligent recommendation method, device and equipment for shared refrigeration house mode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310008399.7A CN115994664B (en) | 2023-01-04 | 2023-01-04 | Intelligent recommendation method, device and equipment for shared refrigeration house mode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115994664A CN115994664A (en) | 2023-04-21 |
| CN115994664B true CN115994664B (en) | 2023-08-08 |
Family
ID=85991677
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310008399.7A Active CN115994664B (en) | 2023-01-04 | 2023-01-04 | Intelligent recommendation method, device and equipment for shared refrigeration house mode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115994664B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117217664B (en) * | 2023-09-18 | 2024-05-14 | 浙江中兴慧农信息科技有限公司 | Processing method and device for storage in refrigeration house, storage medium and electronic equipment |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0118906D0 (en) * | 2000-01-06 | 2001-09-26 | Fujitsu Ltd | Server and method for merchandise item recommendation |
| KR20100018209A (en) * | 2008-08-06 | 2010-02-17 | 부산대학교 산학협력단 | Sysem and method for empty container management |
| JP2017068812A (en) * | 2015-09-28 | 2017-04-06 | 亮 鬼久保 | System of stock calculation and order of specific commodities stored in refrigerator and freezer based on image analysis data of camera |
| CN107316208A (en) * | 2017-06-20 | 2017-11-03 | 国网重庆市电力公司电力科学研究院 | A kind of shared Car sharing distribution and site selection model |
| CN110544139A (en) * | 2018-05-29 | 2019-12-06 | 九阳股份有限公司 | Space sharing method of refrigeration equipment, refrigeration equipment and electronic equipment |
| CN110674405A (en) * | 2019-09-29 | 2020-01-10 | 武汉理工大学 | Shared refrigeration house service platform and use method thereof |
| CN111798291A (en) * | 2020-07-02 | 2020-10-20 | 浙江拉斯贝姆餐饮设备有限公司 | Shared refrigeration house operating system based on internet |
| CN113934947A (en) * | 2021-10-21 | 2022-01-14 | 中国银行股份有限公司 | Method and device for recommending co-hiring area |
| CN115146956A (en) * | 2022-06-30 | 2022-10-04 | 东南大学 | Internet appointment vehicle sharing trip man-vehicle matching method |
| CN115423554A (en) * | 2022-08-30 | 2022-12-02 | 鑫洋互联网科技(广州)有限公司 | Life optimization method based on Internet technology |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140279263A1 (en) * | 2013-03-13 | 2014-09-18 | Truecar, Inc. | Systems and methods for providing product recommendations |
| US20190073713A1 (en) * | 2017-09-01 | 2019-03-07 | Austin Kim | Method of sharing storage space |
| CN111177551B (en) * | 2019-12-27 | 2021-04-16 | 百度在线网络技术（北京）有限公司 | Method, device, equipment and computer storage medium for determining search result |
| CN114169952A (en) * | 2020-09-11 | 2022-03-11 | 京东方科技集团股份有限公司 | Commodity recommendation method, server, shopping cart and shopping system |
-
2023
- 2023-01-04 CN CN202310008399.7A patent/CN115994664B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0118906D0 (en) * | 2000-01-06 | 2001-09-26 | Fujitsu Ltd | Server and method for merchandise item recommendation |
| KR20100018209A (en) * | 2008-08-06 | 2010-02-17 | 부산대학교 산학협력단 | Sysem and method for empty container management |
| JP2017068812A (en) * | 2015-09-28 | 2017-04-06 | 亮 鬼久保 | System of stock calculation and order of specific commodities stored in refrigerator and freezer based on image analysis data of camera |
| CN107316208A (en) * | 2017-06-20 | 2017-11-03 | 国网重庆市电力公司电力科学研究院 | A kind of shared Car sharing distribution and site selection model |
| CN110544139A (en) * | 2018-05-29 | 2019-12-06 | 九阳股份有限公司 | Space sharing method of refrigeration equipment, refrigeration equipment and electronic equipment |
| CN110674405A (en) * | 2019-09-29 | 2020-01-10 | 武汉理工大学 | Shared refrigeration house service platform and use method thereof |
| CN111798291A (en) * | 2020-07-02 | 2020-10-20 | 浙江拉斯贝姆餐饮设备有限公司 | Shared refrigeration house operating system based on internet |
| CN113934947A (en) * | 2021-10-21 | 2022-01-14 | 中国银行股份有限公司 | Method and device for recommending co-hiring area |
| CN115146956A (en) * | 2022-06-30 | 2022-10-04 | 东南大学 | Internet appointment vehicle sharing trip man-vehicle matching method |
| CN115423554A (en) * | 2022-08-30 | 2022-12-02 | 鑫洋互联网科技(广州)有限公司 | Life optimization method based on Internet technology |
Non-Patent Citations (1)
| Title |
|---|
| Hua-ren Zhou et al..Distribution algorithms on on-line lease system.2011 IEEE International Symposium on IT Medicine and Education.2012,235-238. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115994664A (en) | 2023-04-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11829887B2 (en) | Progressive objective addition in multi-objective heuristic systems and methods | |
| US10846926B2 (en) | Systems and methods for filling holes in a virtual reality model | |
| CN115994664B (en) | Intelligent recommendation method, device and equipment for shared refrigeration house mode | |
| Rapolu et al. | Joint pricing, advertisement, preservation technology investment and inventory policies for non-instantaneous deteriorating items under trade credit | |
| JP5859606B2 (en) | Ad source and keyword set adaptation in online commerce platforms | |
| Saló et al. | The second-home rental market: A hedonic analysis of the effect of different characteristics and a high-market-share intermediary on price | |
| CN104636950A (en) | Group object commodity recommendation system and method | |
| KR102257014B1 (en) | Smart locker kiosk system | |
| CN103838775B (en) | Data analysing method and DAF | |
| Onwude et al. | Bottlenecks in Nigeria's fresh food supply chain: What is the way forward? | |
| CN114219520A (en) | Big data analysis-based fresh agricultural product data mining and integrating system | |
| Navarro et al. | A mobile robot vending machine for beaches based on consumers’ preferences and multivariate methods | |
| US11861579B1 (en) | Intelligent inventory system | |
| Buurman | Rural Land Markets; a spatial explanatory model | |
| Kim et al. | Scenario aggregation for supply chain quantity-flexibility contract | |
| Krommyda et al. | Optimal pricing and replenishment policy for noninstantaneous deteriorating items and two levels of storage | |
| US11853960B1 (en) | Systems for selecting items and item placements | |
| KR102626968B1 (en) | Method and device for providing tree information | |
| Hssini et al. | Blood products inventory pickup and delivery problem under time windows constraints | |
| Li et al. | Automatic Pricing and Replenishment Strategy for Vegetable Products Based on Time Series and NSGA-III Algorithm | |
| Pevekar et al. | Inventory model for timely deteriorating products considering penalty cost and shortage cost | |
| Trupo et al. | Southwest Virginia Shipping-point Market Project: Phase Two | |
| Guo | World Resources Determine World Prices | |
| CN114372703A (en) | Agricultural commerce and trade system and method | |
| CN115953223A (en) | Commodity recommendation method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |