CN111861644A - Intelligent recommendation method and system for industrial design products - Google Patents
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Abstract
The invention belongs to the technical field of product recommendation, and discloses an intelligent recommendation method and system for industrial design products, wherein the intelligent recommendation system for industrial design products comprises the following components: the system comprises a client, a demand information acquisition module, a characteristic information acquisition module, a central control module, a demand analysis module, a recommendation strategy acquisition module, a product intelligent recommendation module, a recommendation strategy optimization module, a cloud storage module and a display module. According to the intelligent recommendation method for the industrial design products, the recommendation value of the products is calculated by utilizing a strategy approximation algorithm, the historical behavior characteristics of the users are more emphasized, and more accurate information such as preference and intention of the users can be learned from the historical behavior characteristics of the users, so that the product recommendation accuracy is improved; the customer characteristic information is input into the demand analysis model through the demand analysis model to obtain the product recommendation strategy, so that the workload of collecting customer information and analyzing data is effectively reduced, and proper products and services are provided for customers.
Description
Technical Field
The invention belongs to the technical field of product recommendation, and particularly relates to an intelligent recommendation method and system for industrial design products.
Background
At present, a product recommendation technology is widely applied to various shopping Applications (APPs), and a more valuable product can be recommended to a user according to behavior characteristics of the user, attributes of the product and the like, so that a guiding effect on the user is realized, and purposiveness of purchasing of the user is enhanced. Nowadays, the number of users and the number of products of many shopping applications reach the order of hundreds of millions, and the behavior characteristics of the users and the preferences of the products are rich and diverse. The existing product recommendation technology has low intelligent degree, and when different product sequencing results are provided for different users according to the historical behaviors of the users, the historical behaviors of the users are used once every time the products are sequenced, so that the operation is complicated, and the efficiency is low; meanwhile, the existing technology which pays attention to the action of behavior characteristics of users on product recommendation has not been reported yet. Therefore, a new intelligent recommendation method for industrial design products is needed.
Through the above analysis, the problems and defects of the prior art are as follows: the existing product recommendation technology has low intelligent degree, and when different product sequencing results are provided for different users according to the historical behaviors of the users, the historical behaviors of the users are used once every time the products are sequenced, so that the operation is complicated, and the efficiency is low; meanwhile, the existing technology which pays attention to the action of behavior characteristics of users on product recommendation has not been reported yet.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an intelligent recommendation method and system for industrial design products.
The invention is realized in this way, an industrial design product intelligent recommendation system, the industrial design product intelligent recommendation system includes:
the system comprises a client, a demand information acquisition module, a characteristic information acquisition module, a central control module, a demand analysis module, a recommendation strategy acquisition module, a product intelligent recommendation module, a recommendation strategy optimization module, a cloud storage module and a display module.
The client is connected with the central control module and used for sending a recommendation request of the industrial design product through a request sending program;
the demand information acquisition module is connected with the central control module and used for acquiring demand information of the client sending the product recommendation request on the industrial design product through information acquisition equipment;
the characteristic information acquisition module is connected with the central control module and used for acquiring client characteristic information corresponding to the client number according to the client number in the product demand information through a characteristic acquisition program;
the central control module is connected with the client, the demand information acquisition module, the characteristic information acquisition module, the demand analysis module, the recommendation strategy acquisition module, the intelligent product recommendation module, the recommendation strategy optimization module, the terminal module, the cloud storage module and the display module and is used for controlling the normal operation of each module of the intelligent industrial design product recommendation system through the central processing unit;
The demand analysis module is connected with the central control module and used for acquiring a demand analysis report of the user by using a demand analysis model according to the demand information and the customer characteristic information;
the recommendation strategy acquisition module is connected with the central control module and used for acquiring the industrial design product recommendation strategy corresponding to the client by utilizing a strategy approximation algorithm according to the demand analysis report;
the product intelligent recommendation module is connected with the central control module and used for providing corresponding product recommendation service to the corresponding client through an intelligent recommendation program according to the industrial design product recommendation strategy;
the recommendation strategy optimization module is connected with the central control module and used for optimizing strategy optimization parameters of the strategy approximation algorithm through an optimization program;
the cloud storage module is connected with the central control module and used for storing the acquired demand information, the customer characteristic information, the demand analysis report and the industrial design product recommendation strategy of the client sending the product recommendation request to the industrial design product through a cloud database server;
and the display module is connected with the central control module and is used for displaying the acquired requirement information, the client characteristic information, the requirement analysis report and the real-time data of the industrial design product recommendation strategy of the client sending the product recommendation request on the industrial design product through a display.
Further, the demand analysis module includes:
the acquisition subunit is used for acquiring the historical characteristic information of the client and the historical product recommendation strategy;
and the model training unit is used for training a preset machine learning model according to the historical characteristic information of the client and the historical product recommendation strategy to obtain the demand analysis model.
Further, the central control module includes:
the data analysis unit is used for constructing a parameter optimization part of the strategy approximation algorithm, and the parameter optimization part comprises the strategy optimization parameters;
an optimization target setting unit for setting an optimization target of the parameter optimization part;
and the parameter optimization unit is used for optimizing the strategy optimization parameters according to the optimization target.
Another object of the present invention is to provide an intelligent recommendation method for an industrial design product using the intelligent recommendation system for an industrial design product, the intelligent recommendation method for an industrial design product comprising the following steps:
step one, selecting the type of a recommendation request by a request sending program through a client, setting recommendation request content in the recommendation request, and sending the recommendation request of an industrial design product.
And secondly, acquiring the demand information of the client sending the product recommendation request to the industrial design product by using information acquisition equipment through a demand information acquisition module.
And thirdly, acquiring customer characteristic information corresponding to the customer number according to the customer number in the product demand information by using a characteristic acquisition program through a characteristic information acquisition module.
And step four, controlling the normal operation of each module of the intelligent recommendation system of the industrial design products by using a central processing unit through a central control module.
And fifthly, acquiring a demand analysis report of the user by using a demand analysis model according to the demand information and the customer characteristic information through a demand analysis module.
And step six, obtaining the industrial design product recommendation strategy corresponding to the customer by a recommendation strategy obtaining module according to the demand analysis report by utilizing a strategy approximation algorithm.
And step seven, providing corresponding product recommendation service to the corresponding client by using an intelligent recommendation program through a product intelligent recommendation module according to the industrial design product recommendation strategy.
And step eight, optimizing the strategy optimization parameters of the strategy approximation algorithm by using an optimization program through a recommended strategy optimization module.
And ninthly, storing the acquired demand information, the customer characteristic information, the demand analysis report and the industrial design product recommendation strategy of the client sending the product recommendation request to the industrial design product by using a cloud storage module through a cloud database server.
And step ten, displaying the acquired requirement information, the client characteristic information, the requirement analysis report and the real-time data of the industrial design product recommendation strategy of the client sending the product recommendation request to the industrial design product by using a display through a display module.
Further, in the second step, the requirement information includes a requirement number, a requirement state, a requirement source, a customer number, a customer name, a customer level, a customer category, a product category code, a product category name, a product category, and a product source.
Further, in step five, the demand analysis model is determined according to the following manner:
the first server determines a first historical predicted value of the user according to first historical user behavior data and a first information recommendation model acquired by the first server;
the first server acquires a second historical predicted value determined by the user in a second information recommendation model; the second historical predicted value is determined by the second server according to second historical user behavior data acquired by the second server and a second information recommendation model;
The first server determines the query gain of the first historical user behavior data according to the first historical predicted value and the second historical prediction;
the first server determines a training sample of the demand analysis model according to the first historical user behavior data, the parameters of the first information recommendation model and the corresponding query gain;
and the first server establishes the demand analysis model according to the training sample.
Further, in the sixth step, the method for obtaining the industrial design product recommendation strategy corresponding to the customer by using the strategy approximation algorithm includes:
constructing a strategy generation part of the strategy approximation algorithm, wherein the strategy generation part comprises at least one strategy optimization parameter;
and calculating to obtain a product recommendation strategy corresponding to the product recommendation request by taking the demand analysis report as input data of the strategy generation part.
Further, in the sixth step, the product recommendation strategy includes a product attribute weight vector, and the product attribute weight vector is used for determining the order of the recommended products in the arrangement order; the client is further configured to present the recommended products in the order.
Further, in the seventh step, when providing corresponding product recommendation service to the corresponding client, obtaining a response performance parameter of the product intelligent recommendation module to the received recommendation request, wherein the product intelligent recommendation module stores personalized recommendation data exclusive to each user;
judging whether the response performance parameter is in a preset range;
if so, reducing the quantity of recommendation requests sent to the intelligent product recommendation module so that the intelligent product recommendation module can respond to the rest recommendation requests;
and if not, sending all recommendation requests to the intelligent product recommendation module.
Further, in step eight, the method for optimizing the policy optimization parameter of the policy approximation algorithm includes:
constructing a data sample according to the demand information of the client on the industrial design product;
determining the value of the strategy parameter optimization number when the parameter optimization part reaches the optimization target according to the data sample;
updating the value of the policy optimization parameter to the parameter optimization section.
Another object of the present invention is to provide a computer program product stored on a computer readable medium, which includes a computer readable program for providing a user input interface to implement the intelligent recommendation method for industrial design products when the computer program product is executed on an electronic device.
Another object of the present invention is to provide a computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to execute the intelligent recommendation method for industrial design products.
By combining all the technical schemes, the invention has the advantages and positive effects that: the intelligent recommendation method for the industrial design products can calculate the optimal product recommendation strategy by utilizing a strategy approximation algorithm according to the requirement information of the user in the historical product recommendation strategy. The recommendation value of the product is calculated by using a strategy approximation algorithm, more emphasis is placed on using the historical behavior characteristics of the user, and more accurate information such as preference and intention of the user can be learned from the historical behavior characteristics of the user, so that the product recommendation accuracy is improved. The customer characteristic information is input into the demand analysis model through the demand analysis model to obtain the product recommendation strategy, so that the workload of collecting customer information and analyzing data is effectively reduced, and proper products and services are provided for customers.
Drawings
FIG. 1 is a block diagram of an industrial design product intelligent recommendation system according to an embodiment of the present invention;
In the figure: 1. a client; 2. a demand information acquisition module; 3. a characteristic information acquisition module; 4. a central control module; 5. a demand analysis module; 6. a recommendation strategy acquisition module; 7. a product intelligent recommendation module; 8. a recommendation strategy optimization module; 9. a cloud storage module; 10. and a display module.
Fig. 2 is a block diagram of a central control module according to an embodiment of the present invention.
Fig. 3 is a flowchart of an intelligent recommendation method for an industrial design product according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for obtaining an industrial design product recommendation policy corresponding to the customer by using a policy approximation algorithm according to an embodiment of the present invention.
Fig. 5 is a flowchart of a method for optimizing policy optimization parameters of the policy approximation algorithm according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides an intelligent recommendation method and system for industrial design products, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, an intelligent recommendation system for industrial design products provided by an embodiment of the present invention includes: the system comprises a client 1, a demand information acquisition module 2, a characteristic information acquisition module 3, a central control module 4, a demand analysis module 5, a recommendation strategy acquisition module 6, a product intelligent recommendation module 7, a recommendation strategy optimization module 8, a cloud storage module 9 and a display module 10.
The client 1 is connected with the central control module 4 and used for sending a recommendation request of an industrial design product through a request sending program;
the demand information acquisition module 2 is connected with the central control module 4 and used for acquiring demand information of the client sending the product recommendation request on the industrial design product through information acquisition equipment;
the characteristic information acquisition module 3 is connected with the central control module 4 and is used for acquiring customer characteristic information corresponding to the customer number according to the customer number in the product demand information through a characteristic acquisition program;
the central control module 4 is connected with the client 1, the demand information acquisition module 2, the characteristic information acquisition module 3, the demand analysis module 5, the recommendation strategy acquisition module 6, the intelligent product recommendation module 7, the recommendation strategy optimization module 8, the cloud storage module 9 and the display module 10, and is used for controlling the normal operation of each module of the intelligent industrial design product recommendation system through a central processing unit;
The demand analysis module 5 is connected with the central control module 4 and used for acquiring a demand analysis report of a user by using a demand analysis model according to the demand information and the customer characteristic information;
the recommendation strategy acquisition module 6 is connected with the central control module 4 and used for acquiring the industrial design product recommendation strategy corresponding to the customer by utilizing a strategy approximation algorithm according to the demand analysis report;
the product intelligent recommendation module 7 is connected with the central control module 4 and used for providing corresponding product recommendation service to the corresponding client according to the industrial design product recommendation strategy through an intelligent recommendation program;
the recommendation strategy optimization module 8 is connected with the central control module 4 and used for optimizing strategy optimization parameters of the strategy approximation algorithm through an optimization program;
the cloud storage module 9 is connected with the central control module 4 and used for storing the acquired demand information, the customer characteristic information, the demand analysis report and the industrial design product recommendation strategy of the client sending the product recommendation request to the industrial design product through a cloud database server;
and the display module 10 is connected with the central control module 4 and is used for displaying the acquired requirement information, the client characteristic information, the requirement analysis report and the real-time data of the industrial design product recommendation strategy of the client sending the product recommendation request on the industrial design product through a display.
As shown in fig. 2, the central control module 4 provided in the embodiment of the present invention includes:
a data analysis unit 4-1, configured to construct a parameter optimization part of the policy approximation algorithm, where the parameter optimization part includes the policy optimization parameters;
an optimization target setting unit 4-2 for setting an optimization target of the parameter optimization section;
and the parameter optimization unit 4-3 is used for optimizing the strategy optimization parameters according to the optimization target.
The demand analysis module 5 provided by the embodiment of the invention comprises:
the acquisition subunit 5-1 is used for acquiring the historical characteristic information of the client and the historical product recommendation strategy;
and the model training unit 5-2 is used for training a preset machine learning model according to the historical characteristic information of the client and the historical product recommendation strategy to obtain the demand analysis model.
As shown in fig. 3, the method for intelligently recommending industrial design products according to the embodiment of the present invention includes:
s101, selecting the type of a recommendation request by a request sending program through a client, setting recommendation request content in the recommendation request, and sending a recommendation request of an industrial design product.
And S102, acquiring the demand information of the client sending the product recommendation request to the industrial design product by using information acquisition equipment through a demand information acquisition module.
And S103, acquiring customer characteristic information corresponding to the customer number according to the customer number in the product demand information by using a characteristic acquisition program through a characteristic information acquisition module.
And S104, controlling the normal operation of each module of the intelligent recommendation system of the industrial design products by using a central processing unit through a central control module.
And S105, obtaining a demand analysis report of the user by using a demand analysis model according to the demand information and the customer characteristic information through a demand analysis module.
And S106, obtaining the industrial design product recommendation strategy corresponding to the customer by a recommendation strategy obtaining module according to the demand analysis report by utilizing a strategy approximation algorithm.
And S107, providing corresponding product recommendation service to the corresponding client by using an intelligent recommendation program through a product intelligent recommendation module according to the industrial design product recommendation strategy.
And S108, optimizing the strategy optimization parameters of the strategy approximation algorithm by using an optimization program through a recommended strategy optimization module.
And S109, storing the acquired demand information, the customer characteristic information, the demand analysis report and the industrial design product recommendation strategy of the client sending the product recommendation request to the industrial design product by using the cloud database server through the cloud storage module.
And S110, displaying the acquired requirement information, the client characteristic information, the requirement analysis report and the real-time data of the industrial design product recommendation strategy of the client sending the product recommendation request to the industrial design product by using a display through a display module.
The requirement information in the embodiment of the invention comprises a requirement number, a requirement state, a requirement source, a customer number, a customer name, a customer level, a customer classification, a product type code, a product type name, a product classification and a product source.
In step S105, the demand analysis model is determined according to the following method:
the first server determines a first historical predicted value of the user according to first historical user behavior data and a first information recommendation model acquired by the first server;
the first server acquires a second historical predicted value determined by the user in a second information recommendation model; the second historical predicted value is determined by the second server according to second historical user behavior data acquired by the second server and a second information recommendation model;
the first server determines the query gain of the first historical user behavior data according to the first historical predicted value and the second historical prediction;
The first server determines a training sample of the demand analysis model according to the first historical user behavior data, the parameters of the first information recommendation model and the corresponding query gain;
and the first server establishes the demand analysis model according to the training sample.
Step S107, when providing corresponding product recommendation service to the corresponding client, obtaining response performance parameters of the intelligent product recommendation module to the received recommendation request, wherein the intelligent product recommendation module stores personalized recommendation data exclusive to each user;
judging whether the response performance parameter is in a preset range;
if so, reducing the quantity of recommendation requests sent to the intelligent product recommendation module so that the intelligent product recommendation module can respond to the rest recommendation requests;
and if not, sending all recommendation requests to the intelligent product recommendation module.
As shown in fig. 4, the method for obtaining the recommendation policy of the industrial design product corresponding to the customer by using the policy approximation algorithm according to the embodiment of the present invention includes:
s201, constructing a strategy generation part of the strategy approximation algorithm, wherein the strategy generation part comprises at least one strategy optimization parameter.
S202, the demand analysis report is used as input data of the strategy generation part, and a product recommendation strategy corresponding to the product recommendation request is obtained through calculation.
The product recommendation strategy provided by the embodiment of the invention comprises a product attribute weight vector, wherein the product attribute weight vector is used for determining the sequence of recommended products in the arrangement sequence; the client is further configured to present the recommended products in the order.
As shown in fig. 5, the method for optimizing the policy optimization parameter of the policy approximation algorithm according to the embodiment of the present invention includes:
s301, constructing a data sample according to the requirement information of the client to the industrial design product.
S302, determining the value of the strategy parameter optimization number when the parameter optimization part reaches the optimization target according to the data sample.
S303, updating the value of the strategy optimization parameter to the parameter optimization part.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An intelligent recommendation method for industrial design products is characterized by comprising the following steps:
selecting the type of a recommendation request by a request sending program through a client, setting recommendation request content in the recommendation request, and sending a recommendation request of an industrial design product;
acquiring the demand information of the client sending the product recommendation request to the industrial design product by using information acquisition equipment through a demand information acquisition module; the requirement information comprises a requirement number, a requirement state, a requirement source, a customer number, a customer name, a customer level, a customer classification, a product type code, a product type name, a product classification and a product source;
thirdly, acquiring customer characteristic information corresponding to the customer number according to the customer number in the product demand information by a characteristic information acquisition module through a characteristic acquisition program;
Controlling the normal operation of each module of the intelligent recommendation system of the industrial design products by using a central processing unit through a central control module;
acquiring a demand analysis report of a user by using a demand analysis model according to the demand information and the customer characteristic information through a demand analysis module;
step six, obtaining an industrial design product recommendation strategy corresponding to the customer by a recommendation strategy obtaining module according to the demand analysis report by utilizing a strategy approximation algorithm;
step seven, providing corresponding product recommendation service to the corresponding client by using an intelligent recommendation program through a product intelligent recommendation module according to the industrial design product recommendation strategy;
step eight, optimizing the strategy optimization parameters of the strategy approximation algorithm by using an optimization program through a recommended strategy optimization module;
step nine, the cloud storage module is used for storing the acquired demand information, the customer characteristic information, the demand analysis report and the industrial design product recommendation strategy of the client sending the product recommendation request to the industrial design product by using the cloud database server;
and step ten, displaying the acquired requirement information, the client characteristic information, the requirement analysis report and the real-time data of the industrial design product recommendation strategy of the client sending the product recommendation request to the industrial design product by using a display through a display module.
2. The intelligent recommendation method for industrial design products according to claim 1, wherein in step six, the method for obtaining recommendation strategies for industrial design products corresponding to the customers by using a strategy approximation algorithm comprises:
constructing a strategy generation part of the strategy approximation algorithm, wherein the strategy generation part comprises at least one strategy optimization parameter;
taking the demand analysis report as input data of the strategy generation part, and calculating to obtain a product recommendation strategy corresponding to the product recommendation request;
the product recommendation strategy comprises a product attribute weight vector, and the product attribute weight vector is used for determining the sequence of recommended products in the arrangement sequence; the client is further configured to present the recommended products in the order.
3. The intelligent recommendation method for industrial design products according to claim 1, wherein in step seven, when providing the corresponding product recommendation service to the corresponding client, the response performance parameters of the intelligent product recommendation module to the received recommendation request are obtained, and the intelligent product recommendation module stores personalized recommendation data specific to each user;
judging whether the response performance parameter is in a preset range;
If so, reducing the quantity of recommendation requests sent to the intelligent product recommendation module so that the intelligent product recommendation module can respond to the rest recommendation requests;
and if not, sending all recommendation requests to the intelligent product recommendation module.
4. The intelligent recommendation method for industrial design products according to claim 1, wherein in step eight, the method for optimizing the strategy optimization parameters of the strategy approximation algorithm comprises:
constructing a data sample according to the demand information of the client on the industrial design product;
determining the value of the strategy parameter optimization number when the parameter optimization part reaches the optimization target according to the data sample;
updating the value of the policy optimization parameter to the parameter optimization section.
5. The intelligent recommendation method for industrial design products according to claim 1, wherein in step five, the demand analysis model is determined according to the following manner:
the first server determines a first historical predicted value of the user according to first historical user behavior data and a first information recommendation model acquired by the first server;
the first server acquires a second historical predicted value determined by the user in a second information recommendation model; the second historical predicted value is determined by the second server according to second historical user behavior data acquired by the second server and a second information recommendation model;
The first server determines the query gain of the first historical user behavior data according to the first historical predicted value and the second historical prediction;
the first server determines a training sample of the demand analysis model according to the first historical user behavior data, the parameters of the first information recommendation model and the corresponding query gain;
and the first server establishes the demand analysis model according to the training sample.
6. An intelligent recommendation system for industrial design products, which is used in the intelligent recommendation method for industrial design products claimed in any one of claims 1 to 5, wherein the intelligent recommendation system for industrial design products comprises:
the system comprises a client, a demand information acquisition module, a characteristic information acquisition module, a central control module, a demand analysis module, a recommendation strategy acquisition module, a product intelligent recommendation module, a recommendation strategy optimization module, a cloud storage module and a display module;
the client is connected with the central control module and used for sending a recommendation request of the industrial design product through a request sending program;
the demand information acquisition module is connected with the central control module and used for acquiring demand information of the client sending the product recommendation request on the industrial design product through information acquisition equipment;
The characteristic information acquisition module is connected with the central control module and used for acquiring client characteristic information corresponding to the client number according to the client number in the product demand information through a characteristic acquisition program;
the central control module is connected with the client, the demand information acquisition module, the characteristic information acquisition module, the demand analysis module, the recommendation strategy acquisition module, the intelligent product recommendation module, the recommendation strategy optimization module, the terminal module, the cloud storage module and the display module and is used for controlling the normal operation of each module of the intelligent industrial design product recommendation system through the central processing unit;
the demand analysis module is connected with the central control module and used for acquiring a demand analysis report of the user by using a demand analysis model according to the demand information and the customer characteristic information;
the recommendation strategy acquisition module is connected with the central control module and used for acquiring the industrial design product recommendation strategy corresponding to the client by utilizing a strategy approximation algorithm according to the demand analysis report;
the product intelligent recommendation module is connected with the central control module and used for providing corresponding product recommendation service to the corresponding client through an intelligent recommendation program according to the industrial design product recommendation strategy;
The recommendation strategy optimization module is connected with the central control module and used for optimizing strategy optimization parameters of the strategy approximation algorithm through an optimization program;
the cloud storage module is connected with the central control module and used for storing the acquired demand information, the customer characteristic information, the demand analysis report and the industrial design product recommendation strategy of the client sending the product recommendation request to the industrial design product through a cloud database server;
and the display module is connected with the central control module and is used for displaying the acquired requirement information, the client characteristic information, the requirement analysis report and the real-time data of the industrial design product recommendation strategy of the client sending the product recommendation request on the industrial design product through a display.
7. The intelligent industrial design product recommendation system of claim 6, wherein the demand analysis module comprises:
the acquisition subunit is used for acquiring the historical characteristic information of the client and the historical product recommendation strategy;
and the model training unit is used for training a preset machine learning model according to the historical characteristic information of the client and the historical product recommendation strategy to obtain the demand analysis model.
8. The intelligent industrial design product recommendation system of claim 6, wherein the central control module further comprises:
the data analysis unit is used for constructing a parameter optimization part of the strategy approximation algorithm, and the parameter optimization part comprises the strategy optimization parameters;
an optimization target setting unit for setting an optimization target of the parameter optimization part;
and the parameter optimization unit is used for optimizing the strategy optimization parameters according to the optimization target.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for implementing the method for intelligent recommendation of an industrial design product as claimed in any one of claims 1 to 5 when executed on an electronic device.
10. A computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the intelligent recommendation method for industrial design products according to any one of claims 1 to 5.
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| CN114357014A (en) * | 2022-01-17 | 2022-04-15 | 分享印科技(广州)有限公司 | Cutting die establishing system based on big data |
| CN116742484A (en) * | 2023-08-15 | 2023-09-12 | 希格玛电气(珠海)有限公司 | Circuit connection control system of solid insulation cabinet |
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