CN111275514A - Intelligent purchasing method and system, storage medium and electronic device - Google Patents
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Abstract
The invention provides an intelligent purchasing system, which is characterized in that: the method comprises the following steps: s1: crawling original data of a commodity sales page of the E-commerce platform by using a crawler program; s2: analyzing the original data by using a data analysis program to generate database storage data and storing the database storage data into a database; s3: reading the data stored in the database by using a heat prediction model program and predicting the heat data after a certain time to obtain heat prediction data; s4: calculating heat data after a certain time by using a heat calculation program to obtain heat contrast data; s5: comparing the heat prediction data with the heat comparison data, adjusting parameters of a heat prediction model program if the accuracy is lower than a threshold value, returning to S3, and otherwise, entering S6; s6: the heat prediction model program outputs a heat prediction model to the ERP system; s7: the ERP system recommends the purchased goods using a heat prediction model. The invention has the beneficial effects that: independent of sales personnel, the automatic and accurate commodity popularity prediction can be realized.
Description
Technical Field
The present invention relates to intelligent marketing, and in particular, to an intelligent purchasing method and system, a storage medium, and an electronic device.
Background
The traditional method is that a salesperson deduces the market demand of the next month, quarter or year according to past sales experience and then makes a purchase and sales plan. The mode is limited by self experience of the salesperson, and misjudgment is more. For example, a salesperson would typically infer that future sales would be good based on sales of existing goods, but would ignore sales of similar goods in the marketplace. This would not anticipate the sudden appearance of a competitor. For another example, when a seller is familiar with the sales of a certain commodity in china and a company wants to sell a similar commodity in europe, the seller is unfamiliar with the local market and cannot make a comprehensive and accurate judgment.
Therefore, the market needs a system and a method which can realize automatic and accurate commodity 'hot' prediction without depending on past sales experiences of sales personnel.
Disclosure of Invention
In order to solve the technical problem, the invention discloses an intelligent purchasing system, and the technical scheme of the invention is implemented as follows:
an intelligent purchasing method is characterized in that: the method comprises the following steps: s1: crawling original data of a commodity sales page of the E-commerce platform by using a crawler program; s2: analyzing the original data by using a data analysis program to generate database storage data and storing the database storage data into a database; s3: reading the data stored in the database by using a heat prediction model program and predicting heat data after a certain time to obtain heat prediction data; s4: calculating heat data after a certain time by using a heat calculation program to obtain heat contrast data; s5: comparing the heat prediction data with the heat contrast data, adjusting parameters of the heat prediction model program if the accuracy is lower than a threshold value, returning to S3, and otherwise, entering S6; s6: the heat prediction model program outputs a heat prediction model to the ERP system; s7: the ERP system recommends the purchased goods using the popularity prediction model.
Preferably, further comprising S0; the S0 includes: s01: a data preprocessing program extracts the database storage data and preprocesses the database storage data to generate preprocessed data; s02: importing the preprocessed data into a coding neural network to obtain a heat group; s03: importing the heat group into a decoding neural network, and outputting a latent vector group by the decoding neural network; s04: comparing the feature vector group with the potential vector group to obtain a failure rate; s05: if the error rate is higher than the error threshold, adjusting the encoding neural network and the decoding neural network, and returning to S02; otherwise, the coding neural network is output to the heat calculation module.
Preferably, the preprocessed data is a set of feature vectors; the latent vector group is a set of latent vectors; said feature vector and said membership to vector space
Preferably, the error rate is calculated by
The n is the dimension number of the feature vector.
Preferably, the certain period of time is not less than half a month.
Preferably, the crawler program runs a computer program comprising one or more of a general web crawler, a focused web crawler, an incremental web crawler, or a deep web crawler.
Preferably, the heat prediction model program is a computer program based on a long-short term memory model.
Preferably, the heat calculation program is a neural network-based computer program.
An intelligent purchasing system, an intelligent purchasing method, which is characterized in that: the system comprises the database, a crawler module, a data analysis module, a heat prediction model module and the ERP system; the crawler program is connected with the data analysis module; the data analysis module is connected with the database; the database is connected with the heat prediction model module; the heat prediction model program is connected with the ERP system; the system also comprises a heat calculation module; the heat calculation module is connected with the heat prediction model module; the crawler module runs the crawler program; the data analysis module runs the data analysis program; the heat prediction model module runs the heat prediction model program.
An intelligent procurement storage medium, characterized in that the storage medium has stored therein a computer program, wherein the computer program is arranged to execute an intelligent procurement method when executed.
An intelligent purchasing electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute an intelligent purchasing method.
The technical scheme of the invention can solve the technical problem that automatic and accurate sales condition prediction cannot be realized depending on past sales experiences of sales personnel in the prior art; by implementing the technical scheme of the invention, the technical effect of automatic and accurate judgment of the commodity sales condition without depending on the past sales experience of sales personnel can be realized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only an embodiment of the intelligent purchasing system of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a method flow diagram of an intelligent purchasing method;
fig. 2 is a flowchart of S0 of an intelligent procurement method.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In a specific embodiment, as shown in fig. 1, an intelligent purchasing method is characterized in that: the method comprises the following steps: s1: crawling original data of a commodity sales page of the E-commerce platform by using a crawler program; s2: analyzing the original data by using a data analysis program to generate database storage data and storing the database storage data into a database; s3: reading the data stored in the database by using a heat prediction model program and predicting heat data after a certain time to obtain heat prediction data; s4: calculating heat data after a certain time by using a heat calculation program to obtain heat contrast data; s5: comparing the heat prediction data with the heat contrast data, adjusting parameters of the heat prediction model program if the accuracy is lower than a threshold value, returning to S3, and otherwise, entering S6; s6: the heat prediction model program outputs a heat prediction model to the ERP system; s7: the ERP system recommends the purchased goods using the popularity prediction model.
In the specific embodiment, a crawler program crawls related data of various commodities, namely original data, on a sales page on an e-commerce platform, wherein the original data is generally a character string formed by the whole webpage, but the data type of the original data cannot be directly applied to data analysis, so that the original data is analyzed into a data type which can be used for data analysis through a data analysis program, and the general data analysis program is a character string analysis program based on a regular expression and analyzes data which are contained in the original data and relate to comments, sales volume, price and the like of the commodities; then the analyzed data, namely the database storage data, is stored in the database for future data analysis and use; the heat prediction model program reads the data stored in the database and predicts the heat data after a certain time, namely the heat of each commodity after a certain time, generally speaking, the certain time, namely the time interval is set as one month, and can also be set as other time periods according to the actual situation; then leading in data which belongs to a certain time to the heat degree calculation module so as to obtain heat degree contrast data, then comparing the heat prediction data with the heat contrast data, thereby evaluating the accuracy of the prediction, if the accuracy is lower than the threshold value, adjusting the parameters of the heat prediction model to train again, then comparing the accuracy of the previous time and the next time, if the accuracy is reduced, and the parameters of the heat prediction model are subjected to negative feedback optimization and then trained again, if the accuracy rate is increased, carrying out positive feedback reinforcement on the parameters of the heat prediction model, then carrying out training again, repeating the above processes until a long-term stable prediction model is formed, then the stable heat prediction model is transmitted to an ERP system, and the user predicts the heat of the commodity by using the heat prediction model in the ERP system, so that a corresponding purchasing plan is formed; through the steps, the automation and the accurate judgment of the commodity sales condition are completed, and the corresponding purchase plan is obtained.
In a preferred embodiment, as shown in fig. 2, further comprising S0; the S0 includes: s01: a data preprocessing program extracts the database storage data and preprocesses the database storage data to generate preprocessed data; s02: importing the preprocessed data into a coding neural network to obtain a heat group; s03: importing the heat group into a decoding neural network, and outputting a latent vector group by the decoding neural network; s04: comparing the feature vector group with the potential vector group to obtain a failure rate; s05: if the error rate is higher than the error threshold, adjusting the encoding neural network and the decoding neural network, and returning to S02; otherwise, outputting the coding neural network to a heat calculation module; the preprocessed data is a set of feature vectors; the latent vector group is a set of latent vectors; said feature vector and said membership to vector space
In such a preferred embodiment, the data pre-handler is providedThe method comprises the steps of obtaining database storage data, carrying out data extraction and data cleaning on the database storage data, carrying out normalization to obtain preprocessed data, leading one feature vector in the preprocessed data into a coding neural network to obtain a corresponding latent vector, comparing the corresponding feature vector with the latent vector, and using the latent vector
And calculating the error rate, comparing the error rate with a preset threshold, and if the error rate is lower than the preset error threshold, generally 5%, feeding a failure signal back to the double-layer neural network algorithm model, and enabling the neural network to learn and adjust the algorithm autonomously until the neural network model with the error within the error threshold is obtained.
In a preferred embodiment, the crawler program runs a computer program that includes one or more of a general web crawler, a focused web crawler, an incremental web crawler, or a deep web crawler.
In the preferred embodiment, the crawler program selects the corresponding crawler program or the combination thereof for data crawling according to different requirements of the target website, and the specific type and the combination thereof are selected according to the actual webpage type.
In a preferred embodiment, the heat prediction model program is a computer program based on a long-short term memory model; the certain time is not less than half a month.
In the preferred embodiment, the long-short term memory model is a time cycle neural network, which can solve the long-term dependence problem existing in general RNN, on one hand, the structure is simple and easy to train, on the other hand, the system is suitable for processing and predicting events with very long interval and delay in time series, and the application scene of the system has the characteristic of very long interval and delay in the prediction time series, so the system is very suitable for using the long-short term memory model; generally, the time period, i.e., the time interval, is not less than half a month.
In a preferred embodiment, the heat calculation program is a neural network-based computer program.
In a specific embodiment, an intelligent purchasing system, an intelligent purchasing method, is operated, characterized in that: the system comprises the database, a crawler module, a data analysis module, a heat prediction model module and the ERP system; the crawler program is connected with the data analysis module; the data analysis module is connected with the database; the database is connected with the heat prediction model module; the heat prediction model program is connected with the ERP system; the system also comprises a heat calculation module; the heat calculation module is connected with the heat prediction model module; the crawler module runs the crawler program; the data analysis module runs the data analysis program; the heat prediction model module runs the heat prediction model program.
In the specific embodiment, the crawler program is used for crawling data, the data analysis program is used for converting original data crawled by the crawler program into a data type which can be used for data analysis, then the data type is stored in the database, the heat prediction model program is used for generating a heat prediction model of a target commodity and transmitting the heat prediction model to the ERP system, the heat prediction model program is used for predicting the heat of the commodity which belongs to a certain time after a certain time point, the heat calculation module is used for calculating the heat of the commodity at the time point, and after the ERP system obtains the corresponding heat model, a user uses the heat model to determine a purchasing plan after a certain time in the future; through the interaction among the modules, the automation and the accurate judgment of the commodity sales condition are realized.
In a specific embodiment, an intelligent procurement storage medium is characterized in that the storage medium stores a computer program, wherein the computer program is configured to execute an intelligent procurement method when executed.
In this particular embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
In a specific embodiment, the intelligent purchasing electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute an intelligent purchasing method.
In this specific embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
It should be understood that the above-described embodiments are merely exemplary of the present invention, and are not intended to limit the present invention, and that any modification, equivalent replacement, or improvement made without departing from the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. An intelligent purchasing method is characterized in that: the method comprises the following steps:
s1: crawling original data of a commodity sales page of the E-commerce platform by using a crawler program;
s2: analyzing the original data by using a data analysis program to generate database storage data and storing the database storage data into a database;
s3: reading the data stored in the database by using a heat prediction model program and predicting heat data after a certain time to obtain heat prediction data;
s4: calculating heat data after a certain time by using a heat calculation program to obtain heat contrast data;
s5: comparing the heat prediction data with the heat contrast data, adjusting parameters of the heat prediction model program if the accuracy is lower than a threshold value, returning to S3, and otherwise, entering S6;
s6: the heat prediction model program outputs a heat prediction model to the ERP system;
s7: the ERP system recommends the purchased goods using the popularity prediction model.
2. The intelligent purchasing method as claimed in claim 1, wherein: further comprising S0; the S0 includes:
s01: a data preprocessing program extracts the database storage data and preprocesses the database storage data to generate preprocessed data;
s02: importing the preprocessed data into a coding neural network to obtain a heat group;
s03: importing the heat group into a decoding neural network, and outputting a latent vector group by the decoding neural network;
s04: comparing the feature vector group with the potential vector group to obtain a failure rate;
s05: if the error rate is higher than the error threshold, adjusting the encoding neural network and the decoding neural network, and returning to S02; otherwise, the coding neural network is output to the heat calculation module.
3. The intelligent purchasing method as claimed in claim 2, characterized in that: the preprocessed data is a set of feature vectors; the latent vector group is a set of latent vectors; said feature vector and said membership to vector space
4. The intelligent purchasing method as claimed in claim 3, characterized in that: the error rate is calculated by
The n is the dimension number of the feature vector.
5. The intelligent purchasing method as claimed in any one of claims 1 to 4, including: the crawler program runs a computer program that includes one or more of a general web crawler, a focused web crawler, an incremental web crawler, or a deep web crawler.
6. The intelligent purchasing method as claimed in any one of claims 1 to 4, including: the heat prediction model program is a computer program based on a long-term and short-term memory model;
the certain time is not less than half a month.
7. The intelligent purchasing method as claimed in any one of claims 1 to 4, including: the heat calculation program is a neural network-based computer program.
8. An intelligent purchasing system operating an intelligent purchasing method as claimed in any one of claims 1 to 7, characterized in that: the system comprises the database, a crawler module, a data analysis module, a heat prediction model module and the ERP system; the crawler program is connected with the data analysis module; the data analysis module is connected with the database; the database is connected with the heat prediction model module; the heat prediction model program is connected with the ERP system; the system also comprises a heat calculation module; the heat calculation module is connected with the heat prediction model module;
the crawler module runs the crawler program;
the data analysis module runs the data analysis program;
the heat prediction model module runs the heat prediction model program.
9. An intelligent procurement storage medium characterized by, a computer program stored in the storage medium, wherein the computer program is configured to perform the method of any of claims 1-7 when executed.
10. An intelligent purchasing electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor is arranged to run the computer program to perform the method of any one of claims 1 to 7.
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| CN113643101A (en) * | 2021-08-30 | 2021-11-12 | 北京值得买科技股份有限公司 | Commodity popularity calculation method and system based on graph database |
| CN114820142A (en) * | 2022-06-29 | 2022-07-29 | 国能(北京)商务网络有限公司 | Commodity information recommendation method facing to B-end purchasing user |
| CN116862561A (en) * | 2023-07-10 | 2023-10-10 | 深圳爱巧网络有限公司 | Product heat analysis method and system based on convolutional neural network |
| CN117270794A (en) * | 2023-11-22 | 2023-12-22 | 成都大成均图科技有限公司 | Redis-based data storage method, medium and device |
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| CN113643101A (en) * | 2021-08-30 | 2021-11-12 | 北京值得买科技股份有限公司 | Commodity popularity calculation method and system based on graph database |
| CN114820142A (en) * | 2022-06-29 | 2022-07-29 | 国能(北京)商务网络有限公司 | Commodity information recommendation method facing to B-end purchasing user |
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| CN117270794A (en) * | 2023-11-22 | 2023-12-22 | 成都大成均图科技有限公司 | Redis-based data storage method, medium and device |
| CN117270794B (en) * | 2023-11-22 | 2024-02-23 | 成都大成均图科技有限公司 | Redis-based data storage method, medium and device |
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