CN115983468A - Big data-based information prediction analysis method and system - Google Patents
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Abstract
The invention discloses an information prediction analysis method and system based on big data, which comprises a product information import module, a product information retrieval module, a product analysis module, a model comparison module, a model storage module, a comparison analysis module and a result import and export module; the product information importing module is used for importing product type information to be predicted after a user logs in a system, and the product type information comprises product field information and product name information; the product information retrieval module is used for importing product information into the Internet, retrieving products of the same type of the product information and obtaining the product information of the same type, wherein the product information of the same type comprises product yield information, product gross profit rate information and product sales information; the product analysis module is used for analyzing the same type of product information to obtain real-time product model information. The method has the advantages of higher accuracy of the result of analyzing and predicting the product information and higher reference value.
Description
Technical Field
The invention relates to the field of information analysis, in particular to an information prediction analysis method and system based on big data.
Background
An information prediction analysis method is an information analysis method. The method is a method for scientifically predicting the future development of things by applying scientific theories and technologies to deeply analyze and know the regularity of the evolution of things and deducing unknown information from known information according to information about certain things mastered in the past and at present;
in the actual product sale or production process, an information prediction analysis method is needed to carry out detailed analysis prediction on product information to know the market state of the product, so that the loss caused by blind production is avoided.
The existing information prediction analysis method and system have the defects of larger deviation of the result of prediction analysis, lower reference and certain influence on the use of the information prediction analysis method and system, so the information prediction analysis method and system based on big data are provided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the information prediction analysis method and the information prediction analysis system based on the big data are provided for solving the problems that the existing information prediction analysis method and the existing information prediction analysis system have large deviation of the result obtained by prediction analysis and low reference and bring certain influence on the use of the information prediction analysis method and the information prediction analysis system.
The invention solves the technical problems through the following technical scheme that the system comprises a product information import module, a product information retrieval module, a product analysis module, a model comparison module, a model storage module, a comparison analysis module and a result import and export module;
the product information importing module is used for importing product type information to be predicted after a user logs in a system, and the product type information comprises product field information and product name information;
the product information retrieval module is used for importing product information into the Internet, retrieving products of the same type of the product information and obtaining the product information of the same type, wherein the product information of the same type comprises product yield information, product gross profit rate information and product sales information;
the product analysis module is used for analyzing the information of the same type of products to obtain real-time product model information, and then processing the real-time product model information to obtain product evaluation information;
the model storage module is connected with the Internet database and is used for storing model information of various products;
the model comparison module is used for receiving the real-time product model information and comparing the real-time product model information with the model information in the model storage module to obtain comparison model information;
the comparison analysis module is used for receiving comparison model information and processing the comparison model information to obtain model evaluation information;
and the result exporting module is used for sending the received model evaluation information and the product evaluation information to a preset receiving terminal.
Further, the specific retrieval process of the information of the products of the same type is as follows: the product information retrieval module is used for importing the product information into the Internet, acquiring preset product information of a plurality of products identical to the product information, extracting quantity information of the preset product information, and setting different extraction rules according to the quantity of the quantity information of the preset product information to extract corresponding quantity of the same type of product information.
Further, the extraction rules include a first extraction rule, a second extraction rule, and a third extraction rule, and the first extraction rule, the second extraction rule, and the third extraction rule are selected as follows: the quantity information of the preset product information is extracted, when the quantity information of the preset product information is smaller than the preset quantity, a first extraction rule is selected, and the specific content of the first rule is as follows: exporting all the collected preset product information as the same type of product information;
when the quantity information of the preset product information is within a preset quantity range, selecting a second extraction rule, wherein the specific content of the second rule is as follows: randomly selecting 1/2 of all the collected preset product information to be exported as the same type product information;
when the quantity information of the preset product information is larger than the preset quantity, selecting a third extraction rule, wherein the specific content of the third rule is as follows: and randomly selecting 1/3 of all the collected preset product information to be derived as the same type of product information.
Further, the specific processing procedure of the real-time product model is as follows:
the method comprises the following steps: extracting the collected product information, and acquiring product yield information, product gross interest rate information and product sales information from the product information;
step two: the product yield information is the single-day product yield information of a single product and is marked as Q, the product sales information is the single-day product sales information of the single product and is marked as E, and the product gross profit rate information is the gross profit rate information of the single product and is marked as T;
step four: extracting a maximum value Emax and a minimum value Emin the product single daily sales information E, then extracting single daily product yield information Q corresponding to the Emax and single product gross profit rate information T, calculating a single-day maximum remaining profit space QEMax through a formula Q T-Emax T = QEMax, and then calculating a single-day minimum remaining profit space QEMin through a formula Q T-Emin T = QEMin;
step five: calculating the difference between the maximum residual profit margin QEMax per day and the minimum residual profit margin QEMin per day to obtain a profit difference QE Difference between Single day maximum remaining profit margin QEMax, single day minimum remaining profit margin QEMin and profit margin QE Difference (D) Together forming a real-time product model.
Further, the product evaluation information includes product excellent information, product general information, and product poor information;
the specific processing procedures of the product evaluation information including product excellent information, product general information and product poor information are as follows: extracting the collected real-time product model, and obtaining the maximum residual profit space QAmax, the minimum residual profit space QEMin and the profit difference QE in a single day from the real-time product model Difference (D) ；
When the maximum residual profit margin QAmax of a single day is larger than the preset value, the minimum residual profit margin QEMin of the single day is smaller than the preset value, and the profit difference QE Difference between If the value is larger than the preset value, excellent product information is generated;
when the maximum remains in a single dayThe residual profit margin QEMax is smaller than the preset value, the minimum residual profit margin QEMin per day is larger than the preset value, and the profit difference QE Difference (D) When the product quality is less than the preset value, the product poor information is generated;
when the maximum residual profit margin Qemax, the minimum residual profit margin Qemin and the profit difference QE are applied to each day Difference between When the values are within the preset value range, general product information is generated.
Further, the specific processing procedure of the model comparison module for processing the comparison model information is as follows: extracting the collected real-time product model information, importing the real-time product model information into a model repository, and then extracting the maximum residual profit margin QAmax per day, the minimum residual profit margin QEMin per day and the profit difference QE from the real-time product model information Difference (D) Single day maximum remaining profit margin QEMax, single day minimum remaining profit margin QEMin and profit margin QE Difference (D) Calculating the maximum residual profit margin per day, the minimum residual profit margin per day, the maximum residual profit margin per day QEMax, the minimum residual profit margin per day QEMin, and the profit margin QE of each model in the model library Difference between Obtaining the maximum residual profit contrast parameter, the minimum residual profit parameter and the profit difference parameter in a single day, and then selecting the maximum residual profit contrast parameter, the minimum residual profit parameter and the profit difference parameter in a single day, the maximum residual profit margin QEMax, the minimum residual profit margin QEMin and the profit difference QE at the same time Difference (D) The approximate preset model is comparative model information.
The model evaluation information comprises first-level model information, second-level model information and third-level model information, and the specific processing process of the model evaluation information processed by the comparison analysis module is as follows: extracting contrast model information, importing the contrast model information into a model storage module again, acquiring profit difference information of a product corresponding to the contrast model information in a subsequent preset x days from the model storage module, drawing the profit difference of the subsequent preset x days into a broken line graph, analyzing the broken line graph, generating three-level model information when the profit difference continuously falls for more than m days after being in a rising state, generating primary model information when the profit difference continuously rises for more than m days after being in a falling state, generating secondary model information when the profit difference continuously rises for more than m days after being in a gentle state and rises and falls within a range of preset days, wherein x is more than or equal to 30, and 10 is more than or equal to m and more than 5;
the first-level model information represents that the product has a good prospect, the second-level model information represents that the product has a general prospect, and the third-level model information represents that the product has a poor prospect.
An information prediction analysis method based on big data, the analysis method comprises the following steps:
step (1): importing product type information required to be predicted after a user logs in a system;
step (2): the product information retrieval module leads the product information into the Internet, performs the same type product retrieval of the product information and acquires the same type product information;
and (3): then, the product analysis module analyzes the same type of product information to obtain real-time product model information, and then processes the real-time product model information to obtain product evaluation information;
and (4): the model comparison module is used for comparing the model similarity between the real-time product model information and the models in the model storage module to obtain comparison model information
And (5): the comparison model information is sent to a comparison analysis module, and the comparison analysis module processes the comparison model information to obtain model evaluation information;
and (6): and the result export module sends the received model evaluation information and the product evaluation information to a preset receiving terminal.
Compared with the prior art, the invention has the following advantages: according to the information prediction analysis method and system based on the big data, product information imported by a user is imported into the Internet, the same type of product retrieval of the product information is carried out, the product evaluation information is obtained through refining processing, the user can preliminarily know the actual profit condition information of a product searched and analyzed in the information prediction analysis process through the set product evaluation information, preliminary product layout and the like are carried out, the profit state and the profit space information of the analyzed product are further known through the obtained comparison model information, more detailed reference data are provided for the user to carry out product production or goods input, finally, the comparison model information is evaluated to generate a corresponding prediction result, the recent profit state of the product can be known, the user can more thoroughly know the residual profit space of the product, more useful reference information is provided for the user, the economic loss caused by blind product production or supply of the user is reduced, the product information is more comprehensively analyzed and predicted, and the system is worthy of popularization and use.
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FIG. 1 is a system block diagram of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
As shown in fig. 1, the present embodiment provides a technical solution: an information prediction analysis system based on big data comprises a product information import module, a product information retrieval module, a product analysis module, a model comparison module, a model storage module, a comparison analysis module and a result import and export module;
the product information importing module is used for importing product type information to be predicted after a user logs in a system, and the product type information comprises product field information and product name information;
the product information retrieval module is used for importing product information into the Internet, retrieving products of the same type of the product information and obtaining the product information of the same type, wherein the product information of the same type comprises product yield information, product gross profit rate information and product sales information;
the product analysis module is used for analyzing the same type of product information to obtain real-time product model information, and then processing the real-time product model information to obtain product evaluation information;
the model storage module is connected with the Internet database and is used for storing model information of various products;
the model comparison module is used for receiving the real-time product model information and comparing the similarity of the real-time product model information with the model information in the model storage module to obtain comparison model information;
the comparison analysis module is used for receiving comparison model information and processing the comparison model information to obtain model evaluation information;
the result exporting module is used for sending the received model evaluation information and the product evaluation information to a preset receiving terminal;
according to the method, product information imported by a user is imported into the Internet, the same type of product information is retrieved, thinning processing is carried out to obtain product evaluation information, the user can preliminarily know actual profit condition information of a retrieved and analyzed product in the information prediction analysis process through the set product evaluation information, so that preliminary product layout and the like are carried out, the profit state and the profit space information of the analyzed product are further known through the obtained comparison model information, more detailed reference data are provided for the user to produce or purchase the product, and finally, a corresponding prediction result is generated through evaluating the comparison model information, so that the recent profit state of the product can be known, the user can more thoroughly know the remaining profit space of the product, more functional reference information is provided for the user, economic loss caused by blind production or supply of the product by the user is reduced, the product information is more comprehensively analyzed and predicted, and the system is more worthy of popularization and use.
The specific retrieval process of the same type of product information is as follows: the product information retrieval module is used for importing product information into the Internet, acquiring preset product information of a plurality of products identical to the product information, extracting quantity information of the preset product information, and setting different extraction rules according to the quantity of the preset product information to extract corresponding quantity of the same type of product information;
the extraction rules comprise a first extraction rule, a second extraction rule and a third extraction rule, and the first extraction rule, the second extraction rule and the third extraction rule are selected in the following process: the quantity information of the preset product information is extracted, when the quantity information of the preset product information is smaller than the preset quantity, a first extraction rule is selected, and the specific content of the first rule is as follows: exporting all the collected preset product information as the same type of product information;
when the quantity information of the preset product information is within a preset quantity range, a second extraction rule is selected, and the specific content of the second rule is as follows: randomly selecting 1/2 of all the collected preset product information to be exported as the same type product information;
when the quantity information of the preset product information is larger than the preset quantity, selecting a third extraction rule, wherein the specific content of the third rule is as follows: randomly selecting 1/3 of all the collected preset product information to be derived as the same type product information;
through the process, different extraction rules are set to further optimize the data extraction quantity of the same type of product information, and the subsequent analysis and comparison and the speed of predicting the occurrence of results are accelerated by reducing part of data while the objectivity of the comparison sample is ensured to be enough.
The specific processing procedure of the real-time product model is as follows:
the method comprises the following steps: extracting the collected product information, and acquiring product yield information, product gross interest rate information and product sales information from the product information;
step two: the product yield information is the single-day product yield information of a single product and is marked as Q, the product sales information is the single-day product sales information of the single product and is marked as E, and the product gross profit rate information is the gross profit rate information of the single product and is marked as T;
step four: extracting a maximum value Emax and a minimum value Emin the product single daily sales information E, then extracting single daily product yield information Q corresponding to the Emax and single product gross profit rate information T, calculating a single-day maximum remaining profit space QEMax through a formula Q T-Emax T = QEMax, and then calculating a single-day minimum remaining profit space QEMin through a formula Q T-Emin T = QEMin;
step five: calculating the difference between the maximum residual profit margin QEMax per day and the minimum residual profit margin QEMin per day to obtain a profit difference QE Difference (D) Single day maximum remaining profit margin QEMax, single day minimum remaining profit margin QEMin and profit margin QE Difference (D) Together forming a real-time product model;
through the process, a more accurate real-time product model is obtained, the more accurate real-time product model can ensure that the accuracy of the subsequent analysis and prediction result is high, and the analysis and prediction result can be provided with a higher reference value.
The product evaluation information comprises product excellent information, product general information and product poor information;
the specific processing procedures of the product evaluation information including product excellent information, product general information and product poor information are as follows: extracting the collected real-time product model, and acquiring the maximum residual profit space QEMax, the minimum residual profit space QEMin and the profit difference QE in a single day from the real-time product model Difference (D) ；
When the maximum residual profit space QEMax per day is larger than the preset value, the minimum residual profit space QEMin per day is smaller than the preset value, and the profit difference QE Difference (D) When the value is larger than the preset value, excellent information of the product is generated;
when the maximum residual profit margin QAmax of a single day is smaller than the preset value, the minimum residual profit margin QEMin of the single day is larger than the preset value, and the profit difference QE Difference (D) When the product quality is less than the preset value, the product poor information is generated;
when the maximum residual profit margin Qemax, the minimum residual profit margin Qemin and the profit difference QE are applied to each day Difference (D) When the product information is within the preset value range, the general product information is generated;
through the process, preliminary product evaluation information is generated, and a user can preliminarily know the sales prospect of the product required to be analyzed.
The specific processing process of the model comparison module for processing the comparison model information is as follows: extracting the collected real-time product model information, importing the real-time product model information into a model repository, and then extracting the maximum residual profit space QEMax, the minimum residual profit space QEMin and the profit difference QE from the real-time product model information Difference (D) Single day maximum remaining profit margin QEMax, single day minimum remaining profit margin QEMin and profit margin QE Difference (D) Calculating the maximum residual profit margin and the profit difference per day and the maximum residual profit margin QEmax per day, the minimum residual profit margin QEmin per day and the profit difference QE per day of the real-time model information of each model in the model base Difference (D) Obtaining the maximum residual profit contrast parameter, the minimum residual profit parameter and the profit difference parameter in a single day, and then selecting the maximum residual profit contrast parameter, the minimum residual profit parameter and the profit difference parameter in a single day, the maximum residual profit margin QEMax, the minimum residual profit margin QEMin and the profit difference QE at the same time Difference (D) The approximate preset model is comparison model information;
through the process, the comparison model information is processed more accurately, so that the accuracy of the subsequent analysis and prediction result is higher.
The model evaluation information comprises first-level model information, second-level model information and third-level model information, and the specific processing process of the comparison analysis module for processing the model evaluation information is as follows: extracting contrast model information, importing the contrast model information into a model storage module again, acquiring profit difference information of a product corresponding to the contrast model information in a subsequent preset x days from the model storage module, drawing the profit difference of the subsequent preset x days into a broken line graph, analyzing the broken line graph, generating three-level model information when the profit difference continuously falls for more than m days after being in a rising state, generating primary model information when the profit difference continuously rises for more than m days after being in a falling state, generating secondary model information when the profit difference continuously rises for more than m days after being in a gentle state and rises and falls within a range of preset days, wherein x is more than or equal to 30, and 10 is more than or equal to m and more than 5;
the first-level model information represents that the product has a good prospect, the second-level model information represents that the product has a general prospect, and the third-level model information represents that the product has a poor prospect;
through the process, the generated comparison model information is subjected to refined analysis, so that a user can quickly and intuitively know the prospect of the analyzed and predicted product, and therefore, the product is generated or stocked according to the product prospect, and the economic loss caused by blind production or stocked of the user is reduced.
An information prediction analysis method based on big data, the analysis method comprises the following steps:
step (1): importing product type information required to be predicted after a user logs in a system;
step (2): the product information retrieval module leads the product information into the Internet, performs the same type product retrieval of the product information and acquires the same type product information;
and (3): then, the product analysis module analyzes the same type of product information to obtain real-time product model information, and then processes the real-time product model information to obtain product evaluation information;
and (4): the model comparison module is used for comparing the model similarity between the real-time product model information and the models in the model storage module to obtain comparison model information
And (5): the comparison model information is sent to a comparison analysis module, and the comparison analysis module processes the comparison model information to obtain model evaluation information;
and (6): and the result export module sends the received model evaluation information and the product evaluation information to a preset receiving terminal.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like 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 invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (8)
1. An information prediction analysis system based on big data is characterized by comprising a product information import module, a product information retrieval module, a product analysis module, a model comparison module, a model storage module, a comparison analysis module and a result import and export module;
the product information importing module is used for importing product type information to be predicted after a user logs in a system, and the product type information comprises product field information and product name information;
the product information retrieval module is used for importing product information into the Internet, retrieving products of the same type of the product information and obtaining the product information of the same type, wherein the product information of the same type comprises product yield information, product gross profit rate information and product sales information;
the product analysis module is used for analyzing the information of the same type of products to obtain real-time product model information, and then processing the real-time product model information to obtain product evaluation information;
the model storage module is connected with the Internet database and is used for storing model information of various products;
the model comparison module is used for receiving the real-time product model information and comparing the real-time product model information with the model information in the model storage module to obtain comparison model information;
the comparison analysis module is used for receiving comparison model information and processing the comparison model information to obtain model evaluation information;
and the result deriving module is used for sending the received model evaluation information and the product evaluation information to a preset receiving terminal.
2. The big data-based information prediction analysis method and system according to claim 1, wherein: the specific retrieval process of the information of the products of the same type is as follows: the product information retrieval module is used for importing product information into the Internet, acquiring preset product information of a plurality of products identical to the product information, extracting quantity information of the preset product information, setting different extraction rules according to the quantity of the quantity information of the preset product information, and extracting corresponding quantity of the same type of product information.
3. The big data based information predictive analysis system according to claim 2, wherein: the extraction rules comprise a first extraction rule, a second extraction rule and a third extraction rule, and the first extraction rule, the second extraction rule and the third extraction rule are selected in the following process: the quantity information of the preset product information is extracted, when the quantity information of the preset product information is smaller than the preset quantity, a first extraction rule is selected, and the specific content of the first rule is as follows: exporting all the collected preset product information as the same type of product information;
when the quantity information of the preset product information is within a preset quantity range, a second extraction rule is selected, and the specific content of the second rule is as follows: randomly selecting 1/2 of all the collected preset product information to be exported as the same type product information;
when the quantity information of the preset product information is larger than the preset quantity, a third extraction rule is selected, and the specific content of the third rule is as follows: and randomly selecting 1/3 of all the collected preset product information to be derived as the same type of product information.
4. The big data-based information prediction analysis method and system according to claim 1, wherein: the specific processing procedure of the real-time product model is as follows:
the method comprises the following steps: extracting the collected product information, and acquiring product yield information, product gross profit rate information and product sales information from the product information;
step two: the product yield information is the single-day product yield information of a single product and is marked as Q, the product sales information is the single-day product sales information of the single product and is marked as E, and the product gross profit rate information is the gross profit rate information of the single product and is marked as T;
step four: extracting a maximum value Emax and a minimum value Emin the product single daily sales information E, then extracting single daily product yield information Q corresponding to the Emax and single product gross profit rate information T, calculating a single-day maximum remaining profit space QEMax through a formula Q T-Emax T = QEMax, and then calculating a single-day minimum remaining profit space QEMin through a formula Q T-Emin T = QEMin;
step five: calculating the difference between the maximum residual profit margin QAmax per day and the minimum residual profit margin QEMin per day to obtain a profit difference QE Difference (D) Maximum remaining profit margin Qemax per day, minimum remaining profit margin Qemin per day and profit margin QE Difference (D) Together forming a real-time product model.
5. The big data based information prediction analysis system according to claim 1 or 4, wherein: the product evaluation information comprises product excellent information, product general information and product poor information;
the product evaluation information includes product excellent information, product general information and product poor informationThe specific treatment process is as follows: extracting the collected real-time product model, and acquiring the maximum residual profit space QEMax, the minimum residual profit space QEMin and the profit difference QE in a single day from the real-time product model Difference (D) ；
When the maximum residual profit space QEMax per day is larger than the preset value, the minimum residual profit space QEMin per day is smaller than the preset value, and the profit difference QE Difference (D) When the value is larger than the preset value, excellent information of the product is generated;
when the maximum residual profit space QEMax per day is smaller than the preset value, the minimum residual profit space QEMin per day is larger than the preset value, and the profit difference QE Difference (D) When the product quality is less than the preset value, the product poor information is generated;
when the maximum residual profit margin QEMax, the minimum residual profit margin QEMin and the profit margin QE are applied on a single day Difference (D) When the product information is within the preset value range, the product general information is generated.
6. The big data based information predictive analysis system according to claim 1, wherein: the specific processing process of the model comparison module for processing the comparison model information is as follows: extracting the collected real-time product model information, importing the real-time product model information into a model repository, and then extracting the maximum residual profit space QEMax, the minimum residual profit space QEMin and the profit difference QE from the real-time product model information Difference between Single day maximum remaining profit margin QEMax, single day minimum remaining profit margin QEMin and profit margin QE Difference (D) Calculating the maximum residual profit margin per day, the minimum residual profit margin per day, the maximum residual profit margin per day QEMax, the minimum residual profit margin per day QEMin, and the profit margin QE of each model in the model library Difference (D) Obtaining the maximum residual profit contrast parameter, the minimum residual profit parameter and the profit difference parameter in a single day, and then selecting the maximum residual profit contrast parameter, the minimum residual profit parameter and the profit difference parameter in a single day and simultaneously the maximum residual profit margin QEMax, the minimum residual profit margin QEMin and the profit difference QE in a single day Difference (D) The approximate preset model is comparative model information.
7. The big data based information predictive analysis system according to claim 1, wherein: the model evaluation information comprises first-level model information, second-level model information and third-level model information, and the specific processing process of the model evaluation information processed by the comparison analysis module is as follows: extracting contrast model information, importing the contrast model information into a model storage module again, acquiring profit difference information of a product corresponding to the contrast model information in a subsequent preset x days from the model storage module, drawing the profit difference of the subsequent preset x days into a broken line graph, analyzing the broken line graph, generating three-level model information when the profit difference continuously falls for more than m days after being in a rising state, generating primary model information when the profit difference continuously rises for more than m days after being in a falling state, generating secondary model information when the profit difference continuously rises for more than m days after being in a gentle state and rises and falls within a range of preset days, wherein x is more than or equal to 30, and 10 is more than or equal to m and more than 5;
the first-level model information represents that the product has a good prospect, the second-level model information represents that the product has a general prospect, and the third-level model information represents that the product has a poor prospect.
8. A big data-based information prediction analysis method based on the analysis system of any one of claims 1 to 7, characterized in that: the analysis method comprises the following steps:
step (1): importing product type information required to be predicted after a user logs in a system;
step (2): the product information retrieval module is used for importing the product information into the Internet, retrieving products of the same type of the product information and obtaining the product information of the same type;
and (3): then, the product analysis module analyzes the information of the same type of products to obtain real-time product model information, and then processes the real-time product model information to obtain product evaluation information;
and (4): the model comparison module is used for comparing the similarity between the real-time product model information and the model information in the model storage module to obtain comparison model information
And (5): the comparison model information is sent to a comparison analysis module, and the comparison analysis module processes the comparison model information to obtain model evaluation information;
and (6): and the result export module sends the received model evaluation information and the product evaluation information to a preset receiving terminal.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116976729A (en) * | 2023-07-26 | 2023-10-31 | 速度科技股份有限公司 | Comprehensive calculation method evaluating system based on data analysis |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110071885A1 (en) * | 2009-09-24 | 2011-03-24 | Sap Ag | System and Method for Disaggregating Weekly Forecast Into Daily Components |
| CN110147400A (en) * | 2019-05-10 | 2019-08-20 | 青岛建邦供应链股份有限公司 | Inter-trade data resource integrated system based on big data |
| CN110210689A (en) * | 2019-06-18 | 2019-09-06 | 冶金工业规划研究院 | A kind of carbon emission management data platform |
| CN110852772A (en) * | 2018-08-21 | 2020-02-28 | 北京京东尚科信息技术有限公司 | Dynamic pricing method, system, device and storage medium |
| US20200272971A1 (en) * | 2019-02-21 | 2020-08-27 | The Climate Corporation | Digital modeling and tracking of agricultural fields for implementing agricultural field trials |
| CN115080698A (en) * | 2022-07-01 | 2022-09-20 | 公诚管理咨询有限公司 | Bidding analysis method, system, equipment and storage medium based on big data |
| CN115526443A (en) * | 2022-01-05 | 2022-12-27 | 中科云策(深圳)科技成果转化信息技术有限公司 | Scientific and technological achievement transformation risk information assessment system based on big data |
-
2022
- 2022-12-30 CN CN202211724345.2A patent/CN115983468A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110071885A1 (en) * | 2009-09-24 | 2011-03-24 | Sap Ag | System and Method for Disaggregating Weekly Forecast Into Daily Components |
| CN110852772A (en) * | 2018-08-21 | 2020-02-28 | 北京京东尚科信息技术有限公司 | Dynamic pricing method, system, device and storage medium |
| US20200272971A1 (en) * | 2019-02-21 | 2020-08-27 | The Climate Corporation | Digital modeling and tracking of agricultural fields for implementing agricultural field trials |
| CN110147400A (en) * | 2019-05-10 | 2019-08-20 | 青岛建邦供应链股份有限公司 | Inter-trade data resource integrated system based on big data |
| CN110210689A (en) * | 2019-06-18 | 2019-09-06 | 冶金工业规划研究院 | A kind of carbon emission management data platform |
| CN115526443A (en) * | 2022-01-05 | 2022-12-27 | 中科云策(深圳)科技成果转化信息技术有限公司 | Scientific and technological achievement transformation risk information assessment system based on big data |
| CN115080698A (en) * | 2022-07-01 | 2022-09-20 | 公诚管理咨询有限公司 | Bidding analysis method, system, equipment and storage medium based on big data |
Non-Patent Citations (2)
| Title |
|---|
| GUO YUAN 等: "Evaluation model and algorithm of intelligent manufacturing system based on pattern recognition and big data", 《SOFT COMPUTING》, vol. 27, no. 7, pages 4195 - 4208 * |
| 李征仁 等: "基于Pareto/NBD的用户价值预测模型研究", 北京邮电大学学报(社会科学版), vol. 22, no. 03, pages 7 - 14 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976729A (en) * | 2023-07-26 | 2023-10-31 | 速度科技股份有限公司 | Comprehensive calculation method evaluating system based on data analysis |
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