Electronic product sales data prediction method and system based on artificial intelligence

Abstract

The invention discloses an artificial intelligence-based electronic product sales data prediction method and system, and relates to the technical field of data prediction, the method comprises the following specific steps: S100, data collection and preprocessing: collecting data from an e-commerce platform, social media and a market investigation channel. Model parameters and structures are updated in real time, so that the models can continuously learn from newly added data and optimize themselves, and the method can more accurately capture dynamic features of market changes, including subtle changes of consumer behaviors, sudden turning of market trends and instant adjustment of competitive product strategies; therefore, the prediction accuracy and timeliness of the electronic product sales data are remarkably improved, an enterprise can formulate a more scientific and reasonable sales strategy and inventory management plan based on a more accurate prediction result, the market risk is effectively avoided, and the market competitiveness is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction, and in particular to an electronic product sales data prediction method and system based on artificial intelligence. Background Art

[0002] With the rapid development of information technology, the electronic product market has become highly dynamic and complex. The rapid changes in consumer demand, increasingly fierce market competition, and the continuous emergence of new technologies and new products have made the sales forecast of electronic products an important basis for companies to formulate marketing strategies, inventory management, production plans and other key links. In order to accurately grasp market dynamics and enhance market competitiveness, companies urgently need an efficient and accurate sales forecasting method.

[0003] Traditional electronic product sales forecasting methods mainly rely on historical sales data and make forecasts through simple statistical models and time series analysis methods. Although these methods can reflect sales trends to a certain extent, they have obvious limitations. First, they cannot effectively capture market dynamics, such as rapid changes in consumer preferences and adjustments in competitors' market strategies, resulting in a large deviation between the forecast results and actual sales conditions. Second, traditional methods lack adaptability and cannot adjust the forecast model in real time according to new market data, causing the forecast results to gradually lose timeliness. In addition, traditional methods often ignore the potential value of unstructured data and cannot fully reflect market conditions.

[0004] In response to the above problems, it is necessary to optimize the existing electronic product sales forecasting method, build a forecasting model by real-time collection and processing of market data from multiple data sources, and automatically adjust the model parameters and structure according to new market data and historical feedback results. Therefore, it is of great significance to develop an artificial intelligence-based electronic product sales data forecasting method and system that can comprehensively realize the above characteristics. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide an electronic product sales data prediction method and system based on artificial intelligence. It can collect and process market data from multiple data sources in real time, build a prediction model, and automatically adjust the model parameters and structure according to new market data and historical feedback results. This dynamic adjustment mechanism enables the prediction model to keep up with market dynamics and improve the accuracy and timeliness of the prediction.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a method for predicting electronic product sales data based on artificial intelligence, the method comprising the following specific steps:

[0007] S100, data collection and preprocessing: collect data from e-commerce platforms, social media and market research channels, including historical sales records, consumer behavior data, market trend information and competitive product sales data, and clean, integrate and preprocess the collected data to form a structured data set;

[0008] S200, prediction model construction: Build the initial prediction model based on the preprocessed data, customize the model architecture and parameter settings according to the characteristics and market needs of different electronic products, and design a model adjustment strategy based on new data and historical feedback. By adjusting the model parameters and changing the model structure, the model can adapt to the new market environment and continue to learn from the new data and optimize itself without interrupting the prediction service;

[0009] S300, forecast result output and feedback: Generate forecast results of electronic product sales data according to the adjusted forecast model, and present them to users in the form of charts and reports, and collect user feedback on the forecast results as a basis for further adjustment of the model;

[0010] S400, intelligent decision support: Based on the forecast results, it provides enterprises with intelligent decision support, analyzes market changes in real time, and predicts potential sales opportunities and risks.

[0011] Furthermore, the step S100 includes:

[0012] S101, collects sales data, consumer behavior data, market trend information and competitive product sales data related to electronic products from e-commerce platforms, social media, market research institutions and internal enterprise system channels;

[0013] S102, clean the collected data, remove duplicate, erroneous and invalid data, and integrate the data, convert multi-source heterogeneous data into structured data in a unified format, and perform feature extraction.

[0014] Furthermore, the step S200 includes:

[0015] S201, building a prediction model based on data characteristics and prediction requirements;

[0016] S202, using the training set data to initialize the prediction algorithm, set the initial parameters and structure of the algorithm, and use the training set data to train the model, adjust the parameters of the model through the algorithm, so that the model gradually approaches the distribution of the real data, and evaluate the performance of the model;

[0017] S203, obtaining new market data and historical feedback results in real time through the user interaction interface and automated monitoring tools, and updating the model according to the new data;

[0018] S204, using the updated model to make predictions on the test set and new market data, and re-evaluate the performance of the model.

[0019] Furthermore, the step S201 constructs a prediction model according to data characteristics and prediction requirements, and its calculation formula is: t+1 =α·S t +β·M t +γ·C t , where S t+1 is the predicted sales volume for the next time period (such as week or month), S t is the actual sales volume in the current period, M t It is the market trend index for the current time period, that is, a quantitative indicator that integrates multiple market factors. t It is the consumer behavior index of the current time period, reflecting the consumer's willingness and ability to buy. α, β and γ are weight parameters obtained through historical data training.

[0020] Furthermore, the specific steps of step S202 of training the model using the training set data are:

[0021] (1) Import training set data, preprocess the data, and extract features related to the prediction task from the preprocessed data;

[0022] (2) Use the training set data to train the selected model;

[0023] (3) Monitor the performance indicators of the model during the training process and adjust the parameters or structure of the model based on the monitoring results;

[0024] (4) When the performance of the model on the training set reaches the preset requirements and the training time reaches the preset limit, the training process is stopped.

[0025] Furthermore, the step S202 adjusts the parameters and structure of the model through an algorithm according to the new data, and the calculation formula is: Where: θ t+1 is the updated model parameter, θ t is the current model parameter, η is the learning rate, which controls the step size of parameter update, (D t θ t ) is in the current dataset D t The gradient of the loss function L with respect to the model parameter θ, λ is a weight factor used to balance the impact of new data and historical feedback, Δθ hist It is the model parameter adjustment amount calculated based on the historical feedback results. This formula calculates the gradient of the loss function based on the current data and adjusts the model parameters based on the historical feedback.

[0026] Furthermore, the specific steps of evaluating the performance of the model in step S204 are as follows:

[0027] (1) Collect and organize data sets for evaluating model performance;

[0028] (2) Select evaluation indicators based on the characteristics of the prediction model and business requirements;

[0029] (3) Use the adjusted model to predict the data set and record the prediction results, including the predicted value and the actual value;

[0030] (4) Compare the performance indicators before and after the model adjustment to analyze whether the model performance has been improved;

[0031] (5) If the performance is significantly improved, the model adjustment is effective. If the performance is degraded or the improvement is not obvious, the model is further adjusted and the model parameters are reselected.

[0032] Furthermore, the step S300 includes:

[0033] S301, using the adjusted forecasting model to forecast the sales data of the target electronic product and generate a forecasting result;

[0034] S302, presenting the forecast results to the user in the form of charts and reports, including forecasted sales volume, sales amount and sales trend;

[0035] S303, collecting user feedback on the prediction results through the user interface and API interface, including evaluation of prediction accuracy and practicality, and processing and analyzing the collected feedback as a basis for further adjustment of the model.

[0036] Furthermore, the step S400 includes:

[0037] S401, conduct data analysis based on forecast results and real-time market data to identify sales opportunities and risks;

[0038] S402, based on the data analysis results, formulate intelligent decision-making strategies in inventory management, marketing strategies and production plans, convert the formulated strategies into specific action plans, and coordinate relevant departments and teams to implement them;

[0039] S403, evaluate the effectiveness of strategy implementation, collect data feedback during the implementation process, and provide a basis for subsequent strategy adjustment and optimization.

[0040] On the other hand, an electronic product sales data prediction system based on artificial intelligence, the system comprising: a data collection and preprocessing module, a prediction model building module, a prediction result output and feedback module and the intelligent decision support module;

[0041] The data collection and preprocessing module collects data from e-commerce platforms, social media and market research channels, including historical sales records, consumer behavior data, market trend information and competitive product sales data, and cleans, integrates and preprocesses the collected data to form a structured data set;

[0042] The prediction model building module builds an initial prediction model based on the preprocessed data, customizes the model architecture and parameter settings according to the characteristics and market demands of different electronic products, and designs a model adjustment strategy based on new data and historical feedback. By adjusting the model parameters and changing the model structure, the model can adapt to the new market environment and continuously learn from the newly added data and optimize itself without interrupting the prediction service.

[0043] The prediction result output and feedback module generates prediction results of electronic product sales data according to the adjusted prediction model, and presents them to users in the form of charts and reports, while collecting users' feedback on the prediction results as a basis for further adjustment of the model.

[0044] Compared with the prior art, the electronic product sales data prediction method and system based on artificial intelligence has the following beneficial effects:

[0045] 1. The present invention introduces a dynamically adjusted prediction model and updates the model parameters and structure in real time, so that the model can continuously learn from the newly added data and optimize itself. This method can more accurately capture the dynamic characteristics of market changes, including subtle changes in consumer behavior, sudden turns in market trends, and immediate adjustments to competing product strategies, thereby significantly improving the forecast accuracy and timeliness of electronic product sales data. Enterprises can formulate more scientific and reasonable sales strategies and inventory management plans based on more accurate forecast results, effectively avoid market risks, and enhance market competitiveness.

[0046] 2. Through in-depth analysis of forecast results and real-time market data, the system of the present invention can identify sales opportunities and risks, and provide strong support for enterprises to formulate intelligent decision-making strategies in inventory management, marketing strategies and production plans. At the same time, the system can also evaluate the effectiveness of strategy implementation, collect feedback data, and provide a basis for subsequent strategy adjustment and optimization.

[0047] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 The present invention is a flowchart of an electronic product sales data prediction method based on artificial intelligence.

[0050] Figure 2 A flowchart for building a prediction model for an artificial intelligence-based electronic product sales data prediction method. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Embodiment 1

[0053] This embodiment describes in detail the specific implementation steps of an electronic product sales data prediction method based on artificial intelligence, aiming to achieve accurate prediction of electronic product sales data by building and dynamically adjusting a prediction model.

[0054] In the data collection and preprocessing stage (S100), historical sales records of electronic products, consumer behavior data, market trend information and competitive product sales data are collected from multiple channels such as e-commerce platforms, social media, market research institutions and internal enterprise systems. These data come from various sources and cover various information such as consumer purchasing behavior, market dynamics, and competitive situation. The collected data are strictly cleaned to remove duplicate, erroneous and invalid data items. After that, data integration is performed to convert multi-source heterogeneous data into structured data in a unified format to facilitate subsequent analysis and processing. This process also includes feature extraction to extract key information useful for prediction.

[0055] For the prediction model construction stage (S200), based on the data characteristics and prediction requirements, an initial prediction model is constructed, and the model architecture is customized to capture the complex patterns and trends in the data. The selected algorithm is initialized using the training set data, and the initial parameters are set. Through iterative training, the model parameters are continuously adjusted so that the model gradually approaches the distribution of real data. At the same time, the performance of the model is evaluated using methods such as cross-validation to ensure that the model has good generalization capabilities. New market data and user feedback are obtained in real time through the user interaction interface and automated monitoring tools. Based on these data, the model parameters and structure are automatically adjusted, and online learning technology is introduced to enable the model to continuously learn from the newly added data and optimize itself.

[0056] For forecast result output and feedback (S300), the sales data of the target electronic product is forecasted using the adjusted forecast model to generate forecast results, which include key indicators such as sales volume, sales revenue and sales trends. The forecast results are presented to users in the form of charts and reports. The charts can intuitively display sales trends and key nodes, and the reports contain detailed data analysis and interpretation. User feedback on the forecast results is collected through the user interface and API interface. The feedback includes evaluation of forecast accuracy and practicality. The collected feedback is processed and analyzed as a basis for further adjustment of the model.

[0057] For the intelligent decision support stage (S400), in-depth data analysis is carried out based on the forecast results and real-time market data to identify potential sales opportunities and risk points to provide support for decision-making. According to the data analysis results, intelligent decision-making strategies in inventory management, marketing strategies and production plans are formulated, and the strategies are converted into specific action plans. The relevant departments and teams are coordinated to implement them, the effectiveness of the strategy implementation is evaluated, data feedback is collected during the implementation process, and the strategies are adjusted and optimized according to the feedback results to form a closed-loop intelligent decision support system.

[0058] In summary, through the above embodiments, the present invention realizes an electronic product sales data prediction method based on artificial intelligence. The method improves the accuracy and timeliness of the prediction by dynamically adjusting the prediction model, thereby providing powerful intelligent decision-making support for enterprises.

[0059] Embodiment 2

[0060] Based on the first embodiment, this embodiment describes in detail the construction of a prediction model of an electronic product sales data prediction method based on artificial intelligence.

[0061] In the prediction model building phase (S200), based on data characteristics and prediction requirements, the manufacturer builds an initial prediction model and customizes the model architecture to capture complex patterns and trends in the data. The calculation formula is: St+1 =α·S t +β·M t +γ·C t , where S t+1 is the predicted sales volume for the next time period (such as week or month), S t is the actual sales volume in the current period, M t It is the market trend index for the current time period, that is, a quantitative indicator that integrates multiple market factors. t It is the consumer behavior index of the current time period, reflecting the consumer's willingness and ability to buy. α, β and γ are weight parameters obtained through historical data training.

[0062] Use the training set data to initialize the selected algorithm and set the initial parameters. Through iterative training, continuously adjust the model parameters so that the model gradually approaches the distribution of the real data. The calculation formula is: Where: θ t+1 is the updated model parameter, θ t is the current model parameter, η is the learning rate, which controls the step size of parameter update, In the current dataset D t The gradient of the loss function L with respect to the model parameter θ, λ is a weight factor used to balance the impact of new data and historical feedback, Δθ hist It is the model parameter adjustment amount calculated based on the historical feedback results. This formula calculates the gradient of the loss function based on the current data and adjusts the model parameters based on the historical feedback. At the same time, it evaluates the performance of the model to ensure that the model has good generalization ability. The specific steps are:

[0063] (1) Collect and organize data sets for evaluating model performance;

[0064] (2) Select evaluation indicators based on the characteristics of the prediction model and business requirements;

[0065] (3) Use the adjusted model to predict the data set and record the prediction results, including the predicted value and the actual value;

[0066] (4) Compare the performance indicators before and after the model adjustment to analyze whether the model performance has been improved;

[0067] (5) If the performance is significantly improved, the model adjustment is effective. If the performance is degraded or the improvement is not obvious, the model is further adjusted and the model parameters are reselected.

[0068] Through the user interaction interface and automated monitoring tools, new market data and user feedback are obtained in real time. Based on this data, the model parameters and structure are automatically adjusted. Online learning technology is introduced to enable the model to continuously learn from new data and optimize itself.

[0069] In summary, by implementing the application process of the prediction model construction and adjustment module of the present invention, the accuracy and timeliness of the electronic product sales data prediction service are successfully improved.

[0070] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An electronic product sales data prediction method based on artificial intelligence, characterized in that: The method comprises the following specific steps: S100, data collection and preprocessing: collect data from e-commerce platforms, social media and market research channels, including historical sales records, consumer behavior data, market trend information and competitive product sales data, and clean, integrate and preprocess the collected data to form a structured data set; S200, prediction model construction: Build the initial prediction model based on the preprocessed data, customize the model architecture and parameter settings according to the characteristics and market needs of different electronic products, and design a model adjustment strategy based on new data and historical feedback. By adjusting the model parameters and changing the model structure, the model can adapt to the new market environment and continue to learn from the new data and optimize itself without interrupting the prediction service; S300, forecast result output and feedback: Generate forecast results of electronic product sales data according to the adjusted forecast model, and present them to users in the form of charts and reports, and collect user feedback on the forecast results as a basis for further adjustment of the model; S400, intelligent decision support: Based on the forecast results, it provides enterprises with intelligent decision support, analyzes market changes in real time, and predicts potential sales opportunities and risks.

2. The method for predicting electronic product sales data based on artificial intelligence according to claim 1, characterized in that: The step S100 includes: S101, collects sales data, consumer behavior data, market trend information and competitive product sales data related to electronic products from e-commerce platforms, social media, market research institutions and internal enterprise system channels; S102, clean the collected data, remove duplicate, erroneous and invalid data, and integrate the data, convert multi-source heterogeneous data into structured data in a unified format, and perform feature extraction.

3. The electronic product sales data prediction method based on artificial intelligence according to claim 1 is characterized in that: The step S200 includes: S201, building a prediction model based on data characteristics and prediction requirements; S202, using the training set data to initialize the prediction algorithm, set the initial parameters and structure of the algorithm, and use the training set data to train the model, adjust the parameters of the model through the algorithm, so that the model gradually approaches the distribution of the real data, and evaluate the performance of the model; S203, obtaining new market data and historical feedback results in real time through the user interaction interface and automated monitoring tools, and updating the model according to the new data; S204, using the updated model to make predictions on the test set and new market data, and re-evaluate the performance of the model.

4. The method for predicting electronic product sales data based on artificial intelligence according to claim 3, characterized in that: The step S201 constructs a prediction model according to data characteristics and prediction requirements, and its calculation formula is: t+1 =α·S t +β·M t +γ·C t , where S t+1 is the predicted sales volume for the next time period, S t is the actual sales volume in the current period, M t It is the market trend index for the current time period, that is, a quantitative indicator that integrates multiple market factors. t It is the consumer behavior index of the current time period, reflecting the consumer's willingness and ability to buy. α, β and γ are weight parameters obtained through historical data training.

5. The method for predicting electronic product sales data based on artificial intelligence according to claim 3, characterized in that: The specific steps of step S202 for training the model using the training set data are: (1) Import training set data, preprocess the data, and extract features related to the prediction task from the preprocessed data; (2) Use the training set data to train the model; (3) Monitor the performance indicators of the model during the training process and adjust the parameters and structure of the model based on the monitoring results; (4) When the performance of the model on the training set reaches the preset requirements and the training time reaches the preset limit, the training process is stopped.

6. The method for predicting electronic product sales data based on artificial intelligence according to claim 5, characterized in that: The step S202 adjusts the parameters and structure of the model through an algorithm according to the new data, and the calculation formula is: Where: θ t+1 is the updated model parameter, θ t is the current model parameter, η is the learning rate, which controls the step size of parameter update, In the current dataset D t The gradient of the loss function L with respect to the model parameter θ, λ is a weight factor used to balance the impact of new data and historical feedback, Δθ hist It is the model parameter adjustment amount calculated based on the historical feedback results. This formula calculates the gradient of the loss function based on the current data and adjusts the model parameters based on the historical feedback.

7. The electronic product sales data prediction method based on artificial intelligence according to claim 3 is characterized in that The specific steps of step S204 to evaluate the performance of the model are: (1) Collect and organize data sets for evaluating model performance; (2) Select evaluation indicators based on the characteristics of the prediction model and business requirements; (3) Use the adjusted model to predict the data set and record the prediction results, including the predicted value and the actual value; (4) Compare the performance indicators before and after the model adjustment to analyze whether the model performance has been improved; (5) If the performance is significantly improved, the model adjustment is effective. If the performance is degraded or the improvement is not obvious, the model is further adjusted and the model parameters are reselected.

8. The electronic product sales data prediction method based on artificial intelligence according to claim 1 is characterized in that , the step S300 includes: S301, using the adjusted forecasting model to forecast the sales data of the target electronic product and generate a forecasting result; S302, presenting the forecast results to the user in the form of charts and reports, including forecasted sales volume, sales amount and sales trend; S303, collecting user feedback on the prediction results through the user interface and API interface, including evaluation of prediction accuracy and practicality, and processing and analyzing the collected feedback as a basis for further adjustment of the model.

9. The electronic product sales data prediction method based on artificial intelligence according to claim 1, characterized in that , the step S400 includes: S401, conduct data analysis based on forecast results and real-time market data to identify sales opportunities and risks; S402, based on the data analysis results, formulate intelligent decision-making strategies in inventory management, marketing strategies and production plans, convert the formulated strategies into specific action plans, and coordinate relevant departments and teams to implement them; S403, evaluate the effectiveness of strategy implementation, collect data feedback during the implementation process, and provide a basis for subsequent strategy adjustment and optimization.

10. An electronic product sales data forecasting system based on artificial intelligence, characterized in that ,The system includes: a data collection and preprocessing module, a prediction model building module, a prediction result output and feedback module and the ,intelligent decision support module; The data collection and preprocessing module collects data from e-commerce platforms, social media and market research channels, including historical sales records, consumer behavior data, market trend information and competitive product sales data, and cleans, integrates and preprocesses the collected data to form a structured data set; The prediction model building module builds an initial prediction model based on the preprocessed data, customizes the model architecture and parameter settings according to the characteristics and market demands of different electronic products, and designs a model adjustment strategy based on new data and historical feedback. By adjusting the model parameters and changing the model structure, the model can adapt to the new market environment and continuously learn from the newly added data and optimize itself without interrupting the prediction service. The prediction result output and feedback module generates prediction results of electronic product sales data according to the adjusted prediction model, and presents them to users in the form of charts and reports, while collecting users' feedback on the prediction results as a basis for further adjustment of the model.

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