Sales management and control system and method based on artificial intelligence

Abstract

The invention provides a sales management and control system and method based on artificial intelligence, and the method comprises the steps: obtaining first data related to product sales through a data obtaining module, and transmitting the first data to a data processing module; the data processing module inputs the first data into a prediction model to output a sales prediction result corresponding to the product, and sends the sales prediction result to the strategy generation module; and the strategy generation module is used for allocating inventory resources to the product based on the inventory data and the sales prediction result to obtain a target resource allocation strategy of the product. In the system, the data processing module can determine the sales prediction result corresponding to the product through the pre-trained prediction model, the strategy generation module determines the target resource allocation strategy of the product according to the sales prediction result and the inventory data, the system can dynamically adjust the resource allocation strategy when coping with a complex and changeable market environment, and the resource allocation efficiency is improved. Therefore, real-time market demand changes can be coped with, the resource utilization rate and the resource allocation rate are improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a sales management and control system and method based on artificial intelligence. Background Art

[0002] With the rapid development of the digital field, the demand for sales and distribution management systems continues to increase, especially in large-scale sales networks. Existing sales management systems mainly rely on manual and simple rules to allocate resources, but when dealing with complex and changing market environments, related technologies cannot dynamically adjust resource allocation strategies to cope with real-time changes in market demand, resulting in low resource utilization and resource allocation rates, and increased operating costs. Summary of the invention

[0003] The purpose of the present invention is to provide a sales management and control system and method based on artificial intelligence to dynamically adjust resource allocation strategies to cope with real-time changes in market demand, improve resource utilization and resource allocation rates, and reduce operating costs.

[0004] The present invention provides an artificial intelligence-based sales management and control system, which includes: a data acquisition module, a data processing module and a strategy generation module; the data acquisition module is used to acquire first data associated with product sales, and send the first data to the data processing module; the data processing module is used to input the first data into a pre-trained prediction model to output a sales prediction result corresponding to the product through the prediction model, and send the sales prediction result to the strategy generation module; the strategy generation module is used to acquire inventory data, allocate inventory resources to the product based on the inventory data and the sales prediction result, and obtain the target resource allocation strategy for the product.

[0005] Furthermore, the strategy generation module is also used to: obtain inventory data, allocate inventory resources to products based on the inventory data and sales forecast results, and obtain a first resource allocation strategy for the product; use a reinforcement learning algorithm to process the first resource allocation strategy and determine a second resource allocation strategy; optimize the second resource allocation strategy according to a preset optimization algorithm to obtain a target resource allocation strategy for the product; wherein the preset optimization algorithm includes: a Q-learning algorithm and / or a deep Q network algorithm.

[0006] Furthermore, the system also includes: a budget control module; the budget control module is used to: obtain second data associated with product sales, pre-process the second data, and obtain processed data; determine first feature data based on the processed data; analyze the processed data and the first feature data based on a preset time series analysis algorithm to obtain sales trend forecast results and market change forecast results; generate an initial budget for the product based on the sales trend forecast results and the market change forecast results, as well as pre-acquired unit price information; obtain actual cost expenditures of the product, monitor the actual cost expenditures of the product according to the initial budget, and obtain monitoring results; if the monitoring results indicate that the actual cost expenditures exceed the initial budget, generate an alarm message.

[0007] Furthermore, the budget control module is used to: use a Bayesian network to model the processed data and the first feature data to determine the dependency relationship between target parameters; wherein the target parameters include: sales, marketing activity information and time information.

[0008] Furthermore, the system also includes: a sales optimization module; the sales optimization module is used to: obtain the user's historical behavior data and preference data related to the product; wherein the historical behavior data includes: historical purchase behavior data, historical operation behavior data and historical interaction data; use a collaborative filtering algorithm to analyze the historical behavior data to obtain customer groups associated with the historical behavior data; obtain product information of the first product associated with the customer group; analyze the product information to determine multiple second products associated with the product information; based on the user's preference data, filter out target products that match the preference data from multiple second products, and recommend the target products to the user.

[0009] Furthermore, the sales optimization module is used to: obtain feedback information associated with the product; analyze the feedback information using natural language processing to obtain analysis results; adjust product sales information associated with the product based on the analysis results; wherein the product sales information includes: sales plans and sales strategies.

[0010] Furthermore, the system also includes: a financial voucher processing module; the financial voucher processing module is used to: obtain a first image of a financial voucher associated with a product; identify and process the first image to obtain first information of the financial voucher; wherein the first information includes at least one of the following: invoice number, date, amount and supplier information; classify and process the financial voucher according to the first information to obtain processed financial vouchers; and generate a financial report based on the processed financial voucher.

[0011] Furthermore, the system also includes: a sales monitoring module; the sales monitoring module is used to: obtain product sales data; display sales data through a display interface; use a graph neural network to analyze sales data and output sales trend analysis results.

[0012] Furthermore, different functional modules communicate with each other using a preset data interaction method; wherein the data interaction method includes at least one of the following: API technology, middleware technology and ETL technology; the functional modules include at least two of the following: data acquisition module, data processing module, strategy generation module, budget control module, sales optimization module, financial voucher processing module and sales monitoring module.

[0013] The present invention provides an artificial intelligence-based sales management and control method, which includes: a data acquisition module acquires first data associated with product sales, and sends the first data to a data processing module; the data processing module inputs the first data into a pre-trained prediction model to output a sales prediction result corresponding to the product through the prediction model, and sends the sales prediction result to a strategy generation module; the strategy generation module acquires inventory data, allocates inventory resources to the product based on the inventory data and the sales prediction result, and obtains a target resource allocation strategy for the product.

[0014] The sales control system and method based on artificial intelligence provided by the present invention, the data acquisition module is used to acquire the first data associated with the product sales, and send the first data to the data processing module; the data processing module is used to input the first data into a pre-trained prediction model to output the sales prediction result corresponding to the product through the prediction model, and send the sales prediction result to the strategy generation module; the strategy generation module is used to acquire inventory data, and based on the inventory data and the sales prediction result, allocate inventory resources to the product to obtain the target resource allocation strategy of the product. In this system, the data processing module can determine the sales prediction result corresponding to the product through the pre-trained prediction model, and the strategy generation module determines the target resource allocation strategy of the product based on the sales prediction result and the inventory data. When dealing with a complex and changeable market environment, the system can dynamically adjust the resource allocation strategy to cope with real-time changes in market demand, improve resource utilization and resource allocation rate, and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1A schematic diagram of the architecture of an artificial intelligence-based sales management and control system provided in an embodiment of the present invention;

[0017] Figure 2 A schematic diagram of the architecture of an artificial intelligence-based sales management and control system provided in an embodiment of the present invention;

[0018] Figure 3 A flowchart of a sales management and control method based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. 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.

[0020] With the rapid development of the digital field, the demand for sales and distribution management systems continues to increase, especially in large-scale sales networks. The traditional sales and distribution management systems used by many companies often seem to be unable to cope with the complex and changing market environment. The existing sales management system mainly relies on manual and simple rules to formulate sales strategies and allocate resources, lacking intelligent and automated means.

[0021] In terms of sales forecasting, existing systems usually rely on historical data and simple statistical models for forecasting, which cannot fully consider market changes and various influencing factors, resulting in inaccurate forecast results. In terms of resource allocation, the traditional system has a single allocation strategy and cannot be dynamically adjusted to cope with real-time changes in market demand. In addition, the existing system also has obvious limitations in budget and cost control, sales plan execution, accounting and invoice processing, etc., resulting in low operational efficiency and high costs.

[0022] In summary, in the prior art, the traditional sales and distribution management system has the following main shortcomings when dealing with complex and changing market environments:

[0023] 1. Low sales forecast accuracy: Relying on simple statistical models, it fails to fully consider market changes and various influencing factors, resulting in inaccurate sales forecast results.

[0024] 2. Single resource allocation strategy: The resource allocation strategy cannot be adjusted dynamically to cope with real-time changes in market demand, resulting in low resource utilization and high operating costs.

[0025] 3. Inaccurate budget and cost control: Lack of intelligent means for budget and cost control can easily lead to budget overruns and cost out of control.

[0026] 4. Low efficiency in sales plan execution: There are differences between the plan and actual execution, and inconsistent data leads to poor execution results.

[0027] 5. Cumbersome accounting and invoicing processing: Manual accounting and invoicing processes are prone to errors and inefficient.

[0028] 6. Lack of real-time monitoring and analysis tools: Unable to monitor sales progress, costs and performance in real time, decision-making is delayed and lacks foresight.

[0029] Based on this, an embodiment of the present invention provides a sales management and control system and method based on artificial intelligence. This technology can be applied to applications that require intelligent management and control of sales distribution.

[0030] To facilitate understanding of this embodiment, firstly, an artificial intelligence-based sales management and control system disclosed in an embodiment of the present invention is introduced. Figure 1 As shown, the system includes: a resource allocation strategy optimization module 10, which includes: a data acquisition module 101, a data processing module 102 and a strategy generation module 103; the data acquisition module 101 is used to acquire first data associated with product sales, and send the first data to the data processing module 102; the data processing module 102 is used to input the first data into a pre-trained prediction model to output a sales prediction result corresponding to the product through the prediction model, and send the sales prediction result to the strategy generation module 103; the strategy generation module 103 is used to acquire inventory data, allocate inventory resources to the product based on the inventory data and the sales prediction result, and obtain the target resource allocation strategy for the product.

[0031] The above-mentioned first data may include planned output, customer orders, marketing activities, customer levels, product delivery dates and other data related to product sales; the above-mentioned prediction model can be implemented using an LSTM (Long Short-Term Memory) model, which is a time-recurrent neural network; the above-mentioned sales forecast results may include predicted sales volume, raw material arrival time, etc.; the above-mentioned inventory data generally includes the actual quantity, model, type, etc. of raw materials stored in the warehouse; the above-mentioned target resource allocation strategy can be understood as the quantity and model of raw materials allocated to the product to ensure the normal production of the product.

[0032] The following is an explanation of the training process of the LSTM model. Through data collection, data cleaning and feature extraction, historical sales data, market trends, seasonal changes and other factors can be preprocessed to train and optimize the initial LSTM model. First, the original data related to product sales can be collected from data sources such as internal sales databases, market research reports and external APIs. For example, the original data may include: product market information, sales reports, etc.; the original data is cleaned, missing values ​​and outliers are processed, and the consistency and accuracy of the original data are ensured. Then, key features such as sales volume, sales volume, time features (such as day of the week, month) and market activities are extracted from the original data. The original data and the extracted key features are input into the initial LSTM model, and the training is performed through the back propagation algorithm. The hyperparameters such as the learning rate, batch size and number of hidden units are adjusted to optimize the performance of the initial LSTM model, and finally the trained LSTM model is obtained, that is, the above-mentioned trained prediction model.

[0033] In actual implementation, the data acquisition module 101 can send the acquired first data to the data processing module 102, and the data processing module 102 will input the received first data into the prediction model to obtain the sales forecast result corresponding to the product. The strategy generation module 103 can finally determine the target resource allocation strategy for the product based on the sales forecast result and combined with the inventory data.

[0034] In the above-mentioned sales control system based on artificial intelligence, the data processing module 102 can determine the sales forecast results corresponding to the product through a pre-trained prediction model, and the strategy generation module 103 determines the target resource allocation strategy of the product based on the sales forecast results and inventory data. When dealing with a complex and changing market environment, the system can dynamically adjust the resource allocation strategy to cope with real-time changes in market demand, thereby improving resource utilization and resource allocation rate and reducing operating costs.

[0035] Furthermore, the strategy generation module 103 is also used to: obtain inventory data, allocate inventory resources to products based on the inventory data and sales forecast results, and obtain a first resource allocation strategy for the product; use a reinforcement learning algorithm to process the first resource allocation strategy to determine a second resource allocation strategy; optimize the second resource allocation strategy according to a preset optimization algorithm to obtain a target resource allocation strategy for the product; wherein the preset optimization algorithm includes: a Q-learning algorithm and / or a deep Q network algorithm.

[0036] The above-mentioned reinforcement learning (RL), also known as reinforcement learning, evaluation learning or enhanced learning, is one of the paradigms and methodologies of machine learning, which is used to describe and solve the problem of an intelligent agent maximizing returns or achieving specific goals through learning strategies in the process of interaction with the environment; the above-mentioned Q-Learning algorithm is an algorithm of reinforcement learning, which solves the optimal strategy through continuous trial and error, repeated exploration and learning without prior environmental information; the above-mentioned deep Q network algorithm can also be called DQN (Deep Q-Leaning Network) algorithm, which realizes reinforcement learning in a complex environment by combining deep neural networks with Q learning. In actual implementation, the strategy generation module 103 can first determine the first resource allocation strategy of the product based on inventory data and sales forecast results, and then further use the reinforcement learning algorithm to define states and actions, design reward functions, measure the effect of the first resource allocation strategy, such as measuring resource utilization and sales, etc., to obtain the second resource allocation strategy; and then continuously optimize the second resource allocation strategy in a real-time environment through Q-learning algorithm and / or deep Q network algorithm, etc., to ensure that the best resource utilization is achieved under various market changes. In addition, the system also integrates a real-time data update and feedback mechanism, which receives and processes the latest first data in real time, and dynamically adjusts the product's target resource allocation strategy based on the latest first data. It can monitor actual performance, optimize prediction models and reinforcement learning strategies in real time, ensure efficient operation in various environments, and improve resource utilization and sales management efficiency.

[0037] Existing technologies mainly rely on simple rules and experience to allocate resources, while this system can achieve dynamic and intelligent resource allocation through complex prediction models, which can significantly improve resource utilization and allocation efficiency. The system can also use machine learning algorithms to analyze historical sales data and market trends, and intelligently adjust the allocation strategy of inventory, sales personnel and logistics resources.

[0038] Further, such as Figure 2An architectural diagram of an artificial intelligence-based sales management and control system is shown, and the system also includes: a budget control module 11; the budget control module 11 is used to: obtain second data associated with product sales, pre-process the second data, and obtain processed data; determine first feature data based on the processed data; analyze the processed data and the first feature data based on a preset time series analysis algorithm to obtain sales trend forecast results and market change forecast results; generate an initial budget for the product based on the sales trend forecast results and the market change forecast results, as well as pre-acquired unit price information; obtain the actual cost expenditure of the product, monitor the actual cost expenditure of the product according to the initial budget, and obtain monitoring results; if the monitoring result indicates that the actual cost expenditure exceeds the initial budget, generate an alarm message.

[0039] The second data may include sales data (such as sales, sales volume), market change data (such as customer orders, number of after-sales personnel, maintenance material usage, etc.) and product location data collected from data sources such as financial systems. The product location data can be understood as the geographical location of the products purchased by different customers. The time series analysis algorithm can be implemented using the ARIMA (Autoregressive Integrated Moving Average) model. The basic idea of ​​the ARIMA model is to use the historical information of the data itself to predict the future. The unit price information may include: unit price of raw materials, labor cost per unit time, etc. In actual implementation, the budget control module 11 may first collect the second data, perform data cleaning on the second data to process missing values ​​and outliers, and perform normalization and standardization processing to obtain processed data. Then, extract key features from the processed data, that is, the first feature data, such as sales, sales volume, time features (such as day of the week, month) and market activities. Combined with the preset time series analysis algorithm, the second data and the first feature data are analyzed to predict future sales trend prediction results and market change prediction results. Then, based on the unit price information obtained, the initial budget of the product is automatically generated. When generating the initial budget, the system will consider historical sales data, market trends, seasonal changes, and other relevant factors to ensure the rationality and accuracy of the initial budget. When new actual data is updated, the initial budget can be dynamically adjusted to reflect the latest market conditions and sales. This dynamic adjustment process relies on real-time data input and automated analysis, so that the budget can respond to market changes in a timely manner.

[0040] In addition, the budget control module 11 can track costs through the location-aware technology of LBS (Location Based Services). LBS technology can monitor the cost expenditure of each product project in real time. Specifically, it can obtain the geographic location data of each product project, and monitor the budget execution of each product project in combination with real-time cost expenditure information. In this way, the cost expenditure of each product project can be accurately tracked to ensure that all expenses are within the budget. The real-time feedback mechanism can also be used to continuously optimize budget control. Specifically, the actual cost can be continuously collected and analyzed, including: actual sales data and cost expenditure data, etc., and compared with the initial budget. When a deviation is found, an alarm message can be generated to provide timely adjustments to relevant personnel. The alarm message can be implemented in the form of voice, text or alarm indicator light. In this way, the rationality of resource allocation can be ensured to avoid overspending or waste of resources. The whole process not only improves the efficiency of budget management, but also enhances financial transparency, making financial management more accurate and efficient. Through these technical means, automated budget generation and dynamic adjustment are achieved. At the same time, accurate cost tracking is carried out through LBS technology, combined with a real-time feedback mechanism, to continuously optimize budget control and ensure the rationality of resource allocation and the improvement of management efficiency.

[0041] Existing technologies lack intelligent budgeting and cost control methods. This system can provide more accurate and real-time budget control and reduce financial risks through AI prediction and real-time location perception technology. The system uses AI to analyze sales data and market changes, automatically generate and adjust budgets, and conduct location-based cost tracking.

[0042] Furthermore, the budget control module 11 is used to: use a Bayesian network to model the processed data and the first feature data to determine the dependency relationship between target parameters; wherein the target parameters include: sales, marketing activity information and time information.

[0043] The above-mentioned Bayesian network, also known as the belief network, is an extension of the Bayes method and is currently one of the most effective theoretical models in the field of uncertain knowledge expression and reasoning. In actual implementation, the Bayesian network can be used to model the processed data and the first feature data, and the dependency between variables such as sales, market activity information and time information can be captured through conditional probability distribution.

[0044] Further, such as Figure 2As shown, the system also includes: a sales optimization module 12; the sales optimization module 12 is used to: obtain the user's historical behavior data and preference data related to the product; wherein the historical behavior data includes: historical purchase behavior data, historical operation behavior data and historical interaction data; use a collaborative filtering algorithm to analyze the historical behavior data to obtain a customer group associated with the historical behavior data; obtain product information of a first product associated with the customer group; analyze the product information to determine a plurality of second products associated with the product information; based on the user's preference data, filter out a target product that matches the preference data from a plurality of second products, and recommend the target product to the user.

[0045] The above historical purchase behavior data may include the models and specifications of the products purchased by the user in history; the above historical operation behavior data may include the user's click behavior, sliding operation behavior, etc.; the above historical interaction data may be understood as the data generated when the user performs an interactive operation; the above preference data may be used to indicate the user's preferences, such as the user's preference for a certain function of the product; the above collaborative filtering algorithm is a recommendation system technology that analyzes the user's historical behavior and preferences to find similarities between users, thereby recommending related products or services to customers. The above first product may be understood as a product that is of interest or purchased by a customer group with the same or similar historical behavior data as the user; the above second product may be understood as a product with the same or similar functions, appearance, etc. as the first product; in actual implementation, the collaborative filtering algorithm may be used to analyze the user's historical purchase behavior data, historical operation behavior data, and historical interaction data, etc., to identify customer groups with similar purchase behavior patterns. This personalized recommendation can help companies accurately customize sales strategies and increase users' willingness to buy and transaction volume. Secondly, the content recommendation technology recommends highly relevant second products by analyzing the product information such as the features, tags, and descriptions of the first product associated with the customer group. The content recommendation algorithm is used to recommend target products that meet customer needs based on the product attributes of the second product and the customer's preference data; the target product can include specific products or services. This technology not only improves the personalized service level of sales, but also enhances the user's purchasing experience, thereby promoting sales growth.

[0046] Furthermore, the sales optimization module 12 is used to: obtain feedback information associated with the product; analyze the feedback information using natural language processing to obtain analysis results; adjust product sales information associated with the product according to the analysis results; wherein the product sales information includes: sales plans and sales strategies.

[0047] The above feedback information may include user feedback, market public opinion, social media content, etc.; the above natural language processing may also be referred to as NLP (Natural Language Processing), which studies the understanding and generation of communication between computers and humans; the above analysis results may include user emotional tendencies and opinions, etc.; natural language processing technology plays an important role in sales optimization. By means of natural language processing, the above feedback information is analyzed, and the analysis results such as user emotional tendencies and opinions are identified and understood. Based on these analysis results, sales plans and sales strategies can be dynamically adjusted to respond to market changes and customer needs in a timely manner. For example, negative public opinion can be automatically identified and appropriate remedial measures can be taken, or promotional activities can be adjusted according to positive feedback to enhance market influence. In summary, the sales optimization module 12 realizes in-depth analysis and understanding of customer behavior and market trends through the comprehensive application of collaborative filtering, content recommendation and natural language processing technology. This technical solution not only improves the accuracy and effectiveness of sales strategies, but also optimizes the interactive experience between enterprises and customers and enhances market competitiveness. Through automated and intelligent sales optimization, enterprises can better understand customer needs, improve sales efficiency, and achieve sustained growth and market share expansion.

[0048] Existing technologies mainly rely on experience and simple rules for sales optimization. This system uses advanced recommendation systems and natural language processing technology to achieve dynamic and intelligent sales optimization, improve sales success rate and customer satisfaction. By combining relevant historical behavior data and market trends of customers, AI is used to optimize sales strategies and inventory management, dynamically adjust sales plans and replenishment strategies, etc.

[0049] Further, such as Figure 2 As shown, the system also includes: a financial voucher processing module 13; the financial voucher processing module 13 is used to: obtain a first image of a financial voucher associated with a product; identify and process the first image to obtain first information of the financial voucher; wherein the first information includes at least one of the following: invoice number, date, amount and supplier information; classify and process the financial voucher according to the first information to obtain processed financial vouchers; and generate a financial report based on the processed financial voucher.

[0050] The above-mentioned financial vouchers may include invoices and financial and other related voucher information; in actual implementation, computer vision technology (CV) and robotic process automation (RPA) technology can be used to realize automatic identification, classification and processing of relevant financial vouchers, as well as automatic generation of financial reports, expense tracking and invoice management. This technical solution not only improves the efficiency of financial management, but also reduces manual errors and time costs. First, computer vision technology plays a key role in automatic accounting and invoice management. The first image of the scanned or photographed financial voucher is processed and analyzed by computer vision technology to identify the first information in the financial voucher, such as invoice number, date, amount and supplier information. Computer vision technology can process financial vouchers of different formats and layouts, overcome the recognition difficulties of traditional OCR (Optical Character Recognition) technology when processing complex invoices, and improve accuracy and reliability. Secondly, RPA technology is used to automatically process invoices and financial vouchers. The financial voucher processing module 13 automatically extracts the first information identified by CV technology based on preset rules and processes, and classifies and archives it according to the structure and requirements of the financial system. RPA technology can simulate manual operations and execute processes such as invoice entry, invoice verification, payment and accounting, which greatly reduces the need for manual intervention and improves processing speed and accuracy. In addition, the financial voucher processing module 13 also includes the functions of automatically generating financial reports and real-time expense tracking. By integrating the financial system, the processed financial vouchers are automatically summarized to generate financial reports, including information such as expenditure, accounts receivable and accounts payable, providing real-time financial analysis and decision support for the enterprise management. The expense tracking function helps enterprises manage and control expenditures through monitoring and analysis of real-time data, and promptly discovers and solves expense anomalies and leakage problems. In summary, the financial voucher processing module 13 can realize automatic accounting and invoice management, and through the combined application of CV technology and RPA technology, it realizes the intelligent identification, automatic processing and financial report generation of financial vouchers such as invoices. This technical solution not only improves the efficiency and accuracy of financial management, but also reduces labor costs and error rates, provides enterprises with reliable financial management solutions, and supports enterprises to maintain competitiveness and sustainable development in a highly competitive market environment.

[0051] The existing financial voucher processing process is cumbersome and prone to errors. This system simplifies the process, improves processing efficiency and accuracy, and reduces human errors through CV and RPA technology. AI is used to automatically identify and classify sales transactions, generate financial reports and invoices, simplify accounting processes, and improve accuracy.

[0052] Further, such as Figure 2As shown, the system also includes: a sales monitoring module 14; the sales monitoring module 14 is used to: obtain product sales data; display sales data through a display interface; use a graph neural network to analyze sales data and output sales trend analysis results.

[0053] The above-mentioned Graph Neural Network (GNN) refers to the general term for algorithms that use neural networks to learn graph structure data, extract and discover features and patterns in graph structure data, and meet the needs of graph learning tasks such as clustering, classification, prediction, segmentation, and generation; in actual implementation, the sales monitoring module 14 combines dashboard monitoring, AI predictive analysis, and graph neural network technology, aiming to provide enterprises with instant understanding and in-depth analysis of sales progress, costs, and performance to support decision-making and business optimization. First, real-time monitoring of sales data through display interfaces such as dashboards is a core component of this technical solution. Enterprises can instantly view various sales data, such as sales, sales volume, market share, etc., as well as related cost data and performance indicators through customized display interfaces. The design of display interfaces such as dashboards takes into account user needs, and can intuitively present data trends and changes in key indicators, helping management to respond quickly and adjust strategies. Secondly, the technical solution uses AI for predictive analysis to improve the intelligence level of sales monitoring. The AI ​​algorithm predicts future sales trends and market demand by analyzing factors such as historical sales data, market trends, and seasonal changes. Specifically, neural network models based on deep learning, such as LSTM models or recurrent neural networks (RNN), can be used to model and predict time series data, accurately predict changes in sales and seasonal fluctuations, and provide more reliable decision support for enterprises. In addition, the introduction of graph neural network technology further enhances the ability of sales network analysis. Graph neural network technology can effectively capture and analyze complex relationships and influencing factors in sales networks, such as customer relationship networks, sales channels, and product dependencies. By applying graph neural network models to graph structured data, patterns and rules hidden behind a large amount of interactive data can be discovered, providing more in-depth sales trend analysis and recommendations. In summary, the sales monitoring module 14 can realize real-time sales monitoring and analysis, and through real-time monitoring of display interfaces such as dashboards, AI predictive analysis, and the comprehensive application of graph neural network technology, it provides enterprises with comprehensive sales data management and decision support capabilities. This technical solution not only improves the visualization and real-time nature of sales data, but also helps enterprises accurately grasp market dynamics, optimize sales strategies, and maximize sales efficiency through intelligent prediction and complex network analysis.

[0054] Existing technologies lack real-time monitoring and analysis tools. This system uses display interfaces and graph neural network technology to achieve real-time monitoring and in-depth analysis, improving the timeliness and accuracy of decision-making: real-time monitoring of sales progress, costs and performance through display interfaces such as dashboards, combined with AI for predictive analysis, and providing detailed sales reports.

[0055] Furthermore, different functional modules communicate with each other using a preset data interaction method; wherein the data interaction method includes at least one of the following: API (Application Programming Interface) technology, middleware technology and ETL (Extract, Transform, Load) technology; the functional modules include at least two of the following: data acquisition module 101, data processing module 102, strategy generation module 103, budget control module 11, sales optimization module 12, financial voucher processing module 13 and sales monitoring module 14.

[0056] The above API allows different modules to interact with each other, and establishes connection channels between different modules through API (Application Programming Interface) technology. As an integration method of software components, API allows data exchange and function sharing between modules, ensuring the efficiency and security of data transmission. Through well-defined API endpoints and data formats, different systems can directly read, write and update data, thereby realizing real-time synchronization of sales plan and budget information; the above middleware technology plays an important role in the integration process. As software located between different applications and databases, middleware has functions such as message transmission, data conversion and transaction management. In this technical solution, the middleware is responsible for data transfer and format conversion, converting sales plans, promotion activities and budget data from various systems into a unified standard format to ensure data consistency and availability. The above ETL technology, as the core of data integration, performs data extraction, conversion and loading processes. First, ETL extracts sales plan, promotion activities and budget data from the source system. Then, through data cleaning, conversion and standardization, ETL ensures the quality and consistency of the data so that it fits the format and requirements of the target system. Finally, this technical solution combines ETL technology to achieve automatic synchronization, conversion and integration of data, ensuring seamless connection between different systems and platforms such as sales plans and budgets. ETL loads processed data into the target system to achieve real-time update and sharing of sales plan and budget information. In summary, through the combined application of API and middleware technology, as well as the support of ETL technology, enterprises can achieve seamless integration of sales plans, promotions and budgets. This integration not only improves the efficiency and accuracy of data exchange between various systems within the enterprise, but also ensures the real-time and consistency of data, thereby helping enterprises better manage sales activities, optimize resource allocation, and make timely decisions to respond to market changes. This technical solution is not only of great significance in modern enterprise management, but also provides solid technical support and foundation for the digital transformation of enterprises.

[0057] Existing technologies have defects in data synchronization and integration. This system achieves seamless integration through API and ETL technology, improving the accuracy and consistency of plan execution. The seamless integration of sales plans, promotions and budgets through API and middleware technology is a key technical solution that can improve the efficiency and accuracy of enterprise sales management. Through a unified backend system, sales plans, promotions and budgets are seamlessly integrated, data is automatically synchronized and adjusted in real time.

[0058] It should be noted that although this solution provides a technical solution for a sales control system based on artificial intelligence, in actual applications, other alternative solutions can also be adopted according to specific needs and scenarios, such as:

[0059] 1. Resource optimization and allocation:

[0060] Alternative: Use a rules-based resource allocation system that allocates resources through preset rules and manual adjustments.

[0061] Limitations: Unable to dynamically adapt to market changes and low resource utilization.

[0062] 2. Budget and cost control:

[0063] Alternative: Manual budgeting and cost control using traditional financial management software.

[0064] Limitations: Lack of real-time adjustment and precise control, prone to budget overruns and cost out of control.

[0065] 3. Data interaction method used for planning-sales integration:

[0066] Alternative: Manually synchronize and adjust sales plans, promotions, and budgets.

[0067] Limitations: High risk of data inconsistency, cumbersome operations, and low efficiency.

[0068] 4. Sales Optimization:

[0069] Alternative: Use experience and simple statistical models to optimize sales strategies and inventory management.

[0070] Limitations: Cannot be adjusted dynamically, and the effect is not good.

[0071] 5. Financial voucher management:

[0072] Alternative: Handle accounting and invoicing manually, relying on human review and record keeping.

[0073] Limitations: error-prone and inefficient.

[0074] 6.Sales monitoring and analysis:

[0075] Alternative: Use basic reporting tools for regular sales monitoring and analysis.

[0076] Limitations: Lack of real-time and accuracy, and delayed decision-making.

[0077] Although these alternative solutions can realize some functions of sales and distribution management to a certain extent, they have significant limitations compared to this solution, cannot give full play to the advantages of AI technology, and cannot achieve the same level of intelligence and automation.

[0078] The above-mentioned AI-based sales management and control system can intelligently optimize resource allocation, automate budgeting and cost control, seamlessly integrate sales plans, dynamically optimize sales strategies, and provide real-time sales monitoring and analysis. Through these improvements, this solution can significantly improve the efficiency and accuracy of sales distribution management.

[0079] Based on the application of deep learning and big data analysis, the system has greatly improved the intelligence level of the sales and distribution management system, thereby improving the accuracy of sales forecasts, optimizing resource allocation, automating budget control, and monitoring sales performance in real time.

[0080] The artificial intelligence-based sales control method provided in this embodiment realizes intelligent, efficient and automated management of the sales distribution management system through key technical points such as AI-driven resource optimization and allocation, automatic budgeting and cost control, seamless planning-sales integration, AI-driven sales optimization, automatic financial voucher management, and real-time sales monitoring and analysis.

[0081] The specific effects include:

[0082] 1. Improve resource utilization: Through deep learning and reinforcement learning technology, dynamically optimize resource allocation, significantly improve resource utilization, and reduce operating costs.

[0083] 2. Accurate budgeting and cost control: Through Bayesian networks and LBS technology, budgets are automatically generated and adjusted to achieve real-time cost tracking and optimization, thereby improving the accuracy of cost control and reducing financial risks.

[0084] 3. Consistent and accurate plan execution: Through API and ETL technology, seamless integration of sales plan and budget is achieved, which reduces errors caused by inconsistent data and improves the accuracy of plan execution.

[0085] 4. Optimize sales strategies: Through collaborative filtering, content recommendation and NLP technology, dynamically adjust sales strategies and inventory management, improve sales success rate and reduce inventory backlogs.

[0086] 5. Simplify financial voucher processing: Through computer vision and RPA technology, financial vouchers can be automatically identified and classified, and financial reports can be generated, which improves processing efficiency and accuracy and reduces human errors.

[0087] 6. Real-time decision support: Through the display interface and graph neural network technology, sales progress and performance are monitored in real time, providing in-depth analysis and decision support, improving the timeliness and accuracy of decision-making.

[0088] The embodiment of the present invention provides a sales management method based on artificial intelligence, such as Figure 3 As shown, the method comprises the following steps:

[0089] Step S302, the data acquisition module acquires first data associated with product sales, and sends the first data to the data processing module;

[0090] Step S304, the data processing module inputs the first data into a pre-trained prediction model to output a sales prediction result corresponding to the product through the prediction model, and sends the sales prediction result to the strategy generation module;

[0091] Step S306: The strategy generation module obtains inventory data, allocates inventory resources to the product based on the inventory data and sales forecast results, and obtains a target resource allocation strategy for the product.

[0092] In the above-mentioned sales control method based on artificial intelligence, the data processing module can determine the sales forecast results corresponding to the product through a pre-trained prediction model, and the strategy generation module determines the target resource allocation strategy of the product based on the sales forecast results and inventory data. When dealing with a complex and changing market environment, this method can dynamically adjust the resource allocation strategy to cope with real-time changes in market demand, thereby improving resource utilization and resource allocation rate and reducing operating costs.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sales management and control system based on artificial intelligence, characterized in that: The system comprises: a data acquisition module, a data processing module and a strategy generation module; The data acquisition module is used to acquire first data associated with product sales and send the first data to the data processing module; The data processing module is used to input the first data into a pre-trained prediction model to output a sales prediction result corresponding to the product through the prediction model, and send the sales prediction result to the strategy generation module; The strategy generation module is used to obtain inventory data, allocate inventory resources to the product based on the inventory data and the sales forecast result, and obtain a target resource allocation strategy for the product.

2. The system according to claim 1, characterized in that The strategy generation module is also used for: Acquire inventory data, and allocate inventory resources to the product based on the inventory data and the sales forecast result to obtain a first resource allocation strategy for the product; Using a reinforcement learning algorithm to process the first resource allocation strategy to determine a second resource allocation strategy; According to a preset optimization algorithm, the second resource allocation strategy is optimized to obtain a target resource allocation strategy for the product; wherein the preset optimization algorithm includes: a Q-learning algorithm and / or a deep Q network algorithm.

3. The system according to claim 1, characterized in that The system further comprises: a budget control module; the budget control module is used to: Acquire second data associated with product sales, and pre-process the second data to obtain processed data; determining first feature data based on the processed data; Based on a preset time series analysis algorithm, the processed data and the first feature data are analyzed to obtain sales trend prediction results and market change prediction results; generating an initial budget for the product based on the sales trend forecast result and the market change forecast result, and the pre-acquired unit price information; Acquiring the actual cost expenditure of the product, monitoring the actual cost expenditure of the product according to the initial budget, and obtaining a monitoring result; If the monitoring result indicates that the actual cost expenditure exceeds the initial budget, an alarm message is generated.

4. The system according to claim 3, characterized in that The budget control module is used to: A Bayesian network is used to model the processed data and the first feature data to determine the dependency relationship between target parameters; wherein the target parameters include: sales, marketing activity information and time information.

5. The system according to claim 3, characterized in that The system further comprises: a sales optimization module; the sales optimization module is used to: Acquire the user's historical behavior data and preference data related to the product; wherein the historical behavior data includes: historical purchase behavior data, historical operation behavior data and historical interaction data; Analyzing the historical behavior data using a collaborative filtering algorithm to obtain a customer group associated with the historical behavior data; Acquire product information of a first product associated with the customer group; Analyze the product information to determine a plurality of second products associated with the product information; According to the preference data of the user, a target product matching the preference data is screened out from a plurality of the second products, and the target product is recommended to the user.

6. The system according to claim 5, characterized in that The sales optimization module is used to: obtaining feedback information associated with the product; Analyze the feedback information by natural language processing to obtain analysis results; The product sales information associated with the product is adjusted according to the analysis result; wherein the product sales information includes: a sales plan and a sales strategy.

7. The system according to claim 5, characterized in that The system further comprises: a financial voucher processing module; the financial voucher processing module is used to: acquiring a first image of a financial document associated with the product; Recognize and process the first image to obtain first information of the financial voucher; wherein the first information includes at least one of the following: invoice number, date, amount, and supplier information; Classify and process the financial vouchers according to the first information to obtain processed financial vouchers; Generate financial reports based on the processed financial vouchers.

8. The system according to claim 7, characterized in that The system further comprises: a sales monitoring module; the sales monitoring module is used to: Obtaining sales data of the product; Displaying the sales data through a display interface; The sales data is analyzed using a graph neural network to output sales trend analysis results.

9. The system according to claim 8, characterized in that Different functional modules communicate with each other using a preset data interaction method; wherein the data interaction method includes at least one of the following: API technology, middleware technology and ETL technology; the functional modules include at least two of the following: the data acquisition module, the data processing module, the strategy generation module, the budget control module, the sales optimization module, the financial voucher processing module and the sales monitoring module.

10. A sales management and control method based on artificial intelligence, characterized in that: The method comprises: The data acquisition module acquires first data associated with product sales and sends the first data to the data processing module; The data processing module inputs the first data into a pre-trained prediction model to output a sales prediction result corresponding to the product through the prediction model, and sends the sales prediction result to the strategy generation module; The strategy generation module obtains inventory data, allocates inventory resources to the product based on the inventory data and the sales forecast result, and obtains a target resource allocation strategy for the product.

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