An intelligent information management system for e-commerce

Abstract

The present invention relates to the technical field of business process management, specifically to an intelligent information management system for e-commerce, the system comprising: a transaction behavior monitoring module, an abnormality identification module, a transaction mode optimization module, an inventory optimization module, a risk monitoring module, and an intelligent response module. In the present invention, real-time monitoring and data analysis are used to track e-commerce transaction behaviors, which can instantly discover and screen transaction behaviors that deviate from the norm, improve transaction security and accuracy, use advanced data analysis technology, and effectively identify and manage abnormal transaction patterns through an isolation forest algorithm, which not only helps to prevent fraud and risks, but also optimizes transaction processes and rules, improves transaction efficiency, and can timely implement risk prevention and control measures through real-time monitoring and evaluation of inventory risks, maintain a dynamic balance between inventory and market demand, effectively avoid excessive inventory and out-of-stock situations, and improve the market competitiveness of enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of business process management, and in particular to an intelligent information management system for e-commerce. Background Art

[0002] Business process management is used to make the organization's workflow more efficient and effective. By analyzing existing processes, identifying improvement opportunities, and monitoring the effects of implementation, BPM helps companies achieve higher efficiency and better performance. This technical field integrates information technology and management science, aiming to optimize end-to-end business processes through strategies such as process automation, process redesign, and continuous process improvement. BPM technology involves many aspects, including process modeling, process automation, performance management, and user interaction, and is often used in demand management, service hierarchy management, and resource management.

[0003] Among them, the e-commerce intelligent information management system refers to a system that uses information technology to integrate and automate business processes in an e-commerce environment. The main purpose of this type of system is to improve the efficiency of business operations, ensure the accuracy and timeliness of data, and improve service quality. Through intelligent information management, e-commerce platforms can better manage inventory, orders, customer relationships, and finances, and optimize the entire e-commerce operation process.

[0004] Existing technologies lack real-time data monitoring and efficient risk management mechanisms when processing e-commerce information. In daily operations, this defect leads to data processing delays, failure to detect and respond to abnormal transactions in a timely manner, and increases the fraud and risks faced by enterprises. Traditional inventory management is static and unresponsive, insufficient to cope with the rapid changes in market demand, resulting in excess or shortage of inventory, affecting customer service quality and the economic benefits of enterprises. Technical limitations are particularly prominent in the rapidly developing and highly competitive e-commerce environment, seriously restricting the sustainable development and market adaptability of enterprises. Summary of the invention

[0005] In order to solve the lack of real-time data monitoring and efficient risk management mechanisms in the prior art, in daily operations, this defect leads to data processing delays, and abnormal transactions cannot be discovered and responded to in a timely manner, increasing the fraud and risks faced by enterprises. Traditional inventory management is static and unresponsive, which is insufficient to cope with the rapid changes in market demand, resulting in excess or shortage of inventory, affecting the quality of customer service and the economic benefits of the enterprise. Technical limitations are particularly prominent in the rapidly developing and highly competitive e-commerce environment, which seriously restricts the sustainable development and market adaptability of enterprises. The embodiment of the present invention provides an e-commerce intelligent information management system. The technical solution is as follows: On the one hand, an e-commerce intelligent information management system is provided, the system comprising: The transaction behavior monitoring module tracks e-commerce transaction information in real time, collects transaction time and amount data, calculates averages and variances, and screens out transaction behaviors that deviate from normal patterns to obtain transaction behavior feature records; The abnormality identification module uses the isolation forest algorithm to identify and manage abnormal patterns through the transaction behavior feature records, and locates abnormal transaction behaviors to obtain abnormal transaction behavior location results; The transaction mode optimization module analyzes the loopholes and low efficiency in the abnormal transaction mode according to the abnormal transaction behavior positioning results, adjusts the e-commerce transaction rules, avoids potential risky transaction activities, and obtains a transaction mode adjustment plan; The inventory optimization module implements the transaction pattern adjustment plan, monitors sales trends and inventory levels, automatically adjusts inventory allocation, optimizes the supply chain response of e-commerce, and obtains inventory adjustment records; The risk monitoring module uses the inventory adjustment records to evaluate multiple inventory risk factors, implement risk prevention and control measures, verify the dynamic balance between inventory and market demand, and obtain inventory risk analysis results; The intelligent response module automatically optimizes inventory configuration and e-commerce operation efficiency according to the inventory risk analysis results and the e-commerce inventory information settings, and obtains the e-commerce information management log.

[0006] As a further solution of the present invention, the transaction behavior feature record includes transaction serial number, transaction amount, transaction time point, and indicators of deviation from average transaction behavior; the abnormal transaction behavior positioning result includes the time, amount, and estimated risk level of the abnormal transaction; the transaction pattern adjustment plan includes adjusted transaction review standards, transaction limit adjustments, and risk transaction prevention and control measures; the inventory adjustment record includes adjusted inventory allocation information, inventory level changes, and adjusted inventory replenishment strategies; the inventory risk analysis results include risk levels of multiple inventories, a list of risk factors, and risk mitigation measures; the e-commerce information management log includes inventory adjustment execution records and operational efficiency change data.

[0007] As a further solution of the present invention, the transaction behavior monitoring module includes: The data recording submodule tracks e-commerce transaction information in real time, records the time and amount of transaction events, and updates in real time through continuous recording to obtain the original transaction records; The data analysis submodule analyzes the time series and amount data in the original transaction records, calculates multiple statistical indicators, including the average amount of transactions and the standard deviation of the amount, captures data fluctuations, and forms statistical analysis results; The behavior identification submodule identifies abnormal patterns in amount and time from the statistical analysis results, screens transaction behaviors that deviate from the average level by setting thresholds, identifies the characteristics of transaction behaviors, and obtains transaction behavior characteristic records.

[0008] As a further solution of the present invention, the abnormality identification module includes: The feature analysis submodule performs data standardization processing through the transaction behavior feature records, avoids dimension redundancy, optimizes the model input, and analyzes key features to obtain feature classification records; The abnormal record submodule uses the feature classification records to perform data dimension reduction and unsupervised training, distinguishes normal and abnormal points in the data set by calculating the principal components of the data set, and outputs the abnormal pattern recognition results; The abnormal transaction tracking submodule performs spatial distribution analysis based on the abnormal pattern recognition results, performs geographic calibration on abnormal transaction behaviors, identifies the locations of abnormal transactions through cluster analysis, and obtains abnormal transaction behavior positioning results.

[0009] As a further solution of the present invention, the formula for calculating the principal component of the data set is as follows: ; in, represents the weighted variance of the data set, Representative The original feature vector of the data point, represents the weighted average of the feature vectors of the data points, Representative data points The critical weight of Represents the total number of data points in the dataset.

[0010] As a further solution of the present invention, the transaction mode optimization module includes: The factor analysis submodule analyzes common vulnerabilities and efficiency problems in abnormal transaction patterns through the abnormal transaction behavior positioning results, identifies key factors affecting performance, and obtains vulnerability and efficiency analysis results; Based on the vulnerability and efficiency analysis results, the efficiency optimization submodule adjusts the original transaction rules of e-commerce, adds a data verification layer and a transaction monitoring mechanism, optimizes the overall efficiency and security of the e-commerce transaction verification process, and forms a draft for adjusting the transaction rules; The risk avoidance submodule adopts the trading rule adjustment draft, evaluates the performance of the new rules in real scenarios through simulation tests, regularly updates and evaluates risks, avoids potential risky trading behaviors in future time periods, and obtains a trading model adjustment plan.

[0011] As a further solution of the present invention, the inventory optimization module includes: The sales monitoring submodule implements the transaction model adjustment plan, continuously monitors the sales data and trends of e-commerce, extracts sales indicators, and updates sales dynamics in real time to obtain sales trend analysis results; The market demand matching submodule uses the sales trend analysis results to automatically adjust the inventory allocation of e-commerce, predict the demand in the future time period, implement dynamic inventory allocation to match market demand, and obtain inventory allocation tasks; The supply chain response submodule implements the inventory allocation task, optimizes the supply chain management process, adjusts supplier orders and logistics arrangements, responds to market changes, and obtains inventory adjustment records.

[0012] As a further solution of the present invention, the risk monitoring module includes: The risk factor identification submodule monitors the inventory information of the e-commerce based on the inventory adjustment record, identifies risk factors, including excess and shortage of resources, and evaluates the risk level to obtain a risk level record; The measures formulation submodule uses the risk level records to design and implement risk prevention and control strategies, and obtains risk prevention and control measures by adjusting procurement plans and optimizing inventory turnover speed; The market balance verification submodule implements the risk prevention and control measures, conducts real-time market demand matching inspection, adjusts inventory, verifies the dynamic balance with market demand, and obtains inventory risk analysis results.

[0013] As a further solution of the present invention, the intelligent response module includes: The priority sorting submodule identifies the excess and shortage of e-commerce inventory items based on the inventory risk analysis results, evaluates the factors affecting inventory costs and service levels, and performs inventory classification and priority sorting to obtain an optimized demand list; The risk inventory optimization submodule uses the optimized demand list to calculate the quantity and location of inventory adjustments, cuts risk inventory, increases the stock of shortage risk inventory, optimizes inventory turnover speed and warehouse space utilization, and obtains adjusted inventory configuration; The information update submodule uses the adjusted inventory configuration to update inventory data, synchronize inventory information to sales and supply chain, automatically adjust purchase orders and inventory levels, optimize the overall inventory management process, and obtain e-commerce information management logs.

[0014] As a further solution of the present invention, the formula for calculating the quantity and position of the inventory adjustment is as follows: ; in, Configure for inventory quantity, Represents the forecast demand, Represents real-time inventory levels, represents the safety stock factor, Represents the real-time warehouse location index, Represents the target inventory position index.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: By using real-time monitoring and data analysis to track e-commerce transaction behaviors, it is possible to instantly discover and screen out transaction behaviors that deviate from the norm, thereby improving transaction security and accuracy. Using advanced data analysis technology and the isolation forest algorithm to effectively identify and manage abnormal transaction patterns not only helps prevent fraud and risks, but also optimizes transaction processes and rules and improves transaction efficiency. Implementing automatic inventory adjustment and optimization strategies, dynamically adjusting inventory allocation according to sales trends, and significantly improving the response speed and efficiency of the supply chain. Through real-time monitoring and evaluation of inventory risks, risk prevention and control measures can be implemented in a timely manner to maintain a dynamic balance between inventory and market demand, effectively avoid excessive inventory and out-of-stock situations, and improve the market competitiveness of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the drawings without paying creative work.

[0017] Figure 1 is a schematic diagram of an e-commerce intelligent information management system provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention; Figure 3 This is a flow chart of the transaction behavior monitoring module in the present invention; Figure 4 This is a flow chart of the abnormality identification module in the present invention; Figure 5 This is a flow chart of the transaction mode optimization module in the present invention; Figure 6 This is a flow chart of the inventory optimization module in the present invention; Figure 7 This is a flow chart of the risk monitoring module in the present invention; Figure 8 This is a flow chart of the intelligent response module in the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0021] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0022] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0023] The embodiment of the present invention provides an intelligent e-commerce information management system, such as Figure 1-2 The schematic diagram of the e-commerce intelligent information management system shown in the figure includes: The transaction behavior monitoring module tracks e-commerce transaction information in real time, collects transaction time and amount data, calculates averages and variances, and screens out transaction behaviors that deviate from normal patterns to obtain transaction behavior feature records; The anomaly identification module uses the isolation forest algorithm to identify and manage abnormal patterns through transaction behavior feature records, and locate abnormal transaction behaviors to obtain abnormal transaction behavior location results; The transaction mode optimization module analyzes the loopholes and low efficiency in abnormal transaction modes based on the abnormal transaction behavior positioning results, adjusts the e-commerce transaction rules, avoids potential risky transaction activities, and obtains a transaction mode adjustment plan; The inventory optimization module implements transaction pattern adjustment plans, monitors sales trends and inventory levels, automatically adjusts inventory allocation, optimizes e-commerce supply chain response, and obtains inventory adjustment records; The risk monitoring module uses inventory adjustment records to evaluate multiple inventory risk factors, implement risk prevention and control measures, verify the dynamic balance between inventory and market demand, and obtain inventory risk analysis results;

[0024] The intelligent response module automatically optimizes inventory configuration and e-commerce operation efficiency based on inventory risk analysis results and the e-commerce inventory information settings to obtain e-commerce information management logs.

[0025] Transaction behavior feature records include transaction sequence number, transaction amount, transaction time point, and indicators that deviate from average transaction behavior. Abnormal transaction behavior positioning results include the time, amount, and estimated risk level of abnormal transactions. Transaction model adjustment plans include adjusted transaction review standards, transaction limit adjustments, and risk transaction prevention and control measures. Inventory adjustment records include adjusted inventory allocation information, inventory level changes, and adjusted inventory replenishment strategies. Inventory risk analysis results include risk levels of multiple inventories, risk factor lists, and risk mitigation measures. E-commerce information management logs include inventory adjustment execution records and operational efficiency change data.

[0026] See also Figure 2 , 3 , the transaction behavior monitoring module includes: The data recording submodule tracks the transaction information of e-commerce in real time, records the time and amount information of transaction events, and updates in real time through continuous recording to obtain the execution process of the original transaction record as follows; Track and record e-commerce transaction information, including transaction time and amount, and update transaction data in real time to maintain the accuracy of system information. In this process, data input from the front-end interface is received, classified and stored according to the set data format and timestamp. During the storage process, time tags and amount data are used to generate a continuous transaction data stream. The data stream is verified for integrity and accuracy through internal algorithms to ensure that the data has not been tampered with and is consistent with the actual transaction situation. The transaction time is recorded in Unix timestamp format to ensure uniformity and accurate comparison in different time zones. The amount information is accurately recorded according to the smallest monetary unit. In this way, the original transaction record is obtained.

[0027] The data analysis submodule analyzes the time series and amount data in the original transaction records, calculates multiple statistical indicators, including the average amount of transactions and the standard deviation of the amount, captures data fluctuations, and forms the execution process of statistical analysis results as follows; Process the original transaction records according to the formula: ;and ; Calculate the average transaction amount and the standard deviation of the amount, where Represents the average amount, represents the standard deviation, represents the amount of the qth transaction, Represents the total number of transactions; Extract the amount data of all transaction records, assuming the data is , Represents the total number of transactions, in order to calculate the average amount , add up all transaction amounts to get , divided by the number of transactions For example, if the transaction amount is 100, 200, 300 yuan, and the number of transactions is 3, the average amount is calculated as: Yuan; to calculate the standard deviation , calculate the square of the difference between each transaction amount and the average amount, and sum them up to get , divided by the number of transactions , take the square root, in the previous example, the standard deviation is calculated as: ; Such statistical analysis helps identify fluctuations in trading data and provides important basis for further data analysis and decision-making.

[0028] The behavior recognition submodule identifies abnormal patterns in amount and time from the statistical analysis results, screens out transaction behaviors that deviate from the average level by setting thresholds, identifies the characteristics of transaction behaviors, and obtains the execution process of transaction behavior feature records as follows; Analyze statistical data to identify abnormal patterns of transaction behavior. By comparing the amount and time of each transaction with the established average standard, filter out transaction behaviors that deviate from the normal range, set a threshold based on the distribution of historical data and standard deviation, compare each transaction data with the threshold, record all abnormal data points, and identify abnormal patterns that indicate potential fraud or erroneous operations. In this way, not only can the system be protected from fraud, but also the transaction behavior identification algorithm can be optimized through continuous monitoring, thereby improving the security and efficiency of the system, providing basic data for further analysis, and obtaining transaction behavior feature records.

[0029] See also Figure 2 , 4 , the anomaly recognition module includes: The feature analysis submodule performs data standardization processing through transaction behavior feature records, avoids dimension redundancy, optimizes model input, and analyzes key features to obtain the execution process of feature classification records as follows; Data is standardized to ensure the consistency and accuracy of data input. The standardization process includes adjusting the scale and distribution of data from different sources so that the mean is zero and the standard deviation is one. Dimensionality reduction techniques are also implemented to avoid dimensional redundancy. Commonly used dimensionality reduction techniques include principal component analysis and linear discriminant analysis. The technology helps remove irrelevant features, reduces the difficulty of calculation, and improves the efficiency of subsequent model training. After feature optimization is completed, key features such as transaction amount, time frequency, geographic location, etc. are analyzed through statistical and machine learning methods to identify features that have a significant impact on transaction behavior. This analysis process not only improves the quality of data processing, but also lays a solid foundation for establishing a more accurate prediction model and obtaining feature classification records.

[0030] The abnormal record submodule uses feature classification records to perform data dimension reduction and unsupervised training. It calculates the principal components of the data set, distinguishes normal and abnormal points in the data set, and outputs the abnormal pattern recognition results. The execution process is as follows; The formula for calculating the principal components of a data set is as follows: ;in, represents the weighted variance of the data set, Representative The original feature vector of the data point, represents the weighted average of the feature vectors of the data points, Representative data points The critical weight of Represents the total number of data points in the dataset; Parameter meaning and setting value: Indicates The feature vector of a data point, in practical applications, Acquired through data monitoring and collection, each vector includes multiple measurement dimensions such as temperature and pressure; Represents the weighted average of the feature vectors of all data points, using weights Adjust the contribution of each point; Represents data points based on The importance or reliability of the data, which is set based on the quality or credibility of the data, such as signal strength or measurement accuracy. The weight should ideally be obtained from the data quality monitoring system, for example, higher quality data points have higher weights; Represents the total number of data points in the dataset; Substitute the parameters into the formula for calculation, set a small data set including three data points, the eigenvector for , weight for , calculate the weighted average : ; Calculate weighted variance : ; Results 19 show that the degree of variation of data points relative to the weighted mean reflects the discreteness of the data set, which is crucial for identifying abnormal patterns. It can more accurately assess whether a data point is an anomaly and further guide the application of anomaly detection algorithms.

[0031] The abnormal transaction tracking submodule performs spatial distribution analysis based on the abnormal pattern recognition results, performs geographic calibration on abnormal transaction behaviors, identifies the locations of abnormal transactions through cluster analysis, and obtains the abnormal transaction behavior location results. The execution process is as follows; Perform spatial distribution analysis and focus on the geographic calibration of abnormal trading behaviors. This process uses geographic information systems (GIS) and spatial data processing tools to accurately calibrate the geographic location of abnormal transactions. Through geographic calibration, the distribution of abnormal transactions in different regions can be observed, and cluster analysis is used to further identify the specific location and pattern of abnormal transactions. Cluster analysis uses the K-means algorithm, which can effectively cluster similar points in geographic data and reveal potential hot spots for abnormal transactions. In this way, not only the geographic location of abnormal transactions is accurately identified, but also the spatial distribution pattern is analyzed, providing a scientific basis for subsequent supervision and prevention measures, and obtaining the results of abnormal transaction behavior positioning.

[0032] See also Figure 2 , 5 , the transaction mode optimization module includes: The factor analysis submodule locates the results of abnormal trading behavior, analyzes common vulnerabilities and efficiency issues in abnormal trading patterns, identifies key factors affecting performance, and obtains the execution process of vulnerability and efficiency analysis results as follows; Conduct in-depth analysis of common vulnerabilities and efficiency issues in abnormal trading patterns, identify and evaluate key factors that affect system performance, such as data processing speed, transaction verification accuracy, and system response time. Through analysis, it is possible to identify in detail the specific links that lead to inefficiency or vulnerabilities, and propose corresponding improvement measures. This systematic review helps reveal weaknesses in the trading system and provides targeted solutions, listing the performance impact and improvement plans of each key factor in detail, providing an important basis for subsequent system optimization, and obtaining vulnerability and efficiency analysis results.

[0033] Based on the results of vulnerability and efficiency analysis, the efficiency optimization submodule adjusts the original transaction rules of e-commerce, adds a data verification layer and a transaction monitoring mechanism, optimizes the overall efficiency and security of the e-commerce transaction verification process, and forms the execution process of the transaction rule adjustment draft as follows; Adjust the original transaction rules of e-commerce according to the formula: ; In the formula, Represents the overall efficiency of transaction verification, Representative The efficiency of the verification step, Representative The cost of the verification step, Represents the total number of verification steps; Identify each step in the current transaction verification process, set the efficiency and cost parameters respectively, and set three main verification steps with efficiency values ​​of 0.8, 0.9, and 0.85, and cost values ​​of 10, 15, and 12 respectively. The total efficiency of transaction verification is calculated as: ; Through this weighted sum approach, the contribution of each step can be quantified and the process optimized accordingly, reducing unnecessary costs while improving efficiency. The added data verification layer and transaction monitoring mechanism further ensure the security and reliability of the transaction process, forming a comprehensive and efficient draft of transaction rule adjustments.

[0034] The risk avoidance submodule adopts the draft of trading rule adjustment, evaluates the performance of the new rules in real scenarios through simulation tests, regularly updates and evaluates risks, avoids potential risky trading behaviors in the future time period, and obtains the execution process of the trading model adjustment plan as follows; Evaluate the performance of new rules in real trading scenarios. In this process, by setting up a simulation environment, reproducing trading behaviors and observing the execution effect of new rules, this simulation test can verify the effectiveness and security of the rules in a risk-free environment, ensuring that the new rules can effectively avoid potential risks in actual applications. Risk management strategies are also regularly updated and evaluated to adapt to market and technological changes, and the performance and expected effects of new rules are described in detail to provide a safer and more efficient trading environment for e-commerce platforms and obtain trading model adjustment plans.

[0035] See also Figure 2 , 6 , the inventory optimization module includes: The sales monitoring submodule implements the transaction model adjustment plan, continuously monitors the sales data and trends of e-commerce, extracts sales indicators, and updates sales dynamics in real time. The execution process of obtaining sales trend analysis results is as follows; Continuously monitor e-commerce sales data, according to the formula: ; In the formula, Represents the trend value at a real-time point in time, Represents real-time sales data, represents the smoothing parameter, Represents the trend value at the previous time point; Get real-time sales data , and use the exponential smoothing formula to update the sales trend, setting the initial trend is known, and the smoothing parameter is set is 0.3, for example, the sales trend at the previous time point For 200 units, real-time sales data is 180 units, then the trend value at the real-time point is calculated as: ; This calculation method enables sales trends to reflect the latest market conditions while smoothing out past fluctuations, providing the sales team with real-time updated sales dynamics to assist in decision-making.

[0036] The market demand matching submodule uses the sales trend analysis results to automatically adjust the inventory allocation of e-commerce, predict the demand in the future time period, and implement dynamic inventory allocation to match market demand. The execution process of the inventory allocation task is as follows; Automatically adjust the inventory allocation of e-commerce platforms to cope with changes in market demand. In this process, sales data trends are analyzed through predictive algorithms, and the estimated demand in the future is calculated. Based on the forecast results, the inventory allocation is dynamically adjusted to ensure that the inventory matches the forecasted demand. The adjustment process includes evaluating the current inventory level, comparing the difference between forecasted demand and existing inventory, and formulating replenishment or adjustment plans. In this way, inventory utilization can be optimized, the risk of excess or shortage inventory can be reduced, and an inventory allocation task can be formed.

[0037] The supply chain response submodule implements inventory allocation tasks, optimizes supply chain management processes, adjusts supplier orders and logistics arrangements, responds to market changes, and obtains inventory adjustment records. The execution process is as follows; Adjust the supply chain management process, including order processing, logistics arrangements and inventory control. In this process, analyze the specific requirements of inventory allocation tasks and respond to rapid changes in market demand. This includes communicating with suppliers to adjust purchase orders, optimizing logistics routes and scheduling plans, and ensuring that materials can flow effectively. Supply chain adjustments help companies reduce costs and improve response speed. Through this dynamic management method, not only the flexibility of the supply chain is ensured, but also the ability to adapt to market changes is improved. It reflects the process and results of supply chain adjustments, provides decision-making basis for management, and obtains inventory adjustment records.

[0038] See also Figure 2 , 7 , the risk monitoring module includes: The risk factor identification submodule monitors the inventory information of e-commerce based on inventory adjustment records, identifies risk factors, including excess and shortage of resources, and evaluates the risk level. The execution process of obtaining the risk level record is as follows; Monitor e-commerce inventory information according to the formula: ; In the formula, Represents the risk level, Representative The weight of the factors, Representative The risk value of each factor is represents the total number of risk factors; Extract key risk factors such as resource surplus and shortage from inventory adjustment records, set weights for each risk factor, and set weights for excess resources is 0.4, the weight of resource shortage is 0.6; If the specific risk values ​​are set to 3 and 4 respectively, the risk level calculation is: ; This weighted calculation method takes into account the importance of differentiated risk factors, making the risk level more accurately reflect potential problems with current inventory.

[0039] The measures formulation submodule adopts risk level records to design and implement risk prevention and control strategies. By adjusting the procurement plan and optimizing the inventory turnover rate, the execution process of risk prevention and control measures is as follows; Design risk prevention and control strategies. During this process, analyze risk levels and identify risk types that need to be prioritized. Based on the risk type, formulate specific prevention and control measures, including adjusting procurement plans to deal with resource surpluses or shortages, optimizing inventory turnover speed to improve efficiency, and using strategies to reduce potential risks and enhance the flexibility of inventory management. Instruct how to reduce risks through specific operations, improve the effectiveness of inventory management, provide management with implementation directions and specific operational steps, and obtain risk prevention and control measures.

[0040] The market balance verification submodule implements risk prevention and control measures, conducts real-time market demand matching inspection, adjusts inventory, verifies dynamic balance with market demand, and obtains the execution process of inventory risk analysis results as follows; Conduct market demand matching inspection. During this process, analyze the current market demand and inventory status, check whether inventory adjustments are in dynamic balance with market demand, adjust inventory configuration based on real-time data, and ensure that inventory levels match market demand. In this way, not only can the risks caused by inventory imbalance be reduced, but inventory strategies can also be optimized through continuous market monitoring. The effects of various measures and their impact on market balance are described in detail, providing data support for the next step of strategy adjustment and obtaining inventory risk analysis results.

[0041] See also Figure 2 , 8 , the intelligent response module includes: The prioritization submodule identifies the excess and shortage of e-commerce inventory items based on the inventory risk analysis results, evaluates the factors affecting inventory costs and service levels, and performs inventory classification and priority sorting. The execution process of the optimized demand list is as follows; The inventory items of the e-commerce platform are carefully evaluated and sorted. The process includes identifying and classifying the excess and shortage of each inventory item, determining which inventory is in excess or shortage based on the sales history of the inventory items and market demand forecasts, and evaluating the impact of inventory items on overall inventory costs and service levels. Factors involved include the shelf life of the goods, sales cycle, cost-to-profit ratio, and position in the supply chain. Based on the evaluation results, inventory is classified and sorted according to risk and priority. High-risk or high-value inventory items are given high priority for quick processing to reduce potential losses or take full advantage of market opportunities. On the contrary, low-risk and stable demand inventory items are assigned low priority. Through this priority sorting, inventory costs are effectively controlled, service levels are significantly improved, and an optimized demand list is obtained.

[0042] The risk inventory optimization submodule uses the optimized demand list to calculate the quantity and location of inventory adjustments, cuts risk inventory, increases the stocking of shortage risk inventory, optimizes inventory turnover speed and warehouse space utilization, and obtains the execution process of the adjusted inventory configuration as follows; Not only does it calculate the necessary inventory adjustment quantity, but it also accurately determines the location of the inventory adjustment to ensure the optimal distribution of inventory. During the execution process, inventory marked as high risk is cut, while the inventory of commonly short supply inventory is increased. This strategy not only responds to current inventory risks, but also prevents future inventory problems. Focusing on optimizing inventory turnover speed and improving the utilization efficiency of warehouse space, by improving the inventory management system and adjusting the warehouse layout, ensure that inventory can circulate quickly, reduce stagnation time, reduce costs and increase inventory liquidity. Through comprehensive measures, not only the inventory configuration is optimized, but also the responsiveness and efficiency of the overall supply chain are improved, providing valuable decision-making information for management, helping enterprises maintain their advantages in a highly competitive market environment, and obtain adjusted inventory configuration.

[0043] The information update submodule uses the adjusted inventory configuration to update inventory data, synchronize inventory information to sales and supply chain, automatically adjust purchase orders and inventory levels, optimize the overall inventory management process, and obtain the execution process of e-commerce information management logs as follows; The formula to calculate the quantity and location of inventory adjustments is as follows: ; in, Configure for inventory quantity, Represents the forecast demand, Represents real-time inventory levels, represents the safety stock factor, Represents the real-time warehouse location index, represents the target inventory position index; Parameter meaning and calculation process: (Forecasted demand): This is obtained from past sales data through time series analysis using market analysis tools such as sales forecasting software. This value reflects the expected sales volume within a certain period of time in the future. (Real-time inventory level): This parameter is directly obtained through the inventory management system and represents the total amount of goods currently on hand in all warehouses; Safety Stock Factor: This factor is calculated based on past sales fluctuations and supply chain delays to ensure that inventory can still meet demand in the event of sales forecast errors or supply chain disruptions; and (Warehouse Location Index and Target Inventory Location Index): These two indices take into account the geographical location of the warehouse, transportation costs and time efficiency, and are derived through geographic information systems and logistics analysis. represents the current position index, and It is the target location index set according to the logistics optimization model; Specific calculation example: Through data collection and calculation, the following parameter values ​​are obtained: Unit (monitoring future monthly sales forecast), Unit (read directly from the inventory management system), units (calculated based on sales fluctuations over the past year), (Existing warehouse location index), (target warehouse location index); Substitute the parameters into the formula for calculation: ; The results show that when considering the real-time inventory level, safety stock factor and warehouse location index, the inventory quantity configuration is based on the predicted demand. The numerical value provides a quantitative goal to guide warehouse managers on how to adjust inventory to adapt to market demand while optimizing warehouse space utilization. The calculation method not only accurately reflects the actual needs of e-commerce inventory management, but also demonstrates the necessity and urgency of inventory adjustment through specific numerical values.

[0044] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent e-commerce information management system, characterized in that: The system comprises: a transaction behavior monitoring module that tracks the transaction information of e-commerce in real time, collects transaction time and amount data, calculates average values ​​and variations, screens transaction behaviors that deviate from normal patterns, and obtains transaction behavior feature records; an abnormal identification module that uses the isolation forest algorithm to identify and manage abnormal patterns and locate abnormal transaction behaviors through the transaction behavior feature records, and obtains abnormal transaction behavior location results; a transaction pattern optimization module that analyzes loopholes and low efficiency in abnormal transaction patterns based on the abnormal transaction behavior location results, adjusts e-commerce transaction rules, avoids potential risky transaction activities, and obtains a transaction pattern adjustment plan; an inventory optimization module that implements the transaction pattern adjustment plan, monitors sales trends and inventory levels, automatically adjusts inventory allocation, optimizes the supply chain response of e-commerce, and obtains inventory adjustment records; a risk monitoring module that uses the inventory adjustment records to evaluate risk factors of multiple inventories, implements risk prevention and control measures, verifies the dynamic balance between inventory and market demand, and obtains inventory risk analysis results; an intelligent response module that uses the inventory risk analysis results to automatically optimize inventory configuration and e-commerce operation efficiency according to the inventory information settings of e-commerce, and obtains an e-commerce information management log.

2. The electronic commerce intelligent information management system according to claim 1, characterized in that: The transaction behavior feature record includes the transaction sequence number, transaction amount, transaction time point, and indicators that deviate from the average transaction behavior. The abnormal transaction behavior positioning result includes the time, amount, and estimated risk level of the abnormal transaction. The transaction model adjustment plan includes the adjusted transaction review standards, transaction limit adjustments, and risk transaction prevention and control measures. The inventory adjustment record includes the adjusted inventory allocation information, inventory level changes, and adjusted inventory replenishment strategies. The inventory risk analysis results include the risk levels of multiple inventories, a list of risk factors, and risk mitigation measures. The e-commerce information management log includes the execution record of inventory adjustments and operational efficiency change data.

3. The electronic commerce intelligent information management system according to claim 1, characterized in that: The transaction behavior monitoring module includes: a data recording submodule that tracks e-commerce transaction information in real time, records the time and amount information of transaction events, and updates in real time through continuous recording to obtain original transaction records; a data analysis submodule that analyzes the time series and amount data in the original transaction records, calculates multiple statistical indicators, including the average amount of transactions and the standard deviation of the amount, captures data fluctuations, and forms statistical analysis results; a behavior recognition submodule recognizes abnormal patterns in amount and time from the statistical analysis results, screens transaction behaviors that deviate from the average level by setting thresholds, recognizes the characteristics of transaction behaviors, and obtains transaction behavior characteristic records.

4. The electronic commerce intelligent information management system according to claim 1, characterized in that: The anomaly identification module includes: a feature analysis submodule that performs data standardization processing through the transaction behavior feature records, avoids dimensional redundancy, optimizes the model input, and analyzes key features to obtain feature classification records; an abnormal record submodule uses the feature classification records to perform data dimensionality reduction and unsupervised training, calculates the principal components of the data set, distinguishes normal and abnormal points in the data set, and outputs abnormal pattern recognition results; an abnormal transaction tracking submodule performs spatial distribution analysis based on the abnormal pattern recognition results, performs geographic calibration on abnormal transaction behaviors, identifies the locations of abnormal transactions through cluster analysis, and obtains abnormal transaction behavior positioning results.

5. The electronic commerce intelligent information management system according to claim 4, characterized in that: The formula for calculating the principal components of the data set is as follows: ;in, represents the weighted variance of the data set, Representative The original feature vector of the data point, represents the weighted average of the feature vectors of the data points, Representative data points The critical weight of Represents the total number of data points in the dataset.

6. The electronic commerce intelligent information management system according to claim 1, characterized in that: The transaction mode optimization module includes: a factor analysis submodule that analyzes common vulnerabilities and efficiency issues in abnormal transaction modes through the abnormal transaction behavior positioning results, identifies key factors affecting performance, and obtains vulnerability and efficiency analysis results; an efficiency optimization submodule that adjusts the original transaction rules of e-commerce based on the vulnerability and efficiency analysis results, adds a data verification layer and a transaction monitoring mechanism, optimizes the overall efficiency and security of the e-commerce transaction verification process, and forms a transaction rule adjustment draft; a risk avoidance submodule adopts the transaction rule adjustment draft, evaluates the performance of the new rules in real scenarios through simulation tests, regularly updates and evaluates risks, avoids potential risky transaction behaviors in future time periods, and obtains a transaction mode adjustment plan.

7. The electronic commerce intelligent information management system according to claim 1, characterized in that: The inventory optimization module includes: a sales monitoring submodule that implements the transaction model adjustment plan, continuously monitors the sales data and trends of e-commerce, extracts sales indicators, and updates sales dynamics in real time to obtain sales trend analysis results; a market demand matching submodule that uses the sales trend analysis results to automatically adjust the inventory allocation of e-commerce, predict the demand in the future time period, implement dynamic inventory allocation to match market demand, and obtain inventory allocation tasks; a supply chain response submodule that implements the inventory allocation tasks, optimizes the supply chain management process, adjusts supplier orders and logistics arrangements, responds to market changes, and obtains inventory adjustment records.

8. The electronic commerce intelligent information management system according to claim 1, characterized in that: The risk monitoring module includes: a risk factor identification submodule that monitors the inventory information of e-commerce based on the inventory adjustment record, identifies risk factors, including resource surplus and shortage, and evaluates risk levels to obtain risk level records; a measure formulation submodule that adopts the risk level records to design and implement risk prevention and control strategies, and obtains risk prevention and control measures by adjusting procurement plans and optimizing inventory turnover speeds; a market balance verification submodule that implements the risk prevention and control measures, conducts real-time market demand matching inspections, adjusts inventory, verifies dynamic balance with market demand, and obtains inventory risk analysis results.

9. The electronic commerce intelligent information management system according to claim 1, characterized in that: The intelligent response module includes: a priority sorting submodule, based on the inventory risk analysis results, identifies the surplus and shortage of e-commerce inventory items, evaluates the factors affecting inventory costs and service levels, and classifies and prioritizes inventory to obtain an optimized demand list; a risk inventory optimization submodule uses the optimized demand list to calculate the quantity and location of inventory adjustments, cuts risk inventory, increases the stock of shortage risk inventory, optimizes inventory turnover speed and warehouse space utilization, and obtains an adjusted inventory configuration; an information update submodule uses the adjusted inventory configuration to update inventory data, synchronize inventory information to sales and supply chain, automatically adjust purchase orders and inventory levels, optimize the overall inventory management process, and obtain e-commerce information management logs.

10. The electronic commerce intelligent information management system according to claim 9, characterized in that: The formula for calculating the quantity and position of the inventory adjustment is as follows: ;in, Configure for inventory quantity, Represents the forecast demand, Represents real-time inventory levels, represents the safety stock factor, Represents the real-time warehouse location index, Represents the target inventory position index.

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