A retail commodity sales data statistical analysis system based on big data

Abstract

The present invention relates to the technical field of statistical analysis of retail commodity sales data, and discloses a statistical analysis system for retail commodity sales data based on big data, including a data acquisition module, a data cleaning and preprocessing module, a data storage and management module, a data analysis and mining module, a report generation and visualization module, a permission management module, a real-time monitoring and early warning module, an external data integration module and a decision support module; the data acquisition module is used to collect online and offline sales data in real time or in batches from multiple heterogeneous data sources. The system introduces an adaptive data cleaning mechanism, uses a machine learning algorithm to dynamically identify and repair missing values ​​and abnormal values ​​in the data, and adaptively adjusts the cleaning rules according to the real-time data mode. The intelligent data cleaning system can process structured and unstructured data, and can dynamically respond to data fluctuations from different sources, significantly improve the accuracy and consistency of data analysis, and reduce manual intervention.

Description

Technical Field

[0001] The present invention relates to the technical field of retail commodity sales data statistical analysis, and in particular to a retail commodity sales data statistical analysis system based on big data. Background Art

[0002] With the development of Internet technology and big data analysis, the sales data processing method of the retail industry is moving towards data-driven intelligent direction. Retailers no longer rely solely on traditional sales reports and inventory records, but gradually turn to using big data technology to conduct comprehensive and real-time analysis of massive sales data in order to make more accurate market decisions. However, the existing retail commodity sales data statistical analysis system based on big data still has shortcomings in practical applications.

[0003] Existing sales data analysis systems can usually only process structured data, and lack effective processing mechanisms for unstructured data. At the same time, data quality issues require manual intervention, resulting in low data processing efficiency and prone to errors. For example, the system proposed in patent document CN115689617A integrates and analyzes online and offline sales data, but does not provide a dynamic data cleaning solution, and there is no special processing mechanism for different data types, resulting in the system still having limitations when processing complex data;

[0004] Most existing retail data analysis systems use a static permission management mechanism, usually assigning permissions based on roles, and lack the ability to dynamically adjust data sensitivity and operation time. The system mentioned in patent document CN115689617A mainly focuses on data extraction and analysis, but does not provide detailed instructions for permission management. It does not implement a permission control mechanism down to the data field level, and cannot ensure accurate access to sensitive data and security audits.

[0005] Existing retail commodity sales data analysis systems usually rely on static threshold settings for early warning, and are unable to flexibly adjust early warning parameters based on real-time market dynamics and historical data fluctuations, resulting in frequent false alarms and missed reports. For example, the system mentioned in patent document CN115689617A evaluates and adjusts sales status, but does not introduce a dynamically adjusted early warning mechanism, and is unable to respond to market fluctuations or potential risks in a timely manner.

[0006] Therefore, those skilled in the art provide a retail commodity sales data statistical analysis system based on big data to solve the problems raised in the above background technology. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides a retail commodity sales data statistical analysis system based on big data to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a retail commodity sales data statistical analysis system based on big data, including a data acquisition module, a data cleaning and preprocessing module, a data storage and management module, a data analysis and mining module, a report generation and visualization module, a permission management module, a real-time monitoring and early warning module, an external data integration module and a decision support module;

[0009] The data collection module is used to collect online and offline sales data in real time or in batches from multiple heterogeneous data sources;

[0010] The data cleaning and preprocessing module is based on an adaptive data cleaning mechanism, using a machine learning algorithm to automatically detect and correct outliers and missing values ​​in the data, and adaptively adjusts the cleaning rules through the following formula:

[0011] Among them, w i is the importance weight of the data point, x i is the value of the data point;

[0012] The data storage and management module adopts distributed database technology to support the storage and dynamic expansion of massive data, and stores hot data in efficient storage media and cold data in low-cost media through a hot and cold data separation mechanism;

[0013] The data analysis and mining module combines multi-dimensional sales data and uses machine learning algorithms to perform sales trend forecasting, customer behavior analysis, inventory optimization, and market segmentation. The data analysis results are evaluated using the following formula:

[0014] Among them, α t is the sales influencing factor within the time period, P t Sales forecast value;

[0015] The report generation and visualization module supports drag-and-drop report generation and displays relevant data through interactive visualization tools, and users can dynamically adjust the display dimensions;

[0016] The authority management module implements accurate authority management for different users based on the role-based access control model, and the user's access rights can be adjusted dynamically;

[0017] The real-time monitoring and early warning module combines the dynamic threshold adjustment mechanism, uses time series analysis and machine learning models to monitor the sales status in real time, and dynamically adjusts the early warning threshold according to market changes. The early warning formula is: alert (t) = μ + k·σ,

[0018] Among them, μ is the moving average of sales data, σ is the standard deviation, and k is the warning sensitivity parameter;

[0019] The external data integration module is used to integrate external market data and conduct comprehensive analysis in combination with internal sales data;

[0020] The decision support module combines external data with internal sales data and uses situational analysis and simulation technology to provide scalable strategy recommendations and intelligent sales decision support.

[0021] Preferably, the data partitioning and sharding technology in the data storage and management module performs data sharding and partitioning strategy through a dynamic hash sharding and partitioning algorithm, and the formula is as follows:

[0022] Hash sharding formula: for any data record D i , and calculate its hash value through the following function: H(D i )=hash(K i )modN, where H(D i ) is the data record D i The hash value, K i is the primary key of the record, and N is the total number of shards in the system;

[0023] Consistent hash ring partition formula: used to map shards to storage nodes. Each storage node S j On the hash ring there is a hash value H(S j ), calculated by the following formula:

[0024] Among them, P j It is the storage node S j The partition position on the hash ring, C is the total length of the hash ring;

[0025] Data dynamic partition adjustment formula: When the amount of data increases, the system can dynamically adjust the sharding and partitioning. The adjustment formula is as follows:

[0026] Where N′ is the new number of shards, D t is the total amount of data stored, S max is the storage capacity allowed for each shard;

[0027] Node load balancing formula: To maintain a balanced load on storage nodes, calculate the load balancing coefficient L of each node. j :

[0028] Among them, W(D i ) is the data record D i The weight of is the indicator function.

[0029] Preferably, the report generation and visualization module generates reports through dynamic visualization of multi-dimensional data and natural language query, and its algorithm formula is as follows:

[0030] The multi-dimensional data mapping formula: For a multi-dimensional data set D, the multi-dimensional data needs to be mapped into a two-dimensional or three-dimensional visualization chart. Assuming that the data has n dimensions, the formula is as follows:

[0031] Among them, V i represents the projection value of the i-th data in the visualization space, w k is the weight of the kth dimension, D ik Represents the value of data i in the kth dimension;

[0032] The natural language query parsing formula: The report generation module supports natural language queries. Users dynamically generate reports by inputting natural language. The natural language query parsing algorithm calculates the matching query template through the following formula: Among them, Q t is the optimal matching query template, λ j is the weight, N j is the jth word of the natural language input, T j is the corresponding query variable in the template, similarity(N j , T j ) represents the similarity calculation between words;

[0033] The automatic chart layout optimization formula: If the data visualization module generates multiple charts, the system will automatically optimize the chart layout to maximize the readability of information and space utilization. The layout optimization algorithm is based on the following formula: Among them, L opt is the optimal layout, A i The actual area of ​​the ith graph, S i Indicates the optimal area for graph display, R i is the proportion of data shown in the chart, and n is the total number of charts.

[0034] Preferably, the data focusing and interactive zooming formula: In dynamic visualization, the user interactively determines part of the data to be displayed, and the formula for zooming display is as follows: Where Z represents the scaling factor, x is the focal data value determined by the user, x0 is the median of the data, and α is a parameter that controls the steepness of the scaling curve;

[0035] The time series trend prediction and visualization formula: For the time series trend display of sales data, the system predicts future trends through the following formula and dynamically generates trend charts:

[0036] Among them, P t+h is the predicted value for the next h time steps, P t is the actual value at the time point, X t-i is the data input at the previous time point, β i is the regression coefficient;

[0037] The formula for high-dimensional data dimensionality reduction and heat map generation: For high-dimensional data visualization, principal component analysis or t-SNE algorithm is used for dimensionality reduction. The formula is as follows:

[0038] PCA formula: Z = XW, where X is the original data matrix, W is the projection matrix after dimensionality reduction, and Z is the result of projection into the low-dimensional space;

[0039] t-SNE formula: Among them, P ij represents the similarity between data points i and j in high-dimensional space, σ is the Gaussian kernel parameter;

[0040] The multi-chart collaborative analysis formula: The system supports linkage between multiple charts. When the user determines specific data in a chart, other charts will be automatically updated. The linkage formula is as follows:

[0041] C i =f(G i , H i ), Among them, C i represents the content of the i-th graph, G i is the weighted sum of the data associated with the determined item, H i is the interactive response function of the chart, w k It is the influence weight of the determined item on other charts.

[0042] Preferably, the role-based access control and data sensitivity graded permission management in the permission management module are used to implement detailed permission control. The following is the algorithm formula in this module:

[0043] The authority matrix construction formula: The system constructs the user role, data sensitivity level and operation authority into the authority matrix M, and the formula is as follows:

[0044]

[0045] Where M(u, d, a) represents the permission status of user u to perform operation a on data d, 1 represents permission, and 0 represents no permission;

[0046] The dynamic adjustment formula of permissions: The user's permissions are dynamically adjusted according to the job role, data sensitivity and time period. Assuming that the permissions of user u are affected by the role change, the adjustment formula is:

[0047] P(u, d, t) = R(u)·S(d)·f(t), where P(u, d, t) is the permission value of user u to data d at time t, R(u) is the user's role permission weight, S(d) is the sensitivity level of data d, and f(t) is the time function;

[0048] The data field-level permission refinement formula is: For sensitive data, the system supports permission control refined to the field level. Assume that each field f in the data set D j With different sensitivity weights w j , then the user has a j The access rights A(fj)A(f_j)A(fj) are calculated by the following formula:

[0049] Among them, θ is the permission threshold. If user u has permission to field f j The authority value P(u,f j , t) exceeds the threshold θ, the user accesses the field.

[0050] Preferably, the sensitive data weighted access control formula: when accessing highly sensitive data, the system needs to automatically weight access rights according to the sensitivity of the data, and the formula is as follows:

[0051] Among them, S total is the total sensitivity of the dataset, w i is the sensitivity weight of data field i, A i is the access permission for field i;

[0052] The user behavior audit and anomaly detection formula: To ensure the security of permission management, the system monitors and audits the user's access behavior in real time. The formula is as follows:

[0053] Among them, B(u, t) is the behavior audit score of user u at time t, A i Indicates the user's i The access operation, P(u, f i , t) is the permission value, T i is the field f i Frequency of visits;

[0054] The access log record and security audit formula: When a user accesses data, the system will record the access log and generate a security audit score using the following formula:

[0055] Where L(u, d, t) is the audit score of user u when accessing data d at time t, α iis the field f i The audit weight, A i is an access operation, S(f i ) is the field f i The sensitivity weight of

[0056] The role switching and dynamic permission update formula: The user's role can be switched by time or task, and its permissions can be dynamically updated. The permission update formula after the role switching is:

[0057]

[0058] Among them, P′(u, d, t) is the permission value after role switching, γ is the dynamic update factor, ΔR(u) is the permission change value caused by role switching, T role is the decay coefficient of the role switching time;

[0059] The risk score and access control decision formula: The system conducts a risk assessment on the user's permission access and decides whether to allow access based on the user's risk score. The risk score formula is: Among them, R risk (u, d, t) is the risk score of user u for data d at time t, S(f i ) is the field f i The sensitivity of P(u,f i , t) is the user's authority value, and V(u, t) is the user's credibility score.

[0060] Preferably, the real-time monitoring and early warning module automatically adjusts the early warning threshold and triggers the early warning mechanism based on real-time data. The following is the algorithm formula:

[0061] The multi-dimensional data monitoring formula: The system monitors multiple data in real time and calculates the comprehensive status score S(t). The formula is as follows: Among them, S(t) is the comprehensive status score of the system at time t, w i is the weight of the i-th dimension, x i (t) is the actual value of the i-th dimension at time t;

[0062] The adaptive threshold adjustment formula: The warning threshold is not fixed, but is dynamically adjusted according to real-time data. The system automatically adjusts the threshold by calculating the fluctuation of historical data within the time. The formula is as follows: adaptive (t)=μ(t)+k·σ(t), where, T adaptive (t) is the warning threshold dynamically adjusted at time t, μ(t) is the mean of the data in the time window, σ(t) is the standard deviation of the data, and k is the sensitivity adjustment factor;

[0063] The anomaly detection and warning formula: The system uses an anomaly detection algorithm to determine whether the data exceeds the normal range and triggers an early warning. The anomaly detection adopts the z-score method, and the formula is as follows:

[0064] Among them, Z i (t) is the normalized anomaly score of the i-th dimension at time t, μ i and σ i is the mean and standard deviation of the historical data of the i-th dimension;

[0065] The dynamic adjustment formula of the warning sensitivity: The warning sensitivity is dynamically adjusted according to the system status and historical warning records. The formula is as follows: Where k(t) is the sensitivity factor dynamically adjusted at time t, k0 is the basic sensitivity, and R prev (t) is the occurrence frequency of historical warnings, and N is the standard warning frequency.

[0066] Preferably, the sales trend real-time prediction and early warning formula: In order to predict potential sales anomalies in advance, the system predicts future trends in real time through a time series model, and the formula is as follows:

[0067] P t+h =α·P t +(1-α)·P t-1 , where P t+h is the predicted sales value at h time steps after time t, α is the smoothing coefficient, P t and P t-1 is the sales value;

[0068] The inventory level warning formula: The inventory monitoring can calculate the inventory warning point based on the real-time sales data and the historical average sales speed. The formula is as follows: alert =I current -R s ·T r , where I alert It is the early warning point of inventory. current is the current inventory, R s is the historical average sales velocity, T r is the replenishment cycle;

[0069] The abnormal passenger flow fluctuation detection formula: The abnormal passenger flow fluctuation is detected by the following formula:

[0070] Among them, F anomaly (t) is the passenger flow anomaly score at time t, x traffic (t) is the current passenger flow, μ traffic and is the mean and standard deviation of historical passenger flow;

[0071] Multi-dimensional collaborative early warning formula: In order to comprehensively consider multi-dimensional abnormal situations, the system adopts the following collaborative early warning formula: Where W(t) is the comprehensive warning score at time t, λ i is the weight of the i-th dimension, Z i (t) is the normalized anomaly score of the i-th dimension;

[0072] The real-time response and adjustment formula: The system adjusts the response speed and threshold of the early warning mechanism in real time according to the number of early warnings actually triggered and the effect. The formula is as follows: Among them, ΔT(t) is the dynamic adjustment amplitude of the threshold, N actual (t) is the number of warnings actually triggered at time t, N expected is the expected number of warnings, and γ is the adjustment factor.

[0073] Preferably, the external data integration module is used to combine the external data source with the internal sales data to perform comprehensive analysis and prediction. The following is the algorithm formula:

[0074] The multi-source data fusion formula: The system needs to perform weighted fusion of internal sales data and external data. The fusion formula of multi-source data is:

[0075] Among them, D fused (t) is the comprehensive data at time t, is the i-th dimension of internal sales data, is the jth dimension of the external data, α i and β j is the weight of internal and external data;

[0076] The formula for dynamically adjusting the weight of external data: The weight of external data can be dynamically adjusted according to its relevance to the system. The formula for dynamically adjusting the weight is as follows:

[0077] Among them, β j (t) is the external data at time t The weight of Indicates the correlation coefficient between external data and internal data;

[0078] The competitor strategy analysis and impact prediction formula: The strategy of external competitors will have a direct impact on the sales of the system. The system predicts the impact of competitor strategies on sales through the following formula:

[0079] I competitor (t) = γ·(S competitor (t)-S baseline (t)), where Icompetitor (t) is the impact of competitor strategy on sales at time t, S competitor (t) is the competitor’s strategy score at time t, S baseline 9t) is the system's baseline sales forecast when there is no competitor influence, and γ is the influence coefficient.

[0080] Preferably, the sentiment analysis and sales impact prediction formula: the system integrates social media sentiment data, classifies user sentiment through natural language processing, and predicts the impact of sentiment changes on sales. The sentiment analysis impact prediction formula is:

[0081] Among them, I sentiment (t) is the comprehensive impact of sentiment analysis on sales, S k (t) is the sentiment intensity of the k-th sentiment, δ k is the weight of the impact of emotional intensity on sales;

[0082] The multi-data source time series forecasting formula: Combine external data and internal data for time series forecasting. The system predicts future sales through the weighted average method. The time series forecasting formula is: Among them, P t+h is the sales forecast at the next h time steps, and is the predicted value of internal and external data in the next h time steps, α i and β j is the weight of internal and external data;

[0083] The dynamic data fusion optimization formula: The system dynamically optimizes the data fusion weight by analyzing the synergy between external data and internal data in real time. The optimization formula is:

[0084] Among them, D fused (t) is the fused data at time t, D actual (t) is the actual sales data, α and β are the internal and external data weights that need to be optimized;

[0085] The external data anomaly detection formula: The system needs to detect the anomaly of the external data. The anomaly detection formula of the external data is: Among them, A external (t) is the anomaly score of the external data at time t, μ external and σ external are the mean and standard deviation of the external data.

[0086] The present invention provides a retail commodity sales data statistical analysis system based on big data. It has the following beneficial effects:

[0087] 1. The system of the present invention introduces an adaptive data cleaning mechanism, uses machine learning algorithms to dynamically identify and repair missing values ​​and outliers in the data, and adaptively adjusts the cleaning rules according to the real-time data pattern. The intelligent data cleaning system can process structured and unstructured data, and can dynamically respond to data fluctuations from different sources, significantly improving the accuracy and consistency of data analysis, reducing manual intervention, and reducing the risk of data cleaning errors.

[0088] 2. The present invention implements permission management down to the field level, and can dynamically adjust permissions based on user roles, data sensitivity, and time. Using a refined permission control mechanism, the system can strictly limit access rights to sensitive data, audit user behavior in real time, and automatically detect abnormal access to ensure data security, ensuring accurate data access by different roles in different situations, and improving data security.

[0089] 3. The present invention adopts an adaptive early warning mechanism to dynamically adjust the warning threshold and sensitivity according to historical data and real-time market changes, reduce false alarms and missed alarms, and can predict sales trends based on the data anomaly detection model in combination with time series and trigger early warnings to ensure that the system responds sensitively to market fluctuations, reduce invalid or delayed warnings, and improve response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a system diagram of the present invention. DETAILED DESCRIPTION

[0091] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0092] The present invention is described in detail below in conjunction with the accompanying drawings:

[0093] Example:

[0094] Please see attached Figure 1 , an embodiment of the present invention provides a retail commodity sales data statistical analysis system based on big data, including a data acquisition module, a data cleaning and preprocessing module, a data storage and management module, a data analysis and mining module, a report generation and visualization module, a rights management module, a real-time monitoring and early warning module, an external data integration module and a decision support module;

[0095] The data collection module is used to collect online and offline sales data in real time or batch from multiple heterogeneous data sources;

[0096] The data cleaning and preprocessing module is based on an adaptive data cleaning mechanism. It uses machine learning algorithms to automatically detect and correct outliers and missing values ​​in the data, and adaptively adjusts the cleaning rules through the following formula:

[0097] Among them, w i is the importance weight of the data point, x i is the value of the data point;

[0098] The data storage and management module adopts distributed database technology to support the storage and dynamic expansion of massive data. It also stores hot data in efficient storage media and cold data in low-cost media through the hot and cold data separation mechanism.

[0099] The data analysis and mining module combines multi-dimensional sales data and uses machine learning algorithms to predict sales trends, analyze customer behavior, optimize inventory, and segment the market. The data analysis results are evaluated using the following formula:

[0100] Among them, α t is the sales influencing factor within the time period, P t Sales forecast value;

[0101] The report generation and visualization module supports drag-and-drop report generation and displays relevant data through interactive visualization tools. Users can dynamically adjust the display dimensions.

[0102] The authority management module uses a role-based access control model to achieve precise authority management for different users, and the user's access rights can be adjusted dynamically;

[0103] The real-time monitoring and early warning module combines the dynamic threshold adjustment mechanism, uses time series analysis and machine learning models to monitor sales status in real time, and dynamically adjusts the early warning threshold according to market changes. The early warning formula is: T alert (t) = μ + k·σ,

[0104] Among them, μ is the moving average of sales data, σ is the standard deviation, and k is the warning sensitivity parameter;

[0105] The external data integration module is used to integrate external market data and conduct comprehensive analysis in combination with internal sales data;

[0106] The decision support module combines external data with internal sales data and uses scenario analysis and simulation technology to provide scalable strategy recommendations and intelligent sales decision support.

[0107] The benefit of the data collection module is that it can achieve seamless integration of online and offline sales data, help to fully understand customer behavior and sales dynamics, facilitate real-time analysis and decision-making, and improve the system's response speed to market dynamics; the benefit of the data cleaning and preprocessing module is that intelligent data cleaning reduces dependence on manual intervention and reduces the error rate of data cleaning; the benefit of the data storage and management module is that it supports the storage and management of massive data, has good scalability, and can adapt to the needs of rapid growth in data volume; the benefit of the data analysis and mining module is to optimize inventory management and reduce the risk of inventory backlog and out-of-stock; the benefit of the report generation and visualization module is to deeply explore the potential value of data, improve the readability and analysis ability of data, and help enterprises quickly grasp the operating status; the benefit of the authority management module is to improve the security and flexibility of the system and protect the security of sensitive data; the benefit of the real-time monitoring and early warning module is to reduce false alarms and missed alarms, ensure the accuracy and timeliness of early warnings, and prevent potential risks; the benefit of the external data integration module is to conduct comprehensive analysis in combination with internal sales data, improve the breadth and depth of data analysis, and optimize market response speed and strategic decision-making; the benefit of the decision support module is to reduce decision-making time, ensure that enterprises can respond quickly, and improve overall operational efficiency.

[0108] The data partitioning and sharding technology in the data storage and management module uses dynamic hash sharding and partitioning algorithms to perform data sharding and partitioning strategies. The formula is as follows:

[0109] Hash sharding formula: for any data record D i , and calculate its hash value through the following function: H(D i )=hash(K i )modN, where H(D i ) is the data record D i The hash value, K i is the primary key of the record, and N is the total number of shards in the system;

[0110] Consistent hash ring partition formula: used to map shards to storage nodes. Each storage node S j On the hash ring there is a hash value H(S j ), calculated by the following formula:

[0111] Among them, P j It is the storage node S j The partition position on the hash ring, C is the total length of the hash ring;

[0112] Data dynamic partition adjustment formula: When the amount of data increases, the system can dynamically adjust the sharding and partitioning. The adjustment formula is as follows:

[0113] Where N′ is the new number of shards, D t is the total amount of data stored, S max is the storage capacity allowed for each shard;

[0114] Node load balancing formula: To maintain a balanced load on storage nodes, calculate the load balancing coefficient L of each node. j :

[0115] Among them, W(D i ) is the data record D i The weight of is the indicator function.

[0116] Hash sharding is used to evenly distribute data to different shards to improve data access efficiency; consistent hash rings require data migration when adding or removing storage nodes to ensure system scalability; dynamic partition adjustment helps the system automatically adapt to changes in data volume and ensures that the number of shards can be dynamically increased by the data volume; the load balancing formula ensures that the storage nodes are evenly loaded and avoids load imbalance between nodes.

[0117] The report generation and visualization module generates reports through dynamic visualization of multidimensional data and natural language query. The algorithm formula is as follows:

[0118] Multidimensional data mapping formula: For a multidimensional data set D, it is necessary to map the multidimensional data into a two-dimensional or three-dimensional visualization chart. Assuming that the data has n dimensions, the formula is as follows:

[0119] Among them, V i represents the projection value of the i-th data in the visualization space, w k is the weight of the kth dimension, D ik Represents the value of data i in the kth dimension;

[0120] Natural language query parsing formula: The report generation module supports natural language queries. Users can dynamically generate reports by inputting natural language. The natural language query parsing algorithm calculates the matching query template using the following formula: Among them, Q t is the optimal matching query template, λ j is the weight, N j is the jth word of the natural language input, T j is the corresponding query variable in the template, similarity(N j , T j ) represents the similarity calculation between words;

[0121] Chart automatic layout optimization formula: If the data visualization module generates multiple charts, the system will automatically optimize the chart layout to maximize the readability of information and space utilization. The layout optimization algorithm is based on the following formula: Among them, L opt is the optimal layout, A i The actual area of ​​the ith graph, S i Indicates the optimal area for graph display, R i is the proportion of data shown in the chart, and n is the total number of charts.

[0122] The multidimensional data mapping formula ensures that high-dimensional data can be reasonably displayed in two-dimensional or three-dimensional space. By adjusting the dimension weights, users can flexibly adjust the display content of the visualization chart to highlight the key business dimensions; the natural language query parsing formula enables users to quickly generate reports through natural language, simplify the data query process, and improve the efficiency of report generation; the chart automatic layout optimization formula intelligently optimizes the display space of multiple charts to ensure that the charts match the importance of their information, thereby maximizing the space utilization and readability of the chart display.

[0123] Data focus and interactive zoom formula: In dynamic visualization, users interact to determine part of the data to display. The formula for zooming display is as follows: Where Z represents the scaling factor, x is the focal data value determined by the user, x0 is the median of the data, and α is a parameter that controls the steepness of the scaling curve;

[0124] Time series trend prediction and visualization formula: To display the time series trend of sales data, the system predicts future trends through the following formula and dynamically generates trend charts:

[0125] Among them, P t+h is the predicted value for the next h time steps, P t is the actual value at the time point, X t-i is the data input at the previous time point, β i is the regression coefficient;

[0126] High-dimensional data dimensionality reduction and heat map generation formula: For high-dimensional data visualization, principal component analysis or t-SNE algorithm is used for dimensionality reduction. The formula is as follows:

[0127] PCA formula: Z = XW, where X is the original data matrix, W is the projection matrix after dimensionality reduction, and Z is the result of projection into the low-dimensional space;

[0128] t-SNE formula: Among them, P ij represents the similarity between data points i and j in high-dimensional space, σ is the Gaussian kernel parameter;

[0129] Multi-chart collaborative analysis formula: The system supports linkage between multiple charts. When the user determines specific data in a chart, other charts will be automatically updated. The linkage formula is as follows:

[0130] C i =f(G i , H i ), Among them, C i represents the content of the i-th graph, G i is the weighted sum of the data associated with the determined item, H i is the interactive response function of the chart, w k It is the influence weight of the determined item on other charts.

[0131] The data focus and interactive zoom formulas ensure that users can flexibly zoom in on key data in visualization, improving the flexibility and operability of data display; the time series trend prediction and visualization formula uses time series analysis to generate future sales trend charts to help users understand future sales dynamics; the high-dimensional data dimensionality reduction and heat map generation formula is used to map complex high-dimensional data to low-dimensional space, generate heat maps and other visual charts, and help users understand the internal structure and clustering patterns of the data; the multi-chart collaborative analysis formula uses a multi-chart linkage mechanism to ensure that data in different charts can be updated synchronously during interactive operations, providing stronger interactivity and data analysis depth.

[0132] The role-based access control and data sensitivity classification permission management in the permission management module are used to implement detailed permission control. The following is the algorithm formula in this module:

[0133] Permission matrix construction formula: The system constructs the permission matrix M based on user roles, data sensitivity levels, and operation permissions. The formula is as follows:

[0134]

[0135] Where M(u, d, a) represents the permission status of user u to perform operation a on data d, 1 represents permission, and 0 represents no permission;

[0136] Dynamic adjustment formula for permissions: User permissions are dynamically adjusted based on job roles, data sensitivity, and time periods. Assuming that user u's permissions are affected by role changes, the adjustment formula is:

[0137] P(u, d, t) = R(u)·S(d)·f(t), where P(u, d, t) is the permission value of user u to data d at time t, R(u) is the user's role permission weight, S(d) is the sensitivity level of data d, and f(t) is the time function;

[0138] Formula for refining permissions at the data field level: For sensitive data, the system supports permission control at the field level. Suppose that each field f in the data set D j With different sensitivity weights w j , then the user has a j The access rights A(fj)A(f_j)A(fj) are calculated by the following formula:

[0139] Among them, θ is the permission threshold. If user u has permission to field f j The authority value P(u,f j , t) exceeds the threshold θ, the user accesses the field.

[0140] The permission matrix construction formula ensures the basic definition of permissions, stores the user's permission status in matrix form, and quickly queries a user's permission for a certain operation, laying the foundation for system permission management; the permission dynamic adjustment formula realizes the automatic adjustment of permissions based on roles, data sensitivity and time, so that the system can flexibly manage user permissions according to different situations, prevent excessive or insufficient permissions, and improve data security and the rationality of access control; the permission refinement formula at the data field level further refines permission control to the field level, ensuring that users can access specific fields that match their permission levels. It is suitable for precise protection of sensitive data and enhances the system's privacy protection capabilities.

[0141] Sensitive data weighted access control formula: When accessing highly sensitive data, the system needs to automatically weight access control based on the sensitivity of the data. The formula is as follows:

[0142] Among them, S total is the total sensitivity of the dataset, w i is the sensitivity weight of data field i, A i is the access permission for field i;

[0143] User behavior audit and anomaly detection formula: To ensure the security of permission management, the system monitors and audits user access behavior in real time. The formula is as follows:

[0144] Among them, B(u, t) is the behavior audit score of user u at time t, A i Indicates the user's i The access operation, P(u, f i , t) is the permission value, T i is the field f i Frequency of visits;

[0145] Access log records and security audit formula: When a user accesses data, the system records the access log and generates a security audit score using the following formula:

[0146] Where L(u, d, t) is the audit score of user u when accessing data d at time t, α i is the field f i The audit weight, A i is an access operation, S(f i ) is the field f i The sensitivity weight of

[0147] Role switching and dynamic permission update formula: The user's role can be switched by time or task, and its permissions can be dynamically updated. The permission update formula after role switching is:

[0148]

[0149] Among them, P′(u, d, t) is the permission value after role switching, γ is the dynamic update factor, ΔR(u) is the permission change value caused by role switching, T role is the decay coefficient of the role switching time;

[0150] Risk scoring and access control decision formula: The system conducts risk assessment on user access permissions and decides whether to allow access based on the user's risk score. The risk scoring formula is: Among them, R risk (u, d, t) is the risk score of user u for data d at time t, S(f i ) is the field f i The sensitivity of P(u,f i , t) is the user's authority value, and V(u, t) is the user's credibility score.

[0151] The sensitive data weighted access control formula performs weighted control according to the sensitivity of the data, ensuring that the access rights to sensitive data can be managed in a refined manner to avoid unauthorized access; the user behavior audit and anomaly detection formula monitors the user's access behavior in real time, discovers potential abnormal operations through behavioral audit scores, triggers security alerts in a timely manner, and improves the transparency and security of data access; the access log recording and security audit formula records the user's access behavior and generates a security audit score to help the system evaluate the security risks of user access and ensure effective auditing of access behavior; the role switching and dynamic permission update formula dynamically updates the user's permissions according to the user's role changes, ensuring the flexibility of the system and the adaptability of user permissions, and preventing the risk of permissions being out of control after role switching; the risk scoring and access control decision formula calculates the user's risk score in real time, so that the system can accurately control access to sensitive data and ensure that credible, low-risk access requests are passed, thereby improving the security of the system.

[0152] The real-time monitoring and warning module automatically adjusts the warning threshold and triggers the warning mechanism based on real-time data. The following is the algorithm formula:

[0153] Multi-dimensional data monitoring formula: The system monitors multiple data in real time and calculates the comprehensive status score S(t). The formula is as follows: Among them, S(t) is the comprehensive status score of the system at time t, w i is the weight of the i-th dimension, x i (t) is the actual value of the i-th dimension at time t;

[0154] Adaptive threshold adjustment formula: The warning threshold is not fixed, but is dynamically adjusted according to real-time data. The system automatically adjusts the threshold by calculating the fluctuation of historical data within the time. The formula is as follows: T adaptive (t)=μ(t)+k·σ(t), where, T adaptive (t) is the warning threshold dynamically adjusted at time t, μ(t) is the mean of the data in the time window, σ(t) is the standard deviation of the data, and k is the sensitivity adjustment factor;

[0155] Anomaly detection and warning formula: The system uses an anomaly detection algorithm to determine whether the data exceeds the normal range and triggers an alert. Anomaly detection uses the z-score method. The formula is as follows:

[0156] Among them, Z i (t) is the normalized anomaly score of the i-th dimension at time t, μ i and σ i is the mean and standard deviation of the historical data of the i-th dimension;

[0157] Alert sensitivity dynamic adjustment formula: Alert sensitivity is dynamically adjusted according to system status and historical alert records. The formula is as follows: Where k(t) is the sensitivity factor dynamically adjusted at time t, k0 is the basic sensitivity, and R prev (t) is the occurrence frequency of historical warnings, and N is the standard warning frequency.

[0158] The multi-dimensional data monitoring formula helps the system integrate multi-dimensional data, evaluate the overall status of the system in real time, and ensure that the system can fully cover important monitoring indicators; the adaptive threshold adjustment formula dynamically adjusts the warning threshold to ensure that the system flexibly adapts to changes based on real-time data and historical fluctuations, reducing false alarms and missed reports; the anomaly detection and warning formula uses standardized anomaly scores to effectively detect anomalies in data and trigger warnings in a timely manner to ensure that the system can respond quickly to sudden risks; the warning sensitivity dynamic adjustment formula dynamically adjusts the warning sensitivity according to historical warning records to avoid excessive warnings or missed reports, ensuring that the system can reasonably adjust the response level under different conditions.

[0159] Sales trend real-time prediction and early warning formula: In order to predict potential sales anomalies in advance, the system uses a time series model to predict future trends in real time. The formula is as follows:

[0160] P t+h =α·P t +(1-α)·P t-1 , where P t+h is the predicted sales value at h time steps after time t, α is the smoothing coefficient, P t and P t-1 is the sales value;

[0161] Inventory level warning formula: Inventory monitoring can calculate the inventory warning point based on real-time sales data and historical average sales speed. The formula is as follows: alert =I current -R s ·T r , where I alert It is the early warning point of inventory. current is the current inventory, R s is the historical average sales velocity, T r is the replenishment cycle;

[0162] Passenger flow fluctuation abnormality detection formula: Abnormal fluctuations in passenger flow are detected by the following formula: Among them, F anomaly (t) is the passenger flow anomaly score at time t, x traffic (t) is the current passenger flow, μ traffic and is the mean and standard deviation of historical passenger flow;

[0163] Multi-dimensional collaborative early warning formula: In order to comprehensively consider multi-dimensional abnormal situations, the system adopts the following collaborative early warning formula: Where W(t) is the comprehensive warning score at time t, λ i is the weight of the i-th dimension, Z i (t) is the normalized anomaly score of the i-th dimension;

[0164] Real-time response and adjustment formula: The system adjusts the response speed and threshold of the early warning mechanism in real time according to the actual number of triggered early warnings and their effects. The formula is as follows:

[0165] Among them, ΔT(t) is the dynamic adjustment amplitude of the threshold, N actual (t) is the number of warnings actually triggered at time t, N expected is the expected number of warnings, and γ is the adjustment factor.

[0166] The real-time sales trend prediction and early warning formula predicts future sales trends through time series, helping the system to detect potential sales anomalies in advance and support better business decisions and plan adjustments; the inventory level early warning formula ensures that the system predicts inventory warning points based on real-time sales data and historical sales speeds, prevents insufficient or excessive inventory backlogs, and optimizes inventory management; the customer flow fluctuation anomaly detection formula can promptly detect abnormal fluctuations in customer flow, especially during specific events or promotions, to provide data support for the company's market response; the multi-dimensional collaborative early warning formula provides a more comprehensive early warning mechanism by integrating multi-dimensional abnormal situations, ensuring that the system can accurately identify complex risk signals; the real-time response and adjustment formula dynamically adjusts the response speed and threshold of the early warning mechanism, so that the system can flexibly respond to the actual warning situation and maintain the best warning effect.

[0167] The external data integration module is used to combine external data sources with internal sales data for comprehensive analysis and prediction. The following is the algorithm formula:

[0168] Multi-source data fusion formula: The system needs to perform weighted fusion of internal sales data and external data. The fusion formula for multi-source data is:

[0169] Among them, D fused (t) is the comprehensive data at time t, is the i-th dimension of internal sales data, is the jth dimension of the external data, α i and β j is the weight of internal and external data;

[0170] Dynamic adjustment formula for external data weight: The weight of external data can be dynamically adjusted according to its relevance to the system. The formula for dynamically adjusting the weight is as follows: Among them, β j (t) is the external data at time t The weight of Indicates the correlation coefficient between external data and internal data;

[0171] Competitor strategy analysis and impact prediction formula: The strategy of external competitors will have a direct impact on the sales of the system. The system predicts the impact of competitor strategies on sales through the following formula:

[0172] I competitor (t) = γ·(S competitor (t)-S baseline (t)), where I competitor (t) is the impact of competitor strategy on sales at time t, S competitor (t) is the competitor’s strategy score at time t, S baseline (t) is the baseline sales forecast of the system when there is no competitor influence, and γ is the influence coefficient.

[0173] The multi-source data fusion formula ensures that the system can integrate internal and external data to generate more comprehensive and accurate analysis results. The system can flexibly adjust the weights of internal and external data according to actual business needs to ensure that the fusion of data can reflect the true situation of the market; the external data weight dynamic adjustment formula analyzes the correlation between external and internal data, and the system can dynamically adjust the weight of external data to ensure that highly relevant data sources receive higher attention, thereby improving the precision of data fusion and the accuracy of analysis; the competitor strategy analysis and impact prediction formula analyzes the impact of competitors' strategy changes on their own sales, and the system can help companies predict market changes brought about by competitors' behavior in advance, improve the accuracy of response strategies, and enhance market competitiveness.

[0174] Sentiment analysis and sales impact prediction formula: The system integrates social media sentiment data, classifies user sentiment through natural language processing, and predicts the impact of sentiment changes on sales. The sentiment analysis impact prediction formula is:

[0175] Among them, I sentiment (t) is the comprehensive impact of sentiment analysis on sales, S k (t) is the sentiment intensity of the k-th sentiment, δ k is the weight of the impact of emotional intensity on sales;

[0176] Time series forecasting formula for multiple data sources: Combine external data and internal data for time series forecasting. The system predicts future sales through the weighted average method. The time series forecasting formula is: Among them, P t+h is the sales forecast at the next h time steps, and is the predicted value of internal and external data in the next h time steps, α i and β j is the weight of internal and external data;

[0177] Dynamic data fusion optimization formula: The system dynamically optimizes the data fusion weight by analyzing the synergy between external data and internal data in real time. The optimization formula is:

[0178] Among them, D fused (t) is the fused data at time t, D actual (t) is the actual sales data, α and β are the internal and external data weights that need to be optimized;

[0179] External data anomaly detection formula: The system needs to detect anomalies in external data. The anomaly detection formula for external data is: Among them, A external (t) is the anomaly score of the external data at time t, μ external and σ external are the mean and standard deviation of the external data.

[0180] The sentiment analysis and sales impact prediction formula quantifies the impact of user sentiment on sales, helps the system predict changes in market demand and customer feedback in advance, and optimizes sales and market strategies; the multi-data source time series prediction formula combines internal sales data and external market data, so that the system can accurately predict future sales trends and ensure that the impact of multi-dimensional factors is comprehensively considered; the dynamic data fusion optimization formula optimizes the weights of internal and external data in real time, so that the system can continuously improve the accuracy of data fusion, reduce prediction errors, and enhance the effectiveness of business decisions; the external data anomaly detection formula ensures that the system can detect and handle anomalies in external data in a timely manner, prevent abnormal data from affecting sales forecasts and market decisions, and ensure the reliability of data analysis.

[0181] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A retail commodity sales data statistical analysis system based on big data, characterized in that: It includes data acquisition module, data cleaning and preprocessing module, data storage and management module, data analysis and mining module, report generation and visualization module, authority management module, real-time monitoring and early warning module, external data integration module and decision support module; The data collection module is used to collect online and offline sales data in real time or in batches from multiple heterogeneous data sources; The data cleaning and preprocessing module is based on an adaptive data cleaning mechanism, using a machine learning algorithm to automatically detect and correct outliers and missing values ​​in the data, and adaptively adjusts the cleaning rules through the following formula: Among them, w i is the importance weight of the data point, x i is the value of the data point; The data storage and management module adopts distributed database technology to support the storage and dynamic expansion of massive data, and stores hot data in efficient storage media and cold data in low-cost media through a hot and cold data separation mechanism; The data analysis and mining module combines multi-dimensional sales data and uses machine learning algorithms to perform sales trend forecasting, customer behavior analysis, inventory optimization, and market segmentation. The data analysis results are evaluated using the following formula: Among them, α t is the sales influencing factor within the time period, P t Sales forecast value; The report generation and visualization module supports drag-and-drop report generation and displays relevant data through interactive visualization tools, and users can dynamically adjust the display dimensions; The authority management module implements accurate authority management for different users based on the role-based access control model, and the user's access rights can be adjusted dynamically; The real-time monitoring and early warning module combines the dynamic threshold adjustment mechanism, uses time series analysis and machine learning models to monitor the sales status in real time, and dynamically adjusts the early warning threshold according to market changes. The early warning formula is: alert (t) = μ + k·σ, Among them, μ is the moving average of sales data, σ is the standard deviation, and k is the warning sensitivity parameter; The external data integration module is used to integrate external market data and conduct comprehensive analysis in combination with internal sales data; The decision support module combines external data with internal sales data and uses situational analysis and simulation technology to provide scalable strategy recommendations and intelligent sales decision support.

2. A retail commodity sales data statistical analysis system based on big data according to claim 1, characterized in that: The data partitioning and sharding technology in the data storage and management module uses a dynamic hash sharding and partitioning algorithm to perform data sharding and partitioning strategies. The formula is as follows: Hash sharding formula: for any data record D i , and calculate its hash value through the following function: H(D i )=hash(K i )modN, where H(D i ) is the data record D i The hash value, K i is the primary key of the record, and N is the total number of shards in the system; Consistent hash ring partition formula: used to map shards to storage nodes. Each storage node S j On the hash ring there is a hash value H(S j ), calculated by the following formula: Among them, P j It is the storage node S j The partition position on the hash ring, C is the total length of the hash ring; Data dynamic partition adjustment formula: When the amount of data increases, the system can dynamically adjust the sharding and partitioning. The adjustment formula is as follows: Where N′ is the new number of shards, D t is the total amount of data stored, S max is the storage capacity allowed for each shard; Node load balancing formula: To maintain a balanced load on storage nodes, calculate the load balancing coefficient L of each node. j : Among them, W(D i ) is the data record D i The weight of is the indicator function.

3. The retail commodity sales data statistical analysis system based on big data according to claim 1, characterized in that: The report generation and visualization module generates reports through dynamic visualization of multidimensional data and natural language query. The algorithm formula is as follows: The multi-dimensional data mapping formula: For a multi-dimensional data set D, the multi-dimensional data needs to be mapped into a two-dimensional or three-dimensional visualization chart. Assuming that the data has n dimensions, the formula is as follows: Among them, V i represents the projection value of the i-th data in the visualization space, w k is the weight of the kth dimension, D ik Represents the value of data i in the kth dimension; The natural language query parsing formula: The report generation module supports natural language queries. Users dynamically generate reports by inputting natural language. The natural language query parsing algorithm calculates the matching query template through the following formula: Among them, Q t is the optimal matching query template, λ j is the weight, N j is the jth word of the natural language input, T j is the corresponding query variable in the template, similarity(N j , T j ) represents the similarity calculation between words; The automatic chart layout optimization formula: If the data visualization module generates multiple charts, the system will automatically optimize the chart layout to maximize the readability of information and space utilization. The layout optimization algorithm is based on the following formula: Among them, L opt is the optimal layout, A i The actual area of ​​the ith graph, S i Indicates the optimal area for graph display, R i is the proportion of data shown in the chart, and n is the total number of charts.

4. A retail commodity sales data statistical analysis system based on big data according to claim 3, characterized in that: The data focusing and interactive zooming formula: In dynamic visualization, users interactively determine part of the data to display. The formula for zooming display is as follows: Where Z represents the scaling factor, x is the focal data value determined by the user, x0 is the median of the data, and α is a parameter that controls the steepness of the scaling curve; The time series trend prediction and visualization formula: For the time series trend display of sales data, the system predicts future trends through the following formula and dynamically generates trend charts: Among them, P t+h is the predicted value for the next h time steps, P t is the actual value at the time point, X t-i is the data input at the previous time point, β i is the regression coefficient; The formula for high-dimensional data dimensionality reduction and heat map generation: For high-dimensional data visualization, principal component analysis or t-SNE algorithm is used for dimensionality reduction. The formula is as follows: PCA formula: Z = XW, where X is the original data matrix, W is the projection matrix after dimensionality reduction, and Z is the result of projection into the low-dimensional space; t-SNE formula: Among them, P ij represents the similarity between data points i and j in high-dimensional space, σ is the Gaussian kernel parameter; The multi-chart collaborative analysis formula: The system supports linkage between multiple charts. When the user determines specific data in a chart, other charts will be automatically updated. The linkage formula is as follows: C i =f(G i , H i ), Among them, C i represents the content of the i-th graph, G i is the weighted sum of the data associated with the determined item, H i is the interactive response function of the chart, w k It is the influence weight of the determined item on other charts.

5. The retail commodity sales data statistical analysis system based on big data according to claim 1, characterized in that: The role-based access control and data sensitivity graded permission management in the permission management module are used to implement detailed permission control. The following is the algorithm formula in this module: The authority matrix construction formula: The system constructs the user role, data sensitivity level and operation authority into the authority matrix M, and the formula is as follows: Where M(u, d, a) represents the permission status of user u to perform operation a on data d, 1 represents permission, and 0 represents no permission; The dynamic adjustment formula of permissions: The user's permissions are dynamically adjusted according to the job role, data sensitivity and time period. Assuming that the permissions of user u are affected by the role change, the adjustment formula is: P(u, d, t) = R(u)·S(d)·f(t), where P(u, d, t) is the permission value of user u to data d at time t, R(u) is the user's role permission weight, S(d) is the sensitivity level of data d, and f(t) is the time function; The data field-level permission refinement formula is: For sensitive data, the system supports permission control refined to the field level. Assume that each field f in the data set D j With different sensitivity weights w j , then the user has a j The access rights A(fj)A(f_j)A(fj) are calculated by the following formula: Among them, θ is the permission threshold. If user u has permission to field f j The authority value P(u,f j , t) exceeds the threshold θ, the user accesses the field.

6. A retail commodity sales data statistical analysis system based on big data according to claim 5, characterized in that: The sensitive data weighted access control formula: When accessing highly sensitive data, the system needs to automatically weight access control based on the sensitivity of the data. The formula is as follows: Among them, S total is the total sensitivity of the dataset, w i is the sensitivity weight of data field i, A i is the access permission for field i; The user behavior audit and anomaly detection formula: To ensure the security of permission management, the system monitors and audits the user's access behavior in real time. The formula is as follows: Among them, B(u, t) is the behavior audit score of user u at time t, A i Indicates the user's i The access operation, P(u, f i , t) is the permission value, T i is the field f i Frequency of visits; The access log record and security audit formula: When a user accesses data, the system will record the access log and generate a security audit score using the following formula: Where L(u, d, t) is the audit score of user u when accessing data d at time t, α i is the field f i The audit weight, A i is an access operation, S(f i ) is the field f i sensitivity weights; The role switching and dynamic permission update formula: The user's role can be switched by time or task, and its permissions can be dynamically updated. The permission update formula after the role switching is: Among them, P′(u, d, t) is the permission value after the role switch, γ is the dynamic update factor, ΔR(u is the permission change value caused by the role switch, T role is the decay coefficient of the role switching time; The risk score and access control decision formula: The system conducts a risk assessment on the user's permission access and decides whether to allow access based on the user's risk score. The risk score formula is: Among them, R risk (u, d, t) is the risk score of user u for data d at time t, S(f i ) is the field f i The sensitivity of P(u,f i , t) is the user's authority value, and V(u, t) is the user's credibility score.

7. The retail commodity sales data statistical analysis system based on big data according to claim 1, characterized in that: The real-time monitoring and warning module automatically adjusts the warning threshold and triggers the warning mechanism based on real-time data. The following is the algorithm formula: The multi-dimensional data monitoring formula: The system monitors multiple data in real time and calculates the comprehensive status score S(t). The formula is as follows: Among them, S(t) is the comprehensive status score of the system at time t, w i is the weight of the i-th dimension, x i (t) is the actual value of the i-th dimension at time t; The adaptive threshold adjustment formula: The warning threshold is not fixed, but is dynamically adjusted according to real-time data. The system automatically adjusts the threshold by calculating the fluctuation of historical data within the time. The formula is as follows: T adaptive (t)=μ(t)+k·σ(t), where, T adaptive (t) is the warning threshold dynamically adjusted at time t, μ(t) is the mean of the data in the time window, σ(t) is the standard deviation of the data, and k is the sensitivity adjustment factor; The anomaly detection and warning formula: The system uses an anomaly detection algorithm to determine whether the data exceeds the normal range and triggers an early warning. The anomaly detection adopts the z-score method, and the formula is as follows: Among them, Z i (t) is the normalized anomaly score of the i-th dimension at time t, μ i and σ i is the mean and standard deviation of the historical data of the i-th dimension; The dynamic adjustment formula of the warning sensitivity: The warning sensitivity is dynamically adjusted according to the system status and historical warning records. The formula is as follows: Where k(t) is the sensitivity factor dynamically adjusted at time t, k0 is the basic sensitivity, and R prev (t) is the occurrence frequency of historical warnings, and N is the standard warning frequency.

8. A retail commodity sales data statistical analysis system based on big data according to claim 7, characterized in that: The sales trend real-time prediction and early warning formula: In order to predict potential sales anomalies in advance, the system predicts future trends in real time through a time series model. The formula is as follows: P t+h =α·P t +(1-α)·P t-1 , where P t+h is the predicted sales value at h time steps after time t, α is the smoothing coefficient, P t and P t-1 is the sales value; The inventory level warning formula: The inventory monitoring can calculate the inventory warning point based on the real-time sales data and the historical average sales speed. The formula is as follows: alert =I current -R s ·T r , where I alert It is the early warning point of inventory. current is the current inventory, R s is the historical average sales velocity, T r is the replenishment cycle; The abnormal passenger flow fluctuation detection formula: The abnormal passenger flow fluctuation is detected by the following formula: Among them, F anomaly (t) is the passenger flow anomaly score at time t, x traffic (t) is the current passenger flow, μ traffic and is the mean and standard deviation of historical passenger flow; Multi-dimensional collaborative early warning formula: In order to comprehensively consider multi-dimensional abnormal situations, the system adopts the following collaborative early warning formula: Where W(t) is the comprehensive warning score at time t, λ i is the weight of the i-th dimension, Z i (t) is the normalized anomaly score of the i-th dimension; The real-time response and adjustment formula: The system adjusts the response speed and threshold of the early warning mechanism in real time according to the number of early warnings actually triggered and the effect. The formula is as follows: Among them, ΔT(t) is the dynamic adjustment amplitude of the threshold, N actual (t is the number of warnings actually triggered at time t, N expected is the expected number of warnings, and γ is the adjustment factor.

9. The retail commodity sales data statistical analysis system based on big data according to claim 1, characterized in that: The external data integration module is used to combine external data sources with internal sales data to perform comprehensive analysis and prediction. The following is the algorithm formula: The multi-source data fusion formula: The system needs to perform weighted fusion of internal sales data and external data. The fusion formula of multi-source data is: Among them, D fused (t) is the comprehensive data at time t, is the i-th dimension of internal sales data, is the jth dimension of the external data, α i and β j is the weight of internal and external data; The formula for dynamically adjusting the weight of external data: The weight of external data can be dynamically adjusted according to its relevance to the system. The formula for dynamically adjusting the weight is as follows: Among them, β j (t) is the external data at time t The weight of Indicates the correlation coefficient between external data and internal data; The competitor strategy analysis and impact prediction formula: The strategy of external competitors will have a direct impact on the sales of the system. The system predicts the impact of competitor strategies on sales through the following formula: I competitor (t) = γ·(S competitor (t)-S baseline (t)), where I competitor (t) is the impact of competitor strategy on sales at time t, S competitor (t) is the competitor’s strategy score at time t, S baseline (t) is the baseline sales forecast of the system when there is no competitor influence, and γ is the influence coefficient.

10. A retail commodity sales data statistical analysis system based on big data according to claim 9, characterized in that: The sentiment analysis and sales impact prediction formula: The system integrates social media sentiment data, classifies user sentiment through natural language processing, and predicts the impact of sentiment changes on sales. The sentiment analysis impact prediction formula is: Among them, I sentiment (t) is the comprehensive impact of sentiment analysis on sales, S k (t) is the sentiment intensity of the k-th sentiment, δ k is the weight of the impact of emotional intensity on sales; The multi-data source time series forecasting formula: Combine external data and internal data for time series forecasting. The system predicts future sales through the weighted average method. The time series forecasting formula is: Among them, P t+h is the sales forecast at the next h time steps, and is the predicted value of internal and external data in the next h time steps, α i and β j is the weight of internal and external data; The dynamic data fusion optimization formula: The system dynamically optimizes the data fusion weight by analyzing the synergy between external data and internal data in real time. The optimization formula is: Among them, D fused (t) is the fused data at time t, D actual (t) is the actual sales data, α and β are the internal and external data weights that need to be optimized; The external data anomaly detection formula: The system needs to detect the anomaly of the external data. The anomaly detection formula of the external data is: Among them, A external (t) is the anomaly score of the external data at time t, μ external and σ external are the mean and standard deviation of the external data.

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