A logistics network point customer information intelligent management method and system

Abstract

This invention discloses an intelligent management method and system for customer information at logistics outlets, relating to the field of Internet of Things (IoT) technology. The method includes: acquiring basic customer information, business information, and behavioral information from multiple data sources; cleaning, deduplicating, formatting, and extracting features from the raw data; constructing multi-dimensional customer profiles based on machine learning algorithms; performing hierarchical analysis of the customer profile data to identify high-value customers, potential customers, and ordinary customers; generating targeted service strategy suggestions; and visually displaying the customer profiles, hierarchical results, and service strategy suggestions. This invention achieves automated collection, intelligent analysis, and visualization of customer information at logistics outlets, accurately characterizing customer groups, identifying high-value customers, optimizing service resource allocation, and improving service efficiency and customer satisfaction.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a method and system for intelligent management of customer information at logistics outlets. Background Technology

[0002] With the rapid development of IoT technology, the informatization and intelligentization levels of the logistics industry are continuously improving. As a key node connecting express delivery companies and end customers, the efficiency and accuracy of customer information management at logistics outlets directly affect the overall service quality. Traditional logistics outlets rely on manual recording and basic database queries for customer information management, which is insufficient to cope with the ever-growing user base and diversified service demands. Especially against the backdrop of the rapid development of e-commerce logistics, the daily volume of express deliveries, customer pickup frequency, and delivery preferences processed by outlets are growing exponentially. This places higher demands on the data processing capabilities, real-time analysis capabilities, and personalized service capabilities of customer information management systems.

[0003] However, existing intelligent customer information management systems struggle to meet the diverse business needs of logistics outlets. Firstly, current technologies primarily focus on general customer behavior anomaly detection, failing to optimize for the unique business scenarios of the logistics industry. Key functions tailored to logistics scenarios, such as real-time collection of express delivery records, logistics route analysis, and customer pickup preference identification, are lacking, resulting in the system's inability to provide accurate customer profiles and business support for logistics outlets. Secondly, existing systems lack deep integration with logistics business systems, hindering automated collection, intelligent analysis, and convenient management of customer information, leading to information silos and data fragmentation. Furthermore, existing technologies fail to fully consider the complexity of the customer base at logistics outlets, including the differentiated needs of individual customers, corporate customers, and e-commerce merchants, making it impossible to achieve targeted customer segmentation management and personalized service strategy configuration.

[0004] Therefore, this invention proposes a method and system for intelligent management of customer information at logistics outlets. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent management method and system for customer information of logistics outlets, so as to solve the problem that the existing technology cannot meet the needs of customer information management of logistics outlets.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] According to a first aspect of the present invention, a smart management system for customer information of logistics outlets is provided, comprising:

[0008] Data acquisition device, data preprocessing device, customer profile building device, intelligent analysis device, personalized service recommendation device, and data visualization device;

[0009] The data acquisition device is used to obtain basic information, business information, and behavioral information of customers at logistics outlets from multiple data sources;

[0010] The data preprocessing unit is used to clean, deduplicatize, convert formats, and extract features from the collected raw data to generate standardized data resources;

[0011] The customer profiling building device is used to analyze customer attribute characteristics, behavioral characteristics and preference characteristics based on preprocessed data resources and machine learning algorithms to build multi-dimensional customer profiles.

[0012] The intelligent analysis device is used to perform stratified analysis on customer profile data, identify high-value customers, potential customers and ordinary customers, and generate corresponding customer stratification results;

[0013] The personalized service recommendation device is used to generate targeted service strategy suggestions based on customer profiles and segmentation results;

[0014] Data visualization devices are used to present customer profiles, segmentation results, and service strategy recommendations to logistics network managers in a visual format.

[0015] According to a second aspect of the present invention, a method for intelligent management of customer information at logistics outlets is provided, comprising:

[0016] Step S1: Obtain basic information, business information, and behavioral information of customers at logistics outlets from multiple data sources to form raw data;

[0017] Step S2 involves cleaning, deduplication, format conversion, and feature extraction of the raw data to generate standardized data resources.

[0018] Step S3: Based on standardized data resources, construct multi-dimensional customer profiles using machine learning algorithms;

[0019] Step S4: Perform stratified analysis on the customer profile data to identify high-value customers, potential customers, and ordinary customers, and generate customer stratification results.

[0020] Step S5: Generate targeted service strategy recommendations based on customer profiles and customer segmentation results;

[0021] Step S6: Present customer profiles, customer segmentation results, and service strategy recommendations to logistics network managers in a visual format.

[0022] The data acquisition device includes a personal customer information acquisition module, a corporate customer information acquisition module, and an e-commerce customer information acquisition module.

[0023] The Personal Customer Information Collection module is configured to collect personal customers' names, contact information, shipping addresses, and identity verification information.

[0024] The enterprise customer information collection module is configured to collect enterprise customer information such as company name, company qualifications, contact information, shipping preferences, and billing information.

[0025] The e-commerce customer information collection module is configured to collect information such as store information, product types, shipping frequency, return rate, and customer reviews from e-commerce merchants.

[0026] The data preprocessing unit includes a data cleaning unit, a data deduplication unit, a format conversion unit, and a feature extraction unit.

[0027] The data cleaning unit is configured to identify and process missing values, outliers, and noisy data in the raw data acquired by the data acquisition device.

[0028] The data deduplication unit configuration is used to detect and merge duplicate customer information records, ensuring the uniqueness of customer data.

[0029] The format conversion unit is configured to convert heterogeneous data collected from different data sources into a unified format, which facilitates subsequent processing by machine learning algorithms.

[0030] The feature extraction unit is configured to extract key customer features from standardized data, including pickup frequency features, shipment weight features, delivery time features, and address distribution features.

[0031] The customer profiling device is based on a preset machine learning algorithm. By analyzing the customer's attribute characteristics, behavioral characteristics, and preference characteristics, it constructs a comprehensive customer profile that includes static attribute profiles, dynamic behavioral profiles, and preference prediction profiles.

[0032] Static attribute profiles are used to record customers' basic information and long-term stable attribute characteristics, including customer type, customer level, and contact information.

[0033] Dynamic behavioral profiles are used to record customers' real-time behavioral data and dynamically changing behavioral characteristics, including the average number of packages sent and received per month, delivery route preferences, and pickup time preferences.

[0034] Preference prediction profiles are used to predict customers’ potential needs and service preferences based on historical behavioral data, including the types of value-added services and promotional information that they may be interested in.

[0035] The intelligent analysis device includes a customer segmentation analysis unit, a value assessment unit, and a demand forecasting unit.

[0036] The customer segmentation analysis unit is configured to classify customer profile data based on preset segmentation criteria and identify customer groups of different values.

[0037] The value assessment unit configuration is used to comprehensively evaluate a customer's business contribution, cooperation stability, and growth potential, and generate a customer value score.

[0038] The demand forecasting unit is configured to predict customers’ future service needs based on customer profile data and historical service records.

[0039] The personalized service recommendation device generates service strategy suggestions based on customer segmentation results and value scores, including service priority configuration, resource allocation suggestions, and marketing strategy recommendations.

[0040] Service priority configuration is used to set differentiated service response priorities based on customer value levels, ensuring that high-value customers receive faster service responses.

[0041] Resource allocation recommendations are used to optimize the resource allocation scheme of logistics outlets based on customer distribution characteristics and service demand forecasts.

[0042] Marketing strategy recommendations are used to push targeted promotional information and service upgrade suggestions based on customer preferences.

[0043] The data visualization device includes a customer distribution map module, a hierarchical result chart module, a service strategy dashboard module, a branch archive display module, and a branch operation monitoring module.

[0044] The customer distribution map module is configured to display the distribution of customers within the service area of ​​the logistics network in the form of a geographical distribution map.

[0045] The layered results chart module is configured to display the percentage of each type of customer and their value contribution in the form of statistical charts such as pie charts and bar charts.

[0046] The service strategy dashboard module is configured to display service strategy recommendations and implementation results for different customer groups in a dashboard format.

[0047] Furthermore, to integrate customer information management with the actual operational status of logistics outlets, the data acquisition device is also used to obtain basic information on logistics outlets and operational status information from the outlet management system and the logistics business system. The basic information on logistics outlets includes at least one of the following: outlet identification, outlet type, operational status, responsible person information, service scope, and business hours. The operational status information includes at least one of the following: number of received items, number of sent items, number of dispatched items, number of abnormal items, number of overdue items, number of delayed items, and number of complaints.

[0048] Based on this, the data preprocessing device establishes the association between customers and target logistics outlets according to the matching results of customer addresses and service areas, the service outlets corresponding to customers' historical business records, and the service frequency within a preset statistical period, and generates customer-outlet association identifiers, thereby accurately associating each customer with the logistics outlet that actually provides services to them.

[0049] Based on the established relationship between customers and target logistics outlets, the personalized service recommendation device further compares the customer demand forecast output by the demand forecasting unit with the service capacity threshold of the target logistics outlets. It also generates early warning information, personnel scheduling suggestions, vehicle dispatching suggestions, or customer diversion suggestions by combining the number of abnormal items, overdue items, delayed items, or complaints of the target logistics outlets. This allows the service strategy to be dynamically adjusted according to the actual operating status of the outlets.

[0050] To facilitate managers' intuitive understanding of the relationship between branch operations and customer management, the branch profile display module is used to display the basic profile information of the target logistics branch associated with the customer; the branch operation monitoring module is used to display the business volume trend and abnormal indicators of the target logistics branch within a preset statistical period in the form of line charts, indicator cards or lists, and supports drill-down from customer details to the corresponding target logistics branch's operation details.

[0051] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0052] The intelligent management method and system for customer information of logistics outlets provided by this invention, by executing corresponding processing steps and setting up a data acquisition device, a data preprocessing device, a customer profile building device, an intelligent analysis device, a personalized service recommendation device, and a data visualization device, realizes the automated collection, intelligent analysis, and visual display of customer information of logistics outlets. It effectively solves the problems of insufficient functional targeting, lack of deep integration with logistics business systems, and inability to meet the personalized needs of the logistics industry in the prior art.

[0053] This invention constructs multi-dimensional customer profiles through a customer profiling device, accurately depicting the characteristics of customer groups at logistics outlets and providing a data foundation for personalized services; it achieves customer segmentation and value assessment through an intelligent analysis device, helping logistics outlets identify high-value and potential customers and optimize service resource allocation; and it generates targeted service strategy suggestions through a personalized service recommendation device, improving the service efficiency and customer satisfaction of logistics outlets.

[0054] This invention also establishes a relationship between customers and target logistics outlets, and conducts joint analysis of customer demand forecasts with the actual service capabilities and abnormal operating status of target logistics outlets. This enables the generated service strategies to be dynamically adjusted according to the business load and abnormal indicators of the outlets, thereby reducing the risk of timeouts and delays during peak business periods and improving the timeliness and accuracy of outlet resource allocation. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the overall technical solution architecture of the intelligent management system for customer information of logistics outlets provided by the present invention;

[0056] Figure 2 This is a schematic diagram illustrating the core principle framework of the customer profile building device in this invention, which builds multi-dimensional customer profiles based on machine learning algorithms.

[0057] Figure 3 This is a data flow logic framework diagram of data acquisition, data preprocessing, and customer profile construction in this invention;

[0058] Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow of the intelligent analysis device in this invention for hierarchical analysis, value assessment and demand prediction of customer profile data.

[0059] Figure 5 This is a flowchart illustrating the process of the personalized service recommendation device in this invention generating service strategy suggestions based on customer segmentation results and displaying them through a data visualization device.

[0060] Figure 6 This is a schematic diagram of the intelligent management method for customer information of logistics outlets provided by the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Example 1

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0064] The intelligent customer information management method for logistics outlets provided by this invention corresponds one-to-one with each device in the system described below: Figure 6Steps S1 to S6 of the method shown are respectively executed by the data acquisition device, data preprocessing device, customer profile building device, intelligent analysis device, personalized service recommendation device, and data visualization device in the system. To avoid repetition, the following will first combine... Figures 1 to 5 The specific implementation methods of each device in the system are described in detail, and the same content applies to the explanation of the corresponding method steps.

[0065] like Figure 1 As shown, a smart customer information management system for logistics outlets includes a data acquisition device, a data preprocessing device, a customer profile building device, an intelligent analysis device, a personalized service recommendation device, and a data visualization device. The devices work collaboratively according to the data processing sequence: the data acquisition device provides raw data to the data preprocessing device; the data preprocessing device provides processed data to the customer profile building device; the customer profile building device provides customer profile data to the intelligent analysis device; the intelligent analysis device provides customer segmentation results to the personalized service recommendation device and the data visualization device; and the personalized service recommendation device provides service strategy suggestions to the data visualization device. The data visualization device can obtain customer profile data from the customer profile database and display it in conjunction with the customer segmentation results and service strategy suggestions.

[0066] like Figure 3 As shown, the data acquisition device is used to obtain basic information, business information, and behavioral information of customers at logistics network points from multiple data sources, and sends the collected raw data to the data preprocessing device. The data acquisition device interacts with external business systems through standardized data access protocols, supports multiple data access methods such as RESTful API interfaces, message queue subscriptions, and database CDC change data capture, and automatically triggers access to and data retrieval operations from various data sources according to preset collection cycles and collection strategies.

[0067] The data acquisition device includes modules for collecting personal customer information, enterprise customer information, and e-commerce customer information. These modules are deployed on independent computing nodes, and a load balancer is used to allocate and schedule data acquisition tasks.

[0068] The personal customer information collection module is used to collect the name, contact information, delivery address, and identity verification information of individual customers. This module connects to the customer profile database of the logistics network's business system to obtain basic information submitted by individual customers in real time during business processes such as order placement, package pickup and signing, and package delivery appointment. The module is also equipped with an OCR recognition interface to automatically extract identity information from ID card photos and cross-verify it with the information submitted by the customer. After initial verification, the data collected by the personal customer information collection module is written to a data cache in a structured data format, awaiting further processing.

[0069] The enterprise customer information collection module is used to collect enterprise customer information, including company name, qualifications, contact information, shipping preferences, and payment terms. This module interfaces with the logistics network's enterprise customer management system to obtain core data such as business registration information, cooperation qualification certifications, and payment settlement records. The module also includes a contract management interface to parse the cooperation agreements signed between enterprise customers and logistics networks, automatically extracting key terms such as service levels, billing rules, and payment terms. The collected data includes structured data fields and unstructured agreement documents. The module is equipped with a document parsing engine to extract key information from the agreement documents and convert it into structured data.

[0070] The e-commerce customer information collection module is used to collect information on e-commerce merchants' store information, product types, shipping frequency, return rates, and customer reviews. It connects to the open interfaces of mainstream e-commerce platforms to obtain store operation data from e-commerce merchants with business partnerships with the logistics network. The module is equipped with a data crawling component for automated collection of publicly available store information, product categories, and customer review data. It also has an API interface for connecting to the e-commerce platform's backend system to obtain authorized order data, shipping records, and return processing information from merchants. Given the typically large volume of data collected, the module is equipped with a streaming data processing pipeline for real-time preprocessing and filtering of massive amounts of data.

[0071] In this embodiment, the data acquisition device also establishes a data connection with the branch management system and the logistics business system to obtain basic branch profile information and branch operation status information. Basic branch profile information includes at least one of the following: branch identifier, branch name, branch type, region, operation status, responsible person information, contact number, detailed address, service area, and business hours. Branch operation status information includes at least one of the following: number of received packages, number of sent packages, number of dispatched packages, number of packages delivered, and number of abnormal packages, number of overdue packages, number of delayed packages, and number of complaints, calculated by hour, day, week, or month. Data involving personal contact information is anonymized or access-controlled before entering the analysis process to prevent the complete information from being directly displayed on unauthorized interfaces.

[0072] The data preprocessing unit cleans, deduplicates, converts formats, and extracts features from the collected raw data to generate standardized data resources. The processed data is then sent to the customer profiling unit. The data preprocessing unit maintains a data quality rule engine that automatically detects and labels the input raw data according to preset data quality standards. A data lineage tracking module records the source, transformation process, and processing logic of each data field, ensuring the traceability of the data processing process.

[0073] The data preprocessing unit includes a data cleaning unit, a data deduplication unit, a format conversion unit, and a feature extraction unit. These units are connected in series to form a complete data preprocessing pipeline.

[0074] The data cleaning unit is used to identify and process missing values, outliers, and noisy data in the raw data acquired by the data acquisition device. Internally, the data cleaning unit is equipped with a missing value detection algorithm. This algorithm is configured to traverse each field in the dataset, and when a field value is detected as empty, null, or a default value, it automatically records the missing location and triggers a data imputation strategy. The data cleaning unit also has an outlier identification model, which identifies abnormal records in the data based on a combination of statistical methods and business rules. The data cleaning unit has a standardized data imputation strategy library, employing different imputation methods for different types of missing data, including mean imputation, interpolation imputation, and business default value imputation. For identified noisy data, the data cleaning unit uses smoothing or removal strategies to ensure data usability.

[0075] The data deduplication unit detects and merges duplicate customer information records to ensure the uniqueness of customer data. Internally, it employs a deduplication engine combining fuzzy matching and exact matching algorithms. First, the unit uses an exact matching algorithm to quickly search for completely duplicate records based on key identifier fields such as customer ID number, mobile phone number, and enterprise unified social credit code. For records not identified by exact matching, the unit uses a fuzzy matching algorithm, calculating similarity based on fields such as name, address, and contact information to identify duplicate records that may belong to the same customer. The unit includes a deduplication strategy configuration interface, allowing logistics network managers to adjust matching thresholds and merging rules according to business needs. When merging duplicate records, the unit uses a priority strategy, retaining records with more complete information and more recent updates.

[0076] The format conversion unit is used to convert heterogeneous data collected from different data sources into a unified format, facilitating subsequent processing by machine learning algorithms. Internally, the unit includes a data format standardization engine configured to convert data from various data sources according to a pre-defined standardization model. The unit supports multiple input data formats, including JSON, XML, CSV, and database tables, and outputs a standardized Parquet or ORC columnar storage format after conversion. The unit also includes a data schema management module to maintain the structure definition of the unified data model and provides version control and evolution management functions. During the format conversion process, the unit also performs basic data standardization operations such as data type conversion, encoding standardization, and date format standardization.

[0077] The feature extraction unit extracts key customer features from standardized data, including pickup frequency, shipment weight, delivery time, and address distribution. Internally, the feature extraction unit has a feature engineering engine configured to derive and calculate statistical features from raw data based on business logic and algorithm requirements. For pickup frequency, the unit calculates statistical indicators such as daily, weekly, and monthly average pickup frequency by counting the number of pickups made by customers within a defined time period. For shipment weight, the unit calculates average shipment weight, total shipment weight, and weight distribution by aggregating the weight data from each shipment. For delivery time, the unit identifies preferred pickup times, such as morning, afternoon, and evening, by analyzing the distribution of customer pickup times. For address distribution, the unit extracts address tags such as administrative division, business district distribution, and building type by parsing customer delivery address information. The extracted feature data is stored in vector format for efficient processing by subsequent machine learning algorithms.

[0078] Furthermore, the feature extraction unit calculates the branch load rate and anomaly rate based on the branch operation status information. The branch load rate is the ratio of the predicted customer demand within a preset forecast period to the available processing capacity of the target logistics branch; the anomaly rate is the ratio of the sum of the number of abnormal items, overdue items, and delayed items within a preset statistical period to the total business volume within that statistical period. The branch load rate and anomaly rate, as dynamic environmental features, are input into the intelligent analysis device and personalized service recommendation device along with customer attribute features, behavioral features, and preference features.

[0079] In addition, the data preprocessing unit is equipped with a customer-location association unit to establish the association between customers and target logistics outlets. For any customer and candidate logistics outlet, the customer-location association unit calculates the address coverage matching value, historical business affiliation value, and recent service frequency value, and calculates the association score R according to preset weights.

[0080]

[0081] Where A represents the matching value of the customer's address being within the service area of ​​the candidate logistics outlet, B represents the proportion of the customer's historical business records belonging to the candidate logistics outlet, and F represents the normalized value of the frequency of service provided to the customer by the candidate logistics outlet within the preset statistical period. As weight and The candidate logistics outlet with the highest association score and reaching the association threshold is identified as the target logistics outlet, and a customer-outlet association identifier is generated; when there is no candidate logistics outlet that reaches the association threshold, the customer is marked as pending allocation.

[0082] like Figure 2 As shown, the customer profiling device is used to analyze customer attribute characteristics, behavioral characteristics, and preference characteristics based on preprocessed data resources using machine learning algorithms to construct multi-dimensional customer profiles, and then send the constructed customer profile data to an intelligent analysis device. The customer profiling device is internally equipped with a model management module to manage various machine learning models used in the profile construction process, such as clustering models, classification models, and recommendation models. It supports iterative updates and A / B testing of the models, and establishes a connection with an external feature storage system to read the feature data output by the feature extraction unit in real time, writing the completed customer profile data into the profile database.

[0083] The customer profiling device, based on a pre-defined machine learning algorithm, analyzes customer attributes, behavioral characteristics, and preference characteristics to construct a comprehensive customer profile, including static attribute profiles, dynamic behavioral profiles, and preference prediction profiles. The device employs a multi-model fusion architecture, comprehensively utilizing traditional machine learning algorithms and deep learning algorithms to achieve comprehensive customer profile construction.

[0084] Static attribute profiles are used to record customers' basic information and long-term stable attribute characteristics, including customer type, customer level, and contact information. The construction process of a static attribute profile first aggregates and statistically analyzes customer attribute characteristic data to extract basic customer attribute information. Static attribute profiles include attribute fields such as age range, occupation type, and residential area for individual customers, and attribute fields such as industry type, company size, and business scope for corporate customers. The customer profile building device generates a unique static attribute profile identifier for each customer and establishes a mapping between it and the customer's basic information.

[0085] Dynamic behavioral profiles are used to record customers' real-time behavioral data and dynamically changing behavioral characteristics, including average monthly number of packages sent and received, delivery route preferences, and pickup time preferences. The construction of dynamic behavioral profiles employs a sliding window algorithm to perform time-series analysis on customer behavioral data and extract dynamic changes in behavioral patterns. Dynamic behavioral profiles include customer trends in package sending and receiving activity, regularity in sending times, and migration of frequently used delivery addresses. Dynamic behavioral profiles are updated in real-time using a streaming computing framework to ensure that the profile data promptly reflects the latest behavioral status of customers.

[0086] Preference prediction profiles are used to predict customers' potential needs and service preferences based on historical behavioral data, including the types of value-added services and promotional information they may be interested in. The construction of preference prediction profiles employs collaborative filtering algorithms and deep neural network models to analyze customer behavioral sequences and feature similarities to predict potential customer preferences. Preference prediction profiles include customer interest ratings for different value-added services, predicted response probabilities to various promotional activities, and assessments of acceptance of service upgrades. Preference prediction profiles combine batch computation with real-time prediction to balance computational overhead and prediction timeliness.

[0087] like Figure 4 As shown, the intelligent analysis device performs stratified analysis on customer profile data, identifying high-value customers, potential customers, and ordinary customers, and generating corresponding customer segmentation results. These results are then sent to the personalized service recommendation device and the data visualization device. The intelligent analysis device is internally equipped with an analysis task scheduler to manage the execution scheduling of analysis tasks such as stratified analysis, value assessment, and demand forecasting. It also establishes a real-time connection with the customer profile database, supporting parallel access and processing of the entire customer profile data set.

[0088] The intelligent analysis device includes a customer segmentation analysis unit, a value assessment unit, and a demand forecasting unit. These three units work together to perform intelligent analysis of customer profile data.

[0089] The customer segmentation analysis unit is used to classify customer profile data based on preset segmentation criteria, identifying customer groups of different values. Internally, the unit has a segmentation model that uses clustering or classification algorithms to divide customer groups based on the multidimensional features of customer profiles. The segmentation criteria include multiple dimensions, such as customer business contribution, activity level, loyalty, and growth potential. First, based on business contribution, the unit divides customers into high-contribution customers and ordinary customers. Then, based on activity and loyalty, it identifies high-value and potential customers from the high-contribution customers. Finally, based on growth potential, it identifies potential customers with growth potential from the ordinary customers. The segmentation results are output in structured data format, including each customer's segmentation label and segmentation confidence level.

[0090] The Value Assessment Unit comprehensively evaluates a customer's business contribution, cooperation stability, and growth potential to generate a customer value score. Internally, the unit employs a value assessment model based on customer profile data and a weighted scoring algorithm to calculate the customer's value score. The business contribution assessment is based on financial indicators such as the customer's shipment volume, shipment amount, and settlement cycle. The cooperation stability assessment is based on indicators such as the length of cooperation between the customer and the logistics network, frequency of cooperation, and default records. The growth potential assessment is based on predictions of the customer's business growth trends, demand change patterns, and market environment factors. The unit then weights and combines the assessment results from these three dimensions to generate the final customer value score. The value score is output in a standardized 100-point or 10-point scale for easy prioritization and resource allocation.

[0091] The demand forecasting unit predicts future customer service needs based on customer profile data and historical service records. Internally, it employs a demand forecasting model that combines time series analysis and machine learning algorithms to predict customer service needs over future time periods. The predicted service demands include the expected number of shipments, the expected number of pickups, the types of value-added services expected to be used, and the expected delivery timeliness requirements. The unit utilizes a multi-model ensemble forecasting strategy, combining the results of multiple models to improve the accuracy of demand forecasts. The forecast results are output in tabular form, containing the predicted demand values ​​and confidence intervals for each customer.

[0092] like Figure 5As shown, the personalized service recommendation device generates targeted service strategy suggestions based on customer profiles and segmentation results, and sends these suggestions to the data visualization device. The personalized service recommendation device connects to the intelligent analysis device and analysis result database to obtain customer segmentation results and value score data in real time. It is also equipped with a strategy template library containing strategy templates for different customer groups and business scenarios, supporting flexible configuration and rapid generation of strategies.

[0093] The personalized service recommendation system generates service strategy suggestions based on customer segmentation results and value scores, including service priority configuration, resource allocation recommendations, and marketing strategy recommendations. The system employs a combination of a rule engine and recommendation algorithms to generate accurate personalized service strategies.

[0094] Service priority configuration is used to set differentiated service response priorities based on customer value levels, ensuring that high-value customers receive faster service responses. The service priority configuration module internally configures priority mapping rules, which map customer value scores to service response priority levels. The module assigns the highest priority level to high-value customers, ensuring their shipping requests are processed first, anomalies are addressed first, and complaints and suggestions are handled first. It assigns a medium priority level to potential customers, ensuring their business needs are handled promptly. The module assigns a standard priority level to ordinary customers, responding to them on a first-come, first-served basis. The output of the service priority configuration is written to the service scheduling system to guide the priority allocation of service resources.

[0095] The resource allocation recommendation module optimizes the resource allocation of logistics network points based on customer distribution characteristics and service demand forecasts. Internally, it includes a resource optimization model that generates optimized resource allocation suggestions based on customer geographic distribution, service demand forecasts, and current resource status. The module analyzes customer geographic distribution characteristics, identifying densely populated and sparsely populated areas to provide a basis for network layout optimization. It also analyzes customer express delivery volume forecasts, predicting manpower and transportation capacity needs for different time periods, providing suggestions for personnel scheduling and vehicle dispatching. Furthermore, the module analyzes customer preferences for different value-added services, providing recommendations for the allocation of value-added service equipment and resources.

[0096] The personalized service recommendation device is equipped with rules for judging the load status of logistics outlets. When the load rate of a target logistics outlet reaches a load threshold, it generates resource scheduling suggestions such as increasing staff shifts, allocating vehicles, extending service hours, or diverting some customer demand to nearby outlets. When the anomaly rate of a target logistics outlet reaches an anomaly threshold, or when any of the following reaches a threshold: the number of overdue packages, the number of delayed packages, or the number of complaints, the device increases the service priority of anomaly handling tasks and generates an early warning message for management personnel. When the relevant indicators return to below the threshold, the personalized service recommendation device cancels or adjusts the corresponding temporary scheduling strategy.

[0097] The marketing strategy recommendation module is used to push targeted promotional information and service upgrade suggestions based on customer preferences. Internally, it employs a marketing recommendation algorithm that generates personalized marketing recommendations based on customer profile characteristics and preference predictions. For high-value customers, the module recommends premium value-added services, exclusive promotions, and VIP membership programs to enhance customer loyalty and satisfaction. For potential customers, it recommends cost-effective service packages and limited-time offers to encourage increased business usage. For regular customers, it recommends new customer offers and referral rewards to leverage social media sharing to acquire new customers. The output of the marketing strategy recommendation module is integrated with the customer outreach system to achieve precise delivery of marketing information.

[0098] The data visualization device is used to present customer profiles, segmentation results, and service strategy recommendations to logistics network managers in a visual format. The data visualization device connects with intelligent analysis devices, personalized service recommendation devices, and analysis result databases to acquire the data content to be displayed. It adopts a responsive design to support display on multiple terminals such as PCs, mobile devices, and large screens, and supports exporting visualized data to common formats such as Excel and PDF.

[0099] The data visualization device includes a customer distribution map module, a hierarchical results chart module, a service strategy dashboard module, a branch network file display module, and a branch network operation monitoring module. These five visualization modules work together to provide a comprehensive visual display covering customer information and branch network operation status.

[0100] The Customer Distribution Map module displays the distribution of customers within the service area of ​​the logistics network in the form of a geographical map. It calls a map service interface to mark the location of customers on an electronic map. The module supports filtering and displaying distribution data based on customer type, value level, and business volume. It uses a heatmap to display customer density distribution, identifying areas with high customer concentration. The module supports interactive operations such as zooming and dragging on the map, allowing administrators to view detailed customer distribution information in different areas.

[0101] The tiered results chart module is used to display the quantity and value contribution of various customer types in statistical chart formats such as pie charts and bar charts. This module calls upon chart library components to generate various statistical charts. The tiered results chart module displays a pie chart showing the tiered distribution of customers, intuitively presenting the quantity percentages of high-value customers, potential customers, and ordinary customers. The tiered results chart module displays a bar chart showing the value contribution of each customer group, comparing the business contribution of different groups. The tiered results chart module also displays a histogram showing the distribution pattern of customer value scores. The tiered results chart module supports interactive chart operations, including data filtering, dimension switching, and drill-down.

[0102] The Service Strategy Dashboard module is used to display service strategy recommendations and their implementation effects for different customer groups in a Kanban format. The module uses a Kanban layout design, categorizing service strategy recommendations by customer group and strategy type. It displays the execution status of service priority configurations, including configured, pending configuration, and configuration error statuses. The module shows the specific content of resource allocation recommendations, including manpower demand forecasts, transportation capacity demand forecasts, and network optimization suggestions. It also displays the implementation effects of marketing strategy recommendations, including metrics such as the number of push notifications, response counts, and conversion rates. The module supports strategy editing and adjustment; administrators can directly modify strategy parameters and issue execution commands on the dashboard.

[0103] The branch network profile display module is used to display the target logistics branch network associated with the current customer in the customer details interface, and displays the branch network identifier, branch network type, operational status, person in charge information, service scope, and business hours. The branch network profile display module supports navigation from the customer details interface to the target logistics branch network details interface, but sensitive fields such as contact numbers are hidden, partially anonymized, or fully displayed according to the access user's permission level.

[0104] The branch operation monitoring module displays dynamic operational information for target logistics branches. It uses line graphs to show trends in received, sent, and dispatched parcels within a preset statistical period, and indicator cards to display the number of abnormal parcels, overdue parcels, delayed parcels, and complaints, with different display statuses indicating normal, warning, and over-limit states. When administrators select any trend point or abnormal indicator, they can drill down in the branch operation monitoring module to view the corresponding business records and related customer groups, but complete personal identification information is not displayed in unauthorized interfaces.

[0105] The network operation monitoring module also displays customer demand forecasts, available processing capacity at the network, network load rate, and anomaly rate. When the network load rate or anomaly rate reaches the corresponding threshold, the service strategy dashboard module displays early warning information and resource scheduling suggestions. After management confirms or adjusts the suggestions, the service strategy dashboard module sends personnel scheduling parameters, vehicle dispatching parameters, service time parameters, or customer diversion parameters to the logistics network business system for execution, and records the strategy issuance time, execution status, and implementation effect.

[0106] Example 2

[0107] To meet the deployment needs of logistics networks of different sizes, this embodiment provides a lightweight, merged deployment method for the system, combining the six functional devices in Embodiment 1 into four modules: a data acquisition module, a data processing module, a customer analysis module, and a service presentation module. Specifically, the data acquisition module corresponds to the data acquisition device; the data processing module corresponds to the data preprocessing device; the customer analysis module integrates the functions of a customer profile building device, an intelligent analysis device, and a personalized service recommendation device; and the service presentation module corresponds to the data visualization device. This functional merging is merely a formal integration and does not alter the data processing logic or the collaborative relationships between the functional devices as defined in this invention.

[0108] A smart management system for customer information at logistics outlets includes a data acquisition module, a data processing module, a customer analysis module, and a service presentation module. The data acquisition module is connected to the input terminal of the data processing module. The output terminal of the data processing module is connected to the input terminal of the customer analysis module. The output terminal of the customer analysis module is connected to the input terminal of the service presentation module.

[0109] The functions of each module are the same as those of the corresponding device in Embodiment 1, and will not be described again here.

[0110] Compared to Example 1, Example 2 adopts a lightweight deployment architecture, merging the implementation programs of the six functional devices into four modules, while still executing data acquisition, data preprocessing, customer profile building, intelligent analysis, personalized service recommendation, and data visualization functions respectively. This example is suitable for logistics outlets with smaller business scale and lower data processing volume.

[0111] Example 3

[0112] To meet the needs of large logistics companies for unified management of multiple outlets, this embodiment provides a cloud-based distributed deployment method for the system. The six functional devices in Embodiment 1 are respectively implemented as a distributed data acquisition system, a cloud computing preprocessing system, a machine learning profiling system, an intelligent analysis system, a recommendation engine system, and a cloud visualization system. These six devices correspond to the data acquisition device, data preprocessing device, customer profile building device, intelligent analysis device, personalized service recommendation device, and data visualization device, respectively.

[0113] A smart management system for customer information at logistics outlets includes a distributed data acquisition system, a cloud computing preprocessing system, a machine learning profiling system, an intelligent analysis system, a recommendation engine system, and a cloud visualization system; the systems are connected via encrypted network channels to form a complete data processing and analysis chain.

[0114] The functions of each subsystem are the same as those of the corresponding device in Embodiment 1, and will not be described again here.

[0115] Compared to Embodiments 1 and 2, Embodiment 3 deploys each functional device as a cloud-based distributed system, enabling large logistics enterprises to uniformly collect, analyze, and manage customer information across their entire network. This embodiment boasts high scalability, availability, and processing performance, making it suitable for large-scale business scenarios.

[0116] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart management system for customer information of logistics outlets, characterized in that, include: Data acquisition device, data preprocessing device, customer profile building device, intelligent analysis device, personalized service recommendation device, and data visualization device; The data acquisition device is used to obtain basic information, business information and behavioral information of customers at logistics outlets from multiple data sources, and send the collected raw data to the data preprocessing device. The data preprocessing device is used to clean, deduplicate, convert formats and extract features from the collected raw data to generate standardized data resources, and send the processed data to the customer profile building device. The customer profile building device is used to analyze the customer's attribute characteristics, behavioral characteristics and preference characteristics based on preprocessed data resources and through machine learning algorithms to build a multi-dimensional customer profile, and then send the built customer profile data to the intelligent analysis device. The intelligent analysis device is used to perform hierarchical analysis on customer profile data, identify high-value customers, potential customers and ordinary customers, and generate corresponding customer segmentation results. At the same time, the segmentation results are sent to the personalized service recommendation device and the data visualization device. The personalized service recommendation device is used to generate targeted service strategy suggestions based on customer profiles and stratification results, and send the service strategy suggestions to the data visualization device; The data visualization device is used to display customer profiles, stratification results, and service strategy suggestions to logistics network managers in a visual form.

2. The intelligent management system for customer information of logistics outlets according to claim 1, characterized in that, The data acquisition device includes a personal customer information acquisition module, an enterprise customer information acquisition module, and an e-commerce customer information acquisition module; The personal customer information collection module is used to collect personal customers' names, contact information, shipping addresses, and identity verification information; The enterprise customer information collection module is used to collect enterprise customer information such as company name, company qualifications, contact person information, shipping preferences, and payment terms. The e-commerce customer information collection module is used to collect information such as store information, product types, shipping frequency, return rate, and customer reviews from e-commerce merchants.

3. The intelligent management system for customer information of logistics outlets according to claim 1, characterized in that, The data preprocessing device includes a data cleaning unit, a data deduplication unit, a format conversion unit, and a feature extraction unit; The data cleaning unit is used to identify and repair or remove missing values, outliers and noisy data in the raw data acquired by the data acquisition device; The data deduplication unit is used to detect and merge duplicate customer information records to ensure the uniqueness of customer data; The format conversion unit is used to convert heterogeneous data collected from different data sources into a unified format, which facilitates subsequent machine learning algorithm processing. The feature extraction unit is used to extract key customer features from standardized data, including pickup frequency features, shipment weight features, delivery time features, and address distribution features.

4. The intelligent management system for customer information of logistics outlets according to claim 1, characterized in that, The customer profile building device constructs a comprehensive customer profile based on a preset machine learning algorithm, which includes static attribute profiles, dynamic behavior profiles, and preference prediction profiles. The static attribute profile is used to record the customer's customer type, customer level, and contact information; The dynamic behavioral profile is used to record the customer's average monthly number of packages sent and received, delivery route preferences, and pickup time preferences. The preference prediction profile is used to predict customers' potential needs and service preferences based on their historical behavior data, including the types of value-added services and promotional information that they may be interested in.

5. The intelligent management system for customer information of logistics outlets according to claim 1, characterized in that, The intelligent analysis device includes a customer segmentation analysis unit, a value assessment unit, and a demand forecasting unit. The customer segmentation analysis unit is used to classify customer profile data based on preset segmentation criteria and identify customer groups of different values. The value assessment unit is used to comprehensively evaluate a customer's business contribution, cooperation stability, and growth potential, and generate a customer value score. The demand forecasting unit is used to predict customers' future service needs based on customer profile data and historical service records.

6. The intelligent management system for customer information of logistics outlets according to claim 5, characterized in that, The customer segmentation analysis unit divides customers into groups based on their business contribution indicators, activity indicators, loyalty indicators, and growth indicators. First, based on the business contribution indicators, customers are divided into high-contribution customers and ordinary customers. Then, based on the activity and loyalty indicators, high-value customers and potential customers are identified from the high-contribution customers. Finally, based on the growth indicators, potential customers with development potential are identified from the ordinary customers.

7. The intelligent management system for customer information of logistics outlets according to claim 1, characterized in that, The personalized service recommendation device generates service strategy recommendations, including service priority configuration, resource allocation suggestions, and marketing strategy recommendations, based on customer segmentation results and value scores. The service priority configuration is used to set differentiated service response priorities based on customer value levels, mapping customer value scores to service response priority levels, configuring the highest priority level for high-value customers, the medium priority level for potential customers, and the standard priority level for ordinary customers, ensuring that high-value customers receive faster service responses. The resource allocation recommendations are used to optimize the resource allocation scheme of logistics outlets based on customer distribution characteristics and service demand forecast results. The aforementioned marketing strategy recommendation is used to push targeted promotional information and service upgrade suggestions based on customer preference characteristics.

8. The intelligent management system for customer information of logistics outlets according to claim 1, characterized in that, The data visualization device includes a customer distribution map module, a hierarchical result chart module, a service strategy dashboard module, a branch archive display module, and a branch operation monitoring module. The customer distribution map module is used to display the distribution of customers within the service area of ​​the logistics network in the form of a geographical distribution map. The layered results chart module is used to display the quantity percentage and value contribution of various types of customers in the form of pie charts and bar charts. The service strategy dashboard module is used to display service strategy suggestions and implementation results for different customer groups in a dashboard format; The outlet profile display module is used to display the outlet identifier, outlet type, operating status, person in charge information, service scope and business hours of the target logistics outlets associated with the customer; The network operation monitoring module is used to display the changing trends of the number of received items, sent items, and dispatched items of the target logistics network according to a preset statistical period, as well as at least one of the following: number of abnormal items, number of overdue items, number of delayed items, and number of complaints.

9. The intelligent management system for customer information of logistics outlets according to claim 8, characterized in that, The customer distribution map module displays customer density distribution in the form of a heat map, identifying hotspot areas with high customer density. The hierarchical result chart module supports data filtering, dimension switching, and drill-down operations, and displays the relationship between the current customer and the target logistics network point during drill-down. The service strategy dashboard module allows managers to modify strategy parameters and issue execution orders on the dashboard. The network point operation monitoring module compares the customer demand forecast output by the demand forecasting unit with the service capacity threshold of the target logistics network point, and generates early warning information and resource scheduling suggestions when the customer demand forecast exceeds the service capacity threshold, or when the number of abnormal items, overdue items, delayed items, or complaints exceeds the corresponding abnormal threshold.

10. A method for intelligent management of customer information at logistics outlets, characterized in that, Includes the following steps: Step S1: Obtain basic information, business information, and behavioral information of customers at logistics outlets from multiple data sources to form raw data; Step S2 involves cleaning, deduplicating, formatting, and extracting features from the raw data to generate standardized data resources. Step S3: Based on the standardized data resources, analyze the customer's attribute characteristics, behavioral characteristics, and preference characteristics through machine learning algorithms to construct a multi-dimensional customer profile; Step S4: Perform stratified analysis on the customer profile data to identify high-value customers, potential customers, and ordinary customers, and generate customer stratification results. Step S5: Based on the customer profile and the customer segmentation results, generate targeted service strategy suggestions; Step S6: The customer profile, the customer segmentation results, and the service strategy suggestions are displayed to the logistics network managers in a visual format.

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