Logistics signing rate management method and device based on multiple dimensions, medium and equipment

Abstract

The invention provides a logistics sign-in rate management method and device based on multiple dimensions, a medium and equipment, and the method comprises the steps: obtaining logistics sign-in data, carrying out the preprocessing of the obtained logistics sign-in data, and obtaining the preprocessed logistics sign-in data; constructing a data warehouse based on the preprocessed logistics signing data; based on the data warehouse, logistics signing rate management of each assessment node in multiple dimensions is realized; the multiple dimensions comprise a website dimension, a site dimension and a salesman dimension; the assessment node comprises each network point in a network point dimension; each site in the site dimension; and each salesman in the salesman dimension. According to the invention, by establishing the data warehouse, the logistics signing data from different data sources are stored in a centralized manner, so that the data are managed in a unified manner, the phenomenon of data islands is avoided, and the accessibility and consistency of the data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a multi-dimensional logistics receipt rate management method, device, medium and equipment. Background Art

[0002] With the rapid development of e-commerce and the logistics industry, customers are increasingly demanding the timeliness and reliability of logistics services. As an important indicator of logistics service quality, the logistics receipt rate directly affects customer satisfaction and the competitiveness of enterprises. In this context, efficient management of the logistics receipt rate is particularly important. Previous logistics receipt rate management methods often relied on traditional data collection and processing methods, which have the following limitations: Data silos: The receipt data of various outlets, sites and salesmen may be scattered in different systems, lacking unified integration, making it difficult to conduct comprehensive analysis and real-time monitoring; Statistical lag: In traditional methods, data processing and report generation often take a long time, which makes it impossible for management to obtain the latest receipt rate information in real time, and thus unable to make quick decisions; Dimensional neglect: Most systems only conduct assessments from a single dimension, such as the global level, and cannot break down to the specific performance of outlets, sites and salesmen, resulting in insufficient management accuracy.

[0003] In today's fast-paced market, companies urgently need a method to manage sign-off rates from multiple dimensions. Multi-dimensional management not only provides deeper and broader analysis, helping companies accurately identify problems, but also improves management flexibility and responsiveness. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problems in the existing technology that logistics receipt rate management cannot have both depth and breadth analysis, and is not flexible enough and has insufficient response speed.

[0005] The first aspect of the present invention provides a multi-dimensional logistics receipt rate management method, comprising: Obtaining logistics receipt data, and preprocessing the obtained logistics receipt data to obtain preprocessed logistics receipt data; Build a data warehouse based on pre-processed logistics receipt data; Realize logistics sign-in rate management of each assessment node under multi-dimensional conditions based on data warehouse; The multiple dimensions include: outlet dimension, site dimension and salesperson dimension; The assessment nodes include: each outlet in the outlet dimension; each site in the site dimension; and each salesperson in the salesperson dimension.

[0006] Optionally, in a first implementation of the first aspect of the present invention, obtaining logistics receipt data and preprocessing the obtained logistics receipt data to obtain preprocessed logistics receipt data includes: Obtain logistics receipt data based on various data sources of the logistics business system; The acquired logistics receipt data is cleaned and processed by using ETL tools to obtain cleaned logistics receipt data; the cleaned logistics receipt data is format converted to obtain converted logistics receipt data.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the acquiring of logistics receipt data based on various data sources of the logistics business system includes: When new receipt data changes are generated in the data sources of the logistics business system, they are published to the Kafka distributed message queue in the form of messages; the Kafka distributed message queue is continuously monitored through data change monitoring technology. When data updates are detected in the Kafka distributed message queue, the updated logistics receipt data is obtained in real time.

[0008] Optionally, in a third implementation of the first aspect of the present invention, building a data warehouse based on the preprocessed logistics receipt data includes: Use ETL tools to load pre-processed logistics receipt data and create a master table for receipt rates and associated dimension tables based on the loaded pre-processed logistics receipt data. Build a star- or snowflake-structured data warehouse based on the master table for receipt rates and associated dimension tables through foreign key associations. The main table of receipt rate includes the fields of branch ID, date, receipt quantity and total shipment quantity; The associated dimension tables include a network point information table and a product type table.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the logistics receipt rate management based on the data warehouse includes: multi-dimensional real-time logistics receipt rate query and display, multi-dimensional historical logistics receipt rate query and display, multi-dimensional frequency receipt rate data query and display, frequency linkage, association of order number detail data with receipt rate data, data sorting, and data search and export; The multi-dimensional historical logistics receipt rate query and display includes: using a time series data warehouse to store historical receipt rate data, extracting the logistics receipt rate of the corresponding dimension from the data warehouse according to the time index; for querying summary data across multiple days, using an aggregate function to obtain daily receipt data within the selected time period, and displaying the summary receipt rate results through the front-end; The multi-dimensional frequency acceptance rate data query and display includes: dynamically linking and presenting the frequency acceptance rate data of each assessment node under each dimension according to the frequency assessment configured for each assessment node, and the assessment node under each frequency is updated in real time synchronously with the interface data; The frequency linkage includes: when the interface receives a new frequency assessment data change notification, it triggers the update of the frequency configuration information of the corresponding assessment node, and at the same time, the front-end interface dynamically renders and displays the corresponding frequency acceptance rate data according to the new frequency configuration information by monitoring data change events; The associating of the order number detail data with the receipt rate data includes: at the data storage level, associating the order number detail data with the receipt rate data through a foreign key to establish a strong association relationship; The data sorting includes: the front end uses the JavaScript array sorting method, combined with the customized comparison function, to sort the sign-in rate data obtained from the back end according to the sorting fields and order requirements selected by the user; The data search and export includes: using the front-end drop-down selection component combined with the back-end data warehouse query to filter the corresponding receipt rate data, and return it to the front-end for display; when the user triggers the data export operation, the data is converted into a specified format according to user needs and exported.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the method further includes: Build a prediction model based on random forest; Obtain historical sign-in rate data and corresponding logistics feature data for different assessment nodes in multiple dimensions, and perform missing value filling and feature coding on the obtained historical sign-in rate data and corresponding logistics feature data to obtain processed historical sign-in rate data and corresponding logistics feature data; Use the processed historical sign-in rate data and the corresponding logistics feature data to construct training sets, test sets, and validation sets; The prediction model is trained using the training set, and the prediction model is tested and verified using the test set and validation set to obtain the trained prediction model; The trained prediction model is used to predict the acceptance rate of the assessment nodes under the corresponding dimensions within the preset time period in the future, and the results are displayed in a visual way.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, the method further includes: Use the access control RBAC model to assign specific roles to accounts accessing the data warehouse, and associate each specific role with corresponding menu access permissions; When a user accesses the data warehouse, the user account and password information is obtained through the front-end login interface, and the obtained user account and password information is sent to the back-end authentication service. The back-end authentication service verifies the account and password information. After confirming that the user identity is legal, the role information corresponding to the current account is queried from the user role table of the data warehouse to obtain the role ID; based on the role ID, the role permission set is obtained according to the pre-configured role-permission mapping table; if the target permission is in the current role permission set, the match is successful, and the back-end authentication service returns an access permission instruction to the front-end login interface, and displays the corresponding menu page through the front-end login interface; if the target permission is not in the current role permission set, the back-end authentication service returns an access rejection instruction to the front-end login interface.

[0012] The second aspect of the present invention provides a multi-dimensional logistics receipt rate management device, comprising: The logistics visa data acquisition module is used to obtain logistics receipt data and pre-process the obtained logistics receipt data to obtain pre-processed logistics receipt data; Data warehouse construction module, used to build a data warehouse based on pre-processed logistics receipt data; The logistics receipt rate management module is used to implement multi-dimensional logistics receipt rate management based on the data warehouse.

[0013] Optionally, in a first implementation of the second aspect of the present invention, the logistics visa data acquisition module includes: The data acquisition submodule is used to obtain logistics receipt data based on various data sources of the logistics business system; The pre-processing sub-module is used to clean the acquired logistics receipt data through the ETL tool to obtain the cleaned logistics receipt data; and perform format conversion on the cleaned logistics receipt data to obtain the converted logistics receipt data.

[0014] Optionally, in a second implementation of the second aspect of the present invention, the data acquisition submodule includes: When new receipt data changes are generated in the data sources of the logistics business system, they are published to the Kafka distributed message queue in the form of messages; the Kafka distributed message queue is continuously monitored through data change monitoring technology. When data updates are detected in the Kafka distributed message queue, the updated logistics receipt data is obtained in real time.

[0015] Optionally, in a third implementation of the second aspect of the present invention, the data warehouse construction module includes: Use ETL tools to load pre-processed logistics receipt data and create a master table for receipt rates and associated dimension tables based on the loaded pre-processed logistics receipt data. Build a star- or snowflake-structured data warehouse based on the master table for receipt rates and associated dimension tables through foreign key associations. The main table of receipt rate includes the fields of branch ID, date, receipt quantity and total shipment quantity; The associated dimension tables include a network point information table and a product type table.

[0016] Optionally, in a fourth implementation of the second aspect of the present invention, the logistics receipt rate management module includes: multi-dimensional real-time logistics receipt rate query and display, multi-dimensional historical logistics receipt rate query and display, multi-dimensional frequency receipt rate data query and display, frequency linkage, association of order number detail data with receipt rate data, data sorting, and data search and export; The multi-dimensional historical logistics receipt rate query and display includes: using a time series data warehouse to store historical receipt rate data, extracting the logistics receipt rate of the corresponding dimension from the data warehouse according to the time index; for querying summary data across multiple days, using an aggregate function to obtain daily receipt data within the selected time period, and displaying the summary receipt rate results through the front-end; The multi-dimensional frequency acceptance rate data query and display includes: dynamically linking and presenting the frequency acceptance rate data of each assessment node under each dimension according to the frequency assessment configured for each assessment node, and the assessment node under each frequency is updated in real time synchronously with the interface data; The frequency linkage includes: when the interface receives a new frequency assessment data change notification, it triggers the update of the frequency configuration information of the corresponding assessment node, and at the same time, the front-end interface dynamically renders and displays the corresponding frequency acceptance rate data according to the new frequency configuration information by monitoring data change events; The associating of the order number detail data with the receipt rate data includes: at the data storage level, associating the order number detail data with the receipt rate data through a foreign key to establish a strong association relationship; The data sorting includes: the front end uses the JavaScript array sorting method, combined with the customized comparison function, to sort the sign-in rate data obtained from the back end according to the sorting fields and order requirements selected by the user; The data search and export includes: using the front-end drop-down selection component combined with the back-end data warehouse query to filter the corresponding receipt rate data, and return it to the front-end for display; when the user triggers the data export operation, the data is converted into a specified format according to user needs and exported.

[0017] Optionally, in a fifth implementation of the second aspect of the present invention, the apparatus further comprises: a prediction module configured to predict the sign-off rate of the assessment node under the corresponding dimension within a preset future time period using the prediction model; Model building submodule, used to build prediction models based on random forests; The data acquisition submodule is used to obtain the historical sign-in rate data and corresponding logistics characteristic data of different assessment nodes in multiple dimensions, and perform missing value filling and feature encoding processing on the obtained historical sign-in rate data and corresponding logistics characteristic data to obtain the processed historical sign-in rate data and corresponding logistics characteristic data; The dataset construction submodule is used to construct training sets, test sets, and validation sets using the processed historical sign-in rate data and the corresponding logistics feature data; The model training submodule is used to train the prediction model using the training set, and to test and verify the prediction model using the test set and validation set to obtain the trained prediction model; The prediction submodule is used to use the trained prediction model to predict the sign-in rate of the assessment node under the corresponding dimension within the preset time period in the future, and display it in a visual way.

[0018] Optionally, in a sixth implementation of the second aspect of the present invention, the apparatus further comprises: an access control module configured to assign specific roles to accounts accessing the data warehouse using an access control RBAC model, and to associate each specific role with corresponding menu access rights; When a user accesses the data warehouse, the user account and password information is obtained through the front-end login interface, and the obtained user account and password information is sent to the back-end authentication service. The back-end authentication service verifies the account and password information. After confirming that the user identity is legal, the role information corresponding to the current account is queried from the user role table of the data warehouse to obtain the role ID; based on the role ID, the role permission set is obtained according to the pre-configured role-permission mapping table; if the target permission is in the current role permission set, the match is successful, and the back-end authentication service returns an access permission instruction to the front-end login interface, and displays the corresponding menu page through the front-end login interface; if the target permission is not in the current role permission set, the back-end authentication service returns an access rejection instruction to the front-end login interface.

[0019] A third aspect of the present invention provides an electronic device, comprising a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the electronic device to execute the various steps of the multi-dimensional logistics receipt rate management method as described above.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the various steps of the multi-dimensional logistics receipt rate management method as described above.

[0021] Compared with the prior art, the present invention has the following beneficial effects: 1. By establishing a data warehouse, logistics receipt data from different data sources are centrally stored, enabling unified data management, avoiding data silos, and improving data accessibility and consistency; 2. Using Kafka distributed message queues to monitor and acquire data changes in real time enables managers to obtain the latest receipt data in a timely manner, enhancing the timeliness of decision-making; 3. Supports in-depth analysis of different dimensions of outlets, sites, and sales representatives, helping managers to more comprehensively understand the performance of each assessment node, thereby accurately identifying problems and optimizing improvement directions; 4. Accurately predict future sign-off rates by building a forecasting model, enabling companies to prepare and optimize resource allocation strategies in advance to respond to market changes and potential risks; 5. Through the RBAC model, fine management of user access rights is achieved to ensure that only authorized users can access the corresponding data, reduce the risk of data leakage, and ensure data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings: Figure 1 This is a first flow chart of a multi-dimensional logistics receipt rate management method provided in an embodiment of the present invention.

[0023] Figure 2 This is a second flow chart of the multi-dimensional logistics receipt rate management method provided in an embodiment of the present invention.

[0024] Figure 3 This is a third flow chart of the multi-dimensional logistics receipt rate management method provided in an embodiment of the present invention.

[0025] Figure 4 This is a fourth flow chart of the multi-dimensional logistics receipt rate management method provided in an embodiment of the present invention.

[0026] Figure 5 This is a fifth flow chart of the multi-dimensional logistics receipt rate management method provided in an embodiment of the present invention.

[0027] Figure 6A schematic structural diagram of a multi-dimensional logistics receipt rate management device provided in an embodiment of the present invention.

[0028] Figure 7 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] Embodiments of the present invention provide a multi-dimensional logistics receipt rate management method, apparatus, medium, and equipment, including: obtaining logistics receipt data and preprocessing the obtained logistics receipt data to obtain preprocessed logistics receipt data; constructing a data warehouse based on the preprocessed logistics receipt data; and implementing logistics receipt rate management for each assessment node under multiple dimensions based on the data warehouse; the multiple dimensions include: the network dimension, the site dimension, and the salesperson dimension; the assessment nodes include: each network within the network dimension; each site within the site dimension; and each salesperson within the salesperson dimension. The present invention solves the technical problems in the prior art of logistics receipt rate management, which cannot simultaneously provide in-depth and breadth analysis, and lacks flexibility and responsiveness.

[0030] The terms "first," "second," "third," "fourth," and so on (if any) in the description and claims of the present invention and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that shown or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, product, or apparatus.

[0031] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In the embodiment of the present invention, the first embodiment of the multi-dimensional logistics receipt rate management method includes: 101. Obtain logistics receipt data, and pre-process the obtained logistics receipt data to obtain pre-processed logistics receipt data; In this embodiment, logistics receipt data is obtained based on various data sources of the logistics business system; More specifically, when new changes to the receipt data are generated by the various data sources of the logistics business system, they are published to the Kafka distributed message queue in the form of messages; the Kafka distributed message queue is continuously monitored through data change monitoring technology, and when data updates are detected in the Kafka distributed message queue, the updated logistics receipt data is obtained in real time.

[0032] The acquired logistics receipt data is cleaned and processed by using ETL tools to obtain cleaned logistics receipt data; the cleaned logistics receipt data is format converted to obtain converted logistics receipt data.

[0033] 102. Build a data warehouse based on pre-processed logistics receipt data; In this embodiment, the pre-processed logistics receipt data is loaded through an ETL tool, and a receipt rate master table and associated dimension tables are created based on the loaded pre-processed logistics receipt data. A star or snowflake architecture data warehouse is constructed based on the receipt rate master table and the associated dimension tables through foreign key associations. The main table of receipt rate includes the fields of branch ID, date, receipt quantity and total shipment quantity; The associated dimension tables include a network information table and a product type table; The outlet information table includes: outlet name, region and level; the product type table includes detailed classification of various products.

[0034] 103. Implement logistics sign-in rate management for each assessment node under multiple dimensions based on data warehouse; In this embodiment, the logistics receipt rate management based on the data warehouse includes: multi-dimensional real-time logistics receipt rate query and display, multi-dimensional historical logistics receipt rate query and display, multi-dimensional frequency receipt rate data query and display, frequency linkage, association of order number details and receipt rate data, data sorting, and data search and export; The multi-dimensional historical logistics receipt rate query and display includes: using a time series data warehouse to store historical receipt rate data, extracting the logistics receipt rate of the corresponding dimension from the data warehouse according to the time index; for querying summary data across multiple days, using an aggregate function to obtain daily receipt data within the selected time period, and displaying the summary receipt rate results through the front-end; The multi-dimensional frequency acceptance rate data query and display includes: dynamically linking and presenting the frequency acceptance rate data of each assessment node under each dimension according to the frequency assessment configured for each assessment node, and the assessment node under each frequency is updated in real time synchronously with the interface data; The frequency linkage includes: when the interface receives a new frequency assessment data change notification, it triggers the update of the frequency configuration information of the corresponding assessment node, and at the same time, the front-end interface dynamically renders and displays the corresponding frequency acceptance rate data according to the new frequency configuration information by monitoring data change events; The associating of the order number detail data with the receipt rate data includes: at the data storage level, associating the order number detail data with the receipt rate data through a foreign key to establish a strong association relationship; The data sorting includes: the front end uses the JavaScript array sorting method, combined with the customized comparison function, to sort the sign-in rate data obtained from the back end according to the sorting fields and order requirements selected by the user; The data search and export includes: using the front-end drop-down selection component combined with the back-end data warehouse query to filter the corresponding receipt rate data, and return it to the front-end for display; when the user triggers the data export operation, the data is converted into a specified format according to user needs and exported.

[0035] The multiple dimensions include: outlet dimension, site dimension and salesperson dimension; The assessment nodes include: each outlet in the outlet dimension; each site in the site dimension; and each salesperson in the salesperson dimension.

[0036] See also Figure 2 The second embodiment of the multi-dimensional logistics receipt rate management method of the present invention includes: 201. Obtain logistics receipt data, and pre-process the obtained logistics receipt data to obtain pre-processed logistics receipt data; In this embodiment, historical and real-time receipt data is extracted from various data sources in the logistics business system, cleaned and transformed using ETL tools, and loaded into the database according to preset data structure specifications. This completes data initialization and builds a complete data warehouse. A real-time data synchronization mechanism, leveraging Kafka and data change monitoring technology, enables the immediate delivery of newly generated logistics receipt data. More specifically, the cleaning and conversion using ETL tools includes: When data is missing, for example, some order records may not record the recipient's contact information due to a system failure, then a filling strategy needs to be adopted according to business rules. If other orders from the same shipper have recipient information, it can be filled in as a reference. For erroneous data, such as illogical timestamps in the transport trajectory, they must be identified and corrected through data validation rules, or repaired by combining upstream and downstream data associations. Duplicate data must also be removed, and the order number must be used as the unique identifier to check for duplicates in the entire data. The conversion operation unifies the data formats and encodings of different data sources. For example, the date format is unified to "YYYY-MM-DD", and categorical variables such as cargo type are standardized to ensure consistency in subsequent storage and analysis.

[0037] With Kafka, a high-performance distributed message queue, each data source in the logistics business system publishes new sign-off data changes in the form of messages to the corresponding topics in the Kafka cluster. The deployed data change monitoring component continuously monitors the topics in the Kafka cluster and obtains the sign-off data in real time once a new message is captured. 202. Build a data warehouse based on pre-processed logistics receipt data; In this embodiment, a main table of sign-in rates and associated dimension tables, such as a branch information table and a product type table, are created, and a star-structured or snowflake-structured data warehouse is constructed through foreign key associations.

[0038] Key data fields are extracted based on pre-processed logistics receipt data and converted into a format that can be directly written to the database according to established rules. Through a pre-established database connection, the newly generated logistics receipt data is instantly pushed to the data warehouse to ensure the timeliness of the data, allowing managers at all levels to keep abreast of the latest business dynamics. For example, after a courier completes a delivery and uploads the receipt information, the data update can be seen on the system front end within seconds.

[0039] 203. Implement logistics receipt rate management for each assessment node under multiple dimensions based on data warehouse; Among them, the multiple dimensions include: branch dimension, site dimension and salesperson dimension; the assessment nodes include: each branch in the branch dimension; each site in the site dimension; each salesperson in the salesperson dimension.

[0040] In this embodiment, the logistics receipt rate management of each assessment node under multiple dimensions based on the data warehouse includes: Real-time sign-off rate (DSR) display at the branch level: The default focus is on the real-time sign-off rate for the day, while providing users with a convenient historical data backtracking function. It supports querying the summary sign-off data of any selected historical day or a span of up to 16 natural days to meet the data analysis needs of different time periods.

[0041] Historical Data Query Algorithm: Utilizing a time series database to store historical sign-off rate data, the system constructs query statements based on user-selected historical dates or time periods, accurately extracting corresponding data from the database according to time indexes. For queries of aggregated data spanning multiple days, aggregate functions (such as SUM and AVG) are used to perform statistical calculations on daily sign-off data within the selected time period, generating a summary sign-off rate result that is then fed back to the front-end for real-time display.

[0042] The frequency acceptance rate data is dynamically linked and presented based on the frequency assessment configured individually for each outlet, accurately adapting to the unique business frequency structure of each outlet to ensure that the displayed data is closely related to the actual assessment. The assessment nodes under each frequency are updated in real time with the interface data to ensure data accuracy and timeliness.

[0043] Frequency Linkage Algorithm: The system maintains a frequency configuration table for each branch in the backend, which is bidirectionally linked to the frontend display interface in real time. When the interface receives a new frequency assessment data change notification, it immediately triggers an update to the corresponding branch's frequency configuration information. Simultaneously, the frontend interface monitors data change events and dynamically renders and displays the corresponding frequency acceptance rate data and assessment nodes based on the new frequency configuration, ensuring data consistency and real-time linkage between the frontend and backend.

[0044] Two display tabs are set up, namely, statistics based on the dispatch of first-level outlets and statistics based on actual distribution operations, and the former is displayed first by default. Users can switch freely as needed to gain insights into the details of the receipt rate from different business perspectives.

[0045] Tab switching algorithm: Use state management tools (such as Vuex or Redux) on the front end to record the currently selected tab status. When the user clicks to switch tabs, the system updates the display logic of the interface component according to the current state value, obtains the sign-in rate data from the corresponding statistical perspective from the corresponding data interface on the back end, and re-renders the display to achieve smooth tab switching interaction and provide users with a multi-dimensional data analysis perspective.

[0046] Achieve seamless linkage between the frequency of the branch dimension and the outlet dimension. When the user selects a certain frequency to view in the outlet dimension, the branch acceptance rate data will automatically switch to the corresponding frequency, and vice versa. At the same time, the branch dimension retains an independent frequency switching function without interfering with each other, thereby comprehensively improving the efficiency of data comparison and analysis.

[0047] Branch-outlet frequency linkage algorithm: This algorithm establishes a data linkage model between outlets and branches. Using the outlet ID as the linkage key, the backend maps and stores outlet frequency data with the corresponding branch's acceptance rate data. When a user switches frequency within the outlet dimension, the system quickly locates and updates the corresponding branch acceptance rate data through a linkage query based on the current outlet ID and the selected frequency. Similarly, operations within the outlet dimension follow a similar logic, and by setting independent operation identifiers, this ensures that operations on both sides do not interfere with each other, enabling efficient data linkage and comparison.

[0048] The "View More" interaction logic has been optimized. Operations on the branch or salesperson dimension under the real-time receipt rate tab no longer redirect to a new page. Instead, more detailed data is directly loaded on the current page. Switching between branch dimension tabs is also done smoothly on the current page, reducing page jump waits and enhancing user operation continuity.

[0049] Current page loading algorithm: Using front-end asynchronous data loading technology, such as AJAX or FetchAPI, when the user triggers the "view more" operation, the system sends a data request to the server in the background to obtain more detailed data. At the same time, the front-end uses JavaScript to dynamically operate DOM elements and seamlessly insert new data into the specified position of the current page, avoiding full page refresh and improving user experience.

[0050] For the sign-off rate data in the departmental dimension, the table header is designed to be intelligently updated as the frequency switches, ensuring that the exclusive sign-off node data at each frequency is clearly presented, helping users quickly understand complex data structures.

[0051] Dynamic header update algorithm: The backend predefines the corresponding header template and receipt node data structure for each frequency, storing them in configuration files or database tables. When switching frequencies, the frontend requests the corresponding header information from the backend based on the currently selected frequency. Upon receiving the data, it dynamically renders and updates the header content using a JavaScript template engine (such as Handlebars or EJS) to ensure a perfect match with the data displayed at the current frequency.

[0052] Real-time sign-off rate display of branches / salesmen: It supports interactive drill-down function. Click the branch name or the branch name to go directly to the salesperson dimension page with one click, and conduct in-depth analysis of individual business performance; click "Total" to quickly view the summary of the acceptance rate data of all branches or all salespeople under this branch, and dig deeper into the data layer by layer.

[0053] Drill-down interaction algorithm: Build a page routing system on the front end. When a user clicks on a branch name or a branch name, the system, based on pre-configured routing rules, carries the corresponding parameters (such as the branch ID or branch ID) and jumps to the salesperson dimension page. It also initiates a data request with the parameters to the backend. The backend queries the database based on the parameters and returns the corresponding salesperson's sign-in rate data, enabling fast drill-down display. When clicking "Total", a similar logic is used, except that the query condition is changed to summarize the data of all branches or salespeople under the branch, providing users with an in-depth data analysis path.

[0054] The unsigned data provides an in-depth drill-down path that can directly link to the corresponding order number detail page, accurately locating problem packages and facilitating timely investigation and processing of abnormal logistics items.

[0055] Tracking number detail drill-down algorithm: At the data storage level, tracking number detail data is linked to the receipt rate data via foreign keys, establishing a strong association. When a user clicks the drill-down link for unsigned-for items, the front-end retrieves the tracking number corresponding to the current row and sends a tracking number query request to the back-end via AJAX or FetchAPI. The back-end uses a relational query to retrieve detailed information about the tracking number from the database, including the logistics track and recipient information, and returns it to the front-end for display, helping users accurately trace the problem package.

[0056] It gives users flexible sorting rights and supports ascending or descending sorting by each linked field in the branch header. It defaults to ascending sorting by the unsigned quantity of 1D1400. At the same time, it cleverly excludes the total row from sorting and always displays it at the top, optimizing the data presentation order and highlighting key indicators.

[0057] Sorting algorithm: The front-end uses JavaScript array sorting methods, combined with a custom comparison function, to sort the branch acceptance rate data obtained from the back-end according to the user's selected sorting fields and order requirements. The comparison function determines whether the data is a summary row, excluding it from the sorting range and displaying the summary row separately at the top, ensuring that the data display meets user expectations and improves data readability.

[0058] Equipped with a convenient data export function, users can export branch acceptance rate data into common formats as needed, facilitating offline analysis, reporting or archiving.

[0059] Data export algorithm: The backend integrates mature report generation tools. When the user triggers the data export operation, the system uses these tools to convert the data into a specified format based on user needs and the currently displayed data content, and provides it to the user for download through the browser download function to meet the user's offline use needs.

[0060] A new multi-dimensional search function has been added. In the branch dimension, all (including branch service departments) are displayed by default and support is provided for precise screening by selecting a specific branch, with the help of common components for efficient retrieval. In the salesperson dimension, a search box is cleverly embedded below the frequency, supporting fuzzy search by salesperson or precise fuzzy query by coding, meeting diverse query needs and quickly locating target data.

[0061] Search algorithm: In the department dimension, the front-end drop-down selection component is combined with the back-end database query. When the user selects a specific department, the front-end passes the selection value to the back-end. The back-end filters out the corresponding department's sign-in rate data from the database by constructing an accurate matching query statement and returns it to the front-end for display. In the salesperson dimension, the front-end search box listens to user input events and uses a fuzzy query algorithm to convert the user's fuzzy salesperson information or coded precise information into query conditions. A search request is initiated to the back-end in real time. The back-end quickly retrieves matching data in the database based on the conditions and feeds back to the front-end to update the display results, achieving efficient and accurate search.

[0062] See also Figure 3 The third embodiment of the multi-dimensional logistics receipt rate management method of the present invention includes: 301. Obtain logistics receipt data, and pre-process the obtained logistics receipt data to obtain pre-processed logistics receipt data; 302. Build a data warehouse based on pre-processed logistics receipt data; 303. Implement logistics sign-in rate management for each assessment node in multiple dimensions based on the data warehouse; wherein the multiple dimensions include: outlet dimension, site dimension, and salesperson dimension; the assessment nodes include: each outlet in the outlet dimension; each site in the site dimension; and each salesperson in the salesperson dimension; 304. Use the prediction model to predict the acceptance rate of the assessment node under the corresponding dimension within the preset time period in the future; In this embodiment, a prediction model is constructed based on random forest; Obtain historical sign-in rate data and corresponding logistics characteristic data for different assessment nodes in multiple dimensions, and perform missing value filling and feature encoding on the obtained historical sign-in rate data and corresponding logistics characteristic data to obtain processed historical sign-in rate data and corresponding logistics characteristic data; wherein the logistics characteristic data includes weather conditions, population density in the distribution area, and product type; Use the processed historical sign-in rate data and the corresponding logistics feature data to construct training sets, test sets, and validation sets; The prediction model is trained using the training set, and tested and verified using the test set and validation set to obtain the trained prediction model. For example, the model performance is optimized by adjusting the model hyperparameters, with the goal of minimizing the prediction error. The model accuracy is evaluated using the test set data. When the accuracy reaches the set threshold (such as 85%), the model is saved for subsequent predictions.

[0063] The trained prediction model is used to predict the sign-in rate of the assessment nodes under the corresponding dimensions within the preset time period in the future, and the model is displayed in a visual way to assist first-level branch managers in predicting business trends in advance and formulating targeted strategies.

[0064] See also Figure 4 The fourth embodiment of the multi-dimensional logistics receipt rate management method of the present invention includes: 401. Obtain logistics receipt data, and pre-process the obtained logistics receipt data to obtain pre-processed logistics receipt data; 402. Build a data warehouse based on the pre-processed logistics receipt data; 403. Implementing logistics sign-in rate management for each assessment node in multiple dimensions based on the data warehouse; wherein the multiple dimensions include: outlet dimension, site dimension, and salesperson dimension; the assessment nodes include: each outlet in the outlet dimension; each site in the site dimension; and each salesperson in the salesperson dimension; 404. Use the access control RBAC model to assign specific roles to accounts accessing the data warehouse, and associate each specific role with corresponding menu access permissions; In this embodiment, administrator accounts for first-level branches and branch service departments are finely divided into roles based on their management responsibilities and data requirements. The first-level branch administrator role is granted a global perspective, with associated menu access permissions to view overall, company, and branch sign-off rate data, enabling them to coordinate business operations at all levels. The branch service department administrator role focuses on its own business unit and is only granted access to view its own branch's sign-off rate data to ensure data confidentiality. During system initialization, role information is entered for each administrator account through the management backend configuration interface. This information is stored in a dedicated user role table, with a one-to-one mapping relationship established with the account ID. When a user logs in to the system, the front-end login interface sends the user's entered account and password information to the back-end authentication service. The back-end authentication service first verifies the account and password. After confirming the user's identity, it queries the database's user role table for the corresponding role information and obtains the role ID. Next, based on the role ID, it queries a pre-configured role-permission mapping table. This table details the correspondence between each role and accessible menus. Using the role ID as an index, the target role's permission set can be quickly located. If the target menu permission is in the set, that is, the match is successful, the backend returns an instruction to allow access to the frontend, and the frontend displays the corresponding menu page; if no matching permission is found, the backend returns an access denial prompt to the frontend, informing the user that the permission is insufficient. At the same time, the user can be guided to contact the system administrator to apply for permission or troubleshoot the problem, ensuring strict system permission control and clear processes.

[0065] See also Figure 5The fifth embodiment of the multi-dimensional logistics receipt rate management method of the present invention includes: 501. Obtain logistics receipt data, and pre-process the obtained logistics receipt data to obtain pre-processed logistics receipt data; 502. Build a data warehouse based on the pre-processed logistics receipt data; 503. Implementing logistics sign-in rate management for each assessment node in multiple dimensions based on the data warehouse; wherein the multiple dimensions include: outlet dimension, site dimension, and salesperson dimension; the assessment nodes include: each outlet in the outlet dimension; each site in the site dimension; and each salesperson in the salesperson dimension; 504. Use the access control RBAC model to assign specific roles to accounts accessing the data warehouse, and associate each specific role with corresponding menu access permissions; 505. Use the prediction model to predict the acceptance rate of the assessment node under the corresponding dimension within the preset time period in the future.

[0066] The above describes the logistics receipt rate management method based on multi-dimensionality in the embodiment of the present invention. The following describes the logistics receipt rate management device based on multi-dimensionality in the embodiment of the present invention. Figure 6 In one embodiment of the present invention, a multi-dimensional logistics receipt rate management device includes: The logistics receipt data acquisition module 601 is used to acquire logistics receipt data and pre-process the acquired logistics receipt data to obtain pre-processed logistics receipt data; In this embodiment, the logistics visa data acquisition module 601 includes: The data acquisition submodule 6011 is used to acquire logistics receipt data based on various data sources of the logistics business system; In this embodiment, the data acquisition submodule 6011 includes: when the data sources of the logistics business system generate new receipt data changes, it is published in the form of a message to the Kafka distributed message queue; through the data change monitoring technology, it continuously monitors the Kafka distributed message queue, and when it monitors that the Kafka distributed message queue has data updates, it obtains the updated logistics receipt data in real time; The pre-processing sub-module 6012 is used to clean the acquired logistics receipt data through the ETL tool to obtain the cleaned logistics receipt data; and perform format conversion on the cleaned logistics receipt data to obtain the converted logistics receipt data.

[0067] A data warehouse construction module 602 is used to construct a data warehouse based on the pre-processed logistics receipt data; In this embodiment, the data warehouse construction module 602 includes: Use ETL tools to load pre-processed logistics receipt data and create a master table for receipt rates and associated dimension tables based on the loaded pre-processed logistics receipt data. Build a star- or snowflake-structured data warehouse based on the master table for receipt rates and associated dimension tables through foreign key associations. The main table of receipt rate includes the fields of branch ID, date, receipt quantity and total shipment quantity; The associated dimension tables include a network point information table and a product type table.

[0068] The logistics receipt rate management module 603 is used to implement multi-dimensional logistics receipt rate management based on the data warehouse.

[0069] In this embodiment, the logistics receipt rate management module 603 includes: multi-dimensional real-time logistics receipt rate query and display, multi-dimensional historical logistics receipt rate query and display, multi-dimensional frequency receipt rate data query and display, frequency linkage, association of order number details and receipt rate data, data sorting, and data search and export; The multi-dimensional historical logistics receipt rate query and display includes: using a time series data warehouse to store historical receipt rate data, extracting the logistics receipt rate of the corresponding dimension from the data warehouse according to the time index; for querying summary data across multiple days, using an aggregate function to obtain daily receipt data within the selected time period, and displaying the summary receipt rate results through the front-end; The multi-dimensional frequency acceptance rate data query and display includes: dynamically linking and presenting the frequency acceptance rate data of each assessment node under each dimension according to the frequency assessment configured for each assessment node, and the assessment node under each frequency is updated in real time synchronously with the interface data; The frequency linkage includes: when the interface receives a new frequency assessment data change notification, it triggers the update of the frequency configuration information of the corresponding assessment node, and at the same time, the front-end interface dynamically renders and displays the corresponding frequency acceptance rate data according to the new frequency configuration information by monitoring data change events; The associating of the order number detail data with the receipt rate data includes: at the data storage level, associating the order number detail data with the receipt rate data through a foreign key to establish a strong association relationship; The data sorting includes: the front end uses the JavaScript array sorting method, combined with the customized comparison function, to sort the sign-in rate data obtained from the back end according to the sorting fields and order requirements selected by the user; The data search and export includes: using the front-end drop-down selection component combined with the back-end data warehouse query to filter the corresponding receipt rate data, and return it to the front-end for display; when the user triggers the data export operation, the data is converted into a specified format according to user needs and exported.

[0070] Prediction module 604, used to predict the sign-off rate of the assessment node under the corresponding dimension within a preset time period in the future using the prediction model; In this embodiment, the prediction module 604 includes: A model building submodule 6041 is used to build a prediction model based on random forest; The data acquisition submodule 6042 is used to obtain historical sign-in rate data and corresponding logistics characteristic data for different assessment nodes in multiple dimensions, and perform missing value filling and feature encoding processing on the obtained historical sign-in rate data and corresponding logistics characteristic data to obtain processed historical sign-in rate data and corresponding logistics characteristic data; The data set construction submodule 6043 is used to construct a training set, a test set, and a validation set using the processed historical sign-in rate data and the corresponding logistics feature data; The model training submodule 6044 is used to train the prediction model using the training set, and to test and verify the prediction model using the test set and the validation set to obtain the trained prediction model; The prediction submodule 6045 is used to use the trained prediction model to predict the sign-in rate of the assessment node under the corresponding dimension within a preset time period in the future, and to display it in a visual manner.

[0071] Access control module 605, used to assign specific roles to accounts accessing the data warehouse using the access control RBAC model, and associate each specific role with corresponding menu access rights; In this embodiment, the access control module 605 includes: When a user accesses the data warehouse, the user account and password information is obtained through the front-end login interface, and the obtained user account and password information is sent to the back-end authentication service. The back-end authentication service verifies the account and password information. After confirming that the user identity is legal, the role information corresponding to the current account is queried from the user role table of the data warehouse to obtain the role ID; based on the role ID, the role permission set is obtained according to the pre-configured role-permission mapping table; if the target permission is in the current role permission set, the match is successful, and the back-end authentication service returns an access permission instruction to the front-end login interface, and displays the corresponding menu page through the front-end login interface; if the target permission is not in the current role permission set, the back-end authentication service returns an access rejection instruction to the front-end login interface.

[0072] above Figure 6 The multi-dimensional logistics receipt rate management device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0073] Figure 7is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 700 may vary significantly due to different configurations or performance, and may include one or more processors (central processing units, CPUs) 710 (for example, one or more processors), a memory 720, and one or more storage media 730 (for example, one or more mass storage devices) storing application programs 733 or data 732. The memory 720 and storage medium 730 may be either transient or persistent storage. The program stored in the storage medium 730 may include one or more modules (not shown), each of which may include a series of instruction operations on the electronic device 700. Furthermore, the processor 710 may be configured to communicate with the storage medium 730 to execute the series of instruction operations in the storage medium 730 on the electronic device 700.

[0074] The electronic device 700 may further include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input and output interfaces 750, and / or one or more operating systems 731, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 7 The illustrated electronic device structure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0075] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of a multi-dimensional logistics receipt rate management method.

[0076] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0078] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-dimensional logistics receipt rate management method, characterized in that: include: Obtaining logistics receipt data, and preprocessing the obtained logistics receipt data to obtain preprocessed logistics receipt data; Build a data warehouse based on pre-processed logistics receipt data; Realize logistics sign-in rate management of each assessment node under multi-dimensional conditions based on data warehouse; The multiple dimensions include: outlet dimension, site dimension and salesperson dimension; The assessment nodes include: each outlet in the outlet dimension; each site in the site dimension; and each salesperson in the salesperson dimension.

2. The multi-dimensional logistics receipt rate management method according to claim 1 is characterized in that: The obtaining of logistics receipt data and preprocessing of the obtained logistics receipt data to obtain preprocessed logistics receipt data include: Obtain logistics receipt data based on various data sources of the logistics business system; The acquired logistics receipt data is cleaned and processed by using ETL tools to obtain cleaned logistics receipt data; the cleaned logistics receipt data is format converted to obtain converted logistics receipt data.

3. The multi-dimensional logistics receipt rate management method according to claim 2 is characterized in that: The logistics receipt data is obtained based on various data sources of the logistics business system, including: When new receipt data changes are generated in the data sources of the logistics business system, they are published to the Kafka distributed message queue in the form of messages; the Kafka distributed message queue is continuously monitored through data change monitoring technology. When data updates are detected in the Kafka distributed message queue, the updated logistics receipt data is obtained in real time.

4. The multi-dimensional logistics receipt rate management method according to claim 1 is characterized in that: The data warehouse is constructed based on the pre-processed logistics receipt data, including: Use ETL tools to load pre-processed logistics receipt data and create a master table for receipt rates and associated dimension tables based on the loaded pre-processed logistics receipt data. Build a star- or snowflake-structured data warehouse based on the master table for receipt rates and associated dimension tables through foreign key associations. The main table of receipt rate includes the fields of branch ID, date, receipt quantity and total shipment quantity; The associated dimension tables include a network point information table and a product type table.

5. The multi-dimensional logistics receipt rate management method according to claim 1 is characterized in that: The logistics receipt rate management based on the data warehouse includes: multi-dimensional real-time logistics receipt rate query and display, multi-dimensional historical logistics receipt rate query and display, multi-dimensional frequency receipt rate data query and display, frequency linkage, association of order number details and receipt rate data, data sorting, and data search and export; The multi-dimensional historical logistics receipt rate query and display includes: using a time series data warehouse to store historical receipt rate data, extracting the logistics receipt rate of the corresponding dimension from the data warehouse according to the time index; for querying summary data across multiple days, using an aggregate function to obtain daily receipt data within the selected time period, and displaying the summary receipt rate results through the front-end; The multi-dimensional frequency acceptance rate data query and display includes: dynamically linking and presenting the frequency acceptance rate data of each assessment node under each dimension according to the frequency assessment configured for each assessment node, and the assessment node under each frequency is updated in real time synchronously with the interface data; The frequency linkage includes: when the interface receives a new frequency assessment data change notification, it triggers the update of the frequency configuration information of the corresponding assessment node, and at the same time, the front-end interface dynamically renders and displays the corresponding frequency acceptance rate data according to the new frequency configuration information by monitoring data change events; The associating of the order number detail data with the receipt rate data includes: at the data storage level, associating the order number detail data with the receipt rate data through a foreign key to establish a strong association relationship; The data sorting includes: the front end uses the JavaScript array sorting method, combined with the customized comparison function, to sort the sign-in rate data obtained from the back end according to the sorting fields and order requirements selected by the user; The data search and export includes: using the front-end drop-down selection component combined with the back-end data warehouse query to filter the corresponding receipt rate data, and return it to the front-end for display; when the user triggers the data export operation, the data is converted into a specified format according to user needs and exported.

6. The multi-dimensional logistics receipt rate management method according to claim 1 is characterized in that: The method further comprises: Build a prediction model based on random forest; Obtain historical sign-in rate data and corresponding logistics feature data for different assessment nodes in multiple dimensions, and perform missing value filling and feature coding on the obtained historical sign-in rate data and corresponding logistics feature data to obtain processed historical sign-in rate data and corresponding logistics feature data; Use the processed historical sign-in rate data and the corresponding logistics feature data to construct training sets, test sets, and validation sets; The prediction model is trained using the training set, and the prediction model is tested and verified using the test set and validation set to obtain the trained prediction model; The trained prediction model is used to predict the acceptance rate of the assessment nodes under the corresponding dimensions within the preset time period in the future, and the results are displayed in a visual way.

7. The multi-dimensional logistics receipt rate management method according to claim 1 is characterized in that: The method further comprises: Use the access control RBAC model to assign specific roles to accounts accessing the data warehouse, and associate each specific role with corresponding menu access permissions; When a user accesses the data warehouse, the user account and password information is obtained through the front-end login interface, and the obtained user account and password information is sent to the back-end authentication service. The back-end authentication service verifies the account and password information. After confirming that the user identity is legal, the role information corresponding to the current account is queried from the user role table of the data warehouse to obtain the role ID; based on the role ID, the role permission set is obtained according to the pre-configured role-permission mapping table; if the target permission is in the current role permission set, the match is successful, and the back-end authentication service returns an access permission instruction to the front-end login interface, and displays the corresponding menu page through the front-end login interface; if the target permission is not in the current role permission set, the back-end authentication service returns an access rejection instruction to the front-end login interface.

8. A multi-dimensional logistics receipt rate management device, characterized in that: include: The logistics visa data acquisition module is used to obtain logistics receipt data and pre-process the obtained logistics receipt data to obtain pre-processed logistics receipt data; Data warehouse construction module, used to build a data warehouse based on pre-processed logistics receipt data; The logistics receipt rate management module is used to implement multi-dimensional logistics receipt rate management based on the data warehouse.

9. An electronic device comprising a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the electronic device to execute each step of the multi-dimensional logistics receipt rate management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the multi-dimensional logistics receipt rate management method as described in any one of claims 1 to 7 are implemented.

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