Logistics financial data management method and device, equipment and storage medium

Abstract

The invention relates to the technical field of logistics data management, and discloses a logistics financial data management method, device and equipment and a storage medium, and the method comprises the steps: in response to a page login request, obtaining logistics data information corresponding to a plurality of logistics clients which do not submit logistics, and displaying the logistics data information on a display interface in a target display mode; receiving corresponding target logistics data imported by a target customer which does not submit logistics, and performing standardized verification on the target logistics data; when the standardized verification of the target logistics data is passed, performing data processing in combination with a preset target relation table data structure to obtain a target tax summary table of the target customer who does not submit the logistics; and updating the display interface based on the target tax summary sheet. According to the scheme, the delayed submission rate of logistics customers is reduced, the generation time of the tax summary sheet is shortened, early warning for risk customers is realized, and the management response speed is improved.

Description

Technical Field

[0001] This invention relates to the field of logistics data management technology, and in particular to a method, apparatus, equipment and storage medium for logistics financial data management. Background Technology

[0002] In the financial management, especially the tax management system, of modern logistics enterprises, ensuring that subordinate logistics clients or branches maintain and submit tax-related detailed statements on time and accurately is a crucial link in guaranteeing the overall tax compliance and smooth operation of the enterprise. However, the currently widely used traditional tax processing procedures face serious challenges. First, the process is complex, lengthy, and relies heavily on manual operations. From the collection and verification of tax-related information to data processing, summarization, and reconciliation with taxpayers, the entire process involves numerous steps, often lacking efficient coordination and collaborative supervision, resulting in extremely low overall processing efficiency. Second, the level of data standardization is low. Due to the lack of a unified data exchange standard, the tax information files submitted by various logistics clients vary greatly in format, such as different Excel spreadsheet structures and field definitions, which brings enormous difficulties and additional workload to the headquarters' financial personnel when integrating and analyzing data. Furthermore, existing processes generally lack effective proactive reminders and risk warning mechanisms. Many logistics client companies have not established a systematic reminder system, leading to frequent instances of forgetting or delaying the submission of tax-related data due to negligence. Such delays or omissions can not only directly trigger penalties from tax authorities, causing economic losses to enterprises, but also have a chain reaction on subsequent key financial work such as tax accounting and auditing, bringing numerous troubles and ultimately affecting the long-term stable development and reputation of enterprises. At the same time, there is a lack of real-time data synchronization between headquarters and various logistics customers, making it difficult for management to dynamically and comprehensively grasp the overall progress and risk status of tax processing. In summary, existing financial and tax management methods suffer from problems such as low process efficiency, difficulty in data integration, passive risk control, and insufficient collaborative supervision capabilities. Therefore, developing an intelligent financial management method that can achieve data standardization, process automation, proactive risk warning, and real-time status synchronization has become an urgent technical challenge to be solved in the industry. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, and storage medium for managing logistics financial data, in order to solve the problems of long generation time for tax summary tables and high delay rate in tax data submission by logistics customers in the prior art.

[0004] According to one aspect of this application, a method for managing logistics financial data is disclosed, the method comprising: In response to a login request on the page, the system retrieves the logistics data information corresponding to each of the multiple customers who have not submitted logistics information, and displays the logistics data information on the display interface in a target display manner. Receive the target logistics data of the customers who have not submitted logistics target data, and perform standardized verification on them. The customers who have not submitted logistics target data are one or more of the multiple customers who have not submitted logistics data. When the target logistics data standardization verification is passed, data processing is performed in conjunction with the pre-set target relationship table data structure to obtain the target tax summary table of the unsubmitted logistics target customers; The display interface is updated based on the target tax summary table.

[0005] In some embodiments, obtaining the logistics data information corresponding to each of the multiple customers who have not submitted logistics information, and displaying the logistics data information on the display interface in a targeted display manner includes: Based on AJAX technology, the logistics data information corresponding to each customer who has not submitted logistics information is asynchronously obtained and displayed on the display interface in a responsive layout. The logistics data includes at least the identification code of the customer who has not submitted logistics information, the name of the customer, and the submission status of the tax-related details form. The logistics data is marked with a target tag on the display interface.

[0006] In some embodiments, receiving the target logistics data imported by customers who have not submitted logistics target data, and performing standardized verification on it, includes: The target logistics data is standardized and verified based on preset verification standards. When verification fails, an error log containing the error location and reason is generated, and the error log is fed back to the target customer who has not submitted logistics, so that the target customer who has not submitted logistics can standardize and correct the target logistics data based on the error log.

[0007] In some embodiments, the target relationship table data structure is a three-tiered relationship table data structure consisting of a master table, a detail table, and a summary table. When the target logistics data standardization verification passes, data processing is performed using a pre-defined target relationship table data structure to obtain the target tax summary table for the unsubmitted logistics target customers, including: The main table data is processed based on an anomaly data cleaning algorithm to filter out abnormal data in the main table data and obtain cleaned main table data. Obtain detailed classification business rules, and perform detailed data classification on the cleaned master table data based on the detailed classification business rules, so as to display data that conforms to the detailed classification business rules based on the detailed table, wherein the detailed table includes the actual value of the target tax table; Based on a time series forecasting model, historical tax data is analyzed to predict future fiscal and tax trends, so as to obtain the predicted value of the target tax form. Based on the detailed table, the predicted value, and the actual value, construct the target tax summary table for the target customers who have not submitted logistics information.

[0008] In some embodiments, the logistics financial data management method of this application further includes: Build a multi-level reminder mechanism; Get the current submission status of the logistics data for each customer who has not submitted logistics data; The multi-level reminder mechanism is analyzed to determine the current reminder mechanism corresponding to the current submission status. Based on the current reminder mechanism, a submission reminder is sent to the corresponding customers who have not yet submitted their logistics information.

[0009] In some embodiments, the logistics financial data management method of this application further includes: Based on the established data interaction mechanism with external platforms, data interaction with external platforms is realized, enabling external platforms to adjust their management mechanisms based on the logistics data corresponding to each customer who has not submitted logistics data under dynamic monitoring.

[0010] In some embodiments, the logistics financial data management method of this application further includes: Extract historical data for each customer who has not submitted logistics information. The historical data includes at least historical behavior data, business scale data, geographical location, and data processing time. Obtain a delayed submission risk prediction model, which is pre-trained; The historical data of each customer who has not submitted logistics information is input into the delayed submission risk prediction model to obtain the delayed submission risk probability output by the delayed submission risk prediction model. Obtain the risk level threshold; Based on the correspondence between the probability of delayed submission risk and the risk level threshold, the delayed submission risk level of the corresponding customer who has not submitted logistics is determined; Based on the risk level of delayed submission, a warning is issued to the corresponding customers who have not submitted their logistics information.

[0011] According to another aspect of this application, a logistics financial data management device is also disclosed, characterized in that the device comprises: The login request response module is used to respond to page login requests, obtain logistics data information corresponding to each of the multiple customers who have not submitted logistics information, and display the logistics data information on the display interface in a target display manner. The logistics data includes at least logistics financial data. The data import and receiving module is used to receive the target logistics data of the customers who have not submitted logistics target data, and to perform standardized verification on them. The customers who have not submitted logistics target data are one or more of the multiple customers who have not submitted logistics data. The data processing module is used to process the target logistics data in combination with a pre-set target relationship table data structure when the target logistics data standardization verification is passed, so as to obtain the target tax summary table of the unsubmitted logistics target customers. The display interface update module is used to update the display interface based on the target tax summary table.

[0012] Optionally, the login request response module includes a logistics data acquisition unit, which is used to continuously acquire real-time logistics data of customers who have not submitted logistics information from the background based on AJAX technology in response to the login request. The layout presentation unit is used to display logistics data, including the identification code of customers who have not submitted logistics information, the name of the logistics customer, and the submission status of the tax-related details form, on the display interface in a responsive layout; the target marking unit is used to mark the information of customers who have not submitted logistics information with a conspicuous warning label.

[0013] Optionally, in this embodiment, the standardized verification module includes a data import unit for receiving the corresponding target logistics data of customers who have not submitted logistics target data imports. The intelligent verification unit is used to perform intelligent verification of target logistics data based on preset verification standards, including automatically checking whether the file format is correct, whether the fields are complete, and whether the data type meets the requirements. The log feedback unit is used to generate an error log containing the location and reason of the error after the verification fails, and to feed it back to the target customer who has not submitted the logistics data, so that the target customer who has not submitted the logistics data can standardize and correct the target logistics data based on the error log.

[0014] Optionally, in this embodiment, the target relationship table data structure is a three-tier relationship table data structure consisting of a master table, a detail table, and a summary table. When the target logistics data standardization verification passes, the data processing module includes: The data cleaning unit processes the main table data based on an abnormal data cleaning algorithm to filter out abnormal data in the main table data and obtain cleaned main table data. The data detailing unit is used to obtain the detailing business rules, and to perform data detailing on the cleaned master table data based on the detailing business rules, so as to display the data that conforms to the detailing business rules based on the detail table, wherein the detail table includes the actual value of the target tax table; The forecasting unit is used to analyze historical tax data based on time series forecasting models to predict future tax trends and obtain the predicted value of the target tax return. A construction unit is used to construct a target tax summary table for unsubmitted logistics target customers based on the detailed table, the predicted value, and the actual value.

[0015] Optionally, in this embodiment, the device further includes a multi-level reminder mechanism construction module for constructing a multi-level reminder mechanism; The current submission status acquisition module is used to acquire the current submission status of logistics data for each customer who has not submitted logistics data. The current reminder mechanism parsing module is used to parse the current reminder mechanism corresponding to the current submission state in the multi-level reminder mechanism; The submission reminder sending module is used to send submission reminders to the corresponding customers who have not yet submitted logistics information, based on the current reminder mechanism.

[0016] Optionally, in this embodiment, the device further includes a data interaction module, used to realize data interaction with the external platform based on the constructed data interaction mechanism with the external platform, so that the external platform can adjust its management mechanism based on the logistics data corresponding to each unsubmitted logistics customer under dynamic monitoring.

[0017] Optionally, in this embodiment, the device further includes: The historical data acquisition module is used to extract historical data for each customer who has not submitted logistics information. The historical data includes at least historical behavior data, business scale data, geographical location, and data processing time. The prediction model acquisition module is used to acquire a delayed submission risk prediction model, which is pre-trained. The data input module is used to input the historical data of each customer who has not submitted logistics into the delayed submission risk prediction model to obtain the delayed submission risk probability output by the delayed submission risk prediction model. The risk level threshold acquisition module is used to acquire risk level thresholds; The risk level determination module is used to determine the delayed submission risk level of the corresponding unsubmitted logistics customer based on the correspondence between the delayed submission risk probability and the risk level threshold. The risk level warning module is used to issue risk level warnings to customers who have not submitted logistics based on the risk level of delayed submission.

[0018] According to another aspect of this application, an electronic device is also disclosed, the electronic device including a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory to cause the electronic device to perform the various steps of the logistics financial data management method as described in any of the preceding claims.

[0019] According to another aspect of this application, a computer-readable storage medium is also disclosed, on which instructions are stored, which, when executed by a processor, implement the various steps of the logistics financial data management method as described in any of the preceding claims.

[0020] The present invention includes, but is not limited to, the following beneficial effects: (1) Real-time acquisition of unsubmitted customer data through AJAX technology and real-time display of logistics data information through responsive layout. The headquarters management end can see the data submission status, processing progress, anomaly statistics and risk warnings of each logistics customer in real time and intuitively, which improves the management response speed and decision-making efficiency of the headquarters; (2) Automated checking of imported logistics data using a standardized verification module, including automatic checking of whether the file format is correct, whether the fields are complete, and whether the data type meets the requirements, avoiding repeated manual verification and reducing the overall manual operation time; (3) The data structure adopts a three-level relationship table of main table - detail table - summary table, combined with anomaly data cleaning algorithm, effectively removing outliers and redundancy. (3) Ensure data quality by removing excess data and data with incorrect format; (4) Provide forward-looking financial decision support for enterprises by predicting future financial and tax trends through time series prediction models; (5) Ensure logistics customers submit data on time and reduce the rate of delayed submission by using a multi-level reminder mechanism (primary reminder, secondary reminder, and headquarters intervention); (6) Improve system interoperability and avoid data silos based on the data interaction mechanism built with external platforms; (7) Achieve early warning for risky customers based on the delayed submission risk prediction model; (8) Realize real-time feedback and synchronization of data processing status by pushing the status information of each stage of data processing to the headquarters management terminal in real time. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0022] Figure 1 This is a flowchart of a logistics financial data management method according to an embodiment of this application; Figure 2 This is another flowchart of the logistics financial data management method according to the embodiments of this application; Figure 3 This is another flowchart of the logistics financial data management method according to the embodiments of this application; Figure 4 This is another flowchart of the logistics financial data management method according to the embodiments of this application; Figure 5 This is a structural block diagram of the logistics financial data management device according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0023] This invention provides a method, apparatus, device, and storage medium for managing logistics financial data. The method includes: responding to a page login request, acquiring logistics data information corresponding to multiple customers who have not submitted logistics data, and displaying the logistics data information on a display interface in a target display manner; receiving target logistics data imported by target customers who have not submitted logistics data, and performing standardized verification on them, wherein the target customers who have not submitted logistics data are one or more of multiple customers who have not submitted logistics data; when the standardized verification of the target logistics data passes, performing data processing in conjunction with a pre-set target relationship table data structure to obtain a target tax summary table for the target customers who have not submitted logistics data; and updating the display interface based on the target tax summary table. This solution reduces the delayed submission rate of logistics customers, shortens the time for generating tax summary tables, enables early warning of high-risk customers, and improves management response speed.

[0024] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] For ease of understanding, the specific process of the embodiments of the present invention will be described below. Figure 1 A flowchart for logistics financial data management methods, such as Figure 1 As shown, it includes the following steps: S100: In response to the page login request, obtain the logistics data information corresponding to each of the multiple customers who have not submitted logistics information, and display the logistics data information on the display interface in a target display manner.

[0026] Specifically, headquarters management or finance personnel log in to the system's homepage using their valid account credentials. After verifying the user's identity and permissions, the system backend can immediately trigger an asynchronous data request. This request uses AJAX technology to request a list of "customers who have not submitted logistics reports." The server queries the database, filters out all customer records with a status of "customers who have not submitted logistics reports," and returns data including the identification code, customer name, and tax details submission status for each customer who has not submitted logistics reports. After the user's browser receives the data, there is no need to refresh the entire page. The system adopts a responsive layout to ensure that the logistics data information corresponding to each customer who has not submitted logistics reports is clearly and completely displayed on both large computer screens and small mobile phone screens. For the logistics data information of each customer who has not submitted logistics reports, the system can also use visual markings, such as highlighting the background color with a striking red or placing a red warning icon next to it, to achieve "target display" and intuitively remind managers to pay priority attention.

[0027] S102. Receive the target logistics data imported by customers who have not submitted their logistics target data, and perform standardized verification on it.

[0028] Among them, the target customers who have not submitted logistics information are one or more of a number of customers who have not submitted logistics information.

[0029] Specifically, at the beginning of each month, logistics customer operators can log into their client and access the "Tax Information Import" interface. This interface provides a file upload function, supporting the import of pre-defined formatted Excel files or standardized database files (such as .csv or .xml). After the file is imported, the standardization verification module is activated. Based on preset verification standards, the module automatically checks whether the file format is correct, whether the fields are complete, and whether the data type meets the requirements. For example, it verifies whether the file extension is correct, whether the field names are exactly the same as the preset template, and whether the number of fields is correct. It checks the data type row by row and column by column; for example, the "Tax Amount" field must be in numeric format, while the "Company Code" field must be in text format. If verification fails, the system immediately generates a detailed error log, clearly indicating the row, column, and reason for the error (e.g., "N / A" in column 'Tax Amount' in row 15 is an invalid numeric format"). The error log is then fed back in real-time to the target logistics customers who have not yet submitted their data, allowing them to standardize and correct the target logistics data based on the error log before re-uploading.

[0030] S104. When the standardization verification of the target logistics data is passed, the data is processed in combination with the pre-set target relationship table data structure to obtain the target tax summary table of the target customers who have not submitted logistics data.

[0031] Specifically, the target relational table data structure can be a three-tier relational table data structure consisting of a master table, detail tables, and summary tables, such as... Figure 2 The above is another flowchart of the logistics financial data management method according to an embodiment of this application. When the target relationship table data structure is a three-level relationship table data structure of main table, detail table, and summary table, this flowchart is an exemplary description of step S104, when the target logistics data standardization verification is passed, combining the pre-set target relationship table data structure to perform data processing to obtain the target tax summary table of the unsubmitted logistics target customer. Figure 2 The process includes the following steps: S200. Based on the abnormal data cleaning algorithm, perform data processing on the main table data to filter out abnormal data in the main table data and obtain cleaned main table data.

[0032] Understandably, in logistics financial data, the main table may record a large amount of data such as customer order amounts and shipping times. For example, order amounts are generally in the range of several hundred to tens of thousands of yuan. If an order amount is 0 or an astronomical figure, it may be abnormal data. Anomaly cleaning algorithms can filter out such erroneous data, resulting in accurate cleaned main table data and ensuring the quality of the underlying data for subsequent analysis. Specifically, after the standardization verification of the target logistics data is successfully passed, the system can use a three-tiered relational table data structure of main table, detail table, and summary table to process the data. Imported logistics data first enters the main table, where the data cleaning algorithm automatically processes the main table data to filter out abnormal data, remove obviously unreasonable data, such as records with negative sales, and identify and delete duplicate entries based on key fields (such as invoice number and transaction serial number), thus obtaining cleaned main table data.

[0033] S202. Obtain the detailed classification business rules. Based on the detailed classification business rules, perform detailed classification on the cleaned master table data to display data that conforms to the detailed classification business rules. The detailed table includes the actual value of the target tax table.

[0034] Specifically, further detailed business rules are obtained. In one example, these rules may include, but are not limited to, classification standards for different tax types, corresponding tax reduction and exemption policies, and transportation routes, to divide data into detailed tables. In this example, transportation routes can be used as the detailed business rule for segmentation. After segmentation, the cleaned master table data can be categorized according to transportation routes to generate detailed tables. The detailed tables will present the actual cost, revenue, and other data corresponding to each transportation route, facilitating detailed analysis of the business situation for different routes. The detailed tables can include the actual cost, revenue, and other data corresponding to each transportation route, facilitating detailed analysis of the business situation for different routes. The detailed tables include the actual values ​​from the target tax table.

[0035] S204. Based on the time series forecasting model, analyze historical tax data to predict future fiscal and tax trends, so as to obtain the predicted value of the target tax form.

[0036] Specifically, predictions can be made based on Prophet or LSTM time series forecasting models. By using time series forecasting models for predictive analysis, such as comparing and analyzing the growth or decline trends of tax amounts before and after each year's e-commerce promotional season, and based on these historical patterns, the tax trends during similar promotional seasons in the future can be predicted to obtain the target tax return forecast value, providing a reference for enterprises to make financial plans in advance.

[0037] S206. Construct a target tax summary table for unsubmitted logistics target customers based on the detailed table, forecast values, and actual values.

[0038] Specifically, the predicted value, the actual value, and the difference between the predicted value and the actual value can be marked in the detailed table.

[0039] S106. Update the display interface based on the target tax summary table.

[0040] Specifically, at each key node of data processing (such as "Import Successful", "Validation Passed", "Processing Completed"), the system can push status update messages to the headquarters management server through Kafka or RabbitMQ integrated message queue technology. After receiving the "Processing Completed" message, the headquarters management server will update the submission status of the corresponding logistics customer in the database to "Submitted". At the same time, through the next AJAX polling, this update will be pushed to the front end. On the display interface of the headquarters management personnel, the logistics customer entry that has just been submitted will automatically disappear from the "Unsubmitted" list, or its status marker will change from red warning to green completed icon. At the same time, the statistical data such as "Number of Submitted Customers" on the Kanban board will also be updated in real time.

[0041] Understandably, this embodiment uses AJAX technology to acquire unsubmitted customer data in real time. Combined with a three-tier relational table data structure and standardized verification modules, it automates the entire process from data collection and cleaning to the generation of tax summary reports, reducing manual operation time and improving operational efficiency. It also uses time series forecasting models to predict future financial and tax trends, providing forward-looking financial decision support for enterprises. Furthermore, by pushing the status information of each stage of data processing to the headquarters management terminal in real time, it achieves real-time feedback and synchronization of data processing status.

[0042] Furthermore, Figure 3 This is another flowchart of the logistics financial data management method according to an embodiment of this application, such as... Figure 3 As shown, it includes the following steps: S300, construct a multi-level reminder mechanism.

[0043] Specifically, a multi-level alert mechanism can include primary alerts, secondary alerts, and headquarters intervention.

[0044] S302. Obtain the current submission status of logistics data for each customer who has not submitted logistics data.

[0045] S304. Analyze the current reminder mechanism corresponding to the current submission status in the multi-level reminder mechanism.

[0046] S306. Based on the current reminder mechanism, send a submission reminder to the corresponding customers who have not submitted logistics information.

[0047] Specifically, when a logistics customer is found to have only 3 days left until the deadline for submitting logistics data and the status is still "not submitted," the system automatically triggers the primary reminder mechanism, sending a reminder SMS to the mobile phone number of the person in charge registered with the logistics customer and a reminder email to their email address. If the logistics customer has already exceeded the deadline and still has not submitted, the system will trigger the secondary reminder mechanism, automatically generating a "reminder" task for the regional manager of the logistics customer in the internal management platform and marking its priority as "high." When the system detects that the logistics customer is more than 5 days overdue, it will trigger the highest level of headquarters intervention mechanism, implement risk control operations, automatically call the permission management interface, and temporarily lock the logistics customer's core business permissions (such as suspending its order placement, vehicle dispatch, and other functions in the system) until it completes the data submission and passes the review, as a coercive measure.

[0048] It is understandable that in this embodiment, the multi-level reminder mechanism (primary reminder, secondary reminder, and headquarters intervention) can ensure that logistics customers submit data on time, significantly reducing the delayed submission rate.

[0049] Specifically, Figure 4 This is another flowchart of the logistics financial data management method according to an embodiment of this application, such as... Figure 4 As shown, it includes the following steps: S400: Extract historical data for each customer who has not submitted logistics information.

[0050] Historical data may include, but is not limited to, historical behavioral data, business scale data, geographical location, and data processing time.

[0051] S402. Obtain the delayed submission risk prediction model, which is pre-trained.

[0052] S404. Input the historical data of each customer who has not submitted logistics into the delayed submission risk prediction model to obtain the delayed submission risk probability output by the delayed submission risk prediction model.

[0053] S406. Obtain the risk level threshold.

[0054] S408. Based on the correspondence between the probability of delayed submission risk and the risk level threshold, determine the delayed submission risk level of the corresponding unsubmitted logistics customer.

[0055] S410. Based on the risk level of delayed submission, issue a level warning to the corresponding customers who have not submitted logistics.

[0056] Specifically, the system periodically extracts historical data from the database of all customers who have not submitted logistics reports as features for model training. This data includes historical behavioral data, business scale data, geographical location, and data processing time. Using this data, a delayed submission risk prediction model is built using Random Forest or XGBoost. The model's prediction objective is "whether there will be a delayed submission within the next month." The latest feature data of each logistics customer is input into the trained delayed submission risk prediction model, which outputs a delayed submission risk probability between 0 and 1. The system sets a risk level threshold, such as 80%. For logistics customers whose predicted probability exceeds this threshold, the system classifies them as "high-risk." On the display interface, in addition to a red warning, a flashing icon or highlighted animation is added to provide the highest priority visual alert, prompting managers to pay close attention and intervene in advance.

[0057] Understandably, in this embodiment, a delayed submission risk prediction model is used to predict delay risks based on historical behavioral data, business scale data, geographical location and other characteristics. High-risk customers with a probability of over 80% are marked with flashing icons, which can help enterprises intervene in advance and further reduce tax risks.

[0058] Furthermore, Figure 5 A structural block diagram of a logistics financial data management device, such as Figure 5 As shown, the device includes: The login request response module is used to respond to page login requests, obtain the logistics data information corresponding to each of the multiple customers who have not submitted logistics information, and display the logistics data information on the display interface in a target display manner. The logistics data includes at least logistics financial data. The data import and receiving module is used to receive the target logistics data of customers who have not submitted logistics target data, and to perform standardized verification on them. The customer who has not submitted logistics target data can be one or more of multiple customers who have not submitted logistics data. The data processing module is used to process the target logistics data in combination with the pre-set target relationship table data structure when the target logistics data standardization verification is passed, so as to obtain the target tax summary table of the target customers who have not submitted logistics target data. The display interface update module is used to update the display interface based on the target tax summary table.

[0059] In this embodiment, the login request response module includes a logistics data acquisition unit, which is used to continuously acquire real-time logistics data of customers who have not submitted logistics information from the background based on AJAX technology in response to login requests; a layout presentation unit, which is used to display logistics data, including the identification code of customers who have not submitted logistics information, the name of the logistics customer, and the submission status of the tax-related details form, on the display interface in a responsive layout manner; and a target marking unit, which is used to mark the information of customers who have not submitted logistics information with a conspicuous warning mark.

[0060] In this embodiment, the standardization verification module includes a data import unit for receiving target logistics data imported by customers who have not submitted logistics data; an intelligent verification unit for intelligently verifying the target logistics data based on preset verification standards, including automatically checking whether the file format is correct, whether the fields are complete, and whether the data type meets the requirements; and a log feedback unit for generating an error log containing the error location and reason after verification failure, and feeding it back to the customers who have not submitted logistics data, so that the customers can standardize and correct the target logistics data based on the error log.

[0061] In this embodiment, the target relational table data structure is a three-tier relational table data structure consisting of a main table, detail tables, and a summary table. When the standardization verification of the target logistics data passes, the data processing module includes a data cleaning unit, which processes the main table data based on an anomaly data cleaning algorithm to filter out abnormal data in the main table data and obtain cleaned main table data; a data detail division unit, which obtains detail division business rules and performs data detail division on the cleaned main table data based on the detail division business rules to display data that conforms to the detail division business rules, and the detail tables include the actual values ​​of the target tax table; a prediction unit, which analyzes historical tax data based on a time series prediction model to predict future tax trends and obtain the predicted values ​​of the target tax table; and a construction unit, which constructs a target tax summary table for target customers who have not submitted logistics data based on the detail tables, predicted values, and actual values.

[0062] In this embodiment, the device further includes a multi-level reminder mechanism construction module, used to construct a multi-level reminder mechanism current submission status acquisition module, used to acquire the current submission status corresponding to the logistics data of each customer who has not submitted logistics data; a current reminder mechanism parsing module, used to parse the current reminder mechanism corresponding to the current submission status in the multi-level reminder mechanism; and a submission reminder sending module, used to send a submission reminder to the corresponding customer who has not submitted logistics data based on the current reminder mechanism.

[0063] In this embodiment, the device also includes a data interaction module for constructing a data interaction mechanism with an external platform, enabling data interaction with the external platform so that the external platform can adjust its management mechanism based on the logistics data of each customer who has not submitted logistics data under dynamic monitoring.

[0064] In this embodiment, the device further includes a historical data acquisition module for extracting historical data for each customer who has not submitted logistics data. The historical data includes at least historical behavior data, business scale data, geographical location, and data processing time. A prediction model acquisition module is used to acquire a delayed submission risk prediction model, which is pre-trained. A data input module is used to input the historical data of each customer who has not submitted logistics data into the delayed submission risk prediction model to obtain the delayed submission risk probability output by the model. A risk level threshold acquisition module is used to acquire a risk level threshold. A risk level determination module is used to determine the delayed submission risk level of the corresponding customer who has not submitted logistics data based on the correspondence between the delayed submission risk probability and the risk level threshold. A risk level warning module is used to issue a risk level warning to the corresponding customer who has not submitted logistics data based on the delayed submission risk level.

[0065] In this embodiment, AJAX technology is used to acquire unsubmitted customer data in real time, and a responsive layout is used to display logistics data information in real time. The headquarters management terminal can see the data submission status, processing progress, anomaly statistics, and risk warnings of each logistics customer in real time and intuitively, improving the headquarters' management response speed and decision-making efficiency. A standardized verification module is used to automatically check the imported logistics data, avoiding repeated manual verification and reducing overall manual operation time. The data structure adopts a three-level relationship table of master table, detail table, and summary table. Combined with anomaly data cleaning algorithm, outliers, redundant data, and data with incorrect formatting are effectively removed to ensure data quality. A time series prediction model is used to predict future financial and tax trends, providing enterprises with forward-looking financial decision support. A multi-level reminder mechanism (primary reminder, secondary reminder, and headquarters intervention) ensures that logistics customers submit data on time, reducing the late submission rate. Based on the constructed data interaction mechanism with external platforms, the system interoperability is improved, avoiding data silos. Based on the late submission risk prediction model, early warnings for high-risk customers are achieved. By pushing the status information of each stage of data processing to the headquarters management terminal in real time, real-time feedback and synchronization of data processing status are achieved.

[0066] The application of the relevant modules of the device in this example can be referred to the relevant introduction of the method principle above, and will not be repeated here.

[0067] above Figure 5The logistics financial data management device in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The electronic device in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0068] Figure 6 This is a schematic diagram of the structure of an electronic device 600 provided in an embodiment of the present invention. The electronic device 600 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) for storing application programs 633 or data 632. The memory 620 and storage media 630 can be temporary or persistent storage. The program stored in the storage media 630 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the electronic device 600. Furthermore, the processor 610 may be configured to communicate with the storage media 630 and execute the series of instruction operations in the storage media 630 on the electronic device 600.

[0069] Electronic device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input / output interfaces 660, and / or one or more operating systems 631, such as Windows Server, MacOSX, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 6 The illustrated electronic device structure does not constitute a limitation on electronic devices and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0070] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of a logistics financial data management method.

[0071] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0072] 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 this invention, in essence, or the part 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for managing logistics financial data, characterized in that, The method includes: In response to a login request on the page, the system retrieves the logistics data information corresponding to each of the multiple customers who have not submitted logistics information, and displays the logistics data information on the display interface in a target display manner. Receive the target logistics data of the customers who have not submitted logistics target data, and perform standardized verification on them. The customers who have not submitted logistics target data are one or more of the multiple customers who have not submitted logistics data. When the target logistics data standardization verification is passed, data processing is performed in conjunction with the pre-set target relationship table data structure to obtain the target tax summary table of the unsubmitted logistics target customers; The display interface is updated based on the target tax summary table.

2. The logistics financial data management method according to claim 1, characterized in that, The step of obtaining the logistics data information corresponding to each of multiple customers who have not submitted logistics information, and displaying the logistics data information on the display interface in a targeted display manner includes: Based on AJAX technology, the logistics data information corresponding to each customer who has not submitted logistics information is asynchronously obtained and displayed on the display interface in a responsive layout. The logistics data includes at least the identification code of the customer who has not submitted logistics information, the name of the customer, and the submission status of the tax-related details form. The logistics data is marked with a target tag on the display interface.

3. The logistics financial data management method according to claim 1, characterized in that, The process of receiving the target logistics data imported by customers who have not submitted their logistics targets, and performing standardized verification on it, includes: The target logistics data is standardized and verified based on preset verification standards. When verification fails, an error log containing the error location and reason is generated, and the error log is fed back to the target customer who has not submitted logistics, so that the target customer who has not submitted logistics can standardize and correct the target logistics data based on the error log.

4. The logistics financial data management method according to claim 1, characterized in that, The target relationship table data structure is a three-tiered relationship table data structure consisting of a main table, a detail table, and a summary table. When the target logistics data standardization verification passes, data processing is performed using the pre-set target relationship table data structure to obtain the target tax summary table for the unsubmitted logistics target customers, including: The main table data is processed based on an anomaly data cleaning algorithm to filter out abnormal data in the main table data and obtain cleaned main table data. Obtain detailed classification business rules, and perform detailed data classification on the cleaned master table data based on the detailed classification business rules, so as to display data that conforms to the detailed classification business rules based on the detailed table, wherein the detailed table includes the actual value of the target tax table; Based on a time series forecasting model, historical tax data is analyzed to predict future fiscal and tax trends, so as to obtain the predicted value of the target tax form. Based on the detailed table, the predicted value, and the actual value, construct the target tax summary table for the target customers who have not submitted logistics information.

5. The logistics financial data management method according to claim 1, characterized in that, The method further includes: Build a multi-level reminder mechanism; Get the current submission status of the logistics data for each customer who has not submitted logistics data; The multi-level reminder mechanism is analyzed to determine the current reminder mechanism corresponding to the current submission status. Based on the current reminder mechanism, a submission reminder is sent to the corresponding customers who have not yet submitted their logistics information.

6. The logistics financial data management method according to claim 1, characterized in that, The method further includes: Based on the established data interaction mechanism with external platforms, data interaction with external platforms is realized, enabling external platforms to adjust their management mechanisms based on the dynamically monitored logistics data corresponding to each customer who has not submitted logistics data.

7. The logistics financial data management method according to claim 1, characterized in that, The method further includes: Extract historical data for each customer who has not submitted logistics information. The historical data includes at least historical behavior data, business scale data, geographical location, and data processing time. Obtain a delayed submission risk prediction model, which is pre-trained; The historical data of each customer who has not submitted logistics information is input into the delayed submission risk prediction model to obtain the delayed submission risk probability output by the delayed submission risk prediction model. Obtain the risk level threshold; Based on the correspondence between the probability of delayed submission risk and the risk level threshold, the delayed submission risk level of the corresponding customer who has not submitted logistics is determined; Based on the risk level of delayed submission, a warning is issued to the corresponding customers who have not submitted their logistics information.

8. A logistics financial data management device, characterized in that, The device includes: The login request response module is used to respond to page login requests, obtain logistics data information corresponding to each of the multiple customers who have not submitted logistics information, and display the logistics data information on the display interface in a target display manner. The logistics data includes at least logistics financial data. The data import and receiving module is used to receive the target logistics data of the customers who have not submitted logistics target data, and to perform standardized verification on them. The customers who have not submitted logistics target data are one or more of the multiple customers who have not submitted logistics data. The data processing module is used to process the target logistics data in combination with a pre-set target relationship table data structure when the target logistics data standardization verification is passed, so as to obtain the target tax summary table of the unsubmitted logistics target customers. The display interface update module is used to update the display interface based on the target tax summary table.

9. An electronic device, characterized in that, The electronic device includes a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory to cause the electronic device to perform the steps of the logistics financial data management method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the various steps of the logistics financial data management method as described in any one of claims 1-7.

Images