Digital large screen data management method and device, equipment and storage medium

Abstract

The invention relates to the field of data management, and discloses a digital large screen data management method, device and equipment and a storage medium, and the method is used for collecting and analyzing logistics data and rendering the logistics data to a digital large screen. The method comprises the following steps: converting original data into a standardized real-time data stream; dividing the standardized real-time data stream into macroscopic layer data, mesoscopic layer data and microscopic layer data, constructing three-level data visualization levels, and rendering the three-level data visualization levels to a digital large screen; abnormal events in the microscopic layer data are collected, and abnormal event high-incidence area data are generated; inputting the standardized real-time data flow into a pre-trained LSTM neural network, obtaining a prediction result, obtaining real-time traffic flow data, and generating a region-level risk early warning signal according to the prediction result and the real-time traffic flow data; and according to the area-level risk early warning signal and the abnormal event high-incidence area data, generating a courier task allocation path in combination with a genetic algorithm, generating a dynamic scheduling scheme, and pushing the dynamic scheduling scheme to a digital large screen and a mobile terminal.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a digital large-screen data management method, apparatus, device, and storage medium. Background Technology

[0002] With the booming development of e-commerce, the express delivery and logistics industry has entered a phase of rapid growth, with the daily order volume continuing to climb, and business complexity and management difficulty increasing simultaneously. Traditional express delivery operation and management models are gradually revealing multiple pain points, making it difficult to adapt to the demands of large-scale and refined development.

[0003] From a data management perspective, the express delivery business involves data from multiple stages, including courier pickup, delivery at branch locations, and handling of abnormal events. This data is often scattered across different systems (such as courier mobile apps, branch ERP systems, and customer complaint platforms), resulting in inconsistent formats, poor real-time performance, and weak correlation. Management struggles to grasp the dynamics of the entire business chain in real time, and this data silo phenomenon severely restricts decision-making efficiency. For example, during peak delivery periods at branch locations, managers cannot quickly obtain key data such as the number of pickups in each area and the real-time location of couriers, leading to delays in resource allocation.

[0004] In terms of visual monitoring, traditional management methods rely heavily on static reports or single-dimensional data displays, lacking a multi-level perspective from macro to micro. Management finds it difficult to intuitively grasp the overall situation, such as the total business volume and delivery efficiency distribution across the country / region, and also cannot deeply track detailed information such as individual courier performance and task trajectories. The identification and location of abnormal events (such as delivery delays and customer complaints) are lagging behind, and the problem is often dealt with passively only after it has escalated, affecting service quality.

[0005] From the perspective of risk warning and scheduling optimization, express delivery services are significantly affected by dynamic factors such as traffic conditions, weather changes, and order fluctuations. Traditional scheduling methods are mostly based on experience to allocate tasks, lacking scientific prediction of short-term (e.g., within one hour) network load and task completion rates, and making it difficult to identify high-risk areas in advance. When areas with a high incidence of abnormal events occur, it is impossible to quickly adjust the task routes of couriers, leading to frequent problems such as resource waste and delivery delays. For example, if a sudden traffic congestion in a certain area causes a sharp drop in delivery efficiency, traditional scheduling systems cannot detect this in real time and reallocate surrounding couriers to provide support, thus triggering a chain reaction of delays.

[0006] Therefore, existing technologies still need improvement and development. Summary of the Invention

[0007] This invention provides a digital large screen data management method, apparatus, device, and storage medium for collecting and analyzing logistics data and rendering it onto a digital large screen.

[0008] The first aspect of this invention provides a digital large-screen data management method, comprising: acquiring real-time data on courier order collection status and delivery data from distribution points to obtain raw data, and converting the raw data into a standardized real-time data stream; dividing the standardized real-time data stream into macro-level data, meso-level data, and micro-level data, and using the ECharts map engine to construct a three-level data visualization hierarchy based on the macro-level data, the meso-level data, and the micro-level data, and rendering it onto a digital large screen; collecting abnormal events in the micro-level data, and generating data based on the abnormal events. Data on areas with high incidence of abnormal events; the standardized real-time data stream is input into a pre-trained LSTM neural network to obtain the prediction results output by the LSTM neural network, and real-time traffic flow data is also obtained. Based on the prediction results and the real-time traffic flow data, a regional-level risk warning signal is generated. The prediction results include the network load, courier task completion rate, and probability of abnormal events for a preset time period in the future. Based on the regional-level risk warning signal and the data on areas with high incidence of abnormal events, a genetic algorithm is used to generate courier task allocation paths, generate a dynamic scheduling scheme, and push the dynamic scheduling scheme to digital screens and mobile terminals.

[0009] Optionally, in a first implementation of the first aspect of the present invention, the real-time acquisition of courier order collection status data and branch delivery data to obtain raw data, and the conversion of the raw data into a standardized real-time data stream, includes: constructing a rule engine based on preset business rules; acquiring courier order collection status data and branch delivery data in real time through a lightweight message queue to obtain raw data; and adjusting the data cleaning logic and display criteria of the raw data through the rule engine to obtain a standardized real-time data stream.

[0010] Optionally, in a second implementation of the first aspect of the present invention, the step of dividing the standardized real-time data stream into macro-level data, meso-level data, and micro-level data, and using the ECharts map engine to construct a three-level data visualization hierarchy based on the macro-level data, the meso-level data, and the micro-level data, and rendering it onto a digital screen, includes: hierarchically aggregating the standardized real-time data stream to obtain macro-level data, meso-level data, and micro-level data, wherein the macro-level data includes the total number of scattered orders nationwide / regionally and the heat distribution of delivery efficiency at network points, and the meso-level data includes express delivery... The individual performance indicators for delivery personnel include micro-level data such as real-time GPS location, task trajectory, and abnormal events. Using the ECharts map engine, macro, meso, and micro layers are constructed on a national administrative map. The macro, meso, and micro layer data are then added to each layer in a corresponding manner to create a three-tiered data visualization hierarchy. The macro, meso, and micro layer data within this hierarchy are linked and rendered onto a digital dashboard.

[0011] Optionally, in a third implementation of the first aspect of the present invention, the step of collecting abnormal events in the micro-level data and generating abnormal event high-incidence area data based on the abnormal events includes: extracting abnormal event records from the micro-level data stream, wherein the abnormal event records include spatial information, temporal information, and event attributes; dividing the area covered by the abnormal event records into regular grids, mapping each abnormal event to a corresponding grid, and counting the number of events in each grid; calculating the density value of each grid point based on the number of events in each grid, filtering grids with density values ​​exceeding a threshold, merging adjacent high-risk grids through connected component analysis, and generating abnormal event high-incidence areas.

[0012] Optionally, in a fourth implementation of the first aspect of the present invention, the step of inputting the standardized real-time data stream into a pre-trained LSTM neural network, obtaining the prediction result output by the LSTM neural network, and obtaining real-time traffic flow data, and generating a regional-level risk warning signal based on the prediction result and the real-time traffic flow data, wherein the prediction result includes the network load, courier task completion rate, and probability of abnormal events for a preset future time period, includes: performing feature engineering on the standardized real-time data stream to obtain a real-time data stream feature vector, and converting the real-time data stream feature vector into a time series vector; inputting the time series vector into a pre-trained LSTM neural network, obtaining the prediction result output by the LSTM neural network, wherein the prediction result includes the network load, courier task completion rate, and probability of abnormal events for a preset future time period; obtaining real-time traffic flow data, and generating a regional-level risk warning signal based on the prediction result and the real-time traffic flow data.

[0013] Optionally, in the fifth implementation of the first aspect of the present invention, the step of generating a courier task allocation path based on the regional-level risk warning signal and the data on areas with high incidence of abnormal events, combined with a genetic algorithm, generating a dynamic scheduling scheme, and pushing the dynamic scheduling scheme to a digital screen and a mobile terminal includes: obtaining risk data from the regional-level risk warning signal and the data on areas with high incidence of abnormal events; obtaining a list of currently unfinished tasks and real-time status data of couriers, associating the risk data, the list of currently unfinished tasks, and the real-time status data of couriers to obtain fused data, wherein the list of currently unfinished tasks includes task ID, pickup address, delivery address, and time requirement, and the real-time status data of couriers includes location, assigned task volume, remaining working time, and delivery capacity rating; generating a courier task allocation path based on the fused data and a genetic algorithm, generating a dynamic scheduling scheme, and pushing the dynamic scheduling scheme to a digital screen and a mobile terminal.

[0014] Optionally, in the sixth implementation of the first aspect of the present invention, obtaining risk data from regional risk warning signals and data on areas with high incidence of abnormal events includes: obtaining the risk level, risk type and scope of impact of each region from the regional risk warning signals to obtain first data; extracting specific geographical locations and event type distributions from the data on areas with high incidence of abnormal events to obtain second data; and integrating the first data and the second data into risk data.

[0015] A second aspect of the present invention provides a digital large-screen data management device, comprising: a conversion module, used to acquire in real time courier order collection status data and branch delivery data to obtain raw data, and convert the raw data into a standardized real-time data stream; a construction module, used to divide the standardized real-time data stream into macro-level data, meso-level data and micro-level data, and use the ECharts map engine to construct a three-level data visualization hierarchy based on the macro-level data, the meso-level data and the micro-level data, and render it onto a digital large screen; and a first generation module, used to collect abnormal events in the micro-level data and generate abnormal events based on the abnormal events. The system includes: a high-incidence area data module; a second generation module, which inputs the standardized real-time data stream into a pre-trained LSTM neural network, obtains the prediction results output by the LSTM neural network, and obtains real-time traffic flow data. Based on the prediction results and the real-time traffic flow data, it generates a regional-level risk warning signal, the prediction results including the network load for a preset time period, the courier task completion rate, and the probability of abnormal events; and a scheduling module, which, based on the regional-level risk warning signal and the high-incidence area data of abnormal events, combines a genetic algorithm to generate courier task allocation paths, generates a dynamic scheduling scheme, and pushes the dynamic scheduling scheme to a digital screen and a mobile terminal.

[0016] Optionally, in a first implementation of the second aspect of the present invention, the conversion module includes: a first construction unit, used to construct a rule engine based on preset business rules; a first acquisition unit, used to acquire in real time the courier's scattered order collection status data and the outlet delivery data through a lightweight message queue to obtain raw data; and a conversion unit, used to adjust the data cleaning logic and display criteria of the raw data through the rule engine to obtain a standardized real-time data stream.

[0017] Optionally, in a second implementation of the second aspect of the present invention, the construction module includes: a classification unit, used to aggregate the standardized real-time data stream in layers to obtain macro-level data, meso-level data, and micro-level data, wherein the macro-level data includes the total number of scattered orders nationwide / regionally and the heat distribution of delivery efficiency at network points, the meso-level data includes individual performance indicators of couriers, and the micro-level data includes the real-time GPS location, task trajectory, and abnormal events of couriers; a second construction unit, used to construct the macro-level, meso-level, and micro-level data respectively using the ECharts map engine on the national administrative map, and to add the macro-level data, the meso-level data, and the micro-level data to the macro-level, the meso-level data, and the micro-level data respectively in a one-to-one correspondence to construct a three-level data visualization hierarchy; and a linkage unit, used to link the macro-level data, the meso-level data, and the micro-level data in the three-level data visualization hierarchy, and to render the three-level data visualization hierarchy onto a digital screen.

[0018] Optionally, in a third implementation of the second aspect of the present invention, the first generation module includes: an extraction unit, configured to extract abnormal event records from the micro-level data stream, the abnormal event records including spatial information, temporal information, and event attributes; a statistics unit, configured to divide the area covered by the abnormal event records into regular grids, map each abnormal event to a corresponding grid, and count the number of events in each grid; and a first generation unit, configured to calculate the density value of each grid point based on the number of events in each grid, filter grids with density values ​​exceeding a threshold, merge adjacent high-risk grids through connected component analysis, and generate a high-incidence area of ​​abnormal events.

[0019] Optionally, in a fourth implementation of the second aspect of the present invention, the second generation module includes: a preprocessing unit, configured to perform feature engineering on the standardized real-time data stream to obtain a real-time data stream feature vector, and convert the real-time data stream feature vector into a time series vector; a prediction unit, configured to input the time series vector into a pre-trained LSTM neural network to obtain the prediction result output by the LSTM neural network, the prediction result including the network load for a preset time period, the courier task completion rate, and the probability of abnormal events; and a second generation unit, configured to acquire real-time traffic flow data and generate a regional-level risk warning signal based on the prediction result and the real-time traffic flow data.

[0020] Optionally, in the fifth implementation of the second aspect of the present invention, the scheduling module includes: a second acquisition unit, used to acquire risk data from regional-level risk warning signals and data on areas with high incidence of abnormal events; a fusion unit, used to acquire a list of currently unfinished tasks and real-time status data of couriers, and associate the risk data, the list of currently unfinished tasks, and the real-time status data of couriers to obtain fused data, wherein the list of currently unfinished tasks includes task ID, pickup address, delivery address, and time requirement, and the real-time status data of couriers includes location, assigned task volume, remaining working time, and delivery capacity rating; and a scheduling unit, used to generate courier task allocation paths based on the fused data and combined with a genetic algorithm, generate a dynamic scheduling scheme, and push the dynamic scheduling scheme to a digital screen and a mobile terminal.

[0021] A third aspect of the present invention provides a digital large-screen data management device, comprising: a memory and at least one processor, wherein the memory stores computer-readable instructions, and the memory and the at least one processor are interconnected via a circuit; the at least one processor invokes the computer-readable instructions in the memory to cause the digital large-screen data management device to perform the various steps of the digital large-screen data management method described above.

[0022] A fourth aspect of the present invention provides a computer-readable storage medium storing computer-readable instructions that, when executed on a computer, cause the computer to perform the steps of the digital large screen data management method described above.

[0023] The technical solution provided by this invention acquires and standardizes data on courier order collection and delivery at network points in real time, and uses a three-level data visualization hierarchy to present the operational status at different levels intuitively, facilitating efficient monitoring. Moreover, the collection and analysis of micro-level abnormal events can promptly identify high-incidence areas, enabling targeted responses. The use of LSTM neural networks combined with traffic flow data for prediction and early warning can mitigate risks in advance. Furthermore, the use of genetic algorithms to optimize paths and generate dynamic scheduling schemes can improve task execution efficiency, achieving intelligent and efficient management of the entire express delivery operation chain. Attached Figure Description

[0024] Figure 1 This is a first flowchart of a digital large screen data management method provided in an embodiment of the present invention;

[0025] Figure 2 This is a second flowchart of the digital large screen data management method provided in an embodiment of the present invention;

[0026] Figure 3 This is a third flowchart of the digital large screen data management method provided in the embodiments of the present invention;

[0027] Figure 4 This is a fourth flowchart of the digital large screen data management method provided in the embodiments of the present invention;

[0028] Figure 5 This is a fifth flowchart of the digital large screen data management method provided in an embodiment of the present invention;

[0029] Figure 6 This is a sixth flowchart of the digital large screen data management method provided in an embodiment of the present invention;

[0030] Figure 7 This is a schematic diagram of the structure of the digital large-screen data management device provided in an embodiment of the present invention;

[0031] Figure 8 This is a schematic diagram of the structure of a digital large-screen data management device provided in an embodiment of the present invention. Detailed Implementation

[0032] 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.

[0033] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 The first embodiment of a digital large screen data management method according to the present invention includes:

[0034] S101. Real-time acquisition of courier order collection status data and branch delivery data to obtain raw data, and conversion of raw data into standardized real-time data stream;

[0035] It is understood that the executing entity of this invention can be a digital large-screen data management device, a terminal, or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example.

[0036] In this embodiment, the positioning and status collection module deployed on the courier's mobile terminal and the delivery information management system inside the branch are used to capture real-time status data such as the courier's order collection progress, location, and collection volume, as well as delivery data such as the number, type, and destination of packages to be delivered at the branch, as raw data. Then, standardized processing methods such as data cleaning, format conversion, and field mapping are used to uniformly convert the raw data from different sources and formats into a real-time data stream that conforms to preset specifications, ensuring data consistency and availability.

[0037] S102. Divide the standardized real-time data stream into macro-level data, meso-level data, and micro-level data, and use the ECharts map engine to construct a three-level data visualization hierarchy based on the macro-level data, meso-level data, and micro-level data, and render it onto a digital screen.

[0038] In this embodiment, based on the data's coverage, granularity, and other characteristics, the standardized real-time data stream is classified into macro-level data (such as the overall regional pickup and delivery volume, business growth trends, etc.), meso-level data (such as the pickup and delivery volume of each branch, the flow of express packages within the region, and the individual performance indicators of couriers, etc.), and micro-level data (such as the task progress of a single courier, the status of specific packages, etc.). Then, using the drawing function of the ECharts map engine, corresponding visualization charts are designed for each of the three levels of data (such as using a regional heat map to display the total distribution at the macro level, a bar chart to display the business volume of branches at the meso level, and a dot-line chart to display the courier's trajectory at the micro level), constructing a three-level data visualization hierarchy. These visualization contents are then rendered in real time onto a digital screen, achieving an intuitive presentation and hierarchical display of the data.

[0039] S103. Collect abnormal events in the micro-level data and generate data on high-incidence areas of abnormal events based on the abnormal events.

[0040] In this embodiment, by setting rules for judging abnormal events (such as a courier failing to complete a task on time, abnormal package information, etc.), various abnormal event records are automatically filtered and collected from micro-level data. Then, the frequency and density of abnormal events in each region within a certain period of time are statistically analyzed to generate data on high-incidence areas of abnormal events and identify areas that need to be focused on.

[0041] S104. Input the standardized real-time data stream into the pre-trained LSTM neural network, obtain the prediction results output by the LSTM neural network, and obtain real-time traffic flow data. Generate a regional-level risk warning signal based on the prediction results and real-time traffic flow data. The prediction results include the network load, courier task completion rate and probability of abnormal events for a preset time period in the future.

[0042] In this embodiment, standardized real-time data streams are processed according to time series and then input into a pre-trained LSTM neural network. This network learns patterns from historical data to predict the load of each network point (such as the number of parcels to be processed), the task completion rate of couriers, and the probability of abnormal events within a preset time period. At the same time, real-time traffic flow data (such as road congestion and traffic speed) is accessed. Combined with the above prediction results, a risk assessment model is set up (such as the situation of excessive load, low completion rate, high probability of abnormality, and traffic congestion) to generate regional risk warning signals of different levels (such as blue, yellow, and red warnings).

[0043] S105. Based on regional risk warning signals and data on areas with high incidence of abnormal events, a task allocation path for couriers is generated using a genetic algorithm. A dynamic scheduling scheme is generated and rendered in real time on a digital screen, while also being pushed to mobile terminals for execution.

[0044] In this embodiment, the problem of assigning delivery routes to couriers is transformed into a path optimization model by using regional risk warning signals and data on areas with high incidence of abnormal events as constraints. By utilizing genetic algorithms for selection, crossover, and mutation, the model iteratively generates task assignments and delivery routes for couriers while meeting task requirements and avoiding high-risk areas, thus generating a dynamic scheduling scheme. This scheme is rendered in real time on a digital screen for display and simultaneously pushed to the courier's mobile terminal via a network interface to guide them in executing new task assignments.

[0045] This embodiment provides a digital large-screen data management method, which acquires and standardizes data on courier order collection and delivery at network points in real time. Utilizing a three-level data visualization hierarchy, it presents operational status at different levels intuitively, facilitating efficient monitoring. Furthermore, the collection and analysis of micro-level anomalies allows for timely identification of high-incidence areas, enabling targeted responses. The use of LSTM neural networks combined with traffic flow data for prediction and early warning can mitigate risks in advance. In addition, the generation of dynamic scheduling schemes through genetic algorithm-optimized paths improves task execution efficiency, achieving intelligent and efficient management of the entire express delivery operation chain.

[0046] Please see Figure 2 The second embodiment of the digital large screen data management method in this invention includes:

[0047] S201. Build a rule engine based on preset business rules.

[0048] In this embodiment, a rule engine is built based on preset business rules. The preset business rules cover data format specifications, outlier judgment criteria, data display requirements under different business scenarios, etc. The rule engine can regulate the data processing process according to these rules.

[0049] S202. Obtain the raw data by using a lightweight message queue to retrieve the status data of courier's individual order collection and the data of the network point's delivery.

[0050] In this embodiment, through a lightweight message queue (such as RabbitMQ, Kafka, etc.), the real-time data of the pickup status of individual orders uploaded by the courier's mobile terminal (including pickup time, location, type of package, quantity, etc.) and the data of the dispatch generated by the branch management system (including dispatch number, destination, estimated delivery time, branch, etc.) are received in real time to form raw data.

[0051] S203. Adjust the data cleaning logic and display criteria of the original data through the rule engine to obtain a standardized real-time data stream.

[0052] In this embodiment, the rule engine cleans the raw data according to preset business rules (such as removing duplicate data, correcting data with format errors, and supplementing missing information), and adjusts it according to a unified display standard (such as data field naming, statistical units, etc.) to finally obtain a standardized real-time data stream.

[0053] In this embodiment, a lightweight message queue is used to achieve real-time acquisition of data on courier order collection and delivery at network points, ensuring the timeliness and integrity of the original data. At the same time, the rule engine built on preset business rules can flexibly adjust the data cleaning logic and display criteria, enabling the original data to be efficiently transformed into a standardized real-time data stream. This lays a high-quality data foundation for subsequent multi-level visualization, anomaly monitoring, intelligent prediction, and dynamic scheduling, thereby improving the accuracy and response speed of express delivery operation management as a whole.

[0054] Please see Figure 3 A third embodiment of a digital large-screen data management method according to the present invention includes:

[0055] S301. Standardized real-time data streams are aggregated in layers to obtain macro-level data, meso-level data, and micro-level data. Macro-level data includes the total number of scattered orders nationwide / regionally and the heat map distribution of delivery efficiency at network points. Meso-level data includes individual performance indicators of couriers. Micro-level data includes real-time GPS location of couriers, task trajectory, and abnormal events.

[0056] In this embodiment, standardized real-time data streams are aggregated in layers, and integrated into different levels of data according to the business scope and level of detail reflected by the data. Macro-level data is obtained by summarizing the number of scattered orders collected nationwide or in various regions to obtain the total number of scattered orders nationwide / regionally, and by statistically analyzing the average delivery time, on-time rate, and other indicators of each network point and performing spatial distribution analysis to obtain the heat distribution of network point delivery efficiency; meso-level data is obtained by collecting and calculating the average daily collection and delivery volume and on-time completion rate of each courier to obtain the courier's individual performance indicators; micro-level data directly extracts the GPS location information uploaded in real time by the courier's mobile terminal, the recorded task execution trajectory, and information on abnormal events that have occurred.

[0057] S302. Using the ECharts map engine, construct macro, meso, and micro layers on the national administrative map, and add the macro, meso, and micro data to the macro, meso, and micro layers respectively to construct a three-level data visualization hierarchy.

[0058] In this embodiment, a national administrative map is loaded using the ECharts map engine as the base map. On this base map, a framework of macro, meso, and micro layers is built. Then, the total number of scattered orders nationwide / regionally and the heat distribution of delivery efficiency of network points are added to the macro layer. The individual performance indicators of couriers in the meso layer are added to the meso layer. The real-time GPS location of couriers, task trajectory and abnormal events of couriers in the micro layer are added to the micro layer. This links the data of each level with the map layers to construct a three-level data visualization hierarchy.

[0059] S303. Link the macro-level data, meso-level data and micro-level data in the three-level data visualization hierarchy, and render the three-level data visualization hierarchy onto the digital screen.

[0060] In this embodiment, macro-level data, meso-level data, and micro-level data are linked according to pre-set data linkage rules. For example, clicking on the heat map of a certain area in the macro-level layer will automatically display the personal performance indicators of couriers in that area in the meso-level layer. Clicking on the performance data of a courier in the meso-level layer will display the courier's real-time GPS location, task trajectory, and abnormal events in the micro-level layer. Finally, the three levels of data visualization are displayed in real time on a digital screen using data rendering technology.

[0061] In this embodiment, the pre-defined data linkage rules include trigger conditions and data association fields.

[0062] Example of triggering conditions:

[0063] Macro layer → Meso layer: Click on the "Provincial / Municipal Administrative Region" on the macro layer map (accuracy range: provincial region click range ≥ 100km) 2 City-level areas ≥10km 2 This triggers the loading of mid-level data;

[0064] Meso-level to Micro-level: Click on the "Courier Name / Employee ID" in the meso-level list or the "Courier Identifier" on the map (click error ≤ 5 pixels) to trigger the loading of micro-level data.

[0065] Here is an example of data association fields:

[0066] The macro and meso levels are linked by "regional codes." The "province / city administrative region code" of the macro level (such as Beijing code 110000) corresponds to the "regional code" of the courier in the meso level, allowing the filtering of performance data for all couriers in that region. The meso and micro levels are linked by "courier employee ID." The "courier_id" of the meso level corresponds to the "executor_id" in the task record of the micro level, allowing the filtering of the courier's real-time GPS trajectory, task trajectory, and abnormal events.

[0067] In this embodiment, by aggregating standardized real-time data streams into macro, meso, and micro layers containing different specific content, information acquisition can be met for different management needs. The corresponding layers are constructed on the national administrative map using the ECharts map engine and data is added to build a three-level data visualization hierarchy, realizing the combination of data and geospatial data. Then, through data linkage and rendering to a digital screen, managers can have a comprehensive and coherent grasp of the express delivery business from the whole to the part and from the macro to the micro, improving the intuitiveness of management and decision-making efficiency.

[0068] Please see Figure 4 The fourth embodiment of a digital large screen data management method in this invention includes:

[0069] S401. Extract abnormal event records from the micro-level data stream. The abnormal event records include spatial information, temporal information, and event attributes.

[0070] In this embodiment, abnormal event records are extracted from the micro-level data stream. Based on preset abnormal judgment rules (such as the courier failing to pick up the package within the time limit, or the package information not matching the condition), abnormal events that meet the conditions are filtered out. Each record contains detailed spatial information (such as specific latitude and longitude coordinates), time information (such as the exact time of occurrence), and event attributes (such as abnormality type, severity, etc.) to ensure the comprehensiveness and accuracy of abnormal event information.

[0071] S402. Divide the area covered by the abnormal event record into a regular grid, map each abnormal event to the corresponding grid, and count the number of events in each grid.

[0072] In this embodiment, the area covered by the abnormal event records is divided into several regular grids according to a fixed size (such as 100 meters × 100 meters) to form a grid matrix; then, based on the spatial information in each abnormal event record, the grid to which it belongs is determined, the abnormal events are mapped to the corresponding grids one by one, and the number of abnormal events contained in each grid is counted to achieve spatial quantitative distribution statistics of abnormal events.

[0073] S403. Calculate the density value of each grid point based on the number of events in each grid, and filter grids with density values ​​exceeding the threshold. Merge adjacent high-risk grids through connected component analysis to generate areas with high incidence of abnormal events.

[0074] In this embodiment, the density value of each grid point (i.e., the number of abnormal events per unit area) is calculated based on the number of events within each grid and the grid area.

[0075] In this embodiment, a density threshold (such as 0.05 events per square meter) is preset, and grids with density values ​​exceeding the threshold are selected as high-risk grids.

[0076] Then, a connected component analysis algorithm is used to traverse all high-risk grids. The spatial adjacency relationships (such as shared edges or vertices) between grids are used to determine whether they belong to the same connected component. For example, for a two-dimensional grid matrix, each high-risk grid is checked against its top, bottom, left, right, and four diagonal adjacent grids. If adjacent grids are also high-risk, they are grouped into the same region. During the traversal, each connected component is assigned a unique identifier to distinguish different regions. Finally, all high-risk grids belonging to the same connected component are merged to form a continuous spatial range, thus identifying the high-incidence area of ​​abnormal events.

[0077] In this embodiment, abnormal event records containing spatial, temporal, and event attributes are accurately extracted from the micro-level data stream. Through regular grid division and mapping, density calculation, and connected region analysis, high-incidence areas of abnormal events can be located scientifically and efficiently, providing accurate basis for targeted control and resource allocation, and improving the control and response efficiency of abnormal situations in express delivery business.

[0078] Please see Figure 5 The fifth embodiment of a digital large screen data management method in this invention includes:

[0079] S501. Perform feature engineering on the standardized real-time data stream to obtain the real-time data stream feature vector, and convert the real-time data stream feature vector into a time series vector.

[0080] In this embodiment, feature engineering is performed on the standardized real-time data stream to extract key features related to network load, courier task completion rate, and abnormal events, such as historical pickup and delivery volume, courier historical task completion status, and frequency of past abnormal events. These features are then normalized and standardized to eliminate the influence of units, resulting in a real-time data stream feature vector. The feature vector is then arranged in chronological order and converted into a time series vector that meets the input requirements of an LSTM neural network, ensuring that the data reflects temporal correlation.

[0081] S502. Input the time series vector into the pre-trained LSTM neural network and obtain the prediction results output by the LSTM neural network. The prediction results include the network load, courier task completion rate and probability of abnormal events for a preset time period in the future.

[0082] In this embodiment, the LSTM neural network is trained with a large amount of historical data to learn the time-dependent patterns and latent dynamics within the data. The time series vector is input into the pre-trained LSTM neural network. After processing the input time series vector, the LSTM neural network outputs a prediction result for a future preset duration (e.g., 1 hour). Specifically, this includes the expected number of parcels to be processed at each branch (i.e., branch load), the percentage of tasks expected to be completed by couriers within that duration (i.e., task completion rate), and the probability of abnormal events occurring in that area.

[0083] S503. Obtain real-time traffic flow data and generate regional risk warning signals based on prediction results and real-time traffic flow data.

[0084] In this embodiment, real-time traffic flow data, including traffic volume, average driving speed, and congestion index of each road segment, is obtained through a data interface with the traffic management department or a dedicated traffic data collection device. Then, a risk assessment model is established, which combines the predicted network load, courier task completion rate, and probability of abnormal events with the real-time traffic flow data. For example, when the network load is too high, the task completion rate is low, the probability of abnormal events is high, and the area is congested, it is judged as high risk, and a corresponding regional risk warning signal is generated according to the risk level classification rules (such as low, medium, and high).

[0085] In this embodiment, a time series vector is obtained by performing feature engineering on the standardized real-time data stream. This vector is then input into a pre-trained LSTM neural network, which can accurately predict key indicators such as the network load for a preset period of time in the future. Combined with real-time traffic flow data, a regional-level risk warning signal is generated, enabling early prediction of risks in the express delivery business. This provides reliable support for scientific scheduling and risk avoidance, and improves the stability and efficiency of operations.

[0086] Please see Figure 6 The sixth embodiment of a digital large-screen data management method in this invention includes:

[0087] S601. Obtain risk data from regional risk warning signals and data on areas with a high incidence of abnormal events.

[0088] In this embodiment, risk data is obtained from regional-level risk warning signals and data on areas with high incidence of abnormal events. Specifically, this includes: obtaining the risk level, risk type, and impact range of each region from the regional-level risk warning signals to obtain first data; extracting specific geographical locations and event type distributions from the data on areas with high incidence of abnormal events to obtain second data; and integrating the first data and the second data into risk data.

[0089] In this embodiment, the risk level (e.g., red, yellow, blue), risk type (e.g., traffic congestion, excessive network load, frequent abnormal events), and scope of impact (e.g., specific street, business district boundary) of each region are extracted from the regional risk warning signals.

[0090] In this embodiment, the specific geographical location (such as latitude and longitude range) and event type distribution (such as the proportion of lost packages and delivery delays) of each high-incidence area are parsed from the data of high-incidence areas of abnormal events.

[0091] S602. Obtain the current list of unfinished tasks and the real-time status data of the courier. Associate the risk data, the current list of unfinished tasks and the real-time status data of the courier to obtain fused data. The current list of unfinished tasks includes task ID, pickup address, delivery address and time requirement. The real-time status data of the courier includes location, assigned task volume, remaining working time and delivery capacity rating.

[0092] In this embodiment, a list of currently incomplete tasks is retrieved from the business system database. Each task in the list contains a unique task ID, a specific pickup address (house number, street), a delivery address, and a clear time requirement (such as same-day delivery or next-day delivery).

[0093] In this embodiment, the courier's current location (accurate to the meter level of latitude and longitude), assigned tasks (e.g., 10 orders already completed), remaining working time (e.g., 4 hours of work remaining), and delivery capability rating (e.g., A, B, C levels, based on historical timeliness, positive review rate, etc.) are obtained in real time through the positioning terminal worn by the courier and the business APP.

[0094] In this embodiment, based on preset data association rules, high-risk areas in the risk data are matched with the destinations of tasks in the incomplete task list, and tasks passing through high-risk areas are marked; then, the real-time status data of couriers is associated with the types of tasks they can undertake and the regions, for example, couriers who are good at delivering in a certain region and whose current load is low are associated with tasks in that region, and finally, fused data containing task risk tags and courier suitability is formed.

[0095] S603. Based on the fused data, the system uses a genetic algorithm to generate task allocation paths for couriers, generates a dynamic scheduling scheme, and pushes the dynamic scheduling scheme to digital screens and mobile terminals.

[0096] In this embodiment, a path generation model is constructed based on fused data, with the objective function being "shortest total delivery distance, lowest detour cost for high-risk areas, and balanced courier load." High-risk areas in the risk data are set as path constraints (e.g., prohibited entry or priority detour), and real-time courier status data are used as capacity constraints (e.g., maximum load capacity, maximum working hours). Then, a genetic algorithm is used to solve the model. First, a batch of initial task allocation path schemes are randomly generated as a population. The fitness of each scheme is determined by calculating the objective function value. Schemes with high fitness are selected for crossover (e.g., exchanging some task allocation segments) and mutation (e.g., randomly adjusting the task order). After multiple generations of iteration, the optimal scheme, i.e., the dynamic scheduling scheme, is obtained. Finally, the scheme is pushed to a digital screen in real time for visualization (e.g., path map, task allocation table) through a data interface, and simultaneously pushed to the corresponding couriers through a mobile terminal APP, including task details, optimized route navigation, and risk warnings.

[0097] In this embodiment, by integrating regional risk warning signals, data on areas with high incidence of abnormal events, and the current task and courier status, and by using a genetic algorithm to optimize the task allocation path, the generated dynamic scheduling scheme can effectively avoid high-risk areas and adapt to the real-time capabilities of couriers. This not only improves task execution efficiency but also reduces operational risks. At the same time, through synchronous push notifications on digital screens and mobile terminals, the timely transmission and efficient execution of scheduling instructions are ensured.

[0098] The above describes the digital large screen data management method in the embodiments of the present invention. The following describes the apparatus in the embodiments of the present invention. Please refer to [link / reference]. Figure 7 The implementation methods of the digital large screen data management device in this invention include:

[0099] The conversion module 701 is used to acquire real-time data on the status of express delivery personnel's individual order collection and the data on the delivery of goods at the network points, obtain raw data, and convert the raw data into a standardized real-time data stream.

[0100] The construction module 702 is used to divide the standardized real-time data stream into macro-level data, meso-level data and micro-level data, and use the ECharts map engine to construct a three-level data visualization hierarchy based on the macro-level data, the meso-level data and the micro-level data, and render it to a digital screen.

[0101] The first generation module 703 is used to collect abnormal events in the microscopic layer data and generate abnormal event high-incidence area data based on the abnormal events.

[0102] The second generation module 704 is used to input the standardized real-time data stream into a pre-trained LSTM neural network, obtain the prediction result output by the LSTM neural network, and obtain real-time traffic flow data. Based on the prediction result and the real-time traffic flow data, a regional-level risk warning signal is generated. The prediction result includes the network load, courier task completion rate and probability of abnormal events for a preset time period in the future.

[0103] The scheduling module 705 is used to generate a courier task allocation path based on the regional risk warning signal and the data of areas with high incidence of abnormal events, combined with a genetic algorithm, to generate a dynamic scheduling scheme, and push the dynamic scheduling scheme to the digital screen and mobile terminal.

[0104] In this embodiment, the conversion module 701 includes: a first construction unit 7011, used to build a rule engine based on preset business rules; a first acquisition unit 7012, used to acquire in real time the courier's scattered order collection status data and the network point delivery data through a lightweight message queue to obtain raw data; and a conversion unit 7013, used to adjust the data cleaning logic and display caliber of the raw data through the rule engine to obtain a standardized real-time data stream.

[0105] In this embodiment, the construction module 702 includes: a classification unit 7021, used to aggregate the standardized real-time data stream in layers to obtain macro-level data, meso-level data, and micro-level data. The macro-level data includes the total number of scattered orders nationwide / regionally and the heat distribution of delivery efficiency at network points. The meso-level data includes the individual performance indicators of couriers. The micro-level data includes the real-time GPS location, task trajectory, and abnormal events of couriers. A second construction unit 7022 is used to construct the macro-level, meso-level, and micro-level data on the national administrative map using the ECharts map engine, and to add the macro-level data, meso-level data, and micro-level data to the macro-level, meso-level, and micro-level data respectively, to construct a three-level data visualization hierarchy. A linkage unit 7023 is used to link the macro-level data, meso-level data, and micro-level data in the three-level data visualization hierarchy and render the three-level data visualization hierarchy onto a digital screen.

[0106] In this embodiment, the first generation module 703 includes: an extraction unit 7031, used to extract abnormal event records from the microscopic layer data stream, the abnormal event records including spatial information, temporal information and event attributes; a statistics unit 7032, used to divide the area covered by the abnormal event records into regular grids, map each abnormal event to a corresponding grid, and count the number of events in each grid; and a first generation unit 7033, used to calculate the density value of each grid point based on the number of events in each grid, filter grids with density values ​​exceeding a threshold, merge adjacent high-risk grids through connected component analysis, and generate a high-incidence area of ​​abnormal events.

[0107] In this embodiment, the second generation module 704 includes: a preprocessing unit 7041, used to perform feature engineering on the standardized real-time data stream to obtain a real-time data stream feature vector, and convert the real-time data stream feature vector into a time series vector; a prediction unit 7042, used to input the time series vector into a pre-trained LSTM neural network to obtain the prediction result output by the LSTM neural network, the prediction result including the network load for a preset time period, the courier task completion rate, and the probability of abnormal events; and a second generation unit 7043, used to obtain real-time traffic flow data and generate a regional-level risk warning signal based on the prediction result and the real-time traffic flow data.

[0108] In this embodiment, the scheduling module 705 includes: a second acquisition unit 7051, used to acquire risk data from regional risk warning signals and data on areas with high incidence of abnormal events; a fusion unit 7052, used to acquire a list of currently unfinished tasks and real-time status data of couriers, and associate the risk data, the list of currently unfinished tasks, and the real-time status data of couriers to obtain fused data, wherein the list of currently unfinished tasks includes task ID, pickup address, delivery address, and time requirement, and the real-time status data of couriers includes location, assigned task volume, remaining working time, and delivery capacity rating; and a scheduling unit 7053, used to generate courier task allocation paths based on the fused data and combined with a genetic algorithm, generate a dynamic scheduling scheme, and push the dynamic scheduling scheme to a digital screen and a mobile terminal.

[0109] In this embodiment, real-time acquisition and standardized processing of data on courier order collection and delivery at network points, coupled with a three-level data visualization hierarchy, allows for a clear and intuitive presentation of operational status at different levels, facilitating efficient monitoring. Furthermore, the collection and analysis of micro-level anomalies enables timely identification of high-incidence areas, facilitating targeted responses. The use of LSTM neural networks combined with traffic flow data for prediction and early warning can mitigate risks in advance. In addition, the optimization of paths using genetic algorithms to generate dynamic scheduling schemes improves task execution efficiency, achieving intelligent and efficient management of the entire express delivery operation chain.

[0110] Figure 7 The structure of the digital large screen data management device shown does not constitute a limitation on the digital large screen data management device, and can implement the steps of the digital large screen data management method provided in the above-described method embodiments.

[0111] above Figure 7 The digital large screen data management device in this embodiment of the invention is described in detail from the perspective of modular functional entities. The digital large screen data management device in this embodiment of the invention is described in detail below from the perspective of hardware processing.

[0112] Figure 8This is a schematic diagram of the structure of a digital large-screen data management device provided in an embodiment of the present invention. The device 800 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 810 (e.g., one or more processors) and a memory 820, and one or more storage media 830 (e.g., one or more mass storage devices) for storing application programs 833 or data 832. The memory 820 and storage media 830 can be temporary or persistent storage. The program stored in the storage media 830 may include one or more modules (not shown), each module including a series of instruction operations on the device 800. Furthermore, the processor 810 may be configured to communicate with the storage media 830 and execute the series of instruction operations in the storage media on the device 800.

[0113] Device 800 may also include one or more power supplies 840, one or more wired or wireless network interfaces 850, one or more input / output interfaces 860, and / or one or more operating systems 831, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc.

[0114] This 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. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the digital large screen data management method.

[0115] 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.

[0116] 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, 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 described in the various embodiments of the present 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.

[0117] The above-described 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 data on a digital large screen, characterized in that, The digital large screen data management method includes: Real-time acquisition of courier order collection status data and branch delivery data to obtain raw data, and conversion of the raw data into a standardized real-time data stream; The standardized real-time data stream is divided into macro-level data, meso-level data, and micro-level data. Using the ECharts map engine, a three-level data visualization hierarchy is constructed based on the macro-level data, the meso-level data, and the micro-level data, and then rendered onto a digital screen. Collect abnormal events from the microscopic layer data, and generate data on high-incidence areas of abnormal events based on the abnormal events; The standardized real-time data stream is input into a pre-trained LSTM neural network to obtain the prediction results output by the LSTM neural network and to obtain real-time traffic flow data. A regional risk warning signal is generated based on the prediction results and the real-time traffic flow data. The prediction results include the network load, courier task completion rate and probability of abnormal events for a preset time period in the future. Based on the regional risk warning signal and the data on areas with high incidence of abnormal events, a task allocation path for couriers is generated using a genetic algorithm, a dynamic scheduling scheme is generated, and the dynamic scheduling scheme is pushed to digital screens and mobile terminals.

2. The digital large screen data management method according to claim 1, characterized in that, The process of acquiring real-time data on courier order pickup status and delivery data from distribution points to obtain raw data, and then converting the raw data into a standardized real-time data stream, includes: Build a rule engine based on preset business rules; The raw data is obtained by acquiring real-time data on the status of courier order pickups and delivery data from network points through a lightweight message queue. The rules engine adjusts the data cleaning logic and display criteria of the original data to obtain a standardized real-time data stream.

3. The digital large screen data management method according to claim 1, characterized in that, The process involves dividing the standardized real-time data stream into macro-level, meso-level, and micro-level data, and using the ECharts map engine to construct a three-tiered data visualization hierarchy based on the macro-level, meso-level, and micro-level data, which is then rendered onto a digital dashboard. This includes: The standardized real-time data stream is layered and aggregated to obtain macro-level data, meso-level data, and micro-level data. The macro-level data includes the total number of scattered orders nationwide / regionally and the heat map distribution of delivery efficiency at network points. The meso-level data includes individual performance indicators of couriers. The micro-level data includes the real-time GPS location of couriers, task trajectories, and abnormal events. Using the ECharts map engine, macro, meso, and micro layers are constructed on the national administrative map. The macro, meso, and micro data are then added to the macro, meso, and micro layers respectively to construct a three-level data visualization hierarchy. The macro-level, meso-level, and micro-level data in the three-level data visualization hierarchy are linked together, and the three-level data visualization hierarchy is rendered onto a digital screen.

4. The digital large screen data management method according to claim 1, characterized in that, The process of collecting anomalous events from the microscopic layer data and generating high-incidence area data for anomalous events based on these events includes: Anomaly event records are extracted from the micro-level data stream. These records include spatial information, temporal information, and event attributes. The area covered by the abnormal event records is divided into regular grids, and each abnormal event is mapped to a corresponding grid. The number of events in each grid is counted. The density value of each grid point is calculated based on the number of events in each grid, and grids with density values ​​exceeding the threshold are filtered out. Adjacent high-risk grids are merged through connected component analysis to generate areas with high incidence of abnormal events.

5. The digital large screen data management method according to claim 1, characterized in that, The process involves inputting the standardized real-time data stream into a pre-trained LSTM neural network, obtaining the prediction results output by the LSTM neural network, acquiring real-time traffic flow data, and generating a regional-level risk warning signal based on the prediction results and the real-time traffic flow data. The prediction results include the network load, courier task completion rate, and probability of abnormal events for a preset future time period, including: The standardized real-time data stream is feature-engineered to obtain a real-time data stream feature vector, and then the real-time data stream feature vector is converted into a time series vector. The time series vector is input into a pre-trained LSTM neural network to obtain the prediction results output by the LSTM neural network. The prediction results include the network load, courier task completion rate and probability of abnormal events for a preset time period in the future. Acquire real-time traffic flow data, and generate regional-level risk warning signals based on the prediction results and the real-time traffic flow data.

6. The digital large screen data management method according to claim 1, characterized in that, The process of generating courier task allocation paths based on the regional risk warning signal and the data on areas with high incidence of abnormal events, combined with a genetic algorithm, to generate a dynamic scheduling scheme, and then pushing the dynamic scheduling scheme to digital screens and mobile terminals, includes: Risk data is obtained from regional-level risk warning signals and data on areas with a high incidence of abnormal events; Obtain the current list of unfinished tasks and the real-time status data of couriers. Associate the risk data, the current list of unfinished tasks and the real-time status data of couriers to obtain fused data. The current list of unfinished tasks includes task ID, pickup address, delivery address and time requirement. The real-time status data of couriers includes location, assigned task volume, remaining working time and delivery capacity rating. Based on the fused data, a genetic algorithm is used to generate task allocation paths for couriers, a dynamic scheduling scheme is generated, and the dynamic scheduling scheme is pushed to digital screens and mobile terminals.

7. The digital large screen data management method according to claim 6, characterized in that, Risk data is obtained from regional-level risk warning signals and data on areas with a high incidence of abnormal events, including: First data is obtained by acquiring the risk level, risk type, and impact range of each region from regional risk warning signals; The second set of data is obtained by extracting specific geographical locations and event type distributions from data on areas with high incidence of abnormal events. The first and second data are integrated into risk data.

8. A digital large-screen data management device, characterized in that, include: The conversion module is used to acquire real-time data on the status of express delivery personnel's individual order collection and the data on the delivery of packages at the network points, obtain the raw data, and convert the raw data into a standardized real-time data stream. The module is used to divide the standardized real-time data stream into macro-level data, meso-level data and micro-level data, and use the ECharts map engine to construct a three-level data visualization hierarchy based on the macro-level data, the meso-level data and the micro-level data, and render it to a digital screen. The first generation module is used to collect abnormal events in the microscopic layer data and generate data on high-incidence areas of abnormal events based on the abnormal events. The second generation module is used to input the standardized real-time data stream into a pre-trained LSTM neural network, obtain the prediction results output by the LSTM neural network, and obtain real-time traffic flow data. Based on the prediction results and the real-time traffic flow data, a regional-level risk warning signal is generated. The prediction results include the network load, courier task completion rate and probability of abnormal events for a preset time period in the future. The scheduling module is used to generate a courier task allocation path based on the regional risk warning signal and the data of areas with high incidence of abnormal events, combined with a genetic algorithm, to generate a dynamic scheduling scheme, and push the dynamic scheduling scheme to the digital screen and mobile terminal.

9. A digital large-screen data management device, characterized in that, It includes a memory and at least one processor, wherein the memory stores computer-readable instructions; The at least one processor invokes the computer-readable instructions in the memory to perform the steps of the digital large screen data management method as described in any one of claims 1-7.

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

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