Scene data analysis system, scene data analysis method and computing equipment

Abstract

The embodiment of the invention provides a scene data analysis system, a scene data analysis method and computing equipment, the scene data analysis system comprises an application layer, a computing framework layer and a data layer, and computing interfaces corresponding to a plurality of computing tasks are constructed between the application layer and the computing framework layer; the application layer is used for responding to a data analysis request sent for a target service scene, performing service logic analysis on the target service scene, obtaining at least one target calculation task corresponding to the target service scene, calling a target calculation interface corresponding to each target calculation task to execute data analysis, and obtaining a data analysis result; the computing framework layer is used for responding to the calling of the application layer on each target computing interface, running a data processing module corresponding to each target computing interface, and performing data processing on the target scene data of the target business scene stored in the data layer; and the data layer is used for storing scene data of a plurality of business scenes. And the flexibility, the expandability and the analysis accuracy of the system are improved.

Description

Technical Field

[0001] This specification relates to the technical field of data analysis, and in particular to a scene data analysis system, a scene data analysis method, and a computing device. Background Technology

[0002] With the development of data analytics technology, data analysis is being conducted on more and more business scenario data. For example, by analyzing historical sales data, sales trends can be predicted for a period of time in the future, helping companies optimize inventory management and production plans. Another example is using customer behavior data to segment customers, helping companies to more accurately target their markets and formulate marketing strategies.

[0003] Currently, corresponding computing frameworks are designed for business scenarios and integrated with scenario data analysis systems. These frameworks can provide business stakeholders with scenario simulation, risk prediction, cause analysis, best solution recommendations, and end-to-end case integration to improve their work efficiency and predictability, enabling them to make better decisions.

[0004] However, in order to better and faster adapt to market changes, business scenarios are becoming increasingly complex and dynamic. Designing fixed computing frameworks for specific business scenarios to connect to scenario data analysis systems is insufficient to adapt to the complex and ever-changing business logic and achieve accurate data processing. This results in inadequate accuracy, flexibility, and scalability in scenario data analysis. Therefore, there is an urgent need for a scenario data analysis system with high accuracy, high flexibility, and high scalability. Summary of the Invention

[0005] In view of this, embodiments of this specification provide a scenario data analysis system. One or more embodiments of this specification also relate to a scenario data analysis method, a material data analysis method, a supply chain cost data analysis method, an order data analysis method, a computing device, a computer-readable storage medium, and a computer program product, to address the technical deficiencies existing in the prior art.

[0006] According to a first aspect of the embodiments of this specification, a scene data analysis system is provided, including an application layer, a computing framework layer and a data layer, wherein multiple computing interfaces corresponding to computing tasks are constructed between the application layer and the computing framework layer.

[0007] The application layer is used to respond to data analysis requests sent for target business scenarios, perform business logic analysis on the target business scenarios, obtain at least one target computing task corresponding to the target business scenario, call the target computing interface corresponding to each target computing task to execute data analysis, and obtain data analysis results.

[0008] The computing framework layer is used to respond to the application layer's calls to various target computing interfaces, run the data processing modules corresponding to each target computing interface, and perform data processing on the target scenario data of the target business scenario stored in the data layer.

[0009] The data layer is used to store scenario data for multiple business scenarios.

[0010] According to a second aspect of the embodiments of this specification, a scene data analysis method is provided, applied to a scene data analysis system, including an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are constructed between the application layer and the computing framework layer, including:

[0011] In response to a data analysis request sent for a target business scenario, perform business logic analysis on the target business scenario to obtain at least one target computing task corresponding to the target business scenario;

[0012] The system calls the target computing interface corresponding to each target computing task to perform data analysis, runs the data processing module corresponding to each target computing interface, processes the target scenario data of the target business scenario stored in the data layer, and obtains the data analysis results.

[0013] According to a third aspect of the embodiments of this specification, a material data analysis method is provided, applied to a scenario data analysis system, including an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are constructed between the application layer and the computing framework layer, including:

[0014] In response to a data analysis request sent for a material simulation scenario, perform business logic analysis on the material simulation scenario to obtain at least one target calculation task corresponding to the material simulation scenario;

[0015] The system calls the target calculation interface corresponding to each target calculation task to perform data analysis, runs the data processing module corresponding to each target calculation interface, processes the material data of the material simulation scenario stored in the data layer, and obtains the data analysis results.

[0016] According to a fourth aspect of the embodiments of this specification, a supply chain cost data analysis method is provided, applied to a scenario data analysis system, including an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are constructed between the application layer and the computing framework layer, including:

[0017] In response to a data analysis request sent for a supply chain cost simulation scenario, business logic analysis is performed on the supply chain cost simulation scenario to obtain at least one target calculation task corresponding to the supply chain cost simulation scenario.

[0018] The system calls the target computing interface corresponding to each target computing task to perform data analysis, runs the data processing module corresponding to each target computing interface, processes the supply chain cost data of the supply chain cost simulation scenario stored in the data layer, and obtains the data analysis results.

[0019] According to a fifth aspect of the embodiments of this specification, an order data analysis method is provided, applied to a scenario data analysis system, including an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are constructed between the application layer and the computing framework layer, including:

[0020] In response to the data analysis request sent for the order simulation scenario, perform business logic analysis on the order simulation scenario to obtain at least one target calculation task corresponding to the order simulation scenario;

[0021] The system calls the target computing interface corresponding to each target computing task to perform data analysis, runs the data processing module corresponding to each target computing interface, processes the order data of the order simulation scenario stored in the data layer, and obtains the data analysis results.

[0022] According to a sixth aspect of the embodiments of this specification, a computing device is provided, comprising:

[0023] Memory and processor;

[0024] The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions, which, when executed by the processor, implement the steps of the above method.

[0025] According to a seventh aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0026] According to an eighth aspect of the embodiments of this specification, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0027] In one embodiment of this specification, a scenario data analysis system is provided, comprising an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are constructed between the application layer and the computing framework layer. The application layer, in response to data analysis requests sent for a target business scenario, performs business logic analysis on the target business scenario, obtains at least one target computing task corresponding to the target business scenario, calls the target computing interfaces corresponding to each target computing task to execute data analysis, and obtains data analysis results. The computing framework layer, in response to calls from the application layer to each target computing interface, runs the data processing modules corresponding to each target computing interface and processes the target scenario data of the target business scenario stored in the data layer. The data layer stores scenario data for multiple business scenarios. By performing business logic analysis on the target business scenario, the application layer abstracts at least one target computing task corresponding to the target business scenario, and then determines the corresponding target computing interface. This process, independent of details, achieves isolation between the application layer and the computing framework layer, reduces the coupling between the business scenario services of the application layer and the data processing modules in the computing framework layer, improves the system's flexibility and scalability, and thus completes scenario data analysis for the target business scenario, improving the accuracy of the scenario data analysis. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the structure of a scene data analysis system;

[0029] Figure 2 This is a structural diagram of a scene data analysis system provided in one embodiment of this specification;

[0030] Figure 3 This is a schematic diagram of the front-end structure of a scene data analysis system provided in one embodiment of this specification;

[0031] Figure 4 This is a schematic diagram of the backend structure of a scene data analysis system provided in one embodiment of this specification;

[0032] Figure 5 This is a schematic diagram of the front-end user interface of a scene data analysis system provided in one embodiment of this specification;

[0033] Figure 6 This is a schematic diagram of the front-end user interface of another scenario data analysis system provided in one embodiment of this specification;

[0034] Figure 7 This is a flowchart illustrating a scenario data analysis method provided in one embodiment of this specification;

[0035] Figure 8 This is a flowchart illustrating a material data analysis method provided in one embodiment of this specification;

[0036] Figure 9 This is a flowchart illustrating a supply chain cost data analysis method provided in one embodiment of this specification;

[0037] Figure 10 This is a flowchart illustrating an order data analysis method provided in one embodiment of this specification;

[0038] Figure 11 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0039] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0040] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0041] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0042] Furthermore, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0043] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0044] Abstract Interfaces: An abstract interface defines a set of class declarations for computational tasks (i.e., the names and parameter lists of the computational tasks), but does not provide the implementation details of these computational tasks. The main purpose of an abstract interface is to provide a behavioral specification or protocol for classes that implement the interface, requiring these classes to provide concrete implementations of these computational tasks. For example, an abstract interface written in Java might be:

[0045]

[0046] The Animal interface defines a makeSound method, but it does not provide a concrete implementation of the computation task. The Dog and Cat classes implement the Animal interface and provide concrete implementations of the makeSound computation task.

[0047] ETL (Extract-Transform-Load): Preprocessing operations that extract, transform, and load data from the data source to the target.

[0048] Currently, corresponding computing frameworks are designed for specific business scenarios and integrated with scenario data analysis systems. Taking supply chain data analysis as an example... Figure 1 A schematic diagram of the structure of a scene data analysis system is shown, such as... Figure 1 As shown:

[0049] The shared cloud space includes a private subnet, on which runs the CDH (based on the AWS big data processing platform) hardware system. The shared cloud space is connected to the large-scale data platform hardware system through a database.

[0050] An internet consists of a personal computer hardware system. The internet connects to a large-scale data platform hardware system via network protocols.

[0051] The cloud space comprises a hardware system called a large-scale data platform, on which multiple private virtual networks (VPNs) run. One of these VPNs includes a logical intelligent cloud hardware system, which in turn includes a front-end container data processing module and a micro-frontend underlying module. Another VPN includes a hardware system for accessing microservices, as well as front-end and back-end containers for data processing. The front-end container includes an order data analysis framework underlying module, and the back-end container includes an interface gateway underlying module and an order data analysis module underlying module. All hardware systems and data processing modules within the large-scale data platform are connected via network protocols.

[0052] Cloud space also includes a hardware system for an SQL database and a hardware system for a cache database. The SQL database includes the underlying module MySQL, and the cache database includes the underlying module caching. The SQL database connects to the large-scale data platform via a database connection, while the cache database connects to the large-scale data platform via a network protocol.

[0053] The other internet component comprises the hardware systems of a digital ID platform and an interface platform. The digital ID platform includes a low-level module for receiving payments, and it connects to a large-scale data platform via network protocols. The interface platform connects to the data processing modules of the public API service module and the visualization module via network protocols. The public API service module also connects to the large-scale data platform via network protocols.

[0054] This scenario-based data analysis system integrates multiple business scenarios, processes CDH data using multi-threading technology, and outputs the data analysis results to the application layer.

[0055] However, this scenario-based data analysis system is unable to adapt to the complex and ever-changing business scenarios in supply chain data analysis, and thus cannot complete accurate data processing, resulting in insufficient accuracy, flexibility, and scalability of scenario-based data analysis.

[0056] To address the aforementioned problems in scenario data analysis, this specification provides a scenario data analysis system, a scenario data analysis method, a material data analysis method, a supply chain cost data analysis method, an order data analysis method, a computing device, a computer-readable storage medium, and a computer program product, which will be described in detail in the following embodiments.

[0057] See Figure 2 , Figure 2 The diagram shows a structural diagram of a scene data analysis system provided in one embodiment of this specification. The scene data analysis system 200 includes an application layer 210, a computing framework layer 220 and a data layer 230. Multiple computing interfaces corresponding to computing tasks are constructed between the application layer 210 and the computing framework layer 220.

[0058] Application layer 210 is used to respond to data analysis requests sent for target business scenarios, perform business logic analysis on the target business scenarios, obtain at least one target computing task corresponding to the target business scenarios, call the target computing interface corresponding to each target computing task to perform data analysis, and obtain data analysis results.

[0059] The computing framework layer 220 is used to respond to the application layer 210's calls to each target computing interface, run the data processing module corresponding to each target computing interface, and perform data processing on the target scenario data of the target business scenario stored in the data layer 230.

[0060] Data layer 230 is used to store scenario data for multiple business scenarios.

[0061] The Scenario Data Analysis System 200 is a data analysis system integrating multiple business scenarios. It can perform targeted data processing to complete data analysis for complex and ever-changing business scenarios. For example, a supply chain data analysis system for an automobile manufacturing company can be used for complex and ever-changing business scenarios such as order data simulation, material data simulation, and supply chain cost data simulation. The Scenario Data Analysis System 200 adopts a layered architecture design. By using a computational interface encapsulated with the dependency inversion principle between the application layer 210 and the computational framework layer 220, the data processing modules in the computational framework layer 220 can be reused in different business scenarios, exhibiting high flexibility and scalability.

[0062] Application layer 210 is the top layer of the scenario data analysis system 200. Targeting specific business scenarios, application layer 210 provides scenario data analysis application services to interact with user terminals. These application services can directly call computing interfaces to run the corresponding data processing modules in computing framework layer 220 to execute abstract computing tasks, thereby enabling rapid implementation and iteration of business logic. The functions of application layer 210 include, but are not limited to: receiving requests from user terminals, performing business logic analysis on target business scenarios, outputting data analysis results, and visualizing the data analysis results. Optionally, application layer 210 provides a front-end user interface to facilitate visual interaction. For example, when managers of an automobile manufacturing company want to use system 200 to perform data analysis on target business scenarios such as demand change simulation or breakpoint calculation and simulation, they can send a data analysis request for demand change simulation or breakpoint calculation and simulation to application layer 210 through a user terminal (such as a website or application). This allows for business logic analysis of the demand change simulation or breakpoint calculation and simulation to obtain at least one corresponding target calculation task: Demand change simulation: historical demand analysis, demand forecasting modeling, and sensitivity analysis; Breakpoint calculation and simulation: cost structure analysis, breakpoint calculation, and cost-saving simulation.

[0063] The computing framework layer 220 serves as the middleware layer of the scenario data analysis system 200. It provides unified data processing capabilities to perform scenario data analysis and includes data processing modules corresponding to multiple computing interfaces. Responding to calls to different computing interfaces from the application layer 210, the computing framework layer 220 runs the corresponding data processing modules to execute abstract computing tasks, adapting to different business scenarios and exhibiting high flexibility and scalability. Through the abstract interface defined on the application layer 210, complex and ever-changing business scenarios can be accessed through configuration or extension without modifying the core code. The computing framework layer 220 is capable of large-scale data processing and supports real-time or near-real-time computing requirements. For example, the computing framework layer 220 responds to the application layer 210's calls to the three computing interfaces corresponding to historical demand analysis, demand forecasting modeling, and sensitivity analysis. Among them, the five data processing modules corresponding to historical demand analysis are: data cleaning and preprocessing module, trend analysis module, seasonality analysis module, periodicity analysis module, and anomaly detection module, to specifically perform the abstract historical demand analysis computing tasks and obtain data analysis results to output to the application layer 210.

[0064] Data layer 230 forms the bottom layer of the scenario data analysis system 200, storing scenario data from multiple business scenarios. Optionally, data layer 230 is also used for data management, including but not limited to: data cleaning, preprocessing, data source tagging, and data format conversion. Data layer 230 is highly flexible, supporting multiple data sources and formats while ensuring data quality and consistency. Optionally, data layer 230 encapsulates various data source types, supporting business databases, object databases, and CDH databases. Optionally, data layer 230 utilizes ETL tools for preprocessing to ensure data consistency and integrity. For example, data layer 230 stores scenario data from different business scenarios, such as historical sales data, supplier information, material data, order data, and supply chain cost data, providing data support to the computing framework layer 220.

[0065] A computational task is an abstract operation defined in the scenario data analysis system 200, representing a specific set of data processing operations used to complete data analysis of a business scenario. Each computational task is implemented by a corresponding data processing module as the underlying module. These data processing modules can be flexibly configured or expanded to adapt to the data analysis needs of different business scenarios. Target computational tasks are those determined through business logic analysis of the target business scenario. For example, performing business logic analysis on demand change simulation yields three corresponding target computational tasks: historical demand analysis, demand forecasting modeling, and sensitivity analysis. Similarly, performing business logic analysis on material supply breakpoint calculation and simulation yields three corresponding target computational tasks: cost structure analysis, material supply breakpoint calculation, and cost-saving simulation.

[0066] The computation interface corresponding to the computation task is an abstract interface on the application layer 210 used to execute the computation task. Located between the application layer 210 and the computation framework layer 220, it encapsulates the specific data processing module corresponding to the computation task. The computation interface defines how to call the data processing module in the computation framework layer 220 to execute a specific data analysis task. The design of the computation interface follows the dependency inversion principle, so that the application layer 210 does not need to care about the specific implementation details inside the computation framework layer 220; it only needs to call the computation interface to trigger the required data processing flow. The computation interface isolates the application layer 210 and the computation framework layer 220, reducing the coupling between the business scenario services of the application layer 210 and the data processing module in the computation framework layer 220, and improving the flexibility and scalability of the system 200. For example, the computation interface for cost structure analysis written in Java is as follows:

[0067] import java.util.List;

[0068] public interface CostStructureAnalysisInterface{

[0069] / **

[0070] * Cost element breakdown method.

[0071] *

[0072] *@param costData (cost data)

[0073] *@return List of cost elements after breakdown

[0074] /

[0075] List <string>decomposeCostElements(List <double>costData);

[0076] / **

[0077] *Cost trend analysis methods.

[0078] *

[0079] *@param costData (cost data)

[0080] *@Return Cost Trend Analysis Report

[0081] /

[0082] String analyzeCostTrends(List <double>costData);

[0083] / **

[0084] * Cost prediction modeling methods.

[0085] *

[0086] *@param costData (cost data)

[0087] *@return prediction model

[0088] Among them, CostStructureAnalysisInterface is an abstract interface that defines three core methods for cost structure analysis. The method parameter costData is a List. <double>The type represents a list of cost data. `decomposeCostElements` is a method for decomposing cost elements, returning a List. <string>The type represents the list of cost elements after breakdown. `analyzeCostTrends` is a method for cost trend analysis, returning a String representing the cost trend analysis report. `modelCostPrediction` is a method for cost forecasting modeling, returning a String representing a description of the forecasting model.

[0089] The computing interface corresponding to the target computing task is an abstract interface on the application layer 210 used to execute the target computing task. It can be pre-generated or generated in real time, and there is no limitation here.

[0090] The target business scenario is the business scenario that requires data analysis. For example, for an automobile manufacturing company, the target business scenario is demand change simulation or material supply breakpoint calculation and simulation.

[0091] A data analysis request is a request instruction sent by a user terminal to the scenario data analysis system 200, requesting data analysis for a target business scenario.

[0092] The data analysis results are the outputs of the data processing modules corresponding to the target computing interfaces in the computing framework layer 220 after executing the target computing tasks. The data analysis results can be statistical reports, predictive models, or other forms of data. For example, after completing the data analysis for "demand change simulation," system 200 will output a report that includes historical demand trend analysis, future demand forecasts, and an assessment of the impact of demand changes on the supply chain.

[0093] The data processing module corresponding to the computation interface is a functional module in the computation framework layer 220 that specifically executes the computation tasks corresponding to the computation interface. It is a low-level module, usually containing predefined function calls, and typically utilizes a computation engine to accelerate the processing. For example, the computation interfaces corresponding to the five computation tasks in historical requirements analysis, and the five corresponding data processing modules, include: a data cleaning and preprocessing module: function calls: cleanData() and handleMissingValues(), responsible for cleaning the raw data, handling missing values, outliers, etc., to ensure data quality; a trend analysis module: function call: trendAnalysis(), responsible for identifying long-term trends in the data, etc.

[0094] The data processing module corresponding to the target computation interface is the functional module in the computation framework layer 220 that specifically performs the target computation task.

[0095] Scenario data refers to data related to specific business scenarios and forms the foundation for data analysis. Scenario data typically includes various types of data, such as historical data, real-time data, and external data. This data is usually stored in data layer 230 and preprocessed through ETL tools, including cleaning, transformation, and loading, to ensure data quality and consistency.

[0096] Target scenario data refers to data related to the target business scenario. For example, in the target business scenario of a requirement change simulation, scenario data includes the following types: Historical sales data: Records product sales over a past period, including metrics such as sales revenue and sales volume. For example, monthly car sales records, including data on different models, regions, and sales channels. Supplier information: Contains relevant supplier information, such as supplier name, contact information, and supply capacity. For example, a parts list and price list provided by supplier A. Material data: Records information on raw materials and parts used in the production process, including material numbers, specifications, and quantities. For example, engine parts, tires, and interior materials required for car production. Order data: Contains customer order information, such as order number, quantity ordered, and delivery date. For example, order details for 50 cars ordered by customer B. Supply chain cost data: Records cost information at each stage of the supply chain, such as procurement costs, transportation costs, and warehousing costs. For example, cost records for purchasing parts from suppliers. Market data: Includes external data such as industry reports, competitor information, and consumer surveys. For example, data on expected market growth in the coming year from an automotive industry report.

[0097] To obtain at least one target computing task corresponding to the target business scenario, one option is to perform business logic analysis on the target business scenario based on multiple correspondences between the business scenario and the computing task.

[0098] One option is to run the data processing module corresponding to each target computing interface on computing resources.

[0099] In this embodiment, the application layer 210 performs business logic analysis on the target business scenario, abstracts at least one target computing task corresponding to the target business scenario, and then determines the corresponding target computing interface. This isolation between the application layer 210 and the computing framework layer 220 is achieved without relying on details, reducing the coupling between the business scenario service of the application layer 210 and the data processing module in the computing framework layer 220, improving the flexibility and scalability of the system 200, and thus completing the scenario data analysis for the target business scenario and improving the accuracy of the scenario data analysis.

[0100] In one optional embodiment of this specification, the application layer 210 is specifically used to respond to a data analysis request sent for a target business scenario, query multiple correspondences between pre-built business scenarios and computing tasks, and obtain at least one target computing task corresponding to the target business scenario.

[0101] Multiple correspondences between business scenarios and computational tasks define the computational tasks executed under each business scenario. These computational tasks are typically determined based on the business logic of the business scenario, which helps System 200 better organize and manage computational tasks, and also facilitates the understanding and selection of data analysis services that conform to the business logic. In this way, Application Layer 210 can flexibly select appropriate computational tasks according to different business scenarios and call the corresponding computational interfaces to execute these tasks. For example, querying multiple pre-built correspondences between business scenarios and computational tasks can obtain at least one corresponding target computational task: Demand Change Simulation: Historical Demand Analysis, Demand Forecasting Modeling, and Sensitivity Analysis; Material Supply Breakpoint Calculation and Simulation: Cost Structure Analysis, Material Supply Breakpoint Calculation, and Cost Saving Simulation.

[0102] One possible approach to querying multiple mappings between pre-built business scenarios and computational tasks is to query pre-built database tables or configuration files. For example, a CSV (Comma-Separated Values) file could be used, where each row represents a business scenario and the columns represent the computational tasks within that scenario. Another possible approach is to call a predefined service or application interface to query these mappings. This service or application interface maintains the mappings between business scenarios and computational tasks. For example, a microservice could be dedicated to managing these mappings and providing a query interface. This is not a limitation here.

[0103] In the embodiments described in this specification, the application layer 210 can efficiently and accurately identify computing tasks applicable to specific business scenarios by querying the correspondence between pre-built business scenarios and computing tasks, thereby quickly responding to data analysis requests and providing highly customized data analysis services.

[0104] In one optional embodiment of this specification, the application layer 210 is further configured to respond to an interface generation request sent for a business scenario, determine at least one computing task corresponding to the business scenario from a plurality of preset computing tasks, construct a correspondence between the business scenario and at least one computing task, and generate a computing interface corresponding to at least one computing task.

[0105] The computing framework layer 220 is also used to encapsulate at least one data processing module corresponding to a computing task into the computing interface.

[0106] An interface generation request is a data analysis request sent by a user terminal to the scenario data analysis system 200, requesting the generation of a computational interface corresponding to a computational task. The interface generation request typically contains detailed information about the new business scenario so that the system 200 can determine the required computational tasks and create the corresponding computational interfaces. The interface generation request allows the scenario data analysis system 200 to dynamically expand its functionality to support new business scenarios or data analysis requests under specific scenarios. Interface generation requests can be submitted through the user interface or automatically triggered by the system. For example, suppose an automobile manufacturer wants to add a new business scenario: production planning optimization, which requires computational tasks such as material utilization analysis, production line efficiency assessment, and inventory turnover prediction. In this case, the system administrator or developer can send an interface generation request to the application layer 210 through the user terminal, describing the new business scenario and the required computational tasks. The application layer 210 then constructs the correspondence between the new business scenario and these computational tasks and generates the corresponding computational interfaces.

[0107] Multiple preset computing tasks are a series of predefined computing tasks in the scenario data analysis system 200. These preset computing tasks can be used for data analysis in different business scenarios. Each computing task has a corresponding data processing module in the computing framework layer 220.

[0108] Generate at least one computing interface corresponding to a computing task. One possible approach is to generate the computing interface by defining an abstract interface. Another possible approach is to use dynamic proxy technology to dynamically generate the computing interface based on the task information of the computing task. No specific approach is required here.

[0109] Encapsulate at least one data processing module corresponding to a computing task into the computing interface. One option is to create or select a data processing module corresponding to a preset computing task and bind the data processing module to the computing interface. Another option is to generate a container for the data processing module and inject the container into the computing interface. No limitation is made here.

[0110] In the embodiments described in this specification, by generating a flexible computing framework and computing interface between the application layer, the data analysis development and deployment cycle for new business scenarios is shortened, the flexibility and scalability of the system 200 are improved, and development efficiency and resource utilization efficiency are also effectively enhanced.

[0111] In one optional embodiment of this specification, the application layer 210 is specifically used to define an abstract interface and add class declarations of at least one computing task to the abstract interface to generate a computing interface corresponding to at least one computing task.

[0112] The class declaration for a computational task is a programming language definition that represents the abstract computational logic. For example, for the computational task of material utilization analysis, we can define a class named MaterialUtilizationAnalysis:

[0113]

[0114] CalculationTask is an abstract interface that defines the class declarations that all computation tasks need to implement.

[0115] Define an abstract interface, and add class declarations for at least one computation task to the abstract interface to generate computation interfaces corresponding to at least one computation task. For example, define an abstract interface CalculationTask, which contains class declarations for computation tasks:

[0116] public interface CalculationTask{

[0117] double performCalculation();

[0118] }

[0119] Next, define an abstract interface in application layer 210, and add the class declarations for the above computing tasks to it:

[0120]

[0121]

[0122] The CalculationServiceImpl class implements the CalculationService interface and defines a method createCalculationTask, which accepts a string parameter taskName and returns different calculation task instances based on the passed name.

[0123] In the embodiments described in this specification, the method of adding class declarations of computing tasks to the abstract interface to generate the computing interface realizes modularization and decoupling, improves the flexibility and scalability of the system 200, and enhances the reusability of the computing interface.

[0124] In an optional embodiment of this specification, the application layer 210 is further configured to respond to a data analysis request sent for a target business scenario, query the correspondence between multiple pre-built business scenarios and computing tasks, and if no correspondence is found, determine at least one target computing task corresponding to the target business scenario from multiple pre-set computing tasks, construct the correspondence between the target business scenario and at least one target computing task, and generate a target computing interface corresponding to at least one target computing task.

[0125] The computing framework layer 220 is also used to encapsulate at least one data processing module corresponding to the target computing task into the target computing interface.

[0126] If the pre-built correspondence between multiple business scenarios and computing tasks is not found, it is necessary to build the correspondence between the target business scenario and at least one target computing task to expand the scenario data analysis function of system 200 and meet the data analysis needs of the target business scenario.

[0127] If no target computing task is found, at least one target computing task corresponding to the target business scenario can be determined from multiple preset computing tasks. One possible approach is to match the target computing task from multiple preset computing tasks based on the business logic of the business scenario if no target computing task is found. Another possible approach is to determine the target computing task corresponding to the target business scenario from multiple preset computing tasks through interactive means if no target computing task is found. No specific approach is required here.

[0128] Generate at least one target computing interface corresponding to the target computing task. One option is to generate the target computing interface by defining an abstract interface. Another option is to use dynamic proxy technology to dynamically generate the target computing interface based on the task information of the target computing task. No limitation is made here.

[0129] Encapsulate at least one data processing module corresponding to the target computing task into the target computing interface. One option is to create or select a data processing module corresponding to a preset target computing task and bind the data processing module to the target computing interface. Another option is to generate a container for the data processing module and inject the container for the data processing module into the target computing interface. No limitation is made here.

[0130] In the embodiments of this specification, when no pre-built correspondence between business scenarios and computing tasks is found, the computing tasks corresponding to the target business scenario are determined, and a new correspondence is constructed. This effectively expands the scenario data analysis function of system 200 to meet new business needs, improves the flexibility, scalability and resource utilization efficiency of scenario data analysis system 200, improves user experience, and accelerates the deployment speed of new business scenarios.

[0131] In one optional embodiment of this specification, the application layer 210 is specifically used to define an abstract interface, and add class declarations of at least one target computing task to the abstract interface to generate computing interfaces corresponding to at least one target computing task.

[0132] The embodiments in this specification are similar to the computation interface generation method of the embodiments described above, which define abstract interfaces and add class declarations of computation tasks to generate computation interfaces. Please refer to the description of the embodiments above, which will not be repeated here.

[0133] In the embodiments of this specification, the method of adding the class declaration of the target computing task to the abstract interface to generate the target computing interface not only realizes modularization and decoupling, improving the flexibility and scalability of the system 200, and improving the reusability of the computing interface, but also improves reusability.

[0134] In one optional embodiment of this specification, the application layer 210 is also used for front-end visualization rendering of the data analysis results.

[0135] Front-end visualization rendering is used to graphically display data analysis results on the front-end user interface of the scene data analysis system 200, allowing users to intuitively understand the results. Optionally, front-end visualization rendering typically involves the following steps: Data preparation: Converting the data analysis results into a format suitable for front-end display. Chart type selection: Choosing a suitable chart type based on the characteristics of the data analysis results and the purpose of display, such as line charts, bar charts, pie charts, scatter plots, etc. Using front-end frameworks or libraries: Building charts using front-end development frameworks (such as React, Vue.js) or dedicated data visualization libraries (such as ECharts, D3.js). Interaction design: Designing interactive functions for the charts, allowing users to gain a deeper understanding of the results through chart manipulation. Style customization: Adjusting the chart's colors, fonts, and other styles.

[0136] In the embodiments described in this specification, the data analysis results are visualized and rendered on the front end, which improves the readability of the data analysis results and enhances the user experience.

[0137] In one optional embodiment of this specification, the data layer 230 is also used to clean and / or preprocess the scene data.

[0138] Data cleaning is the process of cleaning and validating scenario data from multiple business scenarios, removing erroneous, incomplete, incorrectly formatted, or duplicate data to improve the quality of the scenario data. For example, a sales data table may contain some incorrect date formats, such as "2023-02-30" (February cannot have 30 days). During data cleaning, these dates will be corrected to the correct format, such as "2023-02-28", or the record will be deleted.

[0139] Data preprocessing is the process of transforming and organizing scenario data to improve its quality and ensure it is suitable for subsequent processing and data analysis. For example, given a dataset containing customer ages ranging from 18 to 90, data preprocessing would segment the ages into several intervals (e.g., 18-30, 31-50, 51 and above).

[0140] In this embodiment of the specification, data cleaning and data preprocessing improve the data quality of the scene data stored in the data layer 230, thereby improving the accuracy of subsequent data analysis.

[0141] In one optional embodiment of this specification, the target business scenario includes at least one of a material simulation scenario, a supply chain cost simulation scenario, and an order simulation scenario, and the scenario data of the target business scenario includes at least one of material data, supply chain cost data, and order data.

[0142] Material simulation scenarios evaluate the effectiveness of material management strategies by simulating the flow of materials during production, storage, and distribution. These scenarios typically involve supply and demand analysis, inventory management, and production planning to ensure efficient material utilization and timely supply. For example, an automotive manufacturer aiming to optimize its parts inventory levels could create a material simulation scenario to model parts flow under different inventory strategies, ultimately identifying an optimization strategy that meets production demands while reducing inventory costs.

[0143] Supply chain cost simulation scenarios assess and optimize supply chain cost data by simulating various activities within the supply chain (such as procurement, production, and logistics). These scenarios typically focus on cost-saving measures, supply chain response time, and cost-benefit analysis. For example, an automobile manufacturer can use supply chain cost simulation scenarios to model the impact of decisions such as choosing different suppliers and changing transportation methods on total costs, aiming to identify lower-cost supply chain configurations.

[0144] Order simulation scenarios evaluate the effectiveness of order processing strategies and optimize them by simulating the receipt, processing, and delivery of orders. These scenarios typically involve order prioritization, delivery scheduling, and inventory matching to improve order processing efficiency and service levels. For example, an automotive manufacturer can use order simulation scenarios to model different order processing strategies (such as sorting orders by value) to assess which strategy maximizes customer satisfaction and reduces the risk of delayed delivery.

[0145] Material data refers to information related to materials, including their basic attributes, inventory status, production consumption, etc. Material data forms the data foundation for material simulation scenarios, used to simulate the flow of materials in the supply chain. For example, material data includes information such as material number, name, specifications, unit price, and inventory quantity. For instance, a material data record might be: "Material Number: 001, Name: Bolt, Specification: M10, Unit Price: 1 yuan, Inventory Quantity: 1000".

[0146] Supply chain cost data comprises cost data related to various activities within the supply chain, including procurement costs, production costs, and logistics costs. Supply chain cost data forms the data foundation for supply chain cost simulation scenarios, used to simulate the composition and optimization potential of supply chain costs. For example, supply chain cost data includes information such as supplier quotes, production cost estimates, and transportation costs. A typical supply chain cost data entry might read: "Supplier quote: 1000 yuan / unit, Production cost: 500 yuan / unit, Transportation cost: 200 yuan / unit."

[0147] Order data consists of information related to orders, including order number, customer information, product information, delivery date, etc. Order data forms the data foundation for order simulation scenarios, used to simulate various decisions and effects during order processing. For example, order data includes information such as order number, order placement time, customer name, product list, and expected delivery date. For instance, an order data record might be: "Order Number: ORD12345, Order Placement Time: 2023-08-01 10:00, Customer Name: Zhang San, Product List: [Product A x 2, Product B x 1], Expected Delivery Date: 2023-08-05".

[0148] In the embodiments described in this specification, by simulating business scenarios such as materials, supply chain costs, and orders, the material management strategy, supply chain cost structure, and order processing strategy were effectively evaluated and optimized, thereby improving material utilization efficiency, reducing supply chain costs, enhancing customer satisfaction, and strengthening the company's competitiveness.

[0149] In one optional embodiment of this specification, the computing framework layer 220 is specifically used to respond to the application layer 210's call to each target computing interface, run the data processing module corresponding to each target computing interface on the elastic framework, and perform data processing on the target scenario data of the target business scenario stored in the data layer 230, wherein the elastic framework is deployed with elastic resources.

[0150] An elastic framework is a framework that automatically adjusts computing resources according to the needs of computing tasks. It can automatically scale resources up or down as the load changes to meet the needs of different computing tasks. Key features of an elastic framework include: Autoscaling: Automatically increasing or decreasing computing resources based on the actual needs of computing tasks. Resource Pooling: Managing computing resources in a pool for more efficient allocation and release. Load Balancing: Distributing tasks evenly across computing nodes using load balancing techniques to fully utilize computing resources. Examples include AWS Glue, Azure Functions, and Azure Spark.

[0151] Elastic resources are computing resources that are dynamically adjusted according to the needs of computing tasks, including computing nodes, memory, and CPU. The characteristics of elastic resources include: Dynamic adjustment: Resource allocation is dynamically adjusted according to the actual needs of computing tasks. High-efficiency utilization: By dynamically adjusting resources, resource utilization is maximized. Cost-effectiveness: By allocating resources on demand, unnecessary resource waste is reduced, and operating costs are lowered.

[0152] Running the data processing modules corresponding to each target computing interface on the elastic framework can be done in two ways. One option is to use containerization technology to run the data processing modules corresponding to each target computing interface on the elastic framework. For example, the data processing modules can be packaged into container images using containerization technology (such as Docker), and then these containers can be deployed on the elastic framework using container orchestration tools (such as Kubernetes). Another option is to use a Function as a Service (FaaS) platform to run the data processing modules corresponding to each target computing interface on the elastic framework. For example, the data processing modules can be deployed as serverless functions using a Function as a Service platform (such as AWS Lambda or Azure Functions). This is not limited to one method.

[0153] In the embodiments described in this specification, by running the data processing modules corresponding to each target computing interface on the elastic framework, the scene data analysis system 200 can better cope with the fluctuations of computing tasks, ensure the effective utilization of computing resources, and improve the response speed and overall performance of the system 200.

[0154] Taking supply chain data analysis as an example, Figure 3 This specification illustrates a schematic diagram of the front-end structure of a scene data analysis system according to one embodiment. Figure 3 As shown:

[0155] The scenario data analysis system includes touchpoints, application layer, and data layer (large-scale data platform).

[0156] The points of contact include the website.

[0157] The application layer includes logical intelligent cloud and supply chain data analytics;

[0158] Logical Intelligence Cloud includes a production project workspace, which includes: production logs, correction dates, project comparisons, cumulative production performance, daily production plans, previous day's production status, transformation models, and link integration, among other target business scenarios.

[0159] Supply chain data analysis includes demand change simulation, specifically: order data simulation, material data simulation, supply chain cost data simulation, and visualization processing. Order data simulation includes calculation tasks such as customer order volume reduction, order volume increase, order volume reduction again, order replacement, and production rescheduling. Material data simulation includes calculation tasks such as on-site operation planning, center operation planning, and order list processing. Supply chain cost data simulation includes calculation tasks such as air freight costs, electronic component costs, and expiration costs. Visualization processing includes calculation tasks using visualization toolkits and visualization dashboards.

[0160] The data layer includes production arrangement simulation and prediction_1, production arrangement simulation and prediction_2, production arrangement simulation and prediction_3, production plan generation and simulation, and an order database provided to the production project workspace. It also includes supply chain orders provided for order data simulation, as well as CEDEC order data and order type grouping provided for material data simulation. The data simulation results are as follows: Impact on existing product capabilities: Material requirement change simulation framework is ready, CEDEC order dataset is ready, and big data cloud computing framework is ready.

[0161] Figure 4 This specification illustrates a schematic diagram of the backend structure of a scene data analysis system according to one embodiment. Figure 4 As shown:

[0162] The cloud space includes two private virtual networks.

[0163] One private virtual network includes a logical intelligent cloud hardware system, which in turn includes a front-end container data processing module, and the front-end container includes a micro-frontend underlying module. The other private virtual network includes an application gateway hardware system and a namespace hardware system for a large-scale data platform. The namespace for the large-scale data platform includes two front-end containers and a back-end container data processing module. One front-end container includes the Vue framework underlying module, and the other includes Spring Boot underlying module. The back-end container includes the Spring Gateway underlying module.

[0164] The logical intelligent cloud and application gateway are connected via Hypertext Transfer Protocol (HTTp). The application gateway and front-end container are connected via HTTp. The data processing modules in the namespace of the large-scale data platform are connected via HTTp.

[0165] The server platform comprises four hardware systems: a database, a monitoring module, storage, and synapses. The database includes the underlying MySQL module, the monitoring module includes log analysis, the storage includes database region tracking, and the synapses include Spark / SQL. Storage and synapses are connected via batch processing.

[0166] The Internet includes large-scale data platforms and hardware systems for data flow.

[0167] The monitoring module and the front-end container security are connected via Transmission Control Protocol (TCP).

[0168] The synapse and the large-scale data platform are connected via batch processing, and the synapse and the data stream are connected via stream processing.

[0169] As mentioned above Figure 4 and Figure 5 As shown, the scene data analysis system adopts a layered architecture design and follows the dependency inversion principle, possessing the following advanced features:

[0170] 1. Microservice architecture: By adopting a microservice architecture, different business scenarios and services are broken down into independent service units. Each service can be deployed, scaled and maintained independently, improving the overall flexibility and maintainability of the system.

[0171] 2. DevOps Practices: Introduce continuous integration / continuous deployment processes to automate testing and deployment, ensuring rapid deployment of new features and timely fix of issues.

[0172] The scenario data analysis system is a supply chain data analysis system that can meet current business needs and has sufficient flexibility and scalability, providing strong support for enterprise decision-making.

[0173] The following technical effects can be achieved:

[0174] 1. Enhance business agility: By building a flexible computing framework and application layer, business teams can respond to market changes and customer needs more quickly, shorten the development and deployment cycle of new features or tools, and improve market competitiveness.

[0175] 2. Enhanced data analysis capabilities: The layered architecture design ensures the efficiency and accuracy of data processing, introduces elastic resources and GPU capabilities, can handle large-scale datasets, provide in-depth data insights and predictive analysis, and support more accurate decision-making.

[0176] 3. Reduced Operation and Maintenance Costs: The adoption of the Inversion Principle and microservice architecture reduces coupling between different components, making the system easier to maintain and upgrade, and reducing downtime and operation and maintenance costs caused by system failures. The introduction of AIOPS end-to-end monitoring ensures the operational quality of each user case, captures performance and abnormal states, and guarantees the availability and service quality of all links.

[0177] 4. Promote cross-departmental collaboration: The unified data layer and computing framework layer provide shared data resources and analysis tools for different business departments, promoting information transparency and cross-departmental collaboration efficiency.

[0178] 5. Optimize resource utilization: Through efficient resource scheduling and load balancing mechanisms, the computing framework layer can make full use of hardware resources, reduce resource waste, and improve the overall system's operating efficiency and cost-effectiveness.

[0179] 6. Enhance user experience: Front-end data visualization and user-friendly interface design improve the work efficiency of business personnel, enabling them to understand data more intuitively and make faster and more accurate decisions.

[0180] Figure 5 This specification illustrates a schematic diagram of the front-end user interface of a scene data analysis system according to one embodiment. Figure 5 As shown:

[0181] The homepage of the front-end user interface includes six functional applications: project planning and production control, logical planning, material and inventory management, strategy and business control, physical logic, and completed vehicle logic.

[0182] Figure 6 This specification illustrates a schematic diagram of the front-end user interface of another scenario data analysis system provided in one embodiment, as shown below. Figure 6 As shown:

[0183] The project and material simulation interface includes six business scenarios: order data simulation, material data simulation, data simulation results, detection and simulation, simulation history, monitoring and management settings.

[0184] Included are explanations:

[0185] "Project and material data simulation is a simulation that relies on order and material data, offering greater transparency and flexibility to meet the requirements of changing market demands."

[0186] * Can perform order data simulation

[0187] * Can perform material data simulation

[0188] *Can perform supply chain cost data simulation

[0189] *A comprehensive evaluation result can be viewed.

[0190] Corresponding to the above system embodiments, this specification also provides embodiments of scene data analysis methods. Figure 7 A flowchart illustrating a scene data analysis method provided in one embodiment of this specification is shown. Figure 7 As shown, this method is applied to a scene data analysis system, which includes an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are established between the application layer and the computing framework layer. The method includes the following specific steps:

[0191] Step 702: In response to the data analysis request sent for the target business scenario, perform business logic analysis on the target business scenario to obtain at least one target computing task corresponding to the target business scenario.

[0192] Step 704: Call the target computing interface corresponding to each target computing task to perform data analysis, run the data processing module corresponding to each target computing interface, process the target scenario data of the target business scenario stored in the data layer, and obtain the data analysis results.

[0193] In the embodiments of this specification, by performing business logic analysis on the target business scenario, at least one target computing task corresponding to the target business scenario is abstracted, and then the corresponding target computing interface is determined. By calling each target computing interface to perform data analysis, running the data processing module corresponding to each target computing interface, the target scenario data of the target business scenario stored in the data layer is processed to obtain data analysis results, thereby completing the scenario data analysis for the target business scenario, improving the accuracy of scenario data analysis, and also enhancing the flexibility and efficiency of scenario data analysis.

[0194] The above is an illustrative scheme of a scene data analysis method according to this embodiment. It should be noted that the technical solution of this scene data analysis method and the technical solution of the scene data analysis system described above belong to the same concept. For details not described in detail in the technical solution of the scene data analysis method, please refer to the description of the technical solution of the scene data analysis system described above.

[0195] Corresponding to the above system embodiments, this specification also provides embodiments of material data analysis methods. Figure 8 A flowchart illustrating a material data analysis method provided in one embodiment of this specification is shown. Figure 8 As shown, this method is applied to a scene data analysis system, which includes an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are established between the application layer and the computing framework layer. The method includes the following specific steps:

[0196] Step 802: In response to the data analysis request sent for the material simulation scenario, perform business logic analysis on the material simulation scenario to obtain at least one target calculation task corresponding to the material simulation scenario.

[0197] For example, an automobile manufacturer wants to optimize its parts inventory levels. The company sends a data analysis request to the application layer, requesting analysis of a material simulation scenario to find the optimal strategy that meets production needs while reducing inventory costs. Based on the request, the application layer performs business logic analysis on the material simulation scenario, identifying several target computational tasks, such as material supply and demand analysis, inventory level optimization, and production plan evaluation.

[0198] Step 804: Call the target calculation interface corresponding to each target calculation task to perform data analysis, run the data processing module corresponding to each target calculation interface, process the material data of the material simulation scenario stored in the data layer, and obtain the data analysis results.

[0199] For example, the application layer calls the target calculation interface for material supply and demand analysis, runs the corresponding material data processing module, and processes the material data stored in the data layer. This material data includes information such as material number, name, specifications, unit price, and inventory quantity. The data processing module analyzes the current material supply and demand situation and predicts future demand based on the production plan, thereby obtaining the results of the material supply and demand analysis.

[0200] In the embodiments of this specification, by performing business logic analysis on the material simulation scenario, at least one target calculation task corresponding to the material simulation scenario is abstracted, and then the corresponding target calculation interface is determined. By calling each target calculation interface to perform data analysis, running the data processing module corresponding to each target calculation interface, the material data of the material simulation scenario stored in the data layer is processed to obtain data analysis results, thereby completing the scenario data analysis for the material simulation scenario, improving the accuracy of scenario data analysis, and also enhancing the flexibility and efficiency of scenario data analysis.

[0201] The above is an illustrative scheme of a supply chain cost data analysis method according to this embodiment. It should be noted that the technical solution of this supply chain cost data analysis method and the technical solution of the aforementioned scenario data analysis system belong to the same concept. Details not described in detail in the technical solution of the supply chain cost data analysis method can be found in the description of the technical solution of the aforementioned scenario data analysis system.

[0202] Corresponding to the above system embodiments, this specification also provides embodiments of supply chain cost data analysis methods. Figure 9 A flowchart illustrating a supply chain cost data analysis method provided in one embodiment of this specification is shown. Figure 9 As shown, this method is applied to a scene data analysis system, which includes an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are established between the application layer and the computing framework layer. The method includes the following specific steps:

[0203] Step 902: In response to the data analysis request sent for the supply chain cost simulation scenario, perform business logic analysis on the supply chain cost simulation scenario to obtain at least one target calculation task corresponding to the supply chain cost simulation scenario.

[0204] For example, an automobile manufacturer wants to reduce its supply chain costs. The company sends a data analysis request to the application layer, requesting analysis of a supply chain cost simulation scenario to find the lowest-cost supply chain configuration. Based on the request, the application layer performs business logic analysis on the supply chain cost simulation scenario, identifying several target calculation tasks, such as cost-saving measure evaluation, supply chain response time optimization, and cost-benefit analysis.

[0205] Step 904: Call the target computing interface corresponding to each target computing task to perform data analysis, run the data processing module corresponding to each target computing interface, process the supply chain cost data of the supply chain cost simulation scenario stored in the data layer, and obtain the data analysis results.

[0206] For example, the application layer invoked the target calculation interface for cost-saving measures assessment, running the corresponding supply chain cost data processing module to process the supply chain cost data stored in the data layer. This supply chain cost data includes information such as supplier quotations, production cost estimates, and transportation costs. The data processing module analyzed the impact of different supplier selections and changes in transportation methods on total costs, thereby obtaining the results of the cost-saving measures assessment.

[0207] In this embodiment of the specification, by performing business logic analysis on the supply chain cost simulation scenario, at least one target calculation task corresponding to the supply chain cost simulation scenario is abstracted, and then the corresponding target calculation interface is determined. By calling each target calculation interface to perform data analysis, running the data processing module corresponding to each target calculation interface, the supply chain cost data of the supply chain cost simulation scenario stored in the data layer is processed to obtain data analysis results, thereby completing the scenario data analysis for the supply chain cost simulation scenario, improving the accuracy of the scenario data analysis, and also enhancing the flexibility and efficiency of the scenario data analysis.

[0208] The above is an illustrative scheme of a supply chain cost data analysis method according to this embodiment. It should be noted that the technical solution of this supply chain cost data analysis method and the technical solution of the aforementioned scenario data analysis system belong to the same concept. Details not described in detail in the technical solution of the supply chain cost data analysis method can be found in the description of the technical solution of the aforementioned scenario data analysis system.

[0209] Corresponding to the above system embodiments, this specification also provides embodiments of order data analysis methods. Figure 10 A flowchart illustrating an order data analysis method provided in one embodiment of this specification is shown. Figure 10 As shown, this method is applied to a scene data analysis system, which includes an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are established between the application layer and the computing framework layer. The method includes the following specific steps:

[0210] Step 1002: In response to the data analysis request sent for the order simulation scenario, perform business logic analysis on the order simulation scenario to obtain at least one target calculation task corresponding to the order simulation scenario.

[0211] For example, an automobile manufacturer wants to improve its order processing efficiency and service levels. The company sends a data analysis request to the application layer, requesting analysis of an order simulation scenario to identify the optimal order processing strategy that maximizes customer satisfaction and reduces the risk of delayed delivery. Based on the request, the application layer performs business logic analysis on the order simulation scenario, identifying several target computational tasks, such as order prioritization, delivery scheduling, and inventory matching.

[0212] Step 1004: Call the target computing interface corresponding to each target computing task to perform data analysis, run the data processing module corresponding to each target computing interface, process the order data of the order simulation scenario stored in the data layer, and obtain the data analysis results.

[0213] For example, the application layer calls the target calculation interface for order priority ranking, runs the corresponding order data processing module, and processes the order data stored in the data layer. This order data includes information such as order number, order time, customer name, product list, and expected delivery date. The data processing module sorts the orders according to their order amounts and evaluates the impact of different sorting strategies on customer satisfaction and the risk of delayed delivery, thereby obtaining the order priority ranking result.

[0214] In this embodiment, by performing business logic analysis on the order simulation scenario, at least one target computation task corresponding to the order simulation scenario is abstracted, and then the corresponding target computation interface is determined. By calling each target computation interface to perform data analysis, running the data processing module corresponding to each target computation interface, and processing the order data of the order simulation scenario stored in the data layer, the data analysis results are obtained, thereby completing the scenario data analysis for the order simulation scenario, improving the accuracy of the scenario data analysis, and also enhancing the flexibility and efficiency of the scenario data analysis.

[0215] The above is an illustrative scheme of an order data analysis method according to this embodiment. It should be noted that the technical solution of this order data analysis method and the technical solution of the above-mentioned scene data analysis system belong to the same concept. For details not described in detail in the technical solution of the order data analysis method, please refer to the description of the technical solution of the above-mentioned scene data analysis system.

[0216] Figure 11 A structural block diagram of a computing device according to one embodiment of this specification is shown. The components of the computing device 1100 include, but are not limited to, a memory 1110 and a processor 1120. The processor 1120 is connected to the memory 1110 via a bus 1130, and a database 1150 is used to store data.

[0217] The computing device 1100 also includes an access device 1140, which enables the computing device 1100 to communicate via one or more networks 1160. Examples of these networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 1140 may include one or more of any type of wired or wireless network interface (e.g., a Network Interface Controller (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0218] In one embodiment of this specification, the aforementioned components of the computing device 1100 and Figure 11 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 11 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0219] The computing device 1100 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 1100 can also be a mobile or stationary server.

[0220] The processor 1120 is used to execute the following computer program / instruction, which, when executed by the processor, implements the steps of the above-mentioned scenario data analysis method, material data analysis method, supply chain cost data analysis method, or order data analysis method.

[0221] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device belongs to the same concept as the technical solutions of the above-mentioned scenario data analysis method, material data analysis method, supply chain cost data analysis method, and order data analysis method. For details not described in detail in the technical solution of the computing device, please refer to the descriptions of the technical solutions of the above-mentioned scenario data analysis method, material data analysis method, supply chain cost data analysis method, or order data analysis method.

[0222] An embodiment of this specification also provides a computer-readable storage medium storing a computer program / instructions that, when executed by a processor, implement the steps of the above-described scenario data analysis method, material data analysis method, supply chain cost data analysis method, or order data analysis method.

[0223] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solutions of the above-mentioned scenario data analysis method, material data analysis method, supply chain cost data analysis method, and order data analysis method. For details not described in detail in the technical solution of the storage medium, please refer to the descriptions of the technical solutions of the above-mentioned scenario data analysis method, material data analysis method, supply chain cost data analysis method, or order data analysis method.

[0224] An embodiment of this specification also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the above-described scenario data analysis method, material data analysis method, supply chain cost data analysis method, or order data analysis method.

[0225] The above is an illustrative scheme of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product belongs to the same concept as the technical solutions of the above-mentioned scenario data analysis method, material data analysis method, supply chain cost data analysis method, and order data analysis method. For details not described in detail in the technical solution of the computer program product, please refer to the descriptions of the technical solutions of the above-mentioned scenario data analysis method, material data analysis method, supply chain cost data analysis method, or order data analysis method.

[0226] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0227] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added or removed according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0228] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0229] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0230] The preferred embodiments disclosed above are merely illustrative of this specification. Optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.< / string> < / double> < / double> < / double> < / string>

Claims

1. A scene data analysis system, characterized in that, It includes an application layer, a computing framework layer, and a data layer, with multiple computing interfaces corresponding to computing tasks built between the application layer and the computing framework layer; The application layer is used to respond to data analysis requests sent for a target business scenario, perform business logic analysis on the target business scenario, obtain at least one target computing task corresponding to the target business scenario, call the target computing interface corresponding to each target computing task to perform data analysis, and obtain data analysis results. The computing framework layer is used to respond to the application layer's call to each target computing interface, run the data processing module corresponding to each target computing interface, and perform data processing on the target scenario data of the target business scenario stored in the data layer; The data layer is used to store scenario data for multiple business scenarios.

2. The system according to claim 1, characterized in that, The application layer is specifically used to respond to data analysis requests sent for a target business scenario, query multiple pre-built correspondences between business scenarios and computing tasks, and obtain at least one target computing task corresponding to the target business scenario.

3. The system according to claim 2, characterized in that, The application layer is also used to respond to an interface generation request sent for a business scenario, determine at least one computing task corresponding to the business scenario from multiple preset computing tasks, construct the correspondence between the business scenario and the at least one computing task, and generate a computing interface corresponding to the at least one computing task. The computing framework layer is also used to encapsulate the data processing module corresponding to the at least one computing task into the computing interface.

4. The system according to claim 3, characterized in that, The application layer is specifically used to define an abstract interface, and add class declarations of at least one computing task to the abstract interface to generate computing interfaces corresponding to the at least one computing task.

5. The system according to claim 1, characterized in that, The application layer is also used to respond to data analysis requests sent for a target business scenario, query the correspondence between multiple pre-built business scenarios and computing tasks, and if no correspondence is found, determine at least one target computing task corresponding to the target business scenario from multiple preset computing tasks, construct the correspondence between the target business scenario and the at least one target computing task, and generate the target computing interface corresponding to the at least one target computing task. The computing framework layer is also used to encapsulate the data processing module corresponding to the at least one target computing task into the target computing interface.

6. The system according to claim 5, characterized in that, The application layer is specifically used to define an abstract interface, and add class declarations of the at least one target computing task to the abstract interface to generate computing interfaces corresponding to the at least one target computing task.

7. The system according to claim 1, characterized in that, The application layer is also used for front-end visualization rendering of the data analysis results.

8. The system according to claim 1, characterized in that, The data layer is also used to clean and / or preprocess the scene data.

9. The system according to claim 1, characterized in that, The target business scenario includes at least one of the following: material simulation scenario, supply chain cost simulation scenario, and order simulation scenario. The scenario data of the target business scenario includes at least one of the following: material data, supply chain cost data, and order data.

10. The system according to any one of claims 1-9, characterized in that, The computing framework layer is specifically used to respond to the application layer's calls to each target computing interface, run the data processing module corresponding to each target computing interface on the elastic framework, and perform data processing on the target scenario data of the target business scenario stored in the data layer, wherein the elastic framework is deployed with elastic resources.

11. A method for scene data analysis, characterized in that, This system, applied to scene data analysis, comprises an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are established between the application layer and the computing framework layer, including: In response to a data analysis request sent for a target business scenario, business logic analysis is performed on the target business scenario to obtain at least one target computing task corresponding to the target business scenario; The target computing interface corresponding to each target computing task is invoked to perform data analysis. The data processing module corresponding to each target computing interface is run to process the target scenario data of the target business scenario stored in the data layer and obtain data analysis results.

12. A material data analysis method, characterized in that, This system, applied to scene data analysis, comprises an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are established between the application layer and the computing framework layer, including: In response to a data analysis request sent for a material simulation scenario, business logic analysis is performed on the material simulation scenario to obtain at least one target calculation task corresponding to the material simulation scenario. The target calculation interface corresponding to each target calculation task is called to perform data analysis, and the data processing module corresponding to each target calculation interface is run to process the material data of the material simulation scenario stored in the data layer to obtain data analysis results.

13. A supply chain cost data analysis method, characterized in that, This system, applied to scene data analysis, comprises an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are established between the application layer and the computing framework layer, including: In response to a data analysis request sent for a supply chain cost simulation scenario, business logic analysis is performed on the supply chain cost simulation scenario to obtain at least one target calculation task corresponding to the supply chain cost simulation scenario. The target computing interface corresponding to each target computing task is invoked to perform data analysis. The data processing module corresponding to each target computing interface is run to process the supply chain cost data of the supply chain cost simulation scenario stored in the data layer and obtain data analysis results.

14. An order data analysis method, characterized in that, This system, applied to scene data analysis, comprises an application layer, a computing framework layer, and a data layer. Multiple computing interfaces corresponding to computing tasks are established between the application layer and the computing framework layer, including: In response to a data analysis request sent for an order simulation scenario, business logic analysis is performed on the order simulation scenario to obtain at least one target computation task corresponding to the order simulation scenario; The target computing interface corresponding to each target computing task is invoked to perform data analysis. The data processing module corresponding to each target computing interface is run to process the order data of the order simulation scenario stored in the data layer and obtain the data analysis results.

15. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 11 to 14.

16. A computer-readable storage medium, characterized in that, It stores a computer program / instructions that, when executed by a processor, implement the steps of the method described in any one of claims 11 to 14.

17. A computer program product, characterized in that, Includes a computer program / instructions that, when executed by a processor, implement the steps of the method according to any one of claims 11 to 14.

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