Method and device for adapting digital transformation to multi-industry scenarios

Abstract

The present application relates to the technical field of digital transformation, in particular to a digital transformation method and device suitable for multiple industry scenarios, wherein the method comprises: obtaining internal operation rules of a target transformation enterprise to determine a plurality of to-be-transformed businesses; performing overall business planning and optimization on the plurality of to-be-transformed businesses to obtain an optimal digital transformation implementation scheme; collecting multi-source data of each to-be-transformed business to construct a data set; constructing an initial business execution model of each to-be-transformed business, training each model using the data set to obtain a business execution model; using each business execution model to make a prediction, performing collaborative optimization on the corresponding business execution model to obtain an optimal business execution model; logically associating the optimal business execution model of each to-be-transformed business to obtain a digital system of the target transformation enterprise. Thus, the problems that the existing digital transformation scheme lacks universality and is difficult to guide different industries to carry out digital transformation are solved.

Description

Technical Field

[0001] This invention relates to the field of digital transformation technology, and in particular to a digital transformation method and apparatus adaptable to multiple industry scenarios. Background Technology

[0002] With the deepening of globalization and the intensification of market competition, enterprises are facing increasing pressure. Digital transformation has become a key way for enterprises to enhance their competitiveness and achieve sustainable development. Digital transformation can help enterprises understand market trends and consumer needs more quickly, thereby flexibly adjusting strategies and products to meet market changes. It can also help enterprises optimize business processes, improve production efficiency and product quality, reduce operating costs, and thus enhance market competitiveness. Furthermore, it can automate and intelligentize enterprise operations, reduce human error and delays, and improve production and operational efficiency.

[0003] However, many enterprises lack clear strategic positioning and path selection during their digital transformation. They may not be clear about their digital goals, nor which technology route and implementation plan to choose. This can lead to inefficient results, or even significant resource investment without achieving the desired outcomes. Therefore, related technologies have proposed digital transformation management methods and construction methods for different industries. However, these solutions can only provide references for enterprises within the same industry, lacking cross-industry guidance and universality. Summary of the Invention

[0004] This invention provides a digital transformation method and apparatus that are adaptable to multiple industry scenarios, in order to solve the problems that existing digital transformation solutions lack universality and are difficult to guide enterprises in different industries to carry out digital transformation.

[0005] A first aspect of this invention provides a digital transformation method adaptable to multiple industry scenarios, comprising the following steps:

[0006] Economic flow characterization is performed on the target transformation enterprise to obtain its internal operating rules.

[0007] Based on the aforementioned internal operating rules, a demand analysis was conducted to identify several business lines that need to be transformed.

[0008] An overall business plan was developed for the multiple businesses to be transformed, resulting in an initial implementation plan for digital transformation.

[0009] Establish a general architecture management system for the multiple businesses to be transformed, and use the general architecture management system to optimize the initial implementation plan for digital transformation to obtain the optimal implementation plan for digital transformation;

[0010] Based on the optimal implementation plan for digital transformation, multi-source data for each business to be transformed are collected to construct a dataset for each business to be transformed.

[0011] Construct an initial business execution model for each business to be transformed, and train the corresponding initial business execution model using the dataset to obtain the business execution model;

[0012] Obtain the actual execution data of each business to be transformed, and use its corresponding business execution model to predict each business to be transformed, thereby obtaining the prediction result of each business to be transformed.

[0013] Based on the prediction results of each business to be transformed, its corresponding business execution model is collaboratively optimized to obtain the optimal business execution model for each business to be transformed.

[0014] By logically associating the optimal business execution models for each business to be transformed, the digital system of the target enterprise is obtained.

[0015] A second aspect of the present invention provides a digital transformation device adapted to multiple industry scenarios, comprising:

[0016] The characterization module is used to characterize the economic flow of the target transformation enterprise in order to obtain the internal operating rules of the target transformation enterprise.

[0017] The demand analysis module is used to perform demand analysis based on the internal operating rules to identify multiple businesses that need to be transformed.

[0018] The business planning module is used to perform overall business planning for the multiple businesses to be transformed, and to obtain an initial implementation plan for digital transformation.

[0019] The solution optimization module is used to establish a general architecture management system for the multiple businesses to be transformed, and to optimize the initial implementation plan for digital transformation using the general architecture management system to obtain the optimal implementation plan for digital transformation.

[0020] The data acquisition module is used to collect multi-source data for each business to be transformed based on the optimal implementation plan for digital transformation, so as to construct a dataset for each business to be transformed.

[0021] The training module is used to construct an initial business execution model for each business to be transformed, and to train the corresponding initial business execution model using the dataset to obtain the business execution model.

[0022] The prediction module is used to obtain the actual execution data of each business to be transformed, and to predict each business to be transformed using its corresponding business execution model, so as to obtain the prediction result of each business to be transformed.

[0023] The collaborative optimization module is used to collaboratively optimize the corresponding business execution model of each business to be transformed based on the prediction results, so as to obtain the optimal business execution model for each business to be transformed.

[0024] The logical association module is used to logically associate the optimal business execution model of each business to be transformed to obtain the digital system of the target enterprise.

[0025] A third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to realize the digital transformation method adapted to multiple industry scenarios as described in the above embodiments.

[0026] A fourth aspect of the present invention provides a computer program product, which, when executed by a processor, implements the above-described digital transformation method adapted to multiple industry scenarios.

[0027] A fifth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described digital transformation method adapted to multiple industry scenarios.

[0028] The digital transformation method and system proposed in this invention, adapted to multiple industry scenarios, ensures close integration of transformation solutions with actual business operations by deeply understanding the characteristics and needs of different industries, avoiding a one-size-fits-all approach. It can be adjusted and optimized according to the specific circumstances of each enterprise to meet their individual needs; it has broad industry applicability, avoiding the need to design separate transformation solutions for each industry, thereby reducing transformation costs. Simultaneously, it reduces duplication of effort and waste, improves resource utilization efficiency, and further lowers transformation costs; it has clear transformation goals and path planning, ensuring that enterprises achieve their transformation goals within a limited timeframe; and it can also help enterprises identify and solve key problems and difficulties in the transformation process, improving the efficiency and success rate of transformation.

[0029] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0030] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0031] Figure 1 A flowchart illustrating a digital transformation method adapted to multiple industry scenarios according to an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of a highly abstract enterprise model provided according to an embodiment of the present invention;

[0033] Figure 3 A general layout diagram of an enterprise as provided in an embodiment of the present invention;

[0034] Figure 4 This is a schematic diagram of an enterprise sales and delivery process model provided according to an embodiment of the present invention;

[0035] Figure 5 This is a schematic diagram of a production process model provided according to an embodiment of the present invention;

[0036] Figure 6 This is a schematic diagram of an enterprise procurement business model provided according to an embodiment of the present invention;

[0037] Figure 7 This is a schematic diagram of a warehouse management model provided according to an embodiment of the present invention;

[0038] Figure 8 This is a schematic diagram of an enterprise import logistics model provided according to an embodiment of the present invention;

[0039] Figure 9 This is a schematic diagram of an enterprise export logistics model provided according to an embodiment of the present invention;

[0040] Figure 10 This is a schematic diagram of the dynamic relationship model of behavior among various links within an enterprise, provided according to an embodiment of the present invention.

[0041] Figure 11 This is a schematic diagram of a customs business architecture provided according to an embodiment of the present invention;

[0042] Figure 12 This is a schematic diagram of a customs IT architecture provided according to an embodiment of the present invention;

[0043] Figure 13 This is a schematic diagram of an IT project management model provided according to an embodiment of the present invention;

[0044] Figure 14 This is a schematic diagram of a general architecture management system for multiple businesses to be transformed, provided according to an embodiment of the present invention;

[0045] Figure 15 This is a schematic diagram of a fully automated design pattern provided according to an embodiment of the present invention;

[0046] Figure 16 This is a schematic diagram of an industry digital system design framework provided according to an embodiment of the present invention;

[0047] Figure 17 A block diagram illustrating a digital transformation device adapted to multiple industry scenarios according to an embodiment of the present invention;

[0048] Figure 18 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0049] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0050] The following description, with reference to the accompanying drawings, describes a digital transformation method and apparatus adapted to multiple industry scenarios according to embodiments of the present invention.

[0051] Figure 1 This is a flowchart illustrating a digital transformation method adapted to multiple industry scenarios provided in an embodiment of the present invention.

[0052] like Figure 1 As shown, this digital transformation method, adapted to multiple industry scenarios, includes the following steps:

[0053] In step S101, economic flow characterization is performed on the target transformation enterprise to obtain its internal operating rules.

[0054] In some embodiments, economic flow characterization is performed on the target transformation firm to obtain its internal operating rules, including:

[0055] Obtain external information and internal structure of the target company undergoing transformation;

[0056] Based on external information and internal structure, the information flow, resource flow, and value flow of the target transformation enterprise are characterized respectively;

[0057] By identifying internal operating rules through information flow, resource flow, and value flow, understanding these rules, analyzing the interplay between various parties, balancing the maximum needs of both stakeholders, seeking solutions at the highest level, and formulating strategic goals and reform strategies, multiple businesses awaiting transformation can be identified.

[0058] In practice, the first step is to identify the main entity undergoing digital transformation, establish a system model around that entity, analyze the system's operational patterns by characterizing the model, identify the entity's preferences and demands, and then formulate reform and development strategies. These strategies mainly include: strategic objectives; business improvement, innovation, and restructuring strategies; and actionable paths composed of key success factors. The strategy design is more adapted to the operation of the entity's system and more in line with the entity's preferences.

[0059] Taking the digital transformation of customs as an example, the core public affairs area involved in customs' lawful performance of its duties is the international trade scenario. International trade activities are interactions between enterprises, thus enterprises are the main participants in international trade. Due to the influence of cultural and legal differences among countries, international trade involves the collaboration and cooperation of departments such as transportation, insurance, banking, taxation, commerce, and customs. It involves large transaction amounts, long performance periods, and overall high trade risks, making it prone to fluctuations. Therefore, enterprises participating in international trade pay extraordinary attention to the stability and order of the international trade market.

[0060] The nature of a company's external behavior depends on its internal structure. Therefore, it is necessary to explain why a company behaves in order to control and predict its behavior by looking at the structural reasons behind the behavior. Collecting information on a company's external behavior is the main data source for risk analysis, and this data can be used to predict the company's behavior.

[0061] There is a close interdependence between corporate behavior and its internal mechanisms. Behavioral patterns can be obtained through model building and analysis. This invention selects the most typical large and medium-sized processing enterprises to build a model. By analyzing the internal economic flows of the enterprise, it dissects the enterprise's internal operations. These internal economic flows are divided into information flow (I), resource flow (R), and value flow (V).

[0062] Information flow (I) is used to describe the flow of information. It is mainly characterized by the relevant documents and data used, associated with, and generated in the current operation. For example, in the warehousing and receiving operation, information flow is characterized by the operation documents for the warehouse management department to register the receiving, such as the receiving slip.

[0063] Resource flow (R) is used to describe the transfer of ownership, and is mainly characterized by information related to the transfer and receipt of ownership. For example, in the warehousing and receiving operation, resource flow is characterized as information records of the physical handover between the receiving department and the warehouse management department, such as receipt slips and handover slips.

[0064] Value stream (V) is used to describe changes in value and ownership, and is characterized by relevant information in the accounting system.

[0065] like Figure 2 and 3As shown, typical business activities of a processing enterprise include sales, production, procurement, warehousing, and logistics. In a market economy, enterprises plan and organize the resources required for production based on the requirements of sales contracts regarding products, specifications, quantity, quality, and delivery time. These resources include production process development, capacity allocation, production line configuration, raw materials, capital, and labor. Therefore, this embodiment of the invention provides an in-depth analysis and characterization of the enterprise's sales, production, procurement, warehousing, and import / export logistics processes, depicting a process model and information flow, resource flow, and value flow models for each process.

[0066] like Figure 4 As shown, most businesses begin their operations with the signing of sales contracts. After receiving a customer's inquiry, the company goes through steps such as quotation and business negotiations before finally signing a sales contract with the customer. A sales contract is an agreement between a company and its downstream customers, including important details such as the products sold, specifications, quantity, unit price, currency, delivery time, and delivery method. Typically, a company only arranges production activities after the sales contract is signed.

[0067] After production is completed, the company will ship the goods to the customer and request payment. If the customer confirms receipt and makes payment, the transaction is considered successfully completed. If the customer refuses to accept all or part of the goods, a return process is initiated. If the customer requests a refund and makes a claim due to product quality or delivery issues, a claims process is initiated.

[0068] like Figure 5 As shown, once all production resources are ready, companies typically initiate production activities by placing orders. Production resources are consumed or transformed during the production process. Through a complete production chain, raw materials are processed into semi-finished products and ultimately become finished products. The production process from raw materials to finished products is the main value creation process for manufacturing companies. Companies with a refined management philosophy will manage the production process, controlling input, output, and production efficiency through methods such as material requisition and work reporting.

[0069] like Figure 6 As shown, during the production planning process, companies determine the demand for raw materials based on production needs. For raw materials that are not in stock, the production planning department will submit a purchase request, thus triggering the procurement process. The procurement department then contacts the supplier and ultimately signs a purchase contract.

[0070] For imported raw materials, the buyer typically opens a letter of credit with its business partner's bank and submits it to the shipper as collateral for payment. In import purchases, the shipper provides a bill of lading and invoice after shipment for the buyer to arrange import customs clearance.

[0071] After import customs clearance is completed, the purchased goods arrive at the buyer's location. The buyer will conduct a quality inspection to determine whether to accept the goods. If accepted, the buyer will pay for the goods; if rejected in whole or in part, the buyer will initiate a return process.

[0072] like Figure 7 As shown, raw materials, semi-finished products, and finished products are the core assets of manufacturing enterprises. The enterprise's warehouse management department is responsible for managing the quantity, unit, and value of inventory. The main functions of warehouse management are managing the receipt, issuance, and inventory of materials.

[0073] Large enterprises may have multiple warehouses or even multi-level warehouses, so they need advanced warehouse management functions to support inventory management in a multi-warehouse model, which can meet the inventory requirements for sales without causing excessive waste.

[0074] Under normal circumstances, outbound shipments reduce the inventory levels of raw materials and semi-finished products, while inbound shipments increase the inventory levels of semi-finished and finished products. However, changes in inventory levels can also be caused by factors such as scrapping, losses, natural causes (e.g., volatile substances), and human factors (e.g., theft).

[0075] like Figure 8 and 9 As shown, the logistics department (or third-party logistics company) is responsible for transporting the company's finished products from the warehouse to the final consignee. In international trade, logistics includes the shipper, freight forwarder, carrier, and consignee. Freight forwarders play a crucial role in the logistics of international trade. They receive shipping orders from shippers, organize transportation capacity according to customer needs, and deliver goods to the consignee via the optimal route. For example... Figure 8 and 9 Taken together, they depict a complete end-to-end international logistics business.

[0076] Assuming the exporter is responsible for all logistics, the exporter first submits a transportation request to a freight forwarding company. The freight forwarding company then plans the route and finds a carrier based on the shipper's needs. After negotiation, the freight company signs a transportation contract with the upstream shipper and, based on the transportation route, signs a transportation contract with the downstream carrier.

[0077] Freight forwarding companies typically use LCL (Less than Container Load) to consolidate goods destined for the same destination but belonging to different shippers into a single container to reduce transportation costs. The forwarding company then arranges for the goods to be transported to the port and assists the shippers with customs clearance. After customs clearance is completed, the exporter transfers the bill of lading and invoice to the importer.

[0078] After the goods arrive, the importing company typically entrusts the same freight forwarding company that handles the export customs declaration to process the export. After the export customs declaration is completed, the importing company confirms receipt of the goods, the exporting company settles the freight charges with the freight forwarding company, and the freight forwarding company settles the freight charges with the carrier.

[0079] like Figure 10 As shown, through analysis and characterization, especially the characterization of information flow, resource flow and value flow, it is found that the interactive operation of various links within the enterprise inevitably leads to the accumulation and storage of some original data and information within the enterprise, which can describe the changes and actions of the enterprise, thereby determining the internal structure of the enterprise and the internal operating rules.

[0080] It's important to note that the flow of enterprise resources often leads to changes in the value stream. Mismatches between information flow, resource flow, and value stream can create management loopholes, significantly increasing the risk of human manipulation and intervention, resulting in inaccurate and unreliable data. Therefore, characterizing information flow, resource flow, and value stream not only helps determine internal operating rules but also allows for the acquisition of enterprise behavioral records, providing valuable insights for building initial implementation plans for digital transformation.

[0081] In step S102, demand analysis is conducted based on internal operating rules to formulate strategic goals and reform strategies in order to identify multiple businesses that need to be transformed.

[0082] In practice, taking the digital transformation of customs as an example, according to the aforementioned internal operating rules, enterprises have requirements to improve trade facilitation and maintain a fair market competition environment, reflecting their basic demand for the optimal allocation of value chains.

[0083] Customs' digital transformation strategy must meet both stringent regulatory requirements and the basic needs of enterprises, promoting a balance between global trade security and facilitation. Therefore, customs must study the patterns of international trade movements, effectively revealing the integrity of enterprises in international trade through the relationships, transformations, and changes in the status of logistics, information flow, and value flow. Based on this, a smart customs system that seamlessly integrates, automatically monitors, and enables unobtrusive customs clearance can be built, thereby effectively solving the win-win problem of strengthening customs supervision and reducing customs clearance costs for enterprises.

[0084] In step S103, an overall business plan is developed for multiple businesses to be transformed, resulting in an initial implementation plan for digital transformation.

[0085] In some embodiments, an overall business plan is developed for multiple businesses to be transformed, resulting in an initial implementation plan for digital transformation, including:

[0086] Determine the basic business processes for several businesses that need to be transformed;

[0087] By associating the basic business processes of each business to be transformed with resources, the overall business architecture can be obtained.

[0088] Analyze the technical means required to implement the basic business processes of each business to be transformed, and build the IT architecture based on the technical means;

[0089] By coupling the overall business architecture and IT architecture through engineering planning, an initial implementation plan for digital transformation is obtained.

[0090] In actual implementation, taking the digital transformation of customs as an example, such as Figure 11 As shown, the customs business architecture is mainly reflected in the business process design, including electronic declaration, electronic document review, risk assessment, physical inspection, anomaly handling, cargo release, post-transfer management, and comprehensive governance. However, with the continuous development of technology and business, this business process design can no longer meet the needs of modernization. A capability-driven business process design is needed, that is, driven by the organization's core capabilities and guided by user needs, to build flexible, efficient, and innovative business processes to complete the resource association of basic business processes and obtain the overall business architecture, as detailed below:

[0091] Shift from a process-centric approach to a user-centric approach:

[0092] In traditional document data business process design, the process is the core of organizational operations, and user needs are often overlooked. However, capability-driven business process design, by deeply understanding user needs, allows for the design of business processes that better meet those needs. Taking customs digital transformation as an example, the goal is to build business processes that seamlessly integrate into the international trade chain, satisfying its own regulatory requirements while minimizing interference with enterprise operations.

[0093] From a fixed process to a dynamic process:

[0094] In traditional document data business process design, processes are typically standardized, regulated, and fixed, requiring organizations to execute them according to the established procedures. This makes it difficult to meet changing user needs. Therefore, capability-driven business process design requires organizations to continuously innovate, adopting more flexible, efficient, and innovative business processes to meet evolving user needs, allowing for adjustments and optimizations based on these changes.

[0095] From standardization to innovation:

[0096] In traditional document data business process design, processes are typically standardized and regulated, and organizations are required to execute them according to these processes. However, user needs are constantly changing. In capability-driven business process design, organizations need to continuously innovate and adopt more flexible, efficient, and innovative business processes to meet the changing needs of users.

[0097] From independent operation to collaborative cooperation:

[0098] In traditional document data business process design, departments often operate independently, with a lack of collaboration between processes. However, in capability-driven business process design, collaboration is crucial. Organizations need to optimize business processes and improve efficiency and quality through collaboration.

[0099] From a fixed process to a dynamic process:

[0100] In traditional document data business process design, processes are typically fixed, and organizations are required to execute them accordingly. However, in modern enterprises, customer needs and market changes are constantly evolving. If companies continue to use fixed processes, it will be difficult to meet the changing needs of users. Therefore, in capability-driven business process design, organizations need to adopt more flexible and dynamic business processes that can be adjusted and optimized according to changes in user needs.

[0101] Next, as Figure 12 As shown, this analysis examines the technical means required to construct the IT architecture for each business to be transformed, encompassing the basic business processes. The IT architecture includes user architecture, application architecture, middleware architecture, and infrastructure architecture.

[0102] The user architecture is user-centric, with a unified user-end design. Following the principles of data-driven, agile development, and accessibility, it forms a "workbench" design pattern for individual, enterprise, and external users. It supports multiple device adaptations and personalized applications, multi-channel information publishing, online help, push notifications, content and report management, and other services.

[0103] The application architecture focuses on meeting business needs. It classifies and designs application systems into five categories: operations, services, management, decision-making, and sharing. According to the requirements of various business operation modes, it conducts hierarchical deployment by network domain, security level, and business line level to form a scientific and reasonable application pattern.

[0104] To promote interconnectivity and collaborative sharing among various business lines, and to effectively address the challenges of high availability, massive volume, and complex business logic, a middle platform architecture is established between the application architecture and the infrastructure architecture. This middle platform provides unified standards, unified business processes, unified data collection, unified interface integration, and unified development and operation management services for interconnectivity among various business lines and related systems. This strengthens cross-level, cross-departmental, cross-regional, and cross-business collaboration capabilities, improves work efficiency, breaks down data barriers, and reduces waste caused by redundant construction.

[0105] Among them, business support is a common capability that sinks common business and applications across multiple business lines, abstracts and microservices them to form general service capabilities, breaks down barriers between business lines, and provides an agile, open, and shared system architecture for upper-layer applications.

[0106] Technical support involves building a general PaaS service suite using various components, providing a common platform for all microservice APIs at the upper layer, allowing the upper layer to focus more on business logic. Data fusion involves collecting, calculating, analyzing, modeling, integrating, and governing massive amounts of data to achieve service-oriented, unified, and platform-based services, providing efficient data-driven services for businesses.

[0107] Infrastructure integrates cloud computing, networking and communications (including IoT and 5G), security and operations, artificial intelligence, big data, mobile internet, and data center environment, and manages basic equipment resources such as servers, storage, and networks in a unified manner, providing various system services and basic security services.

[0108] Next, as Figure 13 As shown, an IT project management model is built based on the overall business architecture and customs IT architecture. An initial implementation plan for digital transformation is designed based on this model, and the implementation of each project is guided. Full lifecycle management of projects is conducted to ensure that each project meets the requirements of the overall plan and ultimately achieves the organization's strategic goals. The specific process is as follows:

[0109] The first step is to develop an implementation plan, focusing on how to implement the business architecture, IT architecture, and business innovation design. This plan includes: a project overview, covering the project's construction goals, overall architecture, and management principles; an organizational structure, clearly defining the composition and responsibilities of the project's leadership at the decision-making, management, and implementation levels; a project plan, specifying the task descriptions, performance targets, responsible departments, business and technical leaders and contact persons, implementation methods, undertaking units, and participating units for each project group and individual project; and a schedule (including investment plans), clarifying the key factors to be considered in the planning and the schedule for specific construction tasks.

[0110] Secondly, organize project implementation and incorporate it into the entire lifecycle management. During this period, focus on the management of core management elements such as requirements, architecture, planning, organization, team, and strategy. Strengthen the coordination of business and technology, implementation management, technology control, and funding procurement. Unite internal resources, make good use of external resources, and build a large engineering construction team. Complete the construction of engineering highlights and key projects in a demand-oriented manner to ensure that projects are completed on schedule, with high quality and quantity, and achieve the expected goals.

[0111] Finally, system integration is crucial, primarily including: interface integration (enabling single sign-on, unified integration of permissions and roles, and unified interface integration); application integration (enabling inter-application function calls, application interaction and data exchange between different levels within the same application system, with all applications built on a unified platform); data integration (enabling data synchronization, data exchange, and data sharing); infrastructure integration (enabling the integration of network systems, computer and storage systems, security architecture, and system software); security integration (enabling user access security, security protection, security risk assessment, and security level evaluation); and operation and maintenance integration (enabling the operation, maintenance, and management of hardware and networks, basic software, and application systems).

[0112] In step S104, a general architecture management system for multiple businesses to be transformed is established. The initial implementation plan for digital transformation is optimized using the general architecture management system to obtain the optimal implementation plan for digital transformation.

[0113] In some embodiments, the overall architecture management system includes an architecture management module, an engineering management module, and a project management module, wherein,

[0114] The architecture management module is used to determine the engineering management requirements based on multiple businesses to be transformed, and to receive the results of simulated projects to compare with the results of target projects. Based on the comparison results, the initial implementation plan for digital transformation is optimized to obtain the optimal implementation plan for digital transformation.

[0115] The project management module is used to build business scope and requirements management, schedule management, risk management, problem management, quality management, performance management, resource management, communication management, financial management, procurement management, comprehensive support, and archives according to project management needs;

[0116] The project management module is used to sort the business scope and requirements management, schedule management, risk management, problem management, quality management, performance management, resource management, communication management, financial management, procurement management, comprehensive support, and document management, obtain the sorting results, and simulate the execution of the above-mentioned items according to the sorting results to obtain simulation results.

[0117] In actual implementation, such as Figure 14 As shown, based on the overall architecture design, a sound architecture management organization is established, management responsibilities are clarified, management processes are implemented, architecture design assets are managed, and management performance is assessed to guide project management. This covers the entire lifecycle of information technology projects, from initiation, design, implementation, to operation and maintenance, ensuring that new projects, projects to be built, and projects already built all follow the overall architecture. Simulated project results are received for comparison with target project results. Based on the comparison results, the initial implementation plan for digital transformation is optimized to obtain the optimal implementation plan for digital transformation.

[0118] The project management module includes business scope and requirements management, schedule management, risk management, problem management, quality management, performance management, resource management, communication management, financial management, procurement management, comprehensive support, and record management. It establishes a project management organizational structure, defines responsibilities, management mechanisms, and management processes, achieving scientific organization and management throughout the entire process. Through the coordinated allocation of human, financial, and material resources, it ensures the efficient and high-quality completion of the project.

[0119] During the project management module's implementation according to plan, it is necessary to thoroughly understand the constraints and standards of the overall architecture to ensure their implementation; at the same time, it is also necessary to promptly feed back the construction results of each project to the overall architecture design team to ensure that each project can be integrated into the overall architecture; and to simulate the execution of the aforementioned projects according to the project ranking results to obtain the simulated project results.

[0120] In step S105, based on the optimal implementation plan for digital transformation, multi-source data for each business to be transformed are collected to construct a dataset for each business to be transformed.

[0121] In actual implementation, technologies such as streaming computing, real-time data synchronization, network acquisition, natural language processing, and computer vision processing are used to collect and preprocess big data information resources from industry intranet application systems, external units, and the Internet, providing rich data support for various business applications.

[0122] Based on the business architecture design, five types of data are collected: operations, management, services, decision-making, and support. Multi-source data can be differentiated based on dimensions such as data source (internal / external, etc.), data type (structured / unstructured, etc.), and collection method (data collection tools / data exchange / purchase / internet collection, etc.).

[0123] After collecting multi-source data for each business to be transformed, the mapping and association of the multi-source datasets is processed. This involves standardizing field values ​​with the same definition in the multi-source data, mapping their specific values ​​to standardized codes or parameter values. This requires establishing and refining a unified code or parameter library in practice.

[0124] For example, in the customs field, standardized mapping of enterprise codes, country (region) codes, and customs district codes is required. Among them, enterprise codes need to be mapped to the latest standardized code, the Unified Social Credit Code.

[0125] Standardized processing refers to operational procedures that comply with laws and regulations, are standardized, and do not involve any abnormal processing behavior. These operational datasets are included in the training dataset for machine learning, playing a positive role in the training process and representing the content of correct business process processing, that is, the "good results" of the "correct behavior" of "good employees".

[0126] For example, during manual customs inspection, containers or parcels are opened and all goods are removed. If necessary, the outer packaging is removed, and the type, specifications, quantity, weight, country of origin, and condition of the goods are meticulously checked against the cargo declaration form to ensure that the goods match the declared information. During customs machine inspection, the inspected goods, vehicles, and parcels are first scanned by non-invasive equipment (such as X-ray machines or CT scanners). If the image reviewer (or artificial intelligence program) determines that there are suspicious items in the image, further inspection by opening the container is carried out. If a large number of image features match the declared information, the goods are released directly without opening the container.

[0127] Data that conforms to the above-mentioned standardized customs operations is considered compliant data. However, abnormal data, such as goods being released due to failure to be inspected when required (or due to operations that do not meet inspection requirements), is not considered compliant data.

[0128] For data from relevant parties, datasets that can be verified by three parties—information flow, logistics, and value flow—should be selected. For example, in cross-border e-commerce trade data, credible data for each transaction should be selected from three sets of data: logistics data provided by transportation companies, transaction order data provided by e-commerce platforms, and payment data provided by payment platforms (such as banks). This data from third parties can be cross-verified and compared with the declared list data.

[0129] For machine-sensing datasets, datasets that trigger anomaly alerts should be selected. For example, during customs inspections, large X-ray machines and CT scanners are used to inspect vehicles and goods. The image data collected by these machines, if identified as abnormal, belongs to the anomaly alert dataset, such as abnormal image data indicating concealed goods, discrepancies in declarations, or smuggling activities.

[0130] In step S106, an initial business execution model is constructed for each business to be transformed, and the corresponding initial business execution model is trained using the dataset to obtain the business execution model.

[0131] In some embodiments, the corresponding initial business execution model is trained using a dataset to obtain a business execution model, including:

[0132] Analyze each feature data in the dataset of each business to be transformed to obtain the distribution and missing rate of each feature data;

[0133] Each feature data is binned according to its distribution and missing rate to obtain multiple categorical variables;

[0134] WOE encoding is performed on multiple categorical variables to obtain the predicted value for each categorical variable;

[0135] Multiple categorical variables are filtered based on the predicted value of each categorical variable to obtain the filtered categorical variables;

[0136] The selected categorical variables are fitted to obtain a logistic regression model;

[0137] The initial business execution model for each business to be transformed is trained using a logistic regression model to obtain the business execution model.

[0138] In step S107, the actual execution data of each business to be transformed is obtained, and the corresponding business execution model is used to predict each business to be transformed to obtain the prediction result of each business to be transformed.

[0139] In step S108, the corresponding business execution model of each business to be transformed is collaboratively optimized based on the prediction results of each business to be transformed, so as to obtain the optimal business execution model for each business to be transformed.

[0140] In actual implementation, a high-performance rule engine and highly accurate algorithms are employed. Originating from rule-based expert systems, the rule engine is a component embedded in information systems. It uses predefined semantic modules to write business rules, separating the creation of business rules from application code. This allows business personnel to create business rules themselves using tools, significantly improving the efficiency of business rule maintenance and the system's responsiveness to changes in business rules. One of the core functions of the rule engine is rule matching, that is, determining which rules are applicable and executed given facts and rules. The rule matching algorithm plays a crucial role in the performance and efficiency of the rule engine. The high-performance rule engine is based on a cloud platform and supported by technologies such as MVEL, Quartz, HBase, Flink, Redis, Kafka, and Docker. It constructs functional services such as rule retrieval, rule performance, rule testing, rule publishing, and rule verification. Through message queues, synchronization interfaces, and page integration, it provides high-performance rule engine service calls for various applications.

[0141] Depending on different business requirements, different models can be called for training and prediction. In turn, different machine learning and training algorithms can be written for the models according to the actual needs of the business. This is what is meant by high-precision algorithms.

[0142] For example, in customs supervision model algorithms, one type of model adopts a screening algorithm centered on dynamic integrity assessment: the dynamic integrity assessment model and compliance management model. This includes an integrity assessment mechanism, an integrity assessment indicator system, an information interconnection mechanism, and a compliance incentive mechanism.

[0143] The integrity assessment mechanism centers on establishing standards and norms for customs-related behavior, creating a complete set of standards for corporate customs-related conduct, and defining the content, format, and channels for collecting corporate integrity information. This not only achieves the structuring and standardization of corporate integrity information for subsequent analysis and sharing, but also reduces the blindness and arbitrariness in customs administrative enforcement, thereby improving the scientific and institutionalized level of customs decision-making and enforcement.

[0144] In the construction of the integrity assessment indicator system, we analyze corporate behavior and grasp key indicators in customs supervision; we analyze the transformation of the physical state of import and export goods, changes in quantity, flow process of routes, as well as cooperation and loading, identify key links, analyze the information in key links, study the mutual corroboration relationship, discover the verification relationship and processing logic of data, and finally form the calculation formula of assessment indicators.

[0145] The information sharing mechanism centers on the recording and collection of information regarding enterprises' customs-related activities. It involves collecting administrative enforcement results from within customs and sharing enterprise integrity information with enforcement departments such as commerce, taxation, foreign exchange, banking, and international customs. This includes extracting information on enterprise procurement, production, sales, warehousing, and logistics from enterprise ERP systems, and obtaining transaction information between enterprises and their upstream and downstream partners from international supply chain network information platforms and e-commerce platforms. This allows for the recording and collection of key aspects of customs-related activities, and the assessment of enterprise integrity. Establishing this information sharing mechanism will help create a collaborative regulatory mechanism among various enforcement departments, effectively deterring enterprises from breaching their trust.

[0146] The compliance incentive mechanism focuses on establishing a trust-based cooperative relationship between customs and enterprises (individuals) to form differentiated management. The more transparent and trustworthy an enterprise's supply chain information is to customs, the more convenient the customs clearance measures will be for the enterprise. This shifts from relying on law enforcement to using big data concepts to encourage industry self-regulation and corporate compliance.

[0147] This algorithm framework integrates logistic regression scoring cards, entropy weight method, analytic hierarchy process (AHP), and Delphi expert method. In practice, the logistic regression scoring card and other methods (entropy weight method, AHP, Delphi expert method) can be selected based on the characteristics of different application industries, choosing the algorithm best suited to their specific needs.

[0148] The analysis process using logistic regression scorecards includes data acquisition and analysis, data preprocessing, feature selection, model training, and score generation.

[0149] Enterprises that have recently engaged in import / export declarations with customs were selected as target companies for this study. Based on the constructed evaluation index system, data from the past two years were used to calculate index values ​​for the target companies, thus determining the sample set for this study. Data on non-customs administrative penalties, criminal penalties, and tax rates were considered external data.

[0150] Analyze the data to understand the missing values, outliers, average, median, maximum, minimum, and distribution of each field, in order to develop a reasonable data preprocessing plan.

[0151] Process the feature variables of each dimension in the current dataset to ensure that the selected samples have practical significance.

[0152] Before formal modeling, statistical methods are used to analyze the features to understand the distribution and missing rate of the indicators.

[0153] During the sample data processing stage, a series of processes are performed on the sample features to derive higher-dimensional sample data from the original features. To ensure the stability of the model after dimensionality expansion and to ensure that the data values ​​of each feature sample meet the requirements of the features in the model, the features are usually binned.

[0154] Selecting variables for model building requires consideration of many factors, such as: the predictive power of the variables, the linear correlation between variables, the simplicity of the variables (ease of generation and use), the robustness of the variables (not easily circumvented), and the interpretability of the variables in business contexts. Among these, the most important and direct metrics are the predictive power and the linear correlation between the variables.

[0155] The selected features are fitted to construct a preliminary logistic regression model. The model is then filtered based on the P-value and the sign of the coefficients of each variable to obtain the final logistic regression model.

[0156] The integrity assessment score of a company is calculated using a logistic regression function. A high score represents low risk, and a low score represents high risk.

[0157] like Figure 15 As shown, steps S105 to S108 promote artificial intelligence (AI) to empower the digital transformation of various industries. Based on big data, it utilizes the machine's capabilities to complete learning and cognitive functions, integrating self-learning, problem-solving, speech recognition, and planning and decision-making. Through self-learning rather than executing fixed procedures, it makes machine decisions more accurate through experience, thereby promoting industrial transformation, giving rise to new business forms and models, and significantly improving social productivity.

[0158] In step S109, the optimal business execution model of each business to be transformed is logically associated to obtain the digital system of the target enterprise to be transformed.

[0159] In a digital system, the algorithmic results obtained from the optimal business execution model for each business to be transformed may be quantitative (e.g., a risk coefficient of 0.6) or qualitative (e.g., result set: compliant or non-compliant). These results are then correlated and fitted using a decision tree algorithm, assigning weights to the branches of the decision tree, and finally calculating a fitted result. The weight calculation process for each branch of the decision tree is essentially a calculation process using the entropy weight method. The formation of this decision tree (including its structure and branch weights) is dynamic and changes repeatedly. It is obtained through expert participation and repeated training with the decision tree machine learning algorithm, and is optimized and adjusted based on feedback from real results to obtain the digital system for the target enterprise undergoing transformation.

[0160] Taking the digital transformation of customs as an example, the results of various model identification, expert judgment, and inspection analysis can all be regarded as the results of multiple business models. Through the fitting calculation of the decision tree algorithm, the fitting decision result of a certain declared goods can be obtained, and the fitting decision result can guide how to automate the business.

[0161] In actual implementation, the optimal business execution model for each business is logically linked to obtain the digital system for the target industry undergoing transformation, and this system is then used to automate the business processes of the target industry undergoing transformation. For example... Figure 16 As shown, referencing the layered design and manufacturing process of integrated circuits, the industry's digital system is planned and integrated in layers to form an organically integrated complete system. Building a digital system is like manufacturing a smart chip; the process is the integrated circuit, parameters, data, knowledge bases, and intelligent models are the components, and the construction tasks of each line and scenario are like inserting these components into different circuit nodes. The system and intelligent equipment are like packaging the integrated circuit. A smart chip containing billions of components can then be delivered for use, and the ecosystem of the digital system is established.

[0162] The first layer of design: Business process streamlining. A business process is like a circuit diagram for an integrated circuit, upon which data, parameters, knowledge bases, intelligent models, and other electronic components are superimposed. Therefore, as the logical starting point of a digital system, the business process is the foundation and basis of digital transformation.

[0163] The second layer of design: Business digitization. Based on the business process roadmap, it relies on data and parameters to achieve digital transformation, that is, to realize business digitization. Data is like electrons flowing in a circuit, and parameters are like the "traffic rules" that constrain the flow of electrons. Through parameters and data, the business process is made to operate automatically.

[0164] The third layer of design: business intelligence. By embedding knowledge bases and intelligent models into the operational links of various business processes, business operations can be processed intelligently, gradually freeing up the physical and mental labor of operators and maximizing the use of computers to assist human brains and machines to replace manual labor.

[0165] The fourth layer of design: Business collaboration. This involves promoting internal and external collaborative governance to address issues such as insufficient precision in external services and poor internal coordination. Externally, it optimizes services to form a ubiquitous service network that is efficient, collaborative, and complementary. Internally, it strengthens operational monitoring, clarifies relationships and responsibilities, enhances supervision and coordination, and simultaneously improves the "problem clearing" mechanism. This ensures that all problems are entered into the system, documented, traceable, and subject to supervision, building a new collaborative governance ecosystem that provides comprehensive, convenient, and efficient services and ensures smooth operation across all areas.

[0166] The fifth layer of design: system usability. Processes, data, parameters, knowledge, models, operation monitoring, and services are contained in a complex backend, and ultimately need to be delivered to front-line users through a user-friendly, simple, streamlined, and intelligent information system and equipment.

[0167] Through the aforementioned design framework, elements such as processes, data, parameters, knowledge bases, and intelligent models are embedded into business systems and equipment facilities, effectively integrating and comprehensively combining digital hardware and software to achieve digital transformation and intelligent upgrading. To continuously promote the iterative upgrading of digital systems, it is also necessary to strengthen relevant institutional mechanisms and establish a sustainable, normalized operation and maintenance mechanism.

[0168] In some embodiments, the present invention further includes:

[0169] The digital system of the target enterprise to be transformed is used to automate the processing of multiple businesses to be transformed, collect unreasonable information in the process, and re-characterize the economic flow of the target enterprise based on the unreasonable information in order to update the digital system of the target enterprise to be transformed.

[0170] Monitor changes in demand within the industry of the target transformation enterprise, and re-profil the economic flow of the target transformation enterprise based on these changes in demand, so as to update the target transformation enterprise's digital system.

[0171] In practice, the digital system of the target enterprise undergoing transformation is used to automate the processing of multiple business processes, collect unreasonable information during the processing, and re-characterize the economic flow of the target enterprise based on the unreasonable information to update the digital system of the target enterprise, including:

[0172] Identifying and addressing issues in automated processing, as well as re-characterizing the target transformation process, involves the following key steps:

[0173] Problem Identification and Diagnosis. Internal Audit: Conduct a comprehensive review of existing business processes, information systems, and data management to identify bottlenecks, redundancies, high-error-rate processes in automation, as well as issues with inaccurate, inconsistent, or difficult-to-integrate data. This may include in-depth analysis of workflows, databases, application programming interfaces (APIs), reporting systems, etc. Employee Feedback: Encourage frontline employees and managers to share automation-related issues they encounter in their daily work, such as excessive manual tasks, slow system response, and cumbersome data entry. This feedback can be collected through questionnaires, focus groups, one-on-one interviews, etc. Technology Assessment: IT experts or third-party consultants assess the existing technology stack to determine whether it meets the needs of future digital transformation and whether there are outdated technologies, compatibility issues, security vulnerabilities, etc.

[0174] Problem Classification and Prioritization. Classification: Identified problems are categorized by type (e.g., process efficiency, data quality, technical architecture, user interface / user experience, compliance, etc.) to allow for targeted solution development. Prioritization: Based on factors such as the severity of the problem's impact on business, the difficulty of resolution, and potential benefits, a priority list is determined. Tools such as risk matrices and the MoSCoW (Must, Should, Can, Won't) method can be used for prioritization.

[0175] Solution design and implementation. Process optimization and automation: For process efficiency issues, technologies such as business process reengineering (BPR), business process management (BPM) tools, and robotic process automation (RPA) can be used to simplify, standardize, and automate processes. Data governance and integration: For data quality issues, a comprehensive data governance framework should be established, including data standards, quality control, metadata management, master data management, etc.; data integration platforms and ETL (extract, transform, load) tools should be used to solve the data silo problem. Technology upgrade and reconstruction: If the existing technical architecture cannot meet the transformation needs, system upgrades, cloud migration, microservice transformation, API interface standardization, etc. may be required. (4) User training and support: Ensure that employees have the ability to use new systems and tools, and provide necessary training and support to improve user acceptance and usage effectiveness.

[0176] Industry Restructuring and Strategic Adjustment. Industry Trend Analysis: Research the latest developments, success stories, and best practices in digital transformation within the target industry, understanding how new technologies (such as artificial intelligence, the Internet of Things, and blockchain) are reshaping industry value chains, business models, and competitive landscapes. Strategic Positioning: Based on the above analysis, clarify the company's unique value proposition, target market, core competencies, and short- and long-term strategic goals in digital transformation. Organizational and Cultural Change: Drive adjustments to organizational structure, roles and responsibilities, decision-making processes, and incentive mechanisms to cultivate an innovative, agile, and data-driven corporate culture to adapt to the needs of digital transformation.

[0177] Continuous monitoring and iterative optimization. KPI setting and tracking: Establish a set of key performance indicators (KPIs) for digital transformation covering business effectiveness, user experience, technical performance, and data quality, and regularly monitor and report progress. Agile methodology application: Adopt agile development, DevOps, continuous integration / continuous deployment (CI / CD) and other methodologies to quickly respond to changes, continuously deliver value, and achieve rapid iterative optimization of products and services. User feedback and data analysis: Continuously collect user feedback and use data analysis tools to deeply mine user behavior, preferences, and pain points to guide product improvement and the development of new features.

[0178] Through the above steps, we can systematically identify the problems in automated processing, propose targeted solutions, and gain a deep understanding and re-characterization of the target industry undergoing transformation to ensure the successful implementation of digital transformation.

[0179] The digital transformation method adapted to multiple industry scenarios proposed in this invention, through in-depth understanding of the characteristics and needs of different industries, ensures that the transformation plan is closely integrated with the actual business of enterprises, avoiding a one-size-fits-all approach; it can be adjusted and optimized according to the specific circumstances of enterprises to meet the personalized needs of different enterprises; it has broad industry applicability, avoiding the need to design separate transformation plans for each industry, thereby reducing transformation costs. At the same time, it can reduce duplication of work and waste, improve resource utilization efficiency, and further reduce transformation costs; it has clear transformation goals and path planning, ensuring that enterprises achieve their transformation goals within a limited time; it can also help enterprises identify and solve key problems and difficulties in the transformation process, improving the efficiency and success rate of transformation.

[0180] Next, referring to the accompanying drawings, a digital transformation device adapted to multiple industry scenarios according to an embodiment of the present invention is described.

[0181] Figure 17 This is a block diagram of a digital transformation device adapted to multiple industry scenarios according to an embodiment of the present invention.

[0182] like Figure 17As shown, the digital transformation device 10, which is adapted to multiple industry scenarios, includes: a characterization module 101, a demand analysis module 102, a business planning module 103, a solution optimization module 104, a data acquisition module 105, a training module 106, a prediction module 107, a collaborative optimization module 108, and a logical association module 109.

[0183] The system comprises the following modules: Characterization module 101 characterizes the economic flow of the target enterprise to obtain its internal operating rules; Demand analysis module 102 performs demand analysis based on these internal operating rules to identify multiple businesses to be transformed; Business planning module 103 performs overall business planning for these businesses to obtain an initial digital transformation implementation plan; Solution optimization module 104 establishes a comprehensive architecture management system for the multiple businesses to be transformed and optimizes the initial digital transformation implementation plan using this system to obtain the optimal implementation plan; Data collection module 105 collects multi-source data for each business to be transformed based on the optimal implementation plan to construct a dataset for each business; Training module 106 constructs an initial business execution model for each business to be transformed and trains it using the dataset to obtain the business execution model; and Prediction module 107 acquires the actual execution data for each business to be transformed and uses its corresponding business execution model to predict the outcome of each business. The collaborative optimization module 108 is used to collaboratively optimize the corresponding business execution model of each business to be transformed based on the prediction results, so as to obtain the optimal business execution model for each business to be transformed. The logical association module 109 is used to logically associate the optimal business execution models of each business to be transformed to obtain the digital system of the target enterprise.

[0184] It should be noted that the foregoing explanation of the digital transformation method embodiment adapted to multiple industry scenarios also applies to the digital transformation device adapted to multiple industry scenarios in this embodiment, and will not be repeated here.

[0185] The digital transformation device adapted to multiple industry scenarios proposed in this invention, through in-depth understanding of the characteristics and needs of different industries, ensures that the transformation plan is closely integrated with the actual business of enterprises, avoiding a one-size-fits-all approach; it can be adjusted and optimized according to the specific circumstances of enterprises to meet the personalized needs of different enterprises; it has broad industry applicability, thus avoiding the need to design separate transformation plans for each industry, thereby reducing transformation costs. At the same time, it can reduce duplication of work and waste, improve resource utilization efficiency, and further reduce transformation costs; it has clear transformation goals and path planning, which can ensure that enterprises achieve transformation goals within a limited time; it can also help enterprises identify and solve key problems and difficulties in the transformation process, improving the efficiency and success rate of transformation.

[0186] Figure 18 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. The electronic device may include:

[0187] The memory 1801, the processor 1802, and the computer program stored on the memory 1801 and executable on the processor 1802.

[0188] When the processor 1802 executes the program, it implements the digital transformation method adapted to multiple industry scenarios provided in the above embodiments.

[0189] Furthermore, electronic devices also include:

[0190] Communication interface 1803 is used for communication between memory 1801 and processor 1802.

[0191] Memory 1801 is used to store computer programs that can run on processor 1802.

[0192] The memory 1801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0193] If the memory 1801, processor 1802, and communication interface 1803 are implemented independently, then the communication interface 1803, memory 1801, and processor 1802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 18 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0194] Optionally, in a specific implementation, if the memory 1801, processor 1802, and communication interface 1803 are integrated on a single chip, then the memory 1801, processor 1802, and communication interface 1803 can communicate with each other through an internal interface.

[0195] The processor 1802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0196] This invention also provides a computer program product, which, when executed by a processor, implements the above-described digital transformation method adapted to multiple industry scenarios.

[0197] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described digital transformation method adapted to multiple industry scenarios.

[0198] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0199] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0200] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0201] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0202] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0203] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0204] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0205] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A digital transformation method adaptable to multiple industry scenarios, characterized in that, Includes the following steps: Economic flow characterization is performed on the target transformation enterprise to obtain its internal operating rules. Based on the aforementioned internal operating rules, a demand analysis was conducted to identify several business lines that need to be transformed. An overall business plan was developed for the multiple businesses to be transformed, resulting in an initial implementation plan for digital transformation, including: Determine the basic business processes for the multiple businesses to be transformed; By associating the basic business processes of each business to be transformed with resources, the overall business architecture can be obtained. Analyze the technical means required to implement the basic business processes of each business to be transformed, and construct the IT architecture based on the technical means; By coupling the overall business architecture and the IT architecture through engineering planning, the initial implementation plan for the digital transformation is obtained; A general architecture management system is established for the multiple businesses to be transformed. This system is then used to optimize the initial digital transformation implementation plan, resulting in the optimal digital transformation implementation plan. The general architecture management system includes an architecture management module, an engineering management module, and a project management module. The architecture management module is used to determine the engineering management requirements based on the multiple businesses to be transformed, and to receive the results of the simulated project to compare with the results of the target project. Based on the comparison results, the initial implementation plan of the digital transformation is optimized to obtain the optimal implementation plan of the digital transformation. The project management module is used to construct business scope and requirements management, schedule management, risk management, problem management, quality management, performance management, resource management, communication management, financial management, procurement management, comprehensive support, and file management according to the project management needs. The project management module is used to sort the business scope and requirements management, the schedule management, the risk management, the problem management, the quality management, the performance management, the resource management, the communication management, the funding management, the procurement management, the comprehensive support, and the file management to obtain a sorting result. Based on the sorting result, the module simulates project execution for each of the following categories to obtain the simulated project results. Based on the optimal implementation plan for digital transformation, multi-source data for each business to be transformed are collected to construct a dataset for each business to be transformed. Construct an initial business execution model for each business to be transformed, and train the corresponding initial business execution model using the dataset to obtain the business execution model; Obtain the actual execution data of each business to be transformed, and use its corresponding business execution model to predict each business to be transformed, thereby obtaining the prediction result of each business to be transformed. Based on the prediction results of each business to be transformed, its corresponding business execution model is collaboratively optimized to obtain the optimal business execution model for each business to be transformed. By logically associating the optimal business execution models for each business to be transformed, the digital system of the target enterprise is obtained.

2. The digital transformation method adaptable to multiple industry scenarios according to claim 1, characterized in that, The process of performing economic flow characterization on the target transformation enterprise to obtain its internal operating rules includes: Obtain the external information and internal structure of the target enterprise undergoing transformation; Based on the external information and internal structure of the enterprise, the information flow, resource flow and value flow of the target enterprise undergoing transformation are respectively characterized; The internal operating rules are determined by the information flow, the resource flow, and the value flow.

3. The digital transformation method adaptable to multiple industry scenarios according to claim 1, characterized in that, The step of training the corresponding initial business execution model using the dataset to obtain the business execution model includes: Analyze each feature data in the dataset of each business to be transformed to obtain the distribution and missing rate of each feature data; Based on the distribution and the missing rate, each feature data is binned to obtain multiple categorical variables; WOE encoding is performed on the multiple categorical variables to obtain the predicted value of each categorical variable; The multiple categorical variables are filtered based on the predicted value of each categorical variable to obtain the filtered categorical variables; The selected categorical variables are fitted to obtain a logistic regression model; The initial business execution model for each business to be transformed is trained using the logistic regression model to obtain the business execution model.

4. The digital transformation method adaptable to multiple industry scenarios according to claim 1, characterized in that, Also includes: The target enterprise digital system is used to automate the processing of the multiple businesses to be transformed, collect unreasonable information in the processing, and re-characterize the economic flow of the target enterprise based on the unreasonable information in order to update the target enterprise digital system. Monitor the demand changes in the industry to which the target transformation enterprise belongs, and re-characterize the economic flow of the target transformation enterprise based on the demand changes in order to update the digital system of the target transformation enterprise.

5. A digital transformation device adaptable to multiple industry scenarios, characterized in that, The digital transformation method adapted to multiple industry scenarios according to any one of claims 1-4 includes: The characterization module is used to characterize the economic flow of the target transformation enterprise in order to obtain the internal operating rules of the target transformation enterprise. The demand analysis module is used to perform demand analysis based on the internal operating rules to identify multiple businesses that need to be transformed. The business planning module is used to perform overall business planning for the multiple businesses to be transformed, and to obtain an initial implementation plan for digital transformation. The solution optimization module is used to establish a general architecture management system for the multiple businesses to be transformed, and to optimize the initial implementation plan for digital transformation using the general architecture management system to obtain the optimal implementation plan for digital transformation. The data acquisition module is used to collect multi-source data for each business to be transformed based on the optimal implementation plan for digital transformation, so as to construct a dataset for each business to be transformed. The training module is used to construct an initial business execution model for each business to be transformed, and to train the corresponding initial business execution model using the dataset to obtain the business execution model. The prediction module is used to obtain the actual execution data of each business to be transformed, and to predict each business to be transformed using its corresponding business execution model, so as to obtain the prediction result of each business to be transformed. The collaborative optimization module is used to collaboratively optimize the corresponding business execution model of each business to be transformed based on the prediction results, so as to obtain the optimal business execution model for each business to be transformed. The logical association module is used to logically associate the optimal business execution model of each business to be transformed, so as to obtain the digital system of the target enterprise.

6. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the digital transformation method adapted to multiple industry scenarios as described in any one of claims 1-4.

7. A computer program product, characterized in that, When the computer program is executed by the processor, it implements the digital transformation method adapted to multiple industry scenarios as described in any one of claims 1-4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the digital transformation method adapted to multiple industry scenarios as described in any one of claims 1-4.

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