Enterprise digital transformation dynamic collaborative management and control method, device, equipment and medium

Abstract

The invention relates to the technical field of enterprise digital transformation. The invention discloses an enterprise digital transformation dynamic collaborative management and control method, device and equipment and a medium, and the method comprises the steps: obtaining enterprise transformation demand information and enterprise basic data, and formulating a digital transformation scheme for an enterprise based on a preset scheme planning model; according to the digital transformation scheme, constructing a sharing platform and an industrial chain virtual twinborn body, and inputting data of the sharing platform into the industrial chain virtual twinborn body for digital simulation; optimizing industrial chain allocation resources according to the data of the sharing platform and the estimated data of the digital simulation; and evaluating the digital transformation scheme based on a preset transformation evaluation model according to the digital transformation scheme and the operation data of the industrial chain allocation resources. According to the method, the collaboration efficiency is improved, the problems of resource mismatching, risk response lag and the like in the transformation process are reduced, and accurate management and control, risk early warning and optimal resource configuration of the transformation process are realized.

Description

Technical Field

[0001] This application relates to the field of enterprise digital transformation technology, and more specifically, to a method, apparatus, equipment, and medium for dynamic collaborative management and control of enterprise digital transformation. Background Technology

[0002] Driven by the digital wave, enterprise digital transformation is undergoing a profound shift from optimizing single links to collaborating across the entire lifecycle and the entire industry chain. Current digital transformation management methods rely on static data integration through shared platforms, driven by traditional data logic, which cannot quickly respond to fluctuations during the transformation process. Insufficient connection and poor collaboration between lifecycle management and industry chain resource integration lead to frequent problems such as resource mismatch and delayed risk response during the transformation process.

[0003] Therefore, there is an urgent need for a digital transformation management method that can achieve precise control, risk warning, and optimal resource allocation during the transformation process in order to cope with multiple challenges such as market fluctuations, supply chain complexity, and data security compliance. Summary of the Invention

[0004] In view of the above situation, the embodiments of this application provide a method, apparatus, equipment and medium for dynamic collaborative management and control of enterprise digital transformation, which aims to solve the above problems or at least partially solve the above problems.

[0005] Firstly, this application provides a dynamic collaborative management and control method for enterprise digital transformation, including: A dynamic collaborative management and control method for enterprise digital transformation includes: Obtain information on enterprise transformation needs and basic enterprise data, and formulate digital transformation solutions for enterprises based on pre-set solution planning models; According to the digital transformation plan, a shared platform and a virtual twin of the industrial chain are constructed, and the data of the shared platform is input into the virtual twin of the industrial chain for digital simulation; Based on the data from the shared platform and the estimated data from the digital simulation, the allocation of resources in the industrial chain is optimized; Based on the digital transformation plan and the operational data of resource allocation in the industrial chain, the digital transformation plan is evaluated using a pre-set transformation evaluation model.

[0006] For example, by acquiring information on enterprise transformation needs and basic enterprise data, and based on a pre-set solution planning model, a digital transformation solution is developed for the enterprise, including: The enterprise's basic data is acquired, and the enterprise's status is determined based on a pre-set enterprise status analysis model. The system acquires information on the enterprise's transformation needs and its status, and determines a digital transformation plan based on a pre-set plan development model.

[0007] For example, according to the digital transformation solution, a shared platform and a virtual twin of the industrial chain are constructed. Data from the shared platform is input into the virtual twin of the industrial chain for digital simulation, including: The shared platform is built based on a microservice architecture, internal enterprise resources, and external enterprise resources. Data encryption technology is used to protect the data stored and transmitted on the shared platform; Role-based access control and data access control technologies are used to allocate permissions to each enterprise user on the shared platform. The data from the shared platform is filtered and transmitted to the blockchain network.

[0008] For example, based on the data from the shared platform and the estimated data from the digital simulation, the resource allocation in the industrial chain is optimized, including: Based on the data from the shared platform and a pre-set resource demand prediction model, the estimated demand information for resource allocation in the current industrial chain is generated. Optimize resource allocation across the industrial chain based on the estimated demand information; Based on the estimated data from the digital simulation and the operational data of the optimized industrial chain resource allocation, the resource demand prediction model is optimized.

[0009] For example, the method further includes: Based on the data from the shared platform and the estimated data from the digital simulation, and using a pre-set anomaly detection model, it is determined whether there are any anomalies in the industrial chain. When an anomaly is detected in the supply chain, the abnormal equipment and its location are determined based on a pre-set anomaly detection model.

[0010] For example, based on the digital transformation plan and the operational data of resource allocation in the industrial chain, and using a pre-set transformation evaluation model, the digital transformation plan is evaluated, including: Based on the digital transformation plan and the integrity assessment model in the transformation assessment model, the enterprise digital integrity index is obtained. Based on the enterprise's digital asset investment data, and using the data envelopment analysis model in the transformation evaluation model, the enterprise's digital transformation efficiency index is obtained. Based on the operational data of resource allocation in the industrial chain, and the industrial chain restructuring model in the transformation assessment model, industrial chain restructuring indicators are obtained. Based on the enterprise digital integrity index, enterprise digital transformation efficiency index, and industrial chain restructuring index, the resource coordination index is obtained; The digital transformation solution is evaluated based on the resource coordination indicators and preset resource coordination indicator thresholds.

[0011] For example, based on the digital transformation solution and the completeness assessment model in the transformation assessment model, an enterprise digital completeness index is obtained, including: A hierarchical model is constructed based on a trusted execution environment and the analytic hierarchy process. Based on the digital transformation plan, determine the target evaluation dimensions and the target indicators corresponding to the target evaluation dimensions; Determine the target evaluation dimension judgment matrix based on the target evaluation dimension, and determine the target indicator judgment matrix based on the target indicators corresponding to the target evaluation dimension. By inputting the target evaluation dimension judgment matrix and the target indicator judgment matrix into the hierarchical structure model, the enterprise digital integrity index is obtained.

[0012] Secondly, this application provides a dynamic collaborative management and control device for enterprise digital transformation, comprising: The solution development module is used to acquire information on enterprise transformation needs and basic enterprise data, and to develop digital transformation solutions for enterprises based on pre-set solution planning models; The collaborative construction module is used to build a shared platform and a virtual twin of the industrial chain according to the digital transformation plan. The data of the shared platform is input into the virtual twin of the industrial chain for digital simulation. The resource optimization module is used to optimize the allocation of resources in the industrial chain based on the data from the shared platform and the estimated data from the digital simulation. The transformation assessment module is used to evaluate the digital transformation plan based on the operational data of the digital transformation plan and the resource allocation of the industrial chain, using a pre-set transformation assessment model.

[0013] Thirdly, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the dynamic collaborative management and control method for enterprise digital transformation as described in the first aspect.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the dynamic collaborative management and control method for enterprise digital transformation as described in the first aspect.

[0015] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: This application introduces a virtual twin of the industry chain for digital simulation and prediction, and then optimizes resources based on data from a shared platform. This enables rapid response to fluctuations during the transformation process, and timely adjustments to the digital transformation plan through evaluation. It facilitates seamless integration of full lifecycle management and industry chain resource consolidation, improving collaborative efficiency and reducing issues such as resource mismatch and delayed risk response during the transformation process. This achieves precise control, risk warning, and optimal resource allocation during the transformation process to address multiple challenges such as market volatility, supply chain complexity, and data security compliance. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of an application environment for the dynamic collaborative management and control method for enterprise digital transformation in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a dynamic collaborative management and control method for enterprise digital transformation in one embodiment of the present invention; Figure 3 yes Figure 2 A flowchart illustrating a specific implementation of the shared platform in step S20; Figure 4 yes Figure 2 A schematic diagram of a specific implementation method for the virtual twin of the industrial chain in step S20; Figure 5 yes Figure 2 A schematic diagram of a specific implementation of step S40; Figure 6 This is a schematic diagram of a dynamic collaborative management and control device for enterprise digital transformation in one embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 8 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0019] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0020] As mentioned earlier, current digital transformation management methods are unable to quickly respond to fluctuations during the transformation process. Insufficient integration and poor coordination between full lifecycle management and supply chain resource integration lead to frequent problems such as resource misallocation and delayed risk response during the transformation process. To address this technical issue, this application provides a dynamic collaborative management method for enterprise digital transformation.

[0021] The dynamic collaborative management and control method for enterprise digital transformation provided in this invention can be applied to, for example... Figure 1In this application environment, the device communicates with the server via a network. The server can obtain enterprise transformation needs information and basic enterprise data through the device, and formulate a digital transformation plan for the enterprise based on a pre-set plan planning model. According to the digital transformation plan, a shared platform and a virtual twin of the industrial chain are constructed. Data from the shared platform is input into the virtual twin of the industrial chain for digital simulation. Based on the data from the shared platform and the estimated data from the digital simulation, the resource allocation of the industrial chain is optimized based on a pre-set resource allocation model. Based on the operational data of the digital transformation plan and the allocated resources of the industrial chain, the digital transformation plan is evaluated based on a pre-set transformation evaluation model. This application introduces a virtual twin of the industrial chain for digital simulation and estimation, and then optimizes resources based on data from the shared platform. This allows for rapid response to fluctuations during the transformation process, and timely adjustments to the digital transformation plan through evaluation. It enables seamless integration of full lifecycle management and industrial chain resource integration, improving collaborative efficiency and reducing problems such as resource mismatch and delayed risk response during the transformation process. It achieves precise control, risk warning, and optimal resource allocation during the transformation process to address multiple challenges such as market fluctuations, supply chain complexity, and data security compliance. The device side can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server side can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0022] Please see Figure 2 As shown, Figure 2 A flowchart illustrating the dynamic collaborative management and control method for enterprise digital transformation provided in this embodiment of the invention includes the following steps: S10: Obtain information on enterprise transformation needs and basic enterprise data, and formulate digital transformation solutions for enterprises based on pre-set solution planning models.

[0023] Specifically, this includes acquiring the enterprise's basic data, determining the enterprise's status based on a pre-set enterprise status analysis model, acquiring the enterprise's transformation needs information and the enterprise's status, and determining a digital transformation plan based on a pre-set plan formulation model.

[0024] In one embodiment, the acquisition of enterprise basic data relies on the target enterprise's existing information systems, including but not limited to ERP, CRM, WMS, MES, SCM, and production monitoring systems. Enterprise basic data includes, but is not limited to, financial data, production data, market data, customer feedback, employee capabilities, organizational structure, management model, business processes, and technology applications.

[0025] In one embodiment, the solution planning model includes an enterprise status analysis model pre-trained based on a large-scale model. The enterprise status analysis model uses artificial intelligence and big data platforms to process and analyze the enterprise's basic data to determine the enterprise's status, which includes the problems and bottlenecks existing in the target enterprise. The big data platform includes, but is not limited to, Alibaba Cloud, SAP HANA, Oracle Big Data Appliance, Hadoop, and Spark. The processing and analysis of the enterprise's basic data includes: data cleaning, processing, and mining (including but not limited to using Python to implement data cleaning, processing, and mining, using the pandas library for data processing, the scikit-learn library for data mining, and the matplotlib library for data visualization; PySpark can be used for ultra-large-scale data scenarios). The enterprise status analysis model discovers problems and bottlenecks in digital transformation through cluster analysis, association rule mining, and trend analysis of historical data.

[0026] In one embodiment, enterprise transformation needs information includes enterprise strategic information, enterprise business information, and enterprise technology information. Obtaining enterprise strategic information includes, but is not limited to, obtaining it through in-depth interviews with senior management, publicly available information, and strategic analysis workshops. Enterprise business information includes, but is not limited to, obtaining it through stratified interviews, questionnaires, process mining, and data analysis. Enterprise technology information includes, but is not limited to, obtaining it through system logs and technical audits, surveys of existing technology trends, and benchmarking against technology trends.

[0027] In one embodiment, the solution planning model includes a solution formulation model pre-trained based on machine learning and natural language processing (NLP) technologies. This model determines digital transformation solutions. NLP technology addresses the understanding and extraction of unstructured requirement text, machine learning addresses the matching of requirements, resources, and bottlenecks, and finally, a sequence generation model outputs a customized solution. The specific training process for the solution formulation model includes: data preprocessing; NLP training; machine learning training; solution generation training; and iterative optimization to obtain the final solution formulation model.

[0028] In one embodiment, a digital transformation solution includes a digital transformation planning document containing strategic goals, implementation paths, key technology selection, budget estimates, and other related content.

[0029] S20: Based on the digital transformation plan, construct a shared platform and a virtual twin of the industrial chain, and input the data of the shared platform into the virtual twin of the industrial chain for digital simulation.

[0030] In one embodiment, such as Figure 3 and Figure 4 As shown, step S20 includes: S21: Build a shared platform.

[0031] S211: The shared platform is built based on a microservice architecture, internal enterprise resources, and external enterprise resources; In one embodiment, by building a shared platform, the efficient aggregation and collaborative optimization of internal and external resources of an enterprise can be achieved, ensuring data sharing among different business departments and users, enabling the connection of supply chain resources, improving collaborative efficiency, and reducing problems such as resource mismatch and delayed risk response during the transformation process.

[0032] In one embodiment, internal enterprise resources include, but are not limited to, internal human resources, technological resources, and financial resources. External enterprise resources include, but are not limited to, external human resources, technological resources, and financial resources.

[0033] In one embodiment, the data exchange between the shared platform and third-party systems uses an API interface.

[0034] S212: Data encryption technology is used to protect the data stored and transmitted on the shared platform; In one embodiment, data encryption technologies include AES encryption and TLS transmission encryption, ensuring privacy protection among different business units and users on the shared platform.

[0035] S213: Role-based access control and data access control technologies are used to allocate permissions to enterprise users on the shared platform; In one embodiment, data on the shared platform is accessed to enterprise users based on their permissions on the platform. Role-based access control (RBAC) and data access control technologies (such as attribute-based access control, ABAC) are used to allocate permissions to enterprise users on the shared platform, ensuring privacy protection and balance among different business units and users within the shared platform.

[0036] S214: Filter the data from the shared platform and transmit it to the blockchain network.

[0037] In one embodiment, blockchain technology is used to ensure the data compliance and transparency of the sharing platform.

[0038] In one embodiment, the consortium blockchain is adapted to meet the controllable sharing requirements of the sharing platform; Core data such as resource collaboration records, core business data, evaluation and decision-making data, and operation logs from the shared platform are selected and transmitted to the blockchain network as on-chain objects.

[0039] During the data collection phase of the shared platform, collection credentials are generated synchronously to trigger on-chain requests. During the data processing phase, data hash values ​​are generated, and the raw data is stored on the shared platform. When the data is used, RBAC / ABAC permission verification is combined with operation logs and recorded on the chain. It works in conjunction with the shared platform's AES encryption, TLS transmission encryption and TEE environment to form a multi-layered protection. Finally, on-chain evidence storage meets the requirements of relevant laws and regulations.

[0040] Authorized queries enable transparent cross-departmental / internal / external collaboration, trace data flow and operations, and ensure data credibility and collaborative compliance.

[0041] By building a shared platform, we can achieve efficient aggregation and collaborative optimization of internal and external resources, and ensure a balance between data sharing and privacy protection among different business departments and users.

[0042] This application builds an enterprise internal and external resource collaboration and sharing platform based on a microservice architecture, integrating a multi-dimensional security mechanism of "access control + data encryption + blockchain notarization" to balance "efficient resource sharing" and "data privacy compliance".

[0043] Access control: A dual access control system of "RBAC (role-based) + ABAC (attribute-based)" is adopted, such as allowing only the production department to access equipment operation data and the finance department to view only budget-related data; Data security: Data is protected by AES encryption (storage) and TLS encryption (transmission), and blockchain technology is used to record resource allocation and data access history to ensure data compliance and transparency; Resource integration: By connecting to third-party systems (such as ERP and SCM) through API interfaces, we integrate human resources, technical resources, and financial resources. Based on machine learning resource demand prediction models, we optimize the allocation of resources in the industrial chain on the shared platform to achieve efficient aggregation of internal and external resources.

[0044] S22: Construct a virtual twin of the industrial chain.

[0045] S221: Obtain physical entity data for each stage within the enterprise.

[0046] In one embodiment, each stage includes, but is not limited to, production, sales, logistics, and organization.

[0047] In one embodiment, physical entity data is multi-source heterogeneous data, including but not limited to production equipment data in the production process, order volume, customer service personnel volume, and personnel location in the sales process, transportation volume and AGV location data in the logistics process, and data from various business systems (such as ERP, CRM, and HR systems), files and documents, real-time streaming data, cross-departmental collaboration data, and external interface data in the organizational process.

[0048] S222: Construct a virtual twin of the industrial chain based on the physical entity data of each link within the enterprise.

[0049] In one embodiment, a digital twin of the industrial chain is constructed through digital modeling (such as building a LOD4 precision virtual device using Unity3D), GIS geographic mapping, and topological structure association, so as to achieve real-time data synchronization between physical entities and virtual images (such as mapping production equipment operating parameters to virtual devices 1:1).

[0050] In one embodiment, digital modeling is used to create virtual models of production equipment, customer service personnel, and AGVs. GIS geographic mapping is then used to digitize the locations of these components, obtaining their corresponding geographic distribution layers. These geographic distribution layers are then offset to ensure they perfectly correspond to the actual locations of the equipment, personnel, and AGVs. The virtual models are then connected based on their actual topology and mapped onto the geographic distribution layers. Finally, the virtual models are stored as objects associated with their respective entities, establishing a digital mapping relationship for the supply chain and ultimately creating a virtual twin of the supply chain.

[0051] The following example uses production equipment data from the production process: A. Production equipment data in the production process includes equipment ledger information, operation and maintenance data, and geographical distribution data of production equipment.

[0052] In one embodiment, the production equipment includes, but is not limited to, various production equipment within the enterprise. For example, production equipment in an automotive parts manufacturing enterprise includes, but is not limited to, stamping presses, welding robots, AGV logistics vehicles, and automatic spray booths for painting lines.

[0053] In one embodiment, data acquisition units (such as IIoT sensors, RFID tags, and UWB positioning devices) are used to obtain equipment ledger information and operation and maintenance data of various production equipment within the enterprise.

[0054] In one embodiment, the equipment ledger includes, but is not limited to, equipment number, model and specifications (e.g., rated pressure of 2000 tons for a stamping machine), rated parameters (e.g., repeatability of ±0.05mm for a welding robot), material properties and process standards.

[0055] In one embodiment, the operation and maintenance data includes, but is not limited to, real-time operating parameters of each production equipment (e.g., 12 strokes / minute for the stamping machine), maintenance records, and corresponding environmental parameters (e.g., workshop temperature and humidity).

[0056] In one embodiment, the geographic distribution data is obtained through GIS technology.

[0057] In one embodiment, the geographic distribution data includes the location distribution information of each production device within the physical production line (such as the location of the stamping workshop and welding stations) and the topology. The topology refers to the process link relationship between each production device (such as "stamping finished product area → welding station → painting buffer zone → AGV loading and unloading point").

[0058] B. Based on the equipment ledger information, operation and maintenance data and geographical distribution data of each production equipment within the enterprise, construct a virtual twin of the production process.

[0059] In one embodiment, the equipment ledger information is input into the 3D modeling software Unity3D, and each production equipment in the physical production line is digitally modeled (LOD4 accuracy) to obtain the corresponding virtual equipment for each production equipment. The virtual equipment is named with the equipment number of the corresponding production equipment.

[0060] In one embodiment, the geographic distribution data is digitized using GIS software to obtain a corresponding geographic distribution layer for each production device. The geographic distribution layers for each production device are then offset to ensure they perfectly correspond to the actual distribution of the production devices on the physical production line. The virtual devices are connected according to their topology and mapped onto the geographic distribution layers. Each production device entity is then associated with and stored as a digital twin model of the physical production line, established using its device number.

[0061] In one embodiment, the digital twin model includes an environment simulation unit. The environment simulation unit simulates the operating environment scenario of the corresponding physical production line based on the collected environmental parameters of each production device, and maps it into the digital twin model. It can obtain the environmental parameters of each production device in the physical production line in real time, giving the digital twin production line the effect of synchronously changing environmental scenarios.

[0062] In one embodiment, corresponding twin data is generated based on the collected operating parameters and maintenance records of each production device, and then updated into the digital twin model to obtain a virtual twin of the production process.

[0063] In one embodiment, the operating parameters, the maintenance records, and the environmental parameters are stored and categorized according to their source.

[0064] This application breaks through the limitations of digital twin technology in terms of "single device / local scene" and constructs a dynamic mapping virtual twin covering the entire industrial chain of enterprise production, sales and logistics, so as to realize multi-dimensional real-time synchronization of "physical entity (equipment + process + organization) - virtual image".

[0065] This application specifically collects multi-source heterogeneous core data from various links in the industry chain, including production links (production volume, processing equipment / personnel), sales links (order volume, operating costs / customer service personnel), logistics links (transportation volume, transportation input parameters), as well as resource data such as the specific location distribution and topology of production equipment in each link, to ensure the accuracy and comprehensiveness of subsequent digital twins.

[0066] This application achieves LOD4 precision digital modeling of physical entities using Unity3D (virtual devices are uniquely associated with physical entity numbers), and combines GIS technology to map the topological structure and geographical distribution between entities into digital layers, synchronizing operating parameters and maintenance records; at the same time, scene synchronization is performed: a built-in "environment simulation unit" dynamically simulates the operation scenario of the physical industrial chain based on environmental parameters such as workshop temperature and humidity, logistics road conditions, etc., ensuring that the "geometric, physical, and behavioral" characteristics of the virtual twin and the physical entity are consistent in real time. S30: Optimize resource allocation in the industrial chain based on the data from the shared platform and the estimated data from the digital simulation; In one embodiment, step S31 includes: S31: Based on the data from the shared platform and a pre-set resource demand prediction model, generate estimated demand information for the current allocation of resources in the industrial chain; S32: Optimize resource allocation across the industrial chain based on the estimated demand information; S33: Optimize the resource demand prediction model based on the estimated data from the digital simulation and the optimized operational data of resource allocation in the industrial chain.

[0067] In one embodiment, after building a shared platform based on the enterprise's digital transformation plan, the resources allocated in the industrial chain are initialized according to the digital transformation plan, and the initial industrial chain resources are used for actual operation, digital twin simulation, and resource demand forecasting.

[0068] In one embodiment, the resource demand prediction model uses a shared platform as the data and implementation carrier, and uses an appropriate machine learning algorithm to accurately predict demand. This includes, but is not limited to, applying algorithms such as Long Short-Term Memory Network (LSTM) and Attention mechanism in production logistics demand. Furthermore, optimization algorithms are used to dynamically allocate resources, including, but not limited to, linear programming (LP), integer programming (IP), reinforcement learning (RL) and other algorithms.

[0069] In one embodiment, the resource demand forecasting model is pre-trained based on a large model, a pre-built knowledge base of supply chain resource allocation needs, machine learning algorithms, and optimization algorithms. For example, a possible demand is: predicting a production gap in the next 24 hours and automatically increasing the capacity of logistics AGVs when optimizing supply chain resource allocation.

[0070] In one embodiment, the resource demand forecasting model is continuously optimized by using estimated data from digital simulation and operational data from optimized supply chain resource allocation, thereby improving the accuracy of forecasts and enabling uninterrupted optimization throughout the entire lifecycle.

[0071] S40: Based on the digital transformation plan and the operational data of resource allocation in the industrial chain, evaluate the digital transformation plan using a pre-set transformation evaluation model.

[0072] In one embodiment, such as Figure 5 As shown, step S40 includes: S41: Based on the digital transformation plan and the integrity assessment model in the transformation assessment model, obtain the enterprise digital integrity index; In one embodiment, step S41 includes: S411: Construct a hierarchical model based on trusted execution environment and analytic hierarchy process; In one embodiment, a Trusted Execution Environment (TEE) is deployed on an edge computing node or in the cloud. This includes, but is not limited to, industrial edge computing nodes based on ARM TrustZone or Intel SGX. The algorithms, models, and data processing logic related to enterprise digital integrity assessment are deployed within the TEE. Furthermore, a hierarchical model is constructed using Analytic Hierarchy Process (AHP) based on the Trusted Execution Environment. This hierarchical model includes a target layer, a criterion layer, and an indicator layer. The target layer includes enterprise digital integrity indicators obtained after the assessment; the criterion layer includes the dimensions of the enterprise digital integrity assessment; and the indicator layer includes the target indicators corresponding to each dimension of the enterprise digital integrity assessment.

[0073] S412: Based on the digital transformation plan, determine the target evaluation dimensions and the target indicators corresponding to the target evaluation dimensions.

[0074] In one embodiment, the target evaluation dimensions are determined according to the digital transformation plan, and after the target evaluation dimensions are determined, the target indicators corresponding to each target evaluation dimension are determined.

[0075] In one embodiment, the target evaluation dimensions include at least one of the following: preset digital asset integrity dimension, preset business process digitization degree dimension, preset organizational structure adaptability dimension, and preset technology application depth dimension. The target indicators corresponding to each target evaluation dimension refer to the specific indicators under each dimension, and are not specifically limited here.

[0076] In one embodiment, the target evaluation dimensions are located in the criteria layer, and the target indicators corresponding to each target evaluation dimension are located in the indicator layer. S413: Determine the target evaluation dimension judgment matrix based on the target evaluation dimension, and determine the target indicator judgment matrix based on the target indicators corresponding to the target evaluation dimension; In one embodiment, the target evaluation dimension judgment matrix includes quantitative values ​​of importance among various target evaluation dimensions, and the target indicator judgment matrix includes quantitative values ​​of importance among various target indicators.

[0077] In one embodiment, the elements of the matrix are determined. To quantify the importance of factors, representing the numerical value of the factors. e Relative factors f The importance of a factor is usually assigned using a 1-9 scale. In the target evaluation dimension judgment matrix, the factors are the target evaluation dimensions; in the target indicator judgment matrix, the factors are the target indicators.

[0078] S414: Input the target evaluation dimension judgment matrix and the target indicator judgment matrix into the hierarchical structure model to obtain the enterprise digital integrity index.

[0079] In one embodiment, the importance quantification values ​​between various target evaluation dimensions are input into the criterion layer, and the importance quantification values ​​between various target indicators are input into the value indicator layer.

[0080] In one embodiment, based on the hierarchical model, and using the judgment matrix elements in the judgment matrix of the target evaluation dimension, the judgment matrix elements in the judgment matrix of the target indicator, and the order of the judgment matrix, the target weights corresponding to the target evaluation dimension and the target indicator are obtained respectively:

[0081] in, W The target weight; To determine the matrix elements; n To determine the order of a matrix; e To determine the target evaluation dimensions or target indicators in the matrix; f To determine the target evaluation dimension or target indicator in the matrix, natural numbers, and f < n ,e ≠ f ; k The sum index is used to iterate through all elements in the same row and calculate the sum of all elements in that row.

[0082] Based on the target evaluation dimensions and the target weights corresponding to each target indicator, the comprehensive score of the enterprise digital integrity indicator is obtained.

[0083] in, These are the weights of the upper-level weights, i.e., the weights of the target evaluation dimensions; is the weight of this layer, that is, the target weight of the target indicator; S is the comprehensive score of the enterprise's digital integrity, that is, the enterprise's digital integrity indicator.

[0084] S42: Based on the enterprise's digital asset investment data and the data envelopment analysis model in the transformation evaluation model, obtain the enterprise's digital transformation efficiency index.

[0085] In one embodiment, encrypted enterprise digital asset investment data is obtained from a shared platform based on the Trusted Execution Environment (TEE). Digital asset investment data refers to various quantifiable resources invested by an enterprise during its digital transformation process, typically stored in encrypted form on the shared platform. Examples include hardware and infrastructure investments (such as the procurement and maintenance costs of hardware like servers, storage devices, and network equipment), software and technology investments (such as the procurement costs of enterprise-level software like ERP, CRM, and data analytics tools), and human resource and training costs (such as educational expenditures for digital skills training and TEE operation training).

[0086] In one embodiment, Data Envelopment Analysis (DEA) is a nonparametric efficiency evaluation method used to measure the relative efficiency of decision units (DMUs) with multiple inputs and multiple outputs. It is used to assess the relative efficiency of similar units (such as businesses, hospitals, and schools) and identify optimal practices (efficiency frontiers) and inefficient units.

[0087] In one embodiment, encrypted enterprise digital asset investment data from each decision-making unit is obtained from the shared platform based on the Trusted Execution Environment (TEE), and input into the Data Envelopment Analysis (DEA) model to obtain the benefit value corresponding to each decision-making unit. The benefit values ​​corresponding to each decision-making unit are then summed to obtain the enterprise digital transformation efficiency index E.

[0088] In one embodiment, the Decision Making Unit (DMU) is a core concept in Data Envelopment Analysis (DEA), referring to an independent entity or unit whose efficiency is being evaluated.

[0089] In one embodiment, the encrypted enterprise digital asset investment data of each decision-making unit is processed based on the Trusted Execution Environment (TEE), and the obtained encrypted enterprise digital asset investment is input into the Data Envelopment Analysis (DEA) model to obtain the benefit value corresponding to each decision-making unit.

[0090] S43: Based on the operational data of resource allocation in the industrial chain, and the industrial chain restructuring model in the transformation assessment model, the industrial chain restructuring indicators are obtained.

[0091] In one embodiment, the supply chain restructuring model includes a data collection unit and a data processing unit.

[0092] In one embodiment, after a period of operation based on resource allocation according to the industrial chain, the data collection unit collects operational data of each link in the target enterprise's industrial chain during a historical standard period. The standard period is artificially set.

[0093] In one embodiment, operational data includes data items such as production volume of a specified product in the production stage, production input parameters, order volume of a specified product in the sales stage, operational input parameters, transportation volume of a specified product in the logistics stage, and transportation input parameters. Among these, production input parameters refer to the number of processing equipment or personnel required to produce the specified product corresponding to the total production volume; operational input parameters refer to the operating costs or customer service personnel required to sell the specified product corresponding to the total order volume; and transportation input parameters refer to the transportation volume of the specified product required to complete the corresponding transportation volume, including but not limited to the number of transported items, transported weight, and the number of transport vehicles, transport personnel, or transportation auxiliary costs required for the total cargo volume corresponding to the transported distance.

[0094] In one embodiment, a data processing unit is used to process and analyze the raw data from each link of the industrial chain to obtain industrial chain restructuring indicators.

[0095] The specific steps are as follows: S431: Within a specified historical period, obtain the number of unique data items for each stage within each historical standard time period.

[0096] For example, consider data items such as the production volume of a specified product in the production process, the order volume of a specified product in the sales process, and the transportation volume of a specified product in the logistics process. The production volume of the specified product within each historical standard time period is labeled Si, the order volume of the specified product within each historical standard time period is labeled Di, and the transportation volume of the specified product within each historical standard time period is labeled Yi, where i = 1, 2, ..., m, and m represents the number of historical standard time periods within the specified historical period.

[0097] S432: Calculate the total number of data items for each data item across all historical standard time periods.

[0098] For example, through Calculate the total production volume SZ of the specified product in the production process, the total order volume DZ of the specified product in the sales process, and the total transportation volume YZ of the specified product in the logistics process within all historical standard time periods in the specified historical period.

[0099] S433: Calculate the total number of data items for each data item in the remaining historical standard time period based on the total number of data items for each data item in all historical standard time periods and the odd number of data items in the current historical standard time period.

[0100] For example, the total number of data items related to the production volume of a specified product within the remaining historical standard time period in the production process is... The total number of data items related to the order volume of a specified product in the sales process within the remaining historical standard period is: The total number of data items related to the transportation volume of a specified product in the logistics process within the remaining historical standard period is: .

[0101] S434: Calculate the percentage of each data item based on the number of data items in the current historical standard time period and the total number of data items in the remaining historical standard time period.

[0102] For example, the ratio of the number of singular data items in the production volume of a specified product within the current historical standard period to the total number of data items within the remaining historical standard period is... In the sales process, the ratio of the number of orders for a specified product in the current historical standard period to the total number of orders in the remaining historical standard period is... The ratio of the number of data items for the transportation volume of a specified product in the current historical standard period to the total number of data items in the remaining historical standard period in the logistics process is... .

[0103] S435: Calculate the difference analysis value between the proportions of any two data items based on the proportion of each data item within each historical standard time period.

[0104] For example, obtain the percentage of each data item within the same historical standard time period, namely, the percentage of production volume of a specified product in the production stage (BSi), the percentage of order volume of a specified product in the sales stage (BDi), and the percentage of transportation volume of a specified product in the logistics stage (BYi); calculate the difference analysis value SDci (production-sales difference analysis value) between the percentage of production volume of a specified product in the production stage (BSi) and the percentage of order volume of a specified product in the sales stage (BDi) using SDci=|BSi-BDi|; calculate the difference analysis value SYci (product difference analysis value) between the percentage of production volume of a specified product in the production stage (BSi) and the percentage of transportation volume of a specified product in the logistics stage (BYi) using SYci=|BSi-BYi|; and calculate the difference analysis value DYci (sales-material difference analysis value) between the percentage of order volume of a specified product in the sales stage (BDi) and the percentage of transportation volume of a specified product in the logistics stage (BYi) using DYci=|BDi-BYi|.

[0105] Similarly, the difference analysis values ​​for each other historical standard period are calculated.

[0106] S436: Based on the difference analysis value between the proportions of any two data items within each historical standard time period, obtain the initial set of each difference analysis value, and calculate the dispersion value of the initial set of each difference analysis value.

[0107] For example, using the variance calculation formula, the dispersion values ​​of each difference analysis value, SDci, SYci, and DYci, can be calculated.

[0108] Let's take calculating the dispersion of the production-sales difference analysis value (SDci) as an example: First, obtain the production-sales difference analysis values ​​(SDci) for multiple historical standard periods and calculate the average of all SDci values. Second, sum the squared deviations of each SDci value from the average of all SDci values ​​and take the average to obtain the variance of the production-sales difference analysis value (SDci). Third, calculate the square root of the variance of the production-sales difference analysis value (SDci). The square root of the variance directly reflects the degree of dispersion.

[0109] S437: Based on the dispersion value of the initial set of each difference analysis value, and based on a pre-set dispersion threshold, update the initial set of each difference analysis value to obtain the set of each difference analysis value.

[0110] Specifically, when the dispersion value of the initial set of each difference analysis value is greater than the dispersion threshold, it is determined that the dispersion value of the current set is too large, and the largest difference analysis value in the current set needs to be deleted, completing one update of the set. Then, the dispersion value of the updated set is calculated again according to step S436, and compared with the dispersion threshold according to step S437. If the comparison result is still greater than the dispersion threshold, the largest difference analysis value in the current set (i.e., the set after one update) is deleted again, completing a second update of the set. This process continues until the dispersion value of the updated set is less than or equal to the dispersion threshold, at which point the update stops, resulting in a set for each difference analysis value. The method for deleting the largest difference analysis value in the set is to sort the values ​​by calculating the absolute value of the difference between each difference analysis value and the average of all difference analysis values.

[0111] For example, taking SDci as an example, if the dispersion value of the current set of SDci is greater than its corresponding dispersion threshold, it means that the dispersion value of the difference analysis value SDci in each historical standard period in the current set is large. Then, it is sorted in descending order according to |SDci-SDcp| (where SDcp represents the average of all production and sales difference analysis values ​​SDci in the set), and then the largest difference analysis value SDci is deleted. After deletion, the set is updated. The dispersion value of the updated set is recalculated and compared with the dispersion threshold. If the calculated dispersion value is still greater than the dispersion threshold, the maximum value of the difference analysis value SDci in the set is found again and deleted. This process continues until the dispersion threshold of the set is less than or equal to the dispersion threshold. The average of all production and sales difference analysis values ​​SDci in the set when the update stops is denoted as SDpp.

[0112] Similarly, the product difference analysis value set SYci and the sales difference analysis value set DYci are updated to obtain the results.

[0113] S438: Based on the set of each difference analysis value, obtain the average value of the set of each difference analysis value.

[0114] For example, the average of all production and sales difference analysis values ​​SDci in the production and sales difference analysis value SDci set is denoted as the average production and sales difference analysis value SDpp; the average of all product difference analysis values ​​SYci in the product difference analysis value SYci set is denoted as the average product difference analysis value SYpp; and the average of all sales difference analysis values ​​DYci in the sales difference analysis value DYci set is denoted as the average sales difference analysis value DYpp.

[0115] S439: Calculate the restructuring index of the industrial chain based on the average value of the set of each difference analysis value.

[0116] For example, the average values ​​of production-sales difference analysis (SDpp), product difference analysis (SYpp), and sales-product difference analysis (DYpp) based on the production, sales, and logistics links are used to obtain the supply chain restructuring index X0 using a formula:

[0117] in, α 1. α 2. α 3 are all preset proportional coefficients; μ is the error correction coefficient, and its value is 2.315.

[0118] S44: Based on the enterprise digital integrity index, enterprise digital transformation efficiency index, and industrial chain restructuring index, the resource coordination index is obtained.

[0119] The resource coordination index is calculated using the following formula:

[0120] in, α 1. α 2. α 3 are all preset proportional coefficients; S is the enterprise digital integrity index; E is the enterprise digital transformation efficiency index.

[0121] Results: By collecting and processing operational data from production, sales, and logistics, companies can more effectively restructure and integrate resources across the supply chain. This helps them identify synergies and differences between stages, thereby optimizing resource allocation. In-depth analysis of historical operational data reveals differences in product volume between stages, allowing for the analysis of whether corresponding input parameters need adjustment. For example, if the production volume SZP is 1000 units and the logistics volume YZP is 900 units, the difference of 100 units indicates a logistics gap of 100 units, requiring an increase in logistics input parameters. Such analysis provides strong data support for management, improving decision-making efficiency. By monitoring and comparing data from different stages, companies can identify potential risks earlier and take measures for risk management and mitigation.

[0122] S45: Evaluate the digital transformation solution based on the resource coordination indicators and the preset resource coordination indicator thresholds.

[0123] In one embodiment, if the resource coordination index is less than or equal to the resource coordination index threshold (set according to industry benchmark data), continuous monitoring is maintained. If the resource coordination index is greater than the resource coordination index threshold, resource restructuring is triggered, and the process re-enters step S10 to analyze and adjust the digital transformation plan, and then re-enters step S20 to update the shared platform and carry out the resource integration phase. Then, the subsequent steps S30 and S40 are repeated to achieve a closed-loop management of the entire lifecycle with organic linkage between each stage.

[0124] Taking the logistics process as an example, if the resource coordination index exceeds the resource coordination index threshold, specifically: The difference between the production volume SZP in the production stage and the production volume YZP in the logistics stage is calculated using supply chain operation data. Specifically: when YZP=900 pieces and SZP=1000 pieces, K2=-100 pieces, indicating a logistics and transportation shortage of 100 pieces; Next, the input parameters are dynamically adjusted: the logistics process is carried out through... Calculate the required additional transportation investment, where YT is the historical average transportation investment. Specifically: when YT = 300,000 yuan, T2 = (-100 × 30) / 900 ≈ -33,000 yuan, meaning an additional AGV capacity of 33,000 yuan is needed. Then, the solution is iterated and resources are re-integrated: the adjusted digital transformation solution is synchronized to the shared platform and re-enters the resource integration stage. The data collection module re-collects the adjusted data, including but not limited to the total AGV transportation volume, AGV transportation input parameters, AGV transportation turnaround time, AGV transportation volume ratio, the data processing module repeatedly calculates the ratio (BSi, BDi, BYi) and reconstructs the indicator X0, forming an iterative closed loop of "adjustment-evaluation-re-adjustment".

[0125] If the resource coordination index is less than the preset benchmark value, continuous monitoring will continue. The dynamic continuous monitoring mechanism will, on the one hand, collect data in real time and provide threshold warnings: the digital twin will periodically synchronize physical production line data, including but not limited to stamping part pass rate, welding positioning deviation, and AGV turnover rate, and compare them with the preset theoretical range. If the indicators are close to the preset theoretical range, the result display module will push warnings through the sharing platform.

[0126] This application will organically link the enterprise digital transformation plan planning, shared platform, and supply chain virtual twin deployment stages through dynamic evaluation models and resource coordination indicators, so as to avoid the disconnect between each link (for example, when the twin detects a production anomaly, the evaluation model will be adjusted and the shared platform will allocate resources simultaneously).

[0127] In one embodiment, the method further includes: determining whether there is an anomaly in the industrial chain based on the data from the shared platform and the estimated data from the digital simulation, using a pre-set anomaly detection model; when an anomaly is determined in the industrial chain, identifying the abnormal equipment and its location based on the pre-set anomaly detection model. The theoretical range of indicators is obtained based on the estimated data from the digital simulation, and the actual indicators for each link are obtained based on the data from the shared platform. The actual indicators are compared with the theoretical range of indicators. When the actual indicators are within the theoretical range, the industrial chain is judged to be operating normally; when the actual indicators exceed the theoretical range, the industrial chain is judged to be abnormal. It is understood that each link has its corresponding indicators. When the industrial chain is abnormal, the abnormal link (e.g., overproduction in the production link, underproduction in the transportation link) and its specific location (e.g., the specific production line location) can be determined based on the indicators. Then, the abnormal equipment and its location are determined based on the actual operating parameters of each piece of equipment at the specific location of the anomaly and the theoretical operating parameters from the simulation.

[0128] In one embodiment, 1) based on a pre-set monitoring period, real-time monitoring data of the physical production line operation in the data of the shared platform within the monitoring period is obtained to obtain the actual production indicators within the corresponding monitoring period, and based on the actual production indicators... ; 2) The production plan data corresponding to the monitoring cycle is used as input parameters and input into the digital twin production line of the virtual twin of the industrial chain corresponding to the physical production line. The digital twin production line performs production simulation operation according to the production plan data, obtains the corresponding simulated production indicators, and marks the obtained simulated production indicators as... ; 3) Based on the simulated production indicators The theoretical production target range is determined and marked as follows: Where A is a preset fixed value (e.g., ±5% deviation); the actual production indicators Within the range of theoretical production indicators Compare; if This indicates that the corresponding physical production line in the industrial chain is operating normally, and it is marked as a normal production state; if This indicates that the corresponding physical production line in the industrial chain is operating abnormally, and it is marked as an abnormal production state. 4) When a physical production line is marked as being in an abnormal production state, obtain the corresponding production efficiency index of the physical production line under the abnormal production state, and mark the production efficiency index as B. Set a production efficiency threshold. (e.g., 0.85) The production efficiency index under abnormal production conditions is compared with the production efficiency threshold to determine whether there is an early warning for the degree of abnormality of the corresponding physical production line. like If a significant abnormality is detected in the corresponding physical production line, a production abnormality warning message is generated and sent to the production line management personnel, and the corresponding physical production line is marked as an abnormal production line. like If no significant abnormality is found in the corresponding physical production line, no further intervention will be performed. 5) The process of determining whether there is a malfunction in the production equipment within an abnormal production line includes: The actual operating parameters of each production equipment in the abnormal production line during the monitoring period are obtained, including but not limited to the actual pressure of the stamping press, welding positioning deviation, and AGV navigation error. At the same time, the theoretical operating parameters of the digital twin production line corresponding to the abnormal production line during the monitoring period are obtained by simulating the operation of each production equipment. The actual operating parameters of each production equipment are compared with the theoretical operating parameters obtained from the simulation operation to obtain the corresponding operating deviation value of each production equipment. Based on the pre-set operating deviation threshold of each production equipment, the corresponding operating deviation value of each production equipment is compared with its operating deviation threshold. If the operating deviation value does not exceed the operating deviation threshold, it indicates that the production equipment is operating normally, and no other operations are performed. If the operating deviation value exceeds the operating deviation threshold, it indicates that the production equipment is operating abnormally. The corresponding operating parameters will be marked as fault parameters, and the corresponding production equipment will be marked as faulty equipment. At the same time, the corresponding equipment fault information will be generated and fed back to the maintenance personnel and the sharing platform.

[0129] In one embodiment, equipment failure information and production line anomaly information are sent to the shared platform to directly trigger resource reconfiguration (such as pushing available solutions, such as "production input reduction solutions", to the workshop management end through the shared platform based on anomaly data analysis), realizing a real-time closed loop of "anomaly detection - resource adjustment" and solving the problem of "resource coordination lag" in the original technology.

[0130] The real-time monitoring and fault analysis of the entire process driven by the virtual twin of the industrial chain in this application realizes the integrated management and control of "real-time monitoring - anomaly judgment - fault location - impact analysis", which breaks through the limitation of "only being able to detect anomalies but not being able to predict the impact of faults".

[0131] Real-time monitoring: Collect actual operational data from each stage at regular intervals, input the data into the virtual twin of the industrial chain for simulation calculations, and generate theoretical data ranges (such as the theoretical output range for the production stage and the theoretical transportation volume range for the logistics stage). Anomaly detection: Compare actual operating data with theoretical data range to determine whether there are anomalies such as "mismatch between production and sales, or imbalance in logistics and transportation"; Fault Analysis: If an anomaly is found, the parameters of the faulty link are collected and compared with the theoretical parameters of the twin to locate faults such as "overproduction and insufficient transportation". The impact of the fault on other links is analyzed through the simulation of the virtual twin of the industrial chain (such as the chain effect of overproduction on warehousing and sales), and a resource restructuring plan including the increase or decrease of input is formulated.

[0132] As can be seen, in the above scheme, for complex insurance entities such as insurance business, the preliminary ranking of candidate questions is first obtained through semantic matching. Then, a scheme to optimize the question answering engine based on entity alignment is proposed. Through entity alignment, the ranking of candidate questions is ranked again, so that more matching candidate questions are selected. This can effectively avoid the generalization ability defects of the model, greatly improve the effect of entity matching, and improve the effect of the question answering engine.

[0133] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0134] In one embodiment, a dynamic collaborative management and control device for enterprise digital transformation is provided, which corresponds one-to-one with the dynamic collaborative management and control method for enterprise digital transformation described in the above embodiments. For example... Figure 6 As shown, the enterprise digital transformation dynamic collaborative management and control device includes a solution formulation module 101, a collaborative construction module 102, a resource optimization module 103, and a transformation evaluation module 104. Detailed descriptions of each functional module are as follows: The solution development module 101 is used to acquire enterprise transformation needs information and basic enterprise data, and to develop digital transformation solutions for enterprises based on pre-set solution planning models; The collaborative construction module 102 is used to construct a shared platform and a virtual twin of the industrial chain according to the digital transformation plan. The data of the shared platform is input into the virtual twin of the industrial chain for digital simulation. The resource optimization module 103 is used to optimize the allocation of resources in the industrial chain based on the data of the shared platform and the estimated data of the digital simulation, and on a pre-set resource allocation model. The transformation assessment module 104 is used to assess the digital transformation plan based on the operational data of the digital transformation plan and the resource allocation of the industrial chain, and based on a pre-set transformation assessment model.

[0135] The solution formulation module 101 is also used to acquire the enterprise's basic data and determine the enterprise's status based on a pre-set enterprise status analysis model; The system acquires information on the enterprise's transformation needs and its status, and determines a digital transformation plan based on a pre-set plan development model.

[0136] The collaborative construction module 102 is also used to build the shared platform based on microservice architecture, internal enterprise resources and external enterprise resources; Data encryption technology is used to protect the data stored and transmitted on the shared platform; Role-based access control and data access control technologies are used to allocate permissions to each enterprise user on the shared platform. The data from the shared platform is filtered and transmitted to the blockchain network.

[0137] The resource optimization module 103 is also used to generate estimated demand information for the current allocation of resources in the industrial chain based on the data of the shared platform and a pre-set resource demand prediction model. Optimize resource allocation across the industrial chain based on the estimated demand information; Based on the estimated data from the digital simulation and the operational data of the optimized industrial chain resource allocation, the resource demand prediction model is optimized.

[0138] The transformation assessment module 104 is also used to obtain the enterprise digital integrity index based on the integrity assessment model in the transformation assessment model, according to the digital transformation plan. Based on the enterprise's digital asset investment data, and using the data envelopment analysis model in the transformation evaluation model, the enterprise's digital transformation efficiency index is obtained. Based on the operational data of resource allocation in the industrial chain, and the industrial chain restructuring model in the transformation assessment model, industrial chain restructuring indicators are obtained. Based on the enterprise digital integrity index, enterprise digital transformation efficiency index, and industrial chain restructuring index, the resource coordination index is obtained; The digital transformation solution is evaluated based on the resource coordination indicators and preset resource coordination indicator thresholds.

[0139] Transformation assessment module 104 is also used to construct a hierarchical model based on trusted execution environment and analytic hierarchy process; Based on the digital transformation plan, determine the target evaluation dimensions and the target indicators corresponding to the target evaluation dimensions; Determine the target evaluation dimension judgment matrix based on the target evaluation dimension, and determine the target indicator judgment matrix based on the target indicators corresponding to the target evaluation dimension. By inputting the target evaluation dimension judgment matrix and the target indicator judgment matrix into the hierarchical structure model, the enterprise digital integrity index is obtained.

[0140] The resource optimization module 103 is also used to determine whether there is an anomaly in the industrial chain based on the data of the shared platform and the estimated data of the digital simulation, and on a pre-set anomaly detection model. When an anomaly is detected in the supply chain, the abnormal equipment and its location are determined based on a pre-set anomaly detection model.

[0141] This invention provides a dynamic collaborative management and control device for enterprise digital transformation. This application introduces a virtual twin of the industrial chain for digital simulation and prediction, and then optimizes resources based on data from a shared platform. It can quickly respond to fluctuations during the transformation process and adjust the digital transformation plan in a timely manner through evaluation. It enables seamless integration of full lifecycle management and industrial chain resource integration, improving collaborative efficiency and reducing problems such as resource mismatch and delayed risk response during the transformation process. It achieves precise control, risk warning, and optimal resource allocation during the transformation process to address multiple challenges such as market fluctuations, supply chain complexity, and data security compliance.

[0142] Specific limitations regarding the dynamic collaborative management and control device for enterprise digital transformation can be found in the limitations of the dynamic collaborative management and control method for enterprise digital transformation mentioned above, and will not be repeated here. Each module in the aforementioned dynamic collaborative management and control device for enterprise digital transformation can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0143] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external devices via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a dynamic collaborative management and control method for enterprise digital transformation on the server side.

[0144] In one embodiment, a computer device is provided, which may be a device terminal, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a dynamic collaborative management and control method for enterprise digital transformation on the device side.

[0145] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: The process involves acquiring enterprise transformation needs and basic enterprise data, developing digital transformation plans based on pre-set planning models, constructing a shared platform and a virtual twin of the industrial chain based on these plans, and inputting data from the shared platform into the virtual twin for digital simulation. Based on the data from the shared platform and the estimated data from the digital simulation, and using a pre-set resource allocation model, the process optimizes resource allocation within the industrial chain. Finally, based on the operational data of the digital transformation plan and the allocated resources within the industrial chain, and using a pre-set transformation evaluation model, the process evaluates the digital transformation plan.

[0146] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: The process involves acquiring enterprise transformation needs and basic enterprise data, developing digital transformation plans based on pre-set planning models, constructing a shared platform and a virtual twin of the industrial chain based on these plans, and inputting data from the shared platform into the virtual twin for digital simulation. Based on the data from the shared platform and the estimated data from the digital simulation, and using a pre-set resource allocation model, the process optimizes resource allocation within the industrial chain. Finally, based on the operational data of the digital transformation plan and the allocated resources within the industrial chain, and using a pre-set transformation evaluation model, the process evaluates the digital transformation plan.

[0147] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and device side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0148] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0149] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0150] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A dynamic collaborative management and control method for enterprise digital transformation, characterized in that, include: Obtain information on enterprise transformation needs and basic enterprise data, and formulate digital transformation solutions for enterprises based on pre-set solution planning models; According to the digital transformation plan, a shared platform and a virtual twin of the industrial chain are constructed, and the data of the shared platform is input into the virtual twin of the industrial chain for digital simulation; Based on the data from the shared platform and the estimated data from the digital simulation, the allocation of resources in the industrial chain is optimized; Based on the digital transformation plan and the operational data of resource allocation in the industrial chain, the digital transformation plan is evaluated using a pre-set transformation evaluation model.

2. The method according to claim 1, characterized in that, Obtain information on enterprise transformation needs and basic enterprise data, and develop digital transformation solutions for enterprises based on pre-set solution planning models, including: The enterprise's basic data is acquired, and the enterprise's status is determined based on a pre-set enterprise status analysis model. The system acquires information on the enterprise's transformation needs and its status, and determines a digital transformation plan based on a pre-set plan development model.

3. The method according to claim 1, characterized in that, According to the aforementioned digital transformation plan, a shared platform and a virtual twin of the industrial chain are constructed. Data from the shared platform is input into the virtual twin of the industrial chain for digital simulation, including: The shared platform is built based on a microservice architecture, internal enterprise resources, and external enterprise resources. Data encryption technology is used to protect the data stored and transmitted on the shared platform; Role-based access control and data access control technologies are used to allocate permissions to each enterprise user on the shared platform. The data from the shared platform is filtered and transmitted to the blockchain network.

4. The method according to claim 1, characterized in that, Based on the data from the shared platform and the estimated data from the digital simulation, the allocation of resources in the industrial chain is optimized, including: Based on the data from the shared platform and a pre-set resource demand prediction model, the estimated demand information for resource allocation in the current industrial chain is generated. Optimize resource allocation across the industrial chain based on the estimated demand information; Based on the estimated data from the digital simulation and the operational data of the optimized industrial chain resource allocation, the resource demand prediction model is optimized.

5. The method according to claim 1, characterized in that, The method further includes: Based on the data from the shared platform and the estimated data from the digital simulation, and using a pre-set anomaly detection model, it is determined whether there are any anomalies in the industrial chain. When an anomaly is detected in the supply chain, the abnormal equipment and its location are determined based on a pre-set anomaly detection model.

6. The method according to claim 1, characterized in that, Based on the aforementioned digital transformation plan and the operational data of resource allocation in the industrial chain, and using a pre-set transformation evaluation model, the digital transformation plan is evaluated, including: Based on the digital transformation plan and the integrity assessment model in the transformation assessment model, the enterprise digital integrity index is obtained. Based on the enterprise's digital asset investment data, and using the data envelopment analysis model in the transformation evaluation model, the enterprise's digital transformation efficiency index is obtained. Based on the operational data of resource allocation in the industrial chain, and the industrial chain restructuring model in the transformation assessment model, industrial chain restructuring indicators are obtained. Based on the enterprise digital integrity index, enterprise digital transformation efficiency index, and industrial chain restructuring index, the resource coordination index is obtained; The digital transformation solution is evaluated based on the resource coordination indicators and preset resource coordination indicator thresholds.

7. The method according to claim 6, characterized in that, Based on the digital transformation plan and the completeness assessment model in the transformation assessment model, the enterprise digital completeness index is obtained, including: A hierarchical model is constructed based on a trusted execution environment and the analytic hierarchy process. Based on the digital transformation plan, determine the target evaluation dimensions and the target indicators corresponding to the target evaluation dimensions; Determine the target evaluation dimension judgment matrix based on the target evaluation dimension, and determine the target indicator judgment matrix based on the target indicators corresponding to the target evaluation dimension. By inputting the target evaluation dimension judgment matrix and the target indicator judgment matrix into the hierarchical structure model, the enterprise digital integrity index is obtained.

8. A dynamic collaborative management and control device for enterprise digital transformation, characterized in that, include: The solution development module is used to acquire information on enterprise transformation needs and basic enterprise data, and to develop digital transformation solutions for enterprises based on pre-set solution planning models; The collaborative construction module is used to build a shared platform and a virtual twin of the industrial chain according to the digital transformation plan. The data of the shared platform is input into the virtual twin of the industrial chain for digital simulation. The resource optimization module is used to optimize the allocation of resources in the industrial chain based on the data from the shared platform and the estimated data from the digital simulation. The transformation assessment module is used to evaluate the digital transformation plan based on the operational data of the digital transformation plan and the resource allocation of the industrial chain, using a pre-set transformation assessment model.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the dynamic collaborative management and control method for enterprise digital transformation as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic collaborative management and control method for enterprise digital transformation as described in any one of claims 1 to 7.

Images