Digital engineering full life cycle management method based on unified data source

Abstract

The invention belongs to the technical field of digital engineering intelligent management, and discloses a digital engineering full life cycle management method based on a unified data source. The method comprises the steps of creating a project on a platform, and determining a project demand; performing task division according to each demand entry in the project demand, and allocating each demand entry in the project demand to a corresponding task; performing priority evaluation on each task according to the demand items; performing model design on each task according to the priority, and obtaining corresponding model data; feeding back the demand item and the model data of each task to the platform; the tasks with the adjacent priorities are interconnected through work packet association in the platform; according to the method, high collaboration of project requirements and tasks is realized, digital engineering full-life-cycle management is realized in a cross-tool and cross-process manner, the problems of data separation, information transmission difficulty and the like are solved, and the project management difficulty is reduced while the project management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital engineering intelligent management, and more specifically, to a digital engineering full life cycle management method based on a unified data source. Background Art

[0002] At present, in the implementation of digital engineering, the traditional product life cycle management method is adopted, which only manages the issuance of tasks and the final product documents, and the process data of the model cannot be tracked and managed; task management is disconnected from model construction, and different tools are used and managed separately, resulting in data separation and island phenomena, that is, the data between task management and model construction cannot be automatically shared or interconnected, which increases the complexity of management and the difficulty of information transmission; data needs to be imported and exported manually, which is not only time-consuming and labor-intensive, but also difficult to ensure the timeliness, accuracy, completeness and traceability of the data; as a result, managers cannot accurately grasp the process data and find it difficult to understand the actual task progress, which in turn affects the control of project risks.

[0003] Of course, there are also intelligent data interconnection methods. For example, the Chinese patent with publication number CN118154119A discloses an intelligent management system for the entire life cycle of water conservancy projects based on digital twins; including: S1: data acquisition module, which collects various indicator data of water conservancy projects in real time through sensors and transmits the data to the system; S2: digital twin modeling module, which associates the actual water conservancy project with its digital model to establish a digital twin model; S3: monitoring and diagnosis module, based on the digital twin model, real-time monitoring and fault diagnosis of the operation status of the water conservancy project; S4: prediction and early warning module, based on historical data and model analysis, predicts future hydrological forecasts and project status, and makes early warnings; it solves the problems of difficult data acquisition, lack of real-time monitoring, slow response, and lack of prediction capabilities in the intelligent management of the entire life cycle of water conservancy projects.

[0004] The above-mentioned technologies realize intelligent data interconnection through digital twin models, but digital twins focus on the virtual representation of specific entities and cannot effectively integrate data collected from different tools and stages, which will still lead to data silos; and digital twin models usually focus on real-time operating status, cannot cover the life cycle management of the entire digital project, and have weak integration and coordination capabilities; in addition, digital twin models are slightly insufficient in the coordination and management of multiple tasks and face collaborative complexity.

[0005] In view of this, the present invention proposes a digital engineering full life cycle management method based on a unified data source to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a digital engineering full life cycle management method based on a unified data source, comprising:

[0007] S1: Create a project on the platform and determine project requirements;

[0008] S2: Divide the tasks according to the requirements in the project requirements and assign each requirement item in the project requirements to the corresponding tasks;

[0009] S3: Prioritize each task based on the requirements;

[0010] S4: Design a model for each task according to the priority and obtain the corresponding model data; feedback the requirement items and model data of each task to the platform;

[0011] S5: Through the work package association in the platform, tasks with adjacent priorities are interconnected, and requirement items and model data are transferred.

[0012] Furthermore, the project includes a project overview and a project plan; the project requirements are the functions and features that the project needs to achieve;

[0013] The platform is a full life cycle management platform for digital engineering; the platform defines API standards and API requirements. API standards are rules and conventions that define and regulate application program interface interactions; each model building tool provides a corresponding API based on the API requirements defined by the platform; the API of each model building tool is integrated into the platform programmatically, and user authentication is synchronized.

[0014] Furthermore, the method for dividing tasks according to each requirement item in the project requirements includes:

[0015] Different digital labels are set for different project names and marked as project labels; different digital labels are set for different requirement items in the project requirements and marked as item labels; task sets are preset, each task set includes tasks corresponding to each requirement item, and task sets correspond to requirement items one by one; different digital labels are set for different task sets and marked as set labels; the project labels, item labels and set labels are all different;

[0016] Take the project label and an item label as a set of analysis data, input each set of analysis data into the trained task prediction model, predict the set label corresponding to each set of analysis data; obtain the task set corresponding to each requirement item according to the set label.

[0017] Furthermore, the training process of the task prediction model includes:

[0018] Collect b groups of analysis data in advance, set corresponding set labels for the b groups of analysis data, where b is an integer greater than 1, and convert the analysis data and the corresponding set labels into a corresponding set of feature vectors;

[0019] Each group of feature vectors is used as the input of the task prediction model. The task prediction model takes a group of prediction set labels corresponding to each group of analysis data as output, and takes the actual set labels corresponding to each group of analysis data as prediction targets, where the actual set labels are pre-set set labels corresponding to the analysis data; minimizing the sum of prediction errors of all analysis data is used as the training target; the task prediction model is trained until the sum of prediction errors reaches convergence and the training is stopped; the task prediction model is a deep neural network model.

[0020] Furthermore, the steps of evaluating the priority of each task include:

[0021] Perform a priority evaluation on the project requirements of the project to obtain the requirement priority of each project requirement; perform a priority evaluation on the requirement items corresponding to each project requirement to obtain the item priority of each requirement item in the corresponding project requirement; perform a priority evaluation on the tasks of each requirement item to obtain the task priority of each task in the corresponding requirement item; multiply the requirement priority, item priority and task priority corresponding to each task in turn to obtain the priority of each task.

[0022] Furthermore, the method for evaluating the priority of project requirements includes:

[0023] The trained word vector conversion model is used to convert the project requirements of the project into corresponding word vectors in turn; the similarity algorithm is used to calculate the semantic similarity between every two word vectors; a similarity threshold is preset, and each semantic similarity is compared with the similarity threshold. The semantic similarities with values ​​greater than or equal to the similarity threshold are marked as similarities of the same type, and the semantic similarities with values ​​less than the similarity threshold are not marked; each project requirement is classified in turn according to the classification rules, and a total of P requirement sets are obtained. There is no identical project requirement in each requirement set, and P is an integer greater than 0;

[0024] A sorting table is generated for each requirement set, and the project requirements in each requirement set are sorted according to the sorting rules; the number of project requirements in each sorting table is counted, and the number of project requirements with the largest value is marked as the maximum number; the maximum number is used as the priority of the project requirement at the front of each sorting table, and the priorities of the remaining project requirements in each sorting table are obtained in sequence according to the positive order of the corresponding sorting table and the preset decreasing range;

[0025] The method of evaluating the priority of the requirement items corresponding to each project requirement and the tasks corresponding to each requirement item is consistent with the method of evaluating the priority of the project requirements.

[0026] Furthermore, the training process of the word vector conversion model includes:

[0027] Collect multiple project requirements in advance; use the WordPi ece algorithm to build a word list containing vocabulary in the field of project requirements; the vocabulary in the field of project requirements is a vocabulary collection of professional vocabulary and terminology related to project requirements; convert project requirements into ID sequences through the word list, and add position codes as inputs; the inputs are deep Transformer encoded through the BERT encoder to obtain latent sentence representation, which is the word vector; randomly select some word masks and predict the corresponding original words as the unsupervised pre-training task target; use cross entropy to calculate the loss function, and back propagate to optimize and modify the parameters of the word vector conversion model to complete the pre-training of the word vector conversion model; collect the word vectors corresponding to the project requirements in advance as annotations and construct classification tasks; add a classification output layer based on the pre-trained word vector conversion model to determine the rate as the task target; fix the BERT encoder and optimize and fine-tune the output layer parameters.

[0028] Furthermore, the classification rules include:

[0029] Mark the project requirements currently being classified as current requirements;

[0030] Analyze the semantic similarity between the current requirement and the project requirements in all requirement sets;

[0031] If there is a semantic similarity that is marked as the same type of similarity, the current requirement is added to the requirement set corresponding to the project requirement corresponding to the same type of similarity;

[0032] If there is no semantic similarity marked as similarity of the same kind, the current requirement is added to a new requirement set;

[0033] If there are multiple semantic similarities marked as similarities of the same kind, then the requirement set corresponding to each project requirement of the same kind of similarity is marked as a similar set, the number of project requirements in each similar set is counted, the similar set with the largest number of project requirements is marked as the maximum set, the current requirement is added to the maximum set, and the project requirements in each similar set that is not marked as the maximum set are added to the maximum set.

[0034] Furthermore, the sorting rules include:

[0035] Set a different digital label for each project requirement in each requirement set and mark it as a requirement label. Input the requirement labels corresponding to each requirement set into the corresponding priority analysis model, predict the requirement label with the highest priority in each requirement set, and mark it as the maximum label. The priority analysis model corresponds to the requirement set one by one. The training process of the priority analysis model is consistent with the training process of the task prediction model, and both are deep neural network models. Generate a sorting table for each requirement set, rank the project requirement corresponding to each maximum label first in the corresponding sorting table, mark the project requirement ranked last in each sorting table as the last requirement, add the project requirement with the greatest similarity to each last requirement to the corresponding sorting table, and add all project requirements to the corresponding sorting table in sequence.

[0036] Furthermore, the priorities of all tasks are sorted from large to small, and a model is designed for each task according to the positive order of priority and in combination with each task and the requirement items corresponding to each task; the model data refers to various data and files generated or used in the modeling process; the work package association is to connect different work packages in project management; the work package is the task in the project, and tasks with adjacent priorities are connected through the work package association; among adjacent tasks, the output of the task with a larger priority is used as the input of the task with a smaller priority.

[0037] The technical effects and advantages of the digital engineering full life cycle management method based on a unified data source of the present invention are as follows:

[0038] A unified data source and API standard are used to achieve seamless integration of various model building tools, and word vector conversion models and similarity algorithms are used to classify and sort requirements in order to develop a multi-layer priority evaluation mechanism to achieve a high degree of coordination between project requirements and tasks. At the same time, tasks are connected in an orderly manner through work package associations, thereby achieving full life cycle management of digital engineering across tools and processes, solving problems such as data separation and difficulty in information transmission, and improving project management efficiency while reducing project management difficulty. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flowchart of a digital engineering full life cycle management method based on a unified data source according to Embodiment 1 of the present invention;

[0040] Figure 2 This is a schematic diagram of the working principle of the digital engineering full life cycle management platform of Example 1 of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Example 1

[0043] See also Figure 1 As shown, the digital engineering full life cycle management method based on a unified data source described in this embodiment includes:

[0044] S1: Create a project on the platform and determine project requirements.

[0045] A project includes a project overview and a project plan; a project overview includes basic project information (such as project name, project description, project type, etc.), project goals, project scope, etc.; a project plan includes project time plan (such as project cycle, time nodes of each task in the project), project resource allocation (such as project team, project budget, project cost, etc.), project requirements (such as technical requirements, functional requirements, etc.), etc.; project requirements are the functions and features that the project needs to achieve, such as providing user feedback function, early warning within 5 seconds, etc.

[0046] The project is created by members of the project team on the platform, which is a full life cycle management platform for digital engineering. The platform defines API (application programming interface) standards and API requirements. API standards define and regulate the rules and conventions for application programming interface interactions, including data formats, protocols, security, etc. API requirements include, for example, how to transfer information for user authentication and how to transfer model data. Each model building tool (such as CAD, CAE, MATLAB, etc.) provides a corresponding API based on the API requirements defined by the platform. The API provided by each model building tool will allow the platform to perform identity authentication and management operations. The APIs of each model building tool are integrated into the platform programmatically, and user authentication is synchronized. For example, when a user logs in to the platform, the platform will send the user's authentication token to all integrated model building tools through the API, so that the user is automatically logged in to all model building tools without having to log in again.

[0047] It should be noted that the purpose of integrating the APIs of various model building tools into the platform is to centrally manage the model data of all model building tools and project-related data in the platform, to unify the data source, to avoid data silos, and the platform can cover the entire life cycle management of digital projects and improve integration and coordination capabilities; the purpose of synchronizing user authentication is to reduce the time required for users to switch between different model building tools, improve work efficiency, enhance user experience, and centralize authentication so that security policies can be managed in one place, which helps to improve the consistency and maintainability of security controls.

[0048] S2: Divide tasks according to each requirement item in the project requirements, and assign each requirement item to the corresponding task to achieve collaborative development of project requirements.

[0049] For example, if the project requirement is to provide user feedback function, then the corresponding requirement items are feedback collection, feedback storage, feedback classification and processing, feedback reply, feedback report generation, etc.; for example, if the requirement item is feedback collection, then the corresponding tasks are designing feedback forms, implementing front-end submission functions, implementing back-end receiving functions, etc.; if the requirement item is feedback storage, then the corresponding tasks are database design, data interface development, storage mechanism implementation, etc.; if the requirement item is feedback classification and processing, then the corresponding tasks are establishing classification rules, developing processing procedures, etc.

[0050] Methods for dividing tasks according to the various requirements in the project requirements include:

[0051] Different digital labels are set for different project names and marked as project labels; different digital labels are set for different requirement items in the project requirements and marked as item labels; task sets are preset, each task set includes tasks corresponding to each requirement item, and the task sets correspond to the requirement items one by one. The task sets are preset by technical personnel in this field according to the requirement items in actual situations; different digital labels are set for different task sets and marked as set labels; the project labels, item labels and set labels are all different;

[0052] Take the project label and an item label as a set of analysis data, input each set of analysis data into the trained task prediction model, predict the set label corresponding to each set of analysis data; obtain the task set corresponding to each requirement item according to the set label.

[0053] The training process of the task prediction model includes:

[0054] Collect b groups of analysis data in advance, set corresponding set labels for the b groups of analysis data, where b is an integer greater than 1, and convert the analysis data and the corresponding set labels into a corresponding set of feature vectors; the set labels corresponding to the analysis data are collected by technicians in this field during the historical project division and operation process, and corresponding set labels are set for each group of analysis data according to actual conditions; corresponding set labels are set for the b groups of analysis data in sequence;

[0055] Each set of feature vectors is used as the input of the task prediction model. The task prediction model takes a set of predicted set labels corresponding to each set of analysis data as output, and takes the actual set labels corresponding to each set of analysis data as the prediction target. The actual set labels are the pre-set set labels corresponding to the analysis data. The training goal is to minimize the sum of the prediction errors of all analysis data. The calculation formula of the prediction error is η K =(β K -ε K ) 2 , where η K is the prediction error, K is the group number of the eigenvector corresponding to the analyzed data, β K is the predicted set label corresponding to the Kth group of analysis data, ε K is the actual set label corresponding to the Kth group of analysis data; the task prediction model is trained until the sum of the prediction errors reaches convergence and the training is stopped.

[0056] The above-mentioned task prediction model is specifically a deep neural network model; it includes an input layer, a hidden layer and an output layer; each hidden layer includes multiple neurons, each neuron is connected to the neurons in the next layer, and the connection contains weights, which determine the importance and influence of data transmission in the neural network; an activation function is applied to each neuron between the hidden layer and the output layer, and the activation function introduces nonlinearity, allowing the network to learn more complex patterns and features.

[0057] It should be noted that the purpose of dividing tasks according to the various requirement items in the project requirements is to break down large project requirements into smaller executable resources, which is conducive to better optimizing resource allocation and task coordination; and breaking down tasks helps to more clearly define project goals and progress, making it easier for project managers to monitor and adjust in real time; at the same time, promoting the correspondence between requirements and tasks, and matching requirement items with specific tasks helps ensure that requirements are fully implemented and covered.

[0058] S3: Prioritize each task based on the requirements.

[0059] The steps to prioritize each task include:

[0060] Perform a priority evaluation on the project requirements of the project to obtain the requirement priority of each project requirement; perform a priority evaluation on the requirement items corresponding to each project requirement to obtain the item priority of each requirement item in the corresponding project requirement; perform a priority evaluation on the tasks of each requirement item to obtain the task priority of each task in the corresponding requirement item; multiply the requirement priority, item priority and task priority corresponding to each task in turn to obtain the priority of each task.

[0061] Exemplarily, the project includes project requirement 1 and project requirement 2, project requirement 1 includes requirement item 1 and requirement item 2, and project requirement 2 includes requirement item 3 and requirement item 4; requirement item 1 includes task 1 and task 2, and similarly each requirement item includes two tasks; the priority of project requirement 1 is 1, the priority of project requirement 2 is 2, the priority of requirement item 1 in project requirement 1 is 1, the priority of requirement item 2 in project requirement 1 is 2, the priority of requirement item 3 in project requirement 2 is 1, the priority of requirement item 4 in project requirement 2 is 2, the priority of task 1 in requirement item 1 is 2, and the priority of task 2 in requirement item is 1. Since task 1 belongs to requirement item 1 and requirement item 1 belongs to project requirement 1, the priority of task 1 should be 1×1×2=2.

[0062] Methods for prioritizing project requirements include:

[0063] Using the trained word vector conversion model, the project requirements of the project are converted into corresponding word vectors in turn; the semantic similarity between every two word vectors is calculated using a similarity algorithm, such as cosine similarity calculation, Mahalanobis distance calculation, Euclidean distance calculation, etc.; a similarity threshold is preset, each semantic similarity is compared with the similarity threshold, and the semantic similarity with a value greater than or equal to the similarity threshold is marked as the same type of similarity, and the semantic similarity with a value less than the similarity threshold is not marked; each project requirement is classified in turn according to the classification rules, and a total of P requirement sets are obtained, and there are no identical project requirements in each requirement set, and P is an integer greater than 0; the similarity threshold is determined by a technician in this field during historical priority evaluation, and multiple requirement sets are collected to obtain the semantic similarity corresponding to every two project requirements in each requirement set, and the semantic similarity with the smallest value is used as the minimum similarity of each requirement set, and the average of multiple minimum similarities is used as the similarity threshold;

[0064] A sorting table is generated for each requirement set, and the project requirements in each requirement set are sorted according to the sorting rules; the number of project requirements in each sorting table is counted, and the number of project requirements with the largest value is marked as the maximum number; the maximum number is used as the priority of the project requirement at the front in each sorting table, and the priorities of the remaining project requirements in each sorting table are obtained in sequence according to the positive order of the corresponding sorting table and the preset decreasing amplitude, and the decreasing amplitude is set by technical personnel in this field according to actual conditions.

[0065] The training process of the word vector conversion model includes:

[0066] Collect multiple project requirements in advance; use the WordPi ece algorithm to build a word list containing vocabulary in the field of project requirements; the vocabulary in the field of project requirements is a vocabulary collection of professional vocabulary and terminology related to project requirements; convert project requirements into ID sequences through the word list, and add position codes as inputs; the inputs are deep Transformer encoded through the BERT encoder to obtain latent sentence representation, which is the word vector; randomly select some word masks and predict the corresponding original words as the unsupervised pre-training task target; use cross entropy to calculate the loss function, and back propagate to optimize and modify the parameters of the word vector conversion model to complete the pre-training of the word vector conversion model; collect the word vectors corresponding to the project requirements in advance as annotations and construct classification tasks; add a classification output layer based on the pre-trained word vector conversion model to determine the rate as the task target; fix the BERT encoder and optimize and fine-tune the output layer parameters.

[0067] The classification rules include:

[0068] Mark the project requirements currently being classified as current requirements;

[0069] Analyze the semantic similarity between the current requirement and the project requirements in all requirement sets;

[0070] If there is a semantic similarity that is marked as the same type of similarity, the current requirement is added to the requirement set corresponding to the project requirement corresponding to the same type of similarity;

[0071] If there is no semantic similarity marked as similarity of the same kind, the current requirement is added to a new requirement set;

[0072] If there are multiple semantic similarities marked as similarities of the same kind, then the requirement set corresponding to each project requirement of the same kind of similarity is marked as a similar set, the number of project requirements in each similar set is counted, the similar set with the largest number of project requirements is marked as the maximum set, the current requirement is added to the maximum set, and the project requirements in each similar set that is not marked as the maximum set are added to the maximum set.

[0073] The method of evaluating the priority of the requirement items corresponding to each project requirement and the tasks corresponding to each requirement item is consistent with the method of evaluating the priority of the project requirements.

[0074] Sorting rules include:

[0075] Set a different digital label for each project requirement in each requirement set and mark it as a requirement label. Input the requirement labels corresponding to each requirement set into the corresponding priority analysis model, predict the requirement label with the highest priority in each requirement set, and mark it as the maximum label. The priority analysis model corresponds to the requirement set one by one. The training process of the priority analysis model is consistent with the training process of the task prediction model, and both are deep neural network models. Generate a sorting table for each requirement set, rank the project requirement corresponding to each maximum label first in the corresponding sorting table, mark the project requirement ranked last in each sorting table as the last requirement, add the project requirement with the greatest similarity to each last requirement to the corresponding sorting table, and add all project requirements to the corresponding sorting table in sequence.

[0076] It should be noted that the purpose of evaluating the priority of each task based on the requirement items is to help determine the order of task execution and ensure the consistency and dependencies between tasks, thereby improving the efficiency of task execution and enhancing the project team's collaboration capabilities.

[0077] S4: Design a model for each task according to the priority and obtain the corresponding model data; feedback the requirement items and model data of each task to the platform. The schematic diagram of the platform working principle is as follows: Figure 2 As shown;

[0078] Sort all tasks by priority from high to low. Modelers in the project team design models for each task in order of priority and in combination with each task and the corresponding requirement items. After the model design is completed, the requirement items and model data of each task are fed back to the platform. Model data refers to various data and files generated or used in the modeling process. Model data includes design drawings, simulation results, parameter settings, data sets, etc. Model data is used to describe the attributes and behaviors of the model and its relationship with other systems or components.

[0079] It should be understood that the purpose of feeding back the requirement items and model data of each task to the platform is to ensure the timeliness, accuracy, completeness and traceability of various data, so that project managers can accurately grasp the process data and timely understand the actual task progress, thereby enhancing the ability to control project risks and reducing the difficulty of project management.

[0080] S5: Through the work package association in the platform, tasks with adjacent priorities are interconnected, requirement items and model data are transferred, and automatic data transfer is achieved.

[0081] Work package association is the process of connecting different work packages in project management to ensure smooth transmission of data and information between work packages. Work packages are tasks in a project. Through work package association, tasks with adjacent priorities are connected. Among adjacent tasks, the output of a task with a higher priority is used as the input of a task with a lower priority, thus achieving data interconnection between adjacent tasks and enhancing the coordination and management capabilities of multiple tasks.

[0082] This embodiment uses a unified data source and API standard to achieve seamless integration of various model building tools, and applies a word vector conversion model and a similarity algorithm to classify and sort requirements, so as to formulate a multi-layer priority evaluation mechanism and achieve a high degree of coordination between project requirements and tasks; at the same time, tasks are connected in an orderly manner through work package associations, thereby achieving full life cycle management of digital engineering across tools and processes, solving problems such as data separation and information transmission difficulties, improving project management efficiency while reducing project management difficulty.

[0083] Example 2

[0084] The present application also provides an electronic device, which may include one or more processors and one or more memories. The memories store computer-readable codes, which, when executed by the one or more processors, may execute a digital engineering full lifecycle management method based on a unified data source as described above.

[0085] The method or system according to the implementation mode of the present application can also be implemented with the help of the architecture of the electronic device. The electronic device may include a bus, one or more CPUs, ROM, RAM, a communication port connected to a network, input / output, a hard disk, etc. A storage device in the electronic device, such as a ROM or a hard disk, can store a digital engineering full life cycle management method based on a unified data source provided in the present application.

[0086] Furthermore, the electronic device may also include a user interface. Of course, the architecture disclosed in the present invention is only exemplary, and when implementing different devices, one or more components in the electronic device disclosed in the present invention may be omitted according to actual needs.

[0087] Example 3

[0088] One embodiment of the present application discloses a computer-readable storage medium. Computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are executed by a processor, a digital engineering full life cycle management method based on a unified data source according to an embodiment of the present application described with reference to the above figures can be executed. The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0089] In addition, according to the implementation of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application, for example: a digital engineering full life cycle management method based on a unified data source. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0090] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0091] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital engineering full life cycle management method based on a unified data source, characterized in that: include: S1: Create a project on the platform and determine project requirements; S2: Divide the tasks according to the requirements in the project requirements and assign each requirement item in the project requirements to the corresponding tasks; S3: Prioritize each task based on the requirements; S4: Design a model for each task according to the priority and obtain the corresponding model data; feedback the requirement items and model data of each task to the platform; S5: Through the work package association in the platform, tasks with adjacent priorities are interconnected, and requirement items and model data are transferred.

2. A digital engineering full life cycle management method based on a unified data source according to claim 1, characterized in that: The project includes a project overview and a project plan; the project requirements are the functions and features that the project needs to achieve; The platform is a digital engineering full life cycle management platform; The platform defines API standards and API requirements. API standards are rules and conventions that define and regulate application program interface interactions. Each model building tool provides corresponding APIs based on the API requirements defined by the platform. Integrate the APIs of various model building tools into the platform programmatically and synchronize user authentication.

3. A digital engineering full life cycle management method based on a unified data source according to claim 2, characterized in that: The method for dividing tasks according to each requirement item in the project requirements includes: Different digital labels are set for different project names and marked as project labels; different digital labels are set for different requirement items in the project requirements and marked as item labels; task sets are preset, each task set includes tasks corresponding to each requirement item, and task sets correspond to requirement items one by one; different digital labels are set for different task sets and marked as set labels; the project labels, item labels and set labels are all different; Take the project label and an item label as a set of analysis data, input each set of analysis data into the trained task prediction model, predict the set label corresponding to each set of analysis data; obtain the task set corresponding to each requirement item according to the set label.

4. A digital engineering full life cycle management method based on a unified data source according to claim 3, characterized in that: The training process of the task prediction model includes: Collect b groups of analysis data in advance, set corresponding set labels for the b groups of analysis data, where b is an integer greater than 1, and convert the analysis data and the corresponding set labels into a corresponding set of feature vectors; Each group of feature vectors is used as the input of the task prediction model. The task prediction model takes a group of prediction set labels corresponding to each group of analysis data as output, and takes the actual set labels corresponding to each group of analysis data as prediction targets, where the actual set labels are pre-set set labels corresponding to the analysis data; minimizing the sum of prediction errors of all analysis data is used as the training target; the task prediction model is trained until the sum of prediction errors reaches convergence and the training is stopped; the task prediction model is a deep neural network model.

5. A digital engineering full life cycle management method based on a unified data source according to claim 4, characterized in that: The steps to prioritize each task include: Perform a priority evaluation on the project requirements of the project to obtain the requirement priority of each project requirement; perform a priority evaluation on the requirement items corresponding to each project requirement to obtain the item priority of each requirement item in the corresponding project requirement; perform a priority evaluation on the tasks of each requirement item to obtain the task priority of each task in the corresponding requirement item; multiply the requirement priority, item priority and task priority corresponding to each task in turn to obtain the priority of each task.

6. A digital engineering full life cycle management method based on a unified data source according to claim 5, characterized in that: The method for prioritizing project requirements of a project includes: The trained word vector conversion model is used to convert the project requirements of the project into corresponding word vectors in turn; the similarity algorithm is used to calculate the semantic similarity between every two word vectors; a similarity threshold is preset, and each semantic similarity is compared with the similarity threshold. The semantic similarities with values ​​greater than or equal to the similarity threshold are marked as similarities of the same type, and the semantic similarities with values ​​less than the similarity threshold are not marked; each project requirement is classified in turn according to the classification rules, and a total of P requirement sets are obtained. There is no identical project requirement in each requirement set, and P is an integer greater than 0; A sorting table is generated for each requirement set, and the project requirements in each requirement set are sorted according to the sorting rules; the number of project requirements in each sorting table is counted, and the number of project requirements with the largest value is marked as the maximum number; the maximum number is used as the priority of the project requirement at the front of each sorting table, and the priorities of the remaining project requirements in each sorting table are obtained in sequence according to the positive order of the corresponding sorting table and the preset decreasing range; The method of evaluating the priority of the requirement items corresponding to each project requirement and the tasks corresponding to each requirement item is consistent with the method of evaluating the priority of the project requirements.

7. A digital engineering full life cycle management method based on a unified data source according to claim 6, characterized in that: The training process of the word vector conversion model includes: Collect multiple project requirements in advance; use the WordPiece algorithm to build a word list containing vocabulary in the field of project requirements; the vocabulary in the field of project requirements is a vocabulary collection that summarizes professional vocabulary and terminology related to project requirements; convert project requirements into ID sequences through the word list, and add position codes as inputs; the inputs are deep Transformer encoded through the BERT encoder to obtain latent sentence representations, which are word vectors; randomly select some word masks and predict the corresponding original words as the unsupervised pre-training task target; use cross entropy to calculate the loss function, and back-propagate to optimize and modify the parameters of the word vector conversion model to complete the pre-training of the word vector conversion model; collect the word vectors corresponding to the project requirements in advance as annotations and construct classification tasks; add a classification output layer based on the pre-trained word vector conversion model to determine the rate as the task target; fix the BERT encoder and optimize and fine-tune the output layer parameters.

8. A digital engineering full life cycle management method based on a unified data source according to claim 7, characterized in that: The classification rules include: Mark the project requirements currently being classified as current requirements; Analyze the semantic similarity between the current requirement and the project requirements in all requirement sets; If there is a semantic similarity that is marked as the same type of similarity, the current requirement is added to the requirement set corresponding to the project requirement corresponding to the same type of similarity; If there is no semantic similarity marked as similarity of the same kind, the current requirement is added to a new requirement set; If there are multiple semantic similarities marked as similarities of the same kind, then the requirement set corresponding to each project requirement of the same kind of similarity is marked as a similar set, the number of project requirements in each similar set is counted, the similar set with the largest number of project requirements is marked as the maximum set, the current requirement is added to the maximum set, and the project requirements in each similar set that is not marked as the maximum set are added to the maximum set.

9. A digital engineering full life cycle management method based on a unified data source according to claim 8, characterized in that: The sorting rules include: Set a different digital label for each project requirement in each requirement set and mark it as a requirement label. Input the requirement labels corresponding to each requirement set into the corresponding priority analysis model, predict the requirement label with the highest priority in each requirement set, and mark it as the maximum label. The priority analysis model corresponds to the requirement set one by one. The training process of the priority analysis model is consistent with the training process of the task prediction model, and both are deep neural network models. Generate a sorting table for each requirement set, rank the project requirement corresponding to each maximum label first in the corresponding sorting table, mark the project requirement ranked last in each sorting table as the last requirement, add the project requirement with the greatest similarity to each last requirement to the corresponding sorting table, and add all project requirements to the corresponding sorting table in sequence.

10. A digital engineering full life cycle management method based on a unified data source according to claim 9, characterized in that: Sort the priorities of all tasks from high to low, and design a model for each task in order of priority and in combination with each task and the requirement items corresponding to each task; the model data refers to various data and files generated or used in the modeling process; the work package association is to connect different work packages in project management; the work package is the task in the project, and tasks with adjacent priorities are connected through work package association; among adjacent tasks, the output of the task with a higher priority is used as the input of the task with a lower priority.

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