Collaborative design management system and method for product life cycle process flow

Abstract

The invention discloses a collaborative design management system and method for a product life cycle process flow, and particularly relates to the technical field of information management, and the method comprises the following steps: assessing information island hidden dangers of all departments in information transmission through a reference data access combination set, collecting and extracting features, and storing the features in a database; calculating an information circulation quality index and an information circulation integration index, inputting the information circulation quality index and the information circulation integration index into a pre-training machine learning model to generate a collaboration quality coefficient, dividing departments with information island hidden dangers in an information transmission process into high-quality collaboration departments and low-quality collaboration departments based on the generated collaboration quality coefficient, and for the low-quality collaboration departments, calculating the information circulation quality index and the information circulation integration index; performing optimization management mode judgment on cooperative work of departments; according to the method, whether the hidden danger of information islands exists in the information transmission process of all the departments or not can be judged, then abnormity analysis is carried out, the departments with the hidden danger are divided into high-quality and low-quality cooperative departments, resource configuration optimization is facilitated, and cooperative and efficient operation of all the departments is ensured.

Description

Technical Field

[0001] The present invention relates to the field of information management technology, and more specifically, to a collaborative design management system and method for a product life cycle process. Background Art

[0002] Collaborative design management of product life cycle process refers to optimizing product design and process through cross-departmental and cross-team collaboration throughout the product life cycle, from concept design, detailed design, manufacturing, use to scrapping. Collaborative design management aims to ensure seamless connection of all links, improve efficiency, reduce costs, and ensure product quality and sustainability. By integrating information technology and management tools, data sharing and real-time communication between design, engineering, manufacturing and other departments can be achieved, promoting scientific design decisions and rapid response to market needs.

[0003] The existing technology has the following shortcomings: Although there are many advanced design and management tools, data and information between different departments and teams are often scattered in their respective department systems, which will lead to information islands, making it difficult to share and circulate data, thereby affecting the efficiency and effectiveness of collaborative work. Information islands lead to information communication between departments and teams, which reduces collaboration efficiency. Team members cannot obtain the required information in a timely manner, increase communication costs, extend product cycles, and affect product development progress and market response speed. Timely discovery of information island problems helps to ensure the collaborative quality of the product life cycle process. Therefore, a collaborative design management system and method for the product life cycle process is proposed. Summary of the invention

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A collaborative design management method for a product life cycle process includes the following steps:

[0006] Determine a set of benchmark data access combinations, and then obtain the benchmark data access combination sets through different departments of collaborative design of product life cycle process, collect access information in real time and conduct preliminary analysis to determine whether there is a hidden danger of information islands in the information transmission process of each department;

[0007] Collect information and extract features from departments with hidden dangers of information islands in the process of information transmission, and then perform anomaly analysis to obtain the information flow quality index and information flow integration index respectively. Then, transmit the information flow quality index and information flow integration index to the pre-trained machine learning model, and generate the collaboration quality coefficient through the pre-trained machine learning model;

[0008] Based on the generated collaboration quality coefficient, the departments with hidden dangers of information islands in the information transmission process are divided into high-quality collaboration departments and low-quality collaboration departments.

[0009] For low-quality collaborative departments, determine how to optimize the management of the department's collaborative work.

[0010] In a preferred embodiment, the steps of collecting access information in real time and performing preliminary analysis to determine whether there are hidden dangers of information islands in the information transmission process of each department are as follows:

[0011] Collect the initial timestamp and completion timestamp of each benchmark data in the benchmark data access combination set obtained by the target department, calculate the difference between the completion timestamp and the initial timestamp, and obtain the total time taken to obtain each benchmark data. Then obtain the total number of links required to obtain each benchmark data in the benchmark data access combination set. Then calculate the ratio of the total time taken to obtain each benchmark data to the total number of links it needs to go through to obtain the acquisition rate. Then calculate the acquisition rate average and acquisition time standard deviation through all acquisition rates. Then compare them with the preset benchmark average and benchmark standard deviation respectively. If the acquisition rate average is less than the preset benchmark average and the acquisition time standard deviation is less than the preset benchmark standard deviation, a normal signal is generated. If the acquisition rate average is not less than the preset benchmark average and the acquisition time standard deviation is not less than the preset benchmark standard deviation, an abnormal signal is generated. When an abnormal signal is generated, it indicates that the department has the hidden danger of information islands in the information transmission process.

[0012] In a preferred implementation, collecting information from a department that has a hidden danger of information islands during information transmission refers to collecting the target department's own information and cross-department information.

[0013] In a preferred embodiment, the logic for obtaining the information flow quality index is:

[0014] The data modification frequency characteristic data and cross-department request failure rate characteristic data in the department information and cross-department information of the target department are extracted respectively, and then the data modification frequency characteristic data and the cross-department request failure rate characteristic data are standardized respectively, so that the data modification frequency characteristic data and the cross-department request failure rate characteristic data are in the same dimension, and then the DBSCAN clustering algorithm is used to perform cluster analysis on the standardized data to identify core points, boundary points and noise points. Based on the clustering results, the average distance between the target department and the core point is calculated to obtain the information flow quality index. The calculation formula of the information flow quality index is: ; represents the number of core points, j is the number of other departments, It represents the Euclidean distance between the target department i and other departments j, and IFQI is the information flow quality index.

[0015] In a preferred embodiment, the logic for obtaining the information circulation integration index is:

[0016] From the cross-departmental information of the target department, the information flow data between it and other departments are obtained, including the information flow time characteristic data and the information flow success rate characteristic data. Then, the information flow time characteristic data and the information flow success rate characteristic data are standardized respectively, so that the information flow time characteristic data and the information flow success rate characteristic data are in the same dimension. The K-means clustering algorithm is used to perform cluster analysis on the standardized data, and the data is divided into normal clusters and abnormal clusters. Based on the clustering results, the average distance between the target department and other departments in the normal cluster is calculated to obtain the information flow integration index. The calculation formula of the information flow integration index is: ; represents the number of departments in the normal cluster, j is the number of other departments, It represents the Euclidean distance between the target department i and other departments j, and IFII is the information flow integration index.

[0017] In a preferred embodiment, based on the generated collaboration quality coefficient, the departments with hidden dangers of information islands in the information transmission process are divided into high-quality collaboration departments and low-quality collaboration departments respectively, which means:

[0018] The generated collaboration quality coefficient is compared with the preset collaboration quality standard reference coefficient. If the generated collaboration quality coefficient is greater than or equal to the preset collaboration quality standard reference coefficient, the department with the hidden danger of information islands in the information transmission process will be classified as a high-quality collaboration department. If the generated collaboration quality coefficient is less than the preset collaboration quality standard reference coefficient, the department with the hidden danger of information islands in the information transmission process will be classified as a low-quality collaboration department.

[0019] In a preferred embodiment, for a low-quality collaborative department, determining an optimal management method for the collaborative work of the department refers to:

[0020] The information flow quality index, information flow integration index, and collaboration quality coefficient of low-quality collaborative departments are obtained, and then fuzzy logic is applied for reasoning to determine how to optimize the management of the collaborative work of the departments.

[0021] In a preferred embodiment, applying fuzzy logic to perform reasoning refers to:

[0022] Define the input variables as the information flow quality index, information flow integration index, and collaboration quality coefficient; define the output variables as the way of optimized management; fuzzify the input variables and convert their values ​​into fuzzy sets; fuzzify the output variables and convert them into fuzzy sets; formulate fuzzy rules to describe the optimization management requirements under different data type combinations; reason the fuzzified input variables through fuzzy rules to determine the way to optimize the collaborative work of departments.

[0023] In a preferred embodiment, a collaborative design management system for a product lifecycle process flow includes a judgment module, an analysis module, a division module, and an optimization module;

[0024] The judgment module is used to obtain a set of reference data access combination sets through different departments of collaborative design of product life cycle process flow according to a determined set of reference data access combination sets, collect access information in real time and conduct preliminary analysis to judge whether there is a hidden danger of information islands in the process of information transmission between departments;

[0025] The analysis module is used to collect information and extract features from departments that have hidden dangers of information islands during information transmission, and then perform anomaly analysis to obtain the information flow quality index and the information flow integration index respectively. Then, the information flow quality index and the information flow integration index are transmitted to the pre-trained machine learning model, and the collaboration quality coefficient is generated through the pre-trained machine learning model;

[0026] Based on the generated collaboration quality coefficient, the division module divides the departments with hidden dangers of information islands in the information transmission process into high-quality collaboration departments and low-quality collaboration departments;

[0027] The optimization module is used to determine the optimal management method for the collaborative work of low-quality collaborative departments.

[0028] Technical effects and advantages of the present invention:

[0029] The present invention evaluates the hidden dangers of information islands in information transmission of each department through a benchmark data access combination set, calculates the information flow quality index and the information flow integration index, and can accurately evaluate the quality of collaboration between departments; the present invention uses fuzzy logic to conduct a comprehensive evaluation of the quality and integration of information flow, and can intelligently identify the information island phenomenon between departments, prompting managers to take targeted measures to break down information islands and promote information sharing and collaborative work.

[0030] Through fuzzy logic reasoning, the present invention can flexibly adjust the optimization strategy according to the process scenarios and requirements of different products, ensuring that in various complex collaborative relationships, the quality and integration of information flow can be effectively improved. By generating a collaboration quality coefficient and comparing it with a preset standard, the present invention can accurately judge the collaboration level of each department, provide scientific management decision support, help enterprises optimize resource allocation, and improve collaboration efficiency. The present invention can provide comprehensive optimization management suggestions for low-quality collaborative departments, including optimization of the department's internal information flow links, optimization of external collaboration links, and comprehensive optimization, to ensure that all departments work together efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0032] Figure 1 A schematic diagram of a collaborative design management method for a product life cycle process flow in the present invention.

[0033] Figure 2 The schematic diagram is a collaborative design management system for a product life cycle process flow in the present invention. DETAILED DESCRIPTION

[0034] 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.

[0035] Reference Figure 1 - Figure 2 The following examples are obtained:

[0036] Embodiment 1: A collaborative design management method for a product life cycle process flow, comprising the following steps:

[0037] Determine a set of benchmark data access combinations, and then obtain the benchmark data access combination sets through different departments of collaborative design of product life cycle process, collect access information in real time and conduct preliminary analysis to determine whether there is a hidden danger of information islands in the information transmission process of each department;

[0038] Collect information and extract features from departments with hidden dangers of information islands in the process of information transmission, and then perform anomaly analysis to obtain the information flow quality index and information flow integration index respectively. Then, transmit the information flow quality index and information flow integration index to the pre-trained machine learning model, and generate the collaboration quality coefficient through the pre-trained machine learning model;

[0039] Based on the generated collaboration quality coefficient, the departments with hidden dangers of information islands in the information transmission process are divided into high-quality collaboration departments and low-quality collaboration departments.

[0040] For low-quality collaborative departments, determine how to optimize the management of the department's collaborative work.

[0041] It should be noted that determining a set of benchmark data access combinations is a key task, which helps to evaluate the information transfer between departments in the collaborative design of product life cycle process. The following are the methods and requirements for determining the benchmark data access combination set:

[0042] Objective: The benchmark data access portfolio should be able to comprehensively cover the key data points in the product lifecycle process and reflect the actual operations and needs of each department in information transfer and collaboration.

[0043] Requirements: Comprehensiveness: The selected data items should cover all stages of the product life cycle, including design, manufacturing, testing, sales and after-sales service.

[0044] Criticality: The selected data items should be the key data that are essential for each department in the collaboration process.

[0045] Representativeness: The data items selected should be representative of the main business activities and information needs of each department.

[0046] Sample data items: Design department: CAD files, design specifications, design change requests; Manufacturing department: production plans, bills of materials, process parameters; Testing department: test plans, test results, quality reports; Sales department: sales orders, customer needs, market feedback; After-sales service department: maintenance records, customer complaints, service feedback.

[0047] Requirements for constructing data access combinations: Frequency and importance: give priority to combinations of data items that are frequently accessed and important; Diversity: ensure that the data items in the combination set are diversified, covering data of different types and sources; Consistency: the selected data items should be able to consistently reflect the collaboration of various departments, avoid data silos, and ultimately obtain a set of benchmark data access combination sets.

[0048] The steps to collect access information in real time and conduct preliminary analysis to determine whether there are hidden dangers of information islands in the information transmission process of each department are as follows:

[0049] Collect the initial timestamp and completion timestamp of each benchmark data in the benchmark data access combination set obtained by the target department, calculate the difference between the completion timestamp and the initial timestamp, and obtain the total time taken to obtain each benchmark data. Then obtain the total number of links required to obtain each benchmark data in the benchmark data access combination set. Then calculate the ratio of the total time taken to obtain each benchmark data to the total number of links it needs to go through to obtain the acquisition rate. Then calculate the acquisition rate average and acquisition time standard deviation through all acquisition rates. Then compare them with the preset benchmark average and benchmark standard deviation respectively. If the acquisition rate average is less than the preset benchmark average and the acquisition time standard deviation is less than the preset benchmark standard deviation, a normal signal is generated. If the acquisition rate average is not less than the preset benchmark average and the acquisition time standard deviation is not less than the preset benchmark standard deviation, an abnormal signal is generated. When an abnormal signal is generated, it indicates that the department has the hidden danger of information islands in the information transmission process.

[0050] The significance of judging whether there are hidden dangers of information islands in the process of information transmission in various departments lies in: by calculating the total time taken to obtain each benchmark data item, the efficiency of each department in obtaining the required data can be accurately evaluated. A longer acquisition time may mean that there is a delay or obstruction in the information transmission process, indicating the possible existence of information islands; by calculating the acquisition rate (the ratio of the total acquisition time to the total number of links passed), abnormal information transmission patterns are detected. By comparing the average value and standard deviation of the acquisition rate with the preset benchmark value, deviations from the normal information transmission pattern can be identified. When the average acquisition rate is low and the standard deviation is small, it means that the information transmission efficiency is high and stable. Otherwise, it indicates the possible existence of information islands. Real-time collection and analysis of data access information can generate abnormal signals in time when abnormal information transmission patterns are found, helping managers to quickly identify and solve problems and prevent the impact of information islands on business processes from expanding.

[0051] Information collection for departments that have hidden dangers of information islands during the information transmission process means: collecting the target department's own information and cross-departmental information to provide data support for subsequent abnormal analysis and optimization.

[0052] The information flow quality index is an important indicator to measure the efficiency and quality of information transmission within an enterprise or across departments. Its significance lies in revealing potential problems in information flow through quantitative analysis, thereby providing data support for optimizing management and decision-making. A high IFQI indicates high information transmission efficiency, high success rate, good data consistency, and high overall information flow quality. The enterprise can achieve efficient information transmission, ensure smooth business processes, and improve operational efficiency; a low IFQI indicates low information transmission efficiency, low success rate, poor data consistency, and low overall information flow quality. The logic for obtaining the information flow quality index is: extract the data modification frequency feature data and cross-department request failure rate feature data from the department information and cross-department information of the target department respectively, and then standardize the data modification frequency feature data and cross-department request failure rate feature data respectively, so that the data modification frequency feature data and cross-department request failure rate feature data are in the same dimension, and then use the DBSCAN clustering algorithm to perform cluster analysis on the standardized data to identify core points, boundary points, and noise points. Based on the clustering results, the average distance between the target department and the core point is calculated to obtain the information flow quality index. The calculation formula of the information flow quality index is: ; represents the number of core points, j is the number of other departments, Represents the Euclidean distance between the target department i and other departments j, and IFQI is the information flow quality index; the DBSCAN algorithm is a density-based clustering method that discovers clusters through density connectivity. The key steps are as follows: define parameters: ϵ: the radius of a point's neighborhood; minPts: the minimum number of neighbors required for a point to be considered a core point; identification of core points, boundary points, and noise points: core point: if the number of points in a point's ϵ neighborhood is greater than or equal to minPts, then the point is a core point; boundary point: if a point is in the ϵ neighborhood of a core point, but the number of points in its own ϵ neighborhood is less than minPts, then the point is a boundary point; noise point: a point that is neither a core point nor a boundary point is a noise point. Clustering process: Randomly select an unvisited point. If the point is a core point, create a new cluster and add all points in the ϵ neighborhood of the point (points directly reachable by density) to the cluster. For each core point in the cluster, all points in its ϵ neighborhood (points directly reachable by density) are also added to the cluster. Repeat this process until there are no more points to join the cluster. Mark all visited points and repeat the above process until all points are visited. Calculate distance: In DBSCAN, first calculate the Euclidean distance between all data points to determine the ϵ neighborhood of each point. Density check: For each data point, check the number of points in its ϵ neighborhood. If it is greater than or equal to minPts, mark it as a core point. Expand clustering: Starting from the core point, expand the clustering and connect other directly density-reachable points through Euclidean distance to form a cluster. The Euclidean distance calculation formula here is: ; and They are the data modification frequency of the target department after standardization and the cross-department request failure rate. represents the frequency of data modification from target department i to other departments j, represents the request failure rate in the cross-departmental requests issued by the target department i. DBSCAN can identify clusters of any shape, not just spherical or elliptical clusters. When analyzing information flow, the distribution of data points may be irregular. DBSCAN can effectively identify clusters of these complex shapes, which helps to more accurately discover anomalies and hidden dangers in information flow. DBSCAN can effectively identify and process noise points (i.e., isolated points), which do not belong to any cluster. In information flow analysis, noise and abnormal points may represent abnormal situations or problems in data transmission. DBSCAN can mark these points separately to help better identify and deal with potential problems. Unlike K-means, DBSCAN does not require the number of clusters to be determined in advance. It automatically determines the number of clusters based on the density of data points. In information flow analysis, when it is not known in advance how many groups the data points need to be divided into, DBSCAN can automatically adjust to provide more flexible and accurate clustering results. Therefore, the DBSCAN algorithm is selected here. DBSCAN can handle clusters of different densities and adapt to density changes in different areas of the data set. In information flow, the information flow frequency and feature density of different departments or links may be different. DBSCAN can identify these differences and provide more detailed clustering analysis.

[0053] The information flow integration index is an indicator used to measure the degree of information system integration and collaborative work efficiency within an enterprise or across departments. IFII can quantify the degree of information integration between departments or systems, and help enterprises understand the level of integration of overall department information. High IFII means that the information system integration is high, the information flow between departments is smooth, and the collaborative work efficiency is high. The enterprise can achieve more efficient information sharing and collaboration, and quickly respond to market demand and business changes; low IFII means that the information system integration is low, there are obstacles to information flow between departments, and the collaborative work efficiency is low. The logic of obtaining the information flow integration index is: obtain the information flow data between the target department and other departments from the cross-departmental information of the target department, including the information flow time characteristic data and the information flow success rate characteristic data, and then standardize the information flow time characteristic data and the information flow success rate characteristic data respectively, so that the information flow time characteristic data and the information flow success rate characteristic data are in the same dimension, and use the K-means clustering algorithm to perform cluster analysis on the standardized data, and divide the data into normal clusters and abnormal clusters. Based on the clustering results, calculate the average distance between the target department and other departments in the normal cluster to obtain the information flow integration index. The calculation formula of the information flow integration index is: ; represents the number of departments in the normal cluster, j is the number of other departments, represents the Euclidean distance between the target department i and other departments j, and IFII is the information flow integration index; the steps of using the K-means clustering algorithm are: initialize the cluster center, select K=2 cluster centers, and then iteratively update the cluster center: Step 1, assign data points to the nearest cluster center: ; is the index of the cluster center to which data point j is assigned, Indicates taking the k that minimizes the objective function value, where k is the index of the cluster center (indicates the kth cluster center). and are the standardized information flow time and the information flow success rate of the kth cluster center, respectively. is the information flow time from the target department i to other departments j after standardized processing, is the success rate of information flow from the target department i to other departments j after standardized processing. Step 2: Update the cluster center: ; ; represents the number of all data points belonging to the kth cluster, represents the sum of the standardized information flow time of all data points j belonging to the kth cluster, represents the sum of the standardized information flow success rates of all data points j belonging to the kth cluster. Each data point j can be represented as a vector , iterate the above steps until the cluster center is stable. The cluster center is the average value of all data point features in the current cluster, indicating the center position of the cluster. In each iteration, the cluster center will be recalculated based on the currently assigned data points to more accurately reflect the distribution of data points. Data point allocation is based on the minimum value principle of Euclidean distance, that is, each data point is assigned to the nearest cluster center. The clustering process is an iterative process, which continuously allocates data points and updates the cluster center until the cluster center is stable (that is, no longer changes significantly). The K-means algorithm is simple and easy to understand, with high computational efficiency, and is particularly suitable for large-scale data sets. When calculating the information flow integration index, it can quickly process a large amount of data and provide analysis results in a timely manner, helping enterprises to quickly identify problems and optimization opportunities in information flow. The K-means clustering results are intuitive and easy to understand. Each data point is assigned to a cluster, and the cluster center represents the characteristic mean of the class. Management and related personnel can intuitively understand and explain the calculation results of the information flow integration index, which is convenient for formulating corresponding optimization measures based on this result. K-means is suitable for processing numerical data, especially for analyzing numerical features such as information flow time and request success rate. When calculating the information flow integration index, it can be directly applied to the standardized information flow time and request success rate indicators to provide accurate analysis results. K-means can automatically divide data points into k clusters, each cluster represents a set of data points with similar characteristics. By automatically dividing the groups, different patterns and problems in information flow can be identified, which helps to take corresponding optimization measures for different types of problems. In addition, K-means provides quantitative analysis results by calculating the distance from the data point to the cluster center. When calculating the information flow integration index, it can provide accurate numerical indicators to help enterprises better understand the integration of information flow.

[0054] The machine learning model is not specifically limited here. Any model that can perform a comprehensive analysis of the information flow quality index and the information flow integration index to generate a collaboration quality coefficient can be used. In order to implement the technical solution of the present invention, the present invention provides a specific implementation method: the information flow quality index and the information flow integration index are substituted into a pre-trained machine learning model. The logic of generating the collaboration quality coefficient is: the information flow quality index and the information flow integration index are standardized so that their data range is within the same scale, and then substituted into the pre-trained machine learning model, such as: , CQC is the collaborative quality coefficient, k1 and k2 are preset proportional coefficients used to adjust the degree of influence of parameters on the results, is the information flow integration index after standardized processing, It is the information flow quality index after standardized processing.

[0055] Based on the generated collaboration quality coefficient, the departments with hidden dangers of information islands in the information transmission process are divided into high-quality collaboration departments and low-quality collaboration departments, which means:

[0056] The generated collaboration quality coefficient is compared with the preset collaboration quality standard reference coefficient. If the generated collaboration quality coefficient is greater than or equal to the preset collaboration quality standard reference coefficient, the department with the hidden danger of information islands in the information transmission process will be classified as a high-quality collaboration department. If the generated collaboration quality coefficient is less than the preset collaboration quality standard reference coefficient, the department with the hidden danger of information islands in the information transmission process will be classified as a low-quality collaboration department. The preset collaboration quality standard reference coefficient is a threshold set by the enterprise according to process design requirements and historical data, which is used to measure the benchmark level of departmental collaboration. The generated collaboration quality coefficient of each department is compared with the preset collaboration quality standard reference coefficient to divide it into high-quality collaboration departments and low-quality collaboration departments. The collaboration quality coefficients of each department can be systematically calculated and analyzed, and compared and divided according to preset standards, so as to identify departments with hidden dangers of information islands in the information transmission process.

[0057] For low-quality collaborative departments, the optimization management method for the collaborative work of the department refers to:

[0058] Obtain the information flow quality index, information flow integration index, and collaboration quality coefficient of the low-quality collaboration department, and then apply fuzzy logic for reasoning to determine the way to optimize the management of the collaboration work of the department. Applying fuzzy logic for reasoning means: defining the input variables as the information flow quality index, information flow integration index, and collaboration quality coefficient, defining the output variables as the way to optimize management, fuzzifying the input variables, converting the values ​​of the input variables into fuzzy sets, fuzzifying the output variables, converting the output variables into fuzzy sets, formulating fuzzy rules, describing the optimization management requirements under different data type combinations, and reasoning the fuzzified input variables through fuzzy rules to determine the way to optimize the collaboration work of the department. way; for example: define the fuzzy set of input variables, such as "low", "medium", "high", information flow quality index (IFQI): low, medium, high; information flow integration index (IFII): low, medium, high; collaboration quality coefficient (CQC): low, medium, high; define the fuzzy set of output variables, such as "internal optimization", "external optimization", "comprehensive optimization", there are three ways of optimization management, namely, optimizing the internal information flow link of the department, namely internal optimization, optimizing the external collaboration link of the department, namely external optimization, optimizing both the internal information flow link and the external collaboration link of the department, namely comprehensive optimization, formulate fuzzy rules, and determine the output variables according to different combinations of input variables:

[0059] Rule 1: If IFQI is low and IFII is low and CQC is low, then the way to optimize management is internal optimization.

[0060] Rule 2: If IFQI is low and IFII is high and CQC is medium, then the optimization management approach is external optimization.

[0061] Rule 3: If IFQI is medium and IFII is medium and CQC is medium, then the optimization management approach is comprehensive optimization.

[0062] Rule 4: If IFQI is high and IFII is low and CQC is high, then the optimization management approach is external optimization.

[0063] Rule 5: If IFQI is high and IFII is high and CQC is high, then the optimal management approach is comprehensive optimization.

[0064] Finally, a specific optimization management suggestion can be obtained. By applying fuzzy logic, the optimal management method of low-quality collaboration departments can be inferred based on the information flow quality index, information flow integration index and collaboration quality coefficient. This method takes into account the fuzziness and uncertainty of the input variables, and can provide flexible and accurate management suggestions to improve departmental collaboration efficiency.

[0065] Embodiment 2: A collaborative design management system for a product lifecycle process flow, comprising a judgment module, an analysis module, a division module, and an optimization module;

[0066] The judgment module is used to obtain a set of reference data access combination sets through different departments of collaborative design of product life cycle process flow according to a determined set of reference data access combination sets, collect access information in real time and conduct preliminary analysis to judge whether there is a hidden danger of information islands in the process of information transmission between departments;

[0067] The analysis module is used to collect information and extract features from departments that have hidden dangers of information islands during information transmission, and then perform anomaly analysis to obtain the information flow quality index and the information flow integration index respectively. Then, the information flow quality index and the information flow integration index are transmitted to the pre-trained machine learning model, and the collaboration quality coefficient is generated through the pre-trained machine learning model;

[0068] Based on the generated collaboration quality coefficient, the division module divides the departments with hidden dangers of information islands in the information transmission process into high-quality collaboration departments and low-quality collaboration departments;

[0069] The optimization module is used to determine the optimal management method for the collaborative work of low-quality collaborative departments.

[0070] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0071] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 application.

[0072] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0073] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0074] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A collaborative design management method for a product life cycle process, characterized in that: The following steps are involved: Determine a set of benchmark data access combinations, and then obtain the benchmark data access combination sets through different departments of collaborative design of product life cycle process, collect access information in real time and conduct preliminary analysis to determine whether there is a hidden danger of information islands in the information transmission process of each department; Collect information and extract features from departments with hidden dangers of information islands in the process of information transmission, and then perform anomaly analysis to obtain the information flow quality index and information flow integration index respectively. Then, transmit the information flow quality index and information flow integration index to the pre-trained machine learning model, and generate the collaboration quality coefficient through the pre-trained machine learning model; Based on the generated collaboration quality coefficient, the departments with hidden dangers of information islands in the information transmission process are divided into high-quality collaboration departments and low-quality collaboration departments. For low-quality collaborative departments, determine how to optimize the management of the department's collaborative work.

2. The collaborative design management method for a product lifecycle process according to claim 1, characterized in that: The steps to collect access information in real time and conduct preliminary analysis to determine whether there are hidden dangers of information islands in the information transmission process of each department are as follows: Collect the initial timestamp and completion timestamp of each benchmark data in the benchmark data access combination set obtained by the target department, calculate the difference between the completion timestamp and the initial timestamp, and obtain the total time taken to obtain each benchmark data. Then obtain the total number of links required to obtain each benchmark data in the benchmark data access combination set. Then calculate the ratio of the total time taken to obtain each benchmark data to the total number of links it needs to go through to obtain the acquisition rate. Then calculate the acquisition rate average and acquisition time standard deviation through all acquisition rates. Then compare them with the preset benchmark average and benchmark standard deviation respectively. If the acquisition rate average is less than the preset benchmark average and the acquisition time standard deviation is less than the preset benchmark standard deviation, a normal signal is generated. If the acquisition rate average is not less than the preset benchmark average and the acquisition time standard deviation is not less than the preset benchmark standard deviation, an abnormal signal is generated. When an abnormal signal is generated, it indicates that the department has the hidden danger of information islands in the information transmission process.

3. The collaborative design management method for a product lifecycle process according to claim 2, characterized in that: Information collection for departments that have the risk of information islands in the process of information transmission refers to: collecting the target department's own information and cross-departmental information.

4. The collaborative design management method for a product lifecycle process according to claim 3, characterized in that: The logic for obtaining the information flow quality index is: The data modification frequency characteristic data and cross-department request failure rate characteristic data in the department information and cross-department information of the target department are extracted respectively, and then the data modification frequency characteristic data and the cross-department request failure rate characteristic data are standardized respectively, so that the data modification frequency characteristic data and the cross-department request failure rate characteristic data are in the same dimension, and then the DBSCAN clustering algorithm is used to perform cluster analysis on the standardized data to identify core points, boundary points and noise points. Based on the clustering results, the average distance between the target department and the core point is calculated to obtain the information flow quality index. The calculation formula of the information flow quality index is: ; represents the number of core points, j is the number of other departments, It represents the Euclidean distance between the target department i and other departments j, and IFQI is the information flow quality index.

5. The collaborative design management method for a product lifecycle process according to claim 4, characterized in that: The logic for obtaining the information circulation integration index is: From the cross-departmental information of the target department, the information flow data between it and other departments are obtained, including the information flow time characteristic data and the information flow success rate characteristic data. Then, the information flow time characteristic data and the information flow success rate characteristic data are standardized respectively, so that the information flow time characteristic data and the information flow success rate characteristic data are in the same dimension. The K-means clustering algorithm is used to perform cluster analysis on the standardized data, and the data is divided into normal clusters and abnormal clusters. Based on the clustering results, the average distance between the target department and other departments in the normal cluster is calculated to obtain the information flow integration index. The calculation formula of the information flow integration index is: ; represents the number of departments in the normal cluster, j is the number of other departments, It represents the Euclidean distance between the target department i and other departments j, and IFII is the information flow integration index.

6. A collaborative design management method for a product lifecycle process according to claim 5, characterized in that: Based on the generated collaboration quality coefficient, the departments with hidden dangers of information islands in the information transmission process are divided into high-quality collaboration departments and low-quality collaboration departments, which means: The generated collaboration quality coefficient is compared with the preset collaboration quality standard reference coefficient. If the generated collaboration quality coefficient is greater than or equal to the preset collaboration quality standard reference coefficient, the department with the hidden danger of information islands in the information transmission process will be classified as a high-quality collaboration department. If the generated collaboration quality coefficient is less than the preset collaboration quality standard reference coefficient, the department with the hidden danger of information islands in the information transmission process will be classified as a low-quality collaboration department.

7. A collaborative design management method for a product lifecycle process according to claim 6, characterized in that: For low-quality collaborative departments, the optimization management method for the collaborative work of the department refers to: The information flow quality index, information flow integration index, and collaboration quality coefficient of low-quality collaboration departments are obtained, and then fuzzy logic is applied for reasoning to determine how to optimize the management of the collaboration work of the departments.

8. The collaborative design management method for a product lifecycle process according to claim 7, characterized in that: Applying fuzzy logic to reasoning means: Define the input variables as the information flow quality index, information flow integration index, and collaboration quality coefficient; define the output variables as the way of optimized management; fuzzify the input variables and convert their values ​​into fuzzy sets; fuzzify the output variables and convert them into fuzzy sets; formulate fuzzy rules to describe the optimization management requirements under different data type combinations; reason the fuzzified input variables through fuzzy rules to determine the way to optimize the collaborative work of departments.

9. A collaborative design management system for a product lifecycle process flow, based on a collaborative design management method for a product lifecycle process flow according to any one of claims 1 to 8, characterized in that: Including judgment module, analysis module, division module and optimization module; The judgment module is used to obtain a set of reference data access combination sets through different departments of collaborative design of product life cycle process flow according to a determined set of reference data access combination sets, collect access information in real time and conduct preliminary analysis to judge whether there is a hidden danger of information islands in the process of information transmission between departments; The analysis module is used to collect information and extract features from departments that have hidden dangers of information islands during information transmission, and then perform anomaly analysis to obtain the information flow quality index and the information flow integration index respectively. Then, the information flow quality index and the information flow integration index are transmitted to the pre-trained machine learning model, and the collaboration quality coefficient is generated through the pre-trained machine learning model; Based on the generated collaboration quality coefficient, the division module divides the departments with hidden dangers of information islands in the information transmission process into high-quality collaboration departments and low-quality collaboration departments; The optimization module is used to determine the optimal management method for the collaborative work of low-quality collaborative departments.

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