Industrial design collaborative optimization system based on cloud computing

Abstract

The invention relates to the technical field of cloud computing, and particularly discloses an industrial design collaborative optimization system based on cloud computing, a data acquisition module acquires key parameters in an industrial design process in real time, and a data processing module manages mass data by adopting a distributed storage technology and performs normalization processing on the acquired parameters to ensure data comparability; the data analysis module constructs three core index model server state indexes for reflecting the load condition of computing resources, a collaborative efficiency index for evaluating collaborative communication and data synchronization efficiency and a design quality index for quantifying task progress and precision errors, and the state evaluation module judges whether the system has risks or not through dynamic threshold comparison and determines whether the system has risks or not. And a potential risk coefficient is calculated, comprehensive early warning is carried out in combination with a reliability index, and efficient management and risk control of an industrial design cooperation process are realized.

Description

Technical Field

[0001] This invention relates to the field of cloud computing technology, specifically to a cloud-based collaborative optimization system for industrial design. Background Technology

[0002] In the current field of industrial design, traditional design models have many limitations. For complex products, multiple designers are often required to design different subsystems. Due to the lack of efficient collaboration mechanisms, seamless integration between the designs of these subsystems is difficult, leading to design conflicts and a decline in overall product performance. Simultaneously, the design process generates massive amounts of data, but the limited processing power of local computing devices makes it difficult to quickly and comprehensively analyze and optimize design solutions. This prolongs product development cycles, increases development costs, and fails to meet the market's demand for rapid product iteration.

[0003] Existing cloud-based collaborative optimization systems for industrial design suffer from the following problems: 1. In traditional industrial design collaboration, the static allocation of cloud computing resources cannot adapt to dynamic loads, leading to insufficient resources under high loads and wasted resources under low loads; 2. Risk response in traditional industrial design collaboration is lagging, relying on manual monitoring, making timely detection difficult. Therefore, this invention proposes a cloud-based collaborative optimization system for industrial design. Summary of the Invention

[0004] The purpose of this invention is to provide a cloud computing-based collaborative optimization system for industrial design, thereby solving the above-mentioned technical problems: The objective of this invention can be achieved through the following technical solutions: A cloud computing-based industrial design collaborative optimization system, the system comprising a data acquisition module, a data processing module, a data analysis module, a status assessment module, a user collaboration terminal, and a central control module; The data acquisition module is used to collect various parameters during the industrial design process; The data processing module, connected to the data acquisition module, is used to store and process the acquired data, employing distributed storage technology to store massive amounts of data. Furthermore, various parameters in the industrial design process are normalized to ensure that different parameters are under the same standard. The data analysis module is used to analyze the processed parameters and extract relevant indicators for collaborative optimization of industrial design. The status assessment module is used to assess whether there are risks in the industrial design collaboration process based on relevant indicators of industrial design collaboration optimization, and to issue corresponding warnings and optimizations. The user collaboration terminal is used to receive early warnings and optimization suggestions, and to provide feedback on manual operation data; The central control module is used to output control commands to the user's collaborative terminal.

[0005] As a further description of the technical solution of the present invention, the various parameters in the industrial design process include: computational resource parameters, collaborative efficiency parameters, and design quality parameters; The computing resource parameters include: CPU utilization, GPU utilization, memory usage, and storage space utilization. The collaborative efficiency parameters include: file synchronization delay time, real-time communication delay time, and data download speed; The design quality parameters include: parallel task progress and accuracy.

[0006] As a further description of the technical solution of the present invention, the working process of the data analysis module includes: To obtain real-time CPU utilization, GPU utilization, memory usage, and storage utilization of a cloud computing server, a server status indicator calculation model is constructed, expressed as: ; In the formula, It is the server resource utilization rate. , , and These are server CPU utilization, GPU utilization, memory usage, and storage space utilization, respectively. It is the server resource availability rate. These are the weighting coefficients for various server resources. This refers to server status indicators.

[0007] As a further description of the technical solution of the present invention, the working process of the data analysis module also includes: To calculate the collaborative efficiency index, we obtain the current server file synchronization delay, real-time communication delay, and data download speed, and construct the collaborative efficiency index calculation model, expressed as: ; In the formula, For data download speed, For file synchronization delay time, For real-time communication delay time, , and These are the weighting coefficients corresponding to server file synchronization latency, real-time communication latency, and data download speed, respectively. This is a collaborative efficiency indicator.

[0008] As a further description of the technical solution of the present invention, the working process of the data analysis module also includes: Obtain the number of tasks currently running in parallel and number them sequentially as 1, 2, ..., n. Obtain the progress and accuracy error of each task, and construct a design quality index calculation model, expressed as: ; In the formula, Let be the real-time accuracy error of the j-th task. This is a quantified value representing the cumulative change in the work progress of the j-th task. Quantify the cumulative change in the work progress of the j-th task as defined by the system. Let be the weight coefficient corresponding to the j-th task, where j belongs to n. To design quality indicators.

[0009] As a further description of the technical solution of the present invention, the quantified value of the cumulative change in the work progress of the j-th task is... The acquisition process includes: Obtain the progress of the j-th task over time, and generate a real-time curve of the progress of the j-th task over time in the coordinate system xoy; The area enclosed by the real-time curve of the progress of task j over time and the x-axis is the result. ; The quantitative standard value for the cumulative change in the progress of the j-th task is the area enclosed by the standard curve of the progress of the j-th task over time set by the washing liquid and the x-axis.

[0010] As a further description of the technical solution of the present invention, the working process of the state assessment module includes: The server status indicators, collaboration efficiency indicators, and design quality indicators are compared with the corresponding standards set by the system. If any of the server status indicators, collaboration efficiency indicators, and design quality indicators fails to meet the corresponding standards, it indicates that there is a risk in the current industrial design collaboration process, and an early warning is immediately issued for the indicators that do not meet the standards. If the server status indicators, collaboration efficiency indicators, and design quality indicators all meet the corresponding standards, then assess whether there are potential risks in the industrial design collaboration process.

[0011] As a further description of the technical solution of the present invention, the process of assessing whether there are potential risks in the industrial design collaboration process includes: A model for calculating the potential risk coefficient in the collaborative industrial design process is constructed, and its expression is as follows: ; In the formula, As a system reliability indicator, , and The weighting coefficients for server status indicators, collaboration efficiency indicators, and design quality indicators are, in order. The potential risk coefficient of the industrial design collaboration process is compared with the threshold set by the system. If the potential risk coefficient of the industrial design collaboration process is greater than or equal to the threshold set by the system, it indicates that there is a potential risk in the industrial design collaboration process.

[0012] As a further description of the technical solution of the present invention, the process of obtaining the system reliability index includes: Obtain historical reliability parameters of the system, including: task completion rate, average fault recovery time, and user operation error rate; The system reliability coefficient calculation model is constructed, and the expression is: ; In the formula, For task completion rate, Mean time to recovery from failure, To account for user operation error rate , and These are the weighting coefficients corresponding to task completion rate, average fault recovery time, and user operation error rate, respectively.

[0013] The beneficial effects of this invention are: This invention's data acquisition module collects key parameters in the industrial design process in real time. The data processing module uses distributed storage technology to manage massive amounts of data and normalizes the collected parameters to ensure data comparability. The data analysis module constructs three core indicator models: server status indicators reflecting computing resource load, collaboration efficiency indicators evaluating collaborative communication and data synchronization efficiency, and design quality indicators quantifying task progress and accuracy errors. The status assessment module uses dynamic threshold comparisons to determine whether there are risks in the system, calculates potential risk coefficients, and combines reliability indicators for comprehensive early warning, achieving efficient management and risk control of the industrial design collaboration process. Attached Figure Description

[0014] The invention will now be further described with reference to the accompanying drawings.

[0015] Figure 1 This is a schematic diagram of the structure of the cloud computing-based collaborative optimization system for industrial design of this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 As shown, the present invention provides a cloud computing-based industrial design collaborative optimization system, the system comprising a data acquisition module, a data processing module, a data analysis module, a status assessment module, a user collaboration terminal, and a central control module; The data acquisition module is used to collect various parameters during the industrial design process; The data processing module, connected to the data acquisition module, is used to store and process the acquired data, employing distributed storage technology to store massive amounts of data. Furthermore, various parameters in the industrial design process are normalized to ensure that different parameters are under the same standard. The data analysis module is used to analyze the processed parameters and extract relevant indicators for collaborative optimization of industrial design. The status assessment module is used to assess whether there are risks in the industrial design collaboration process based on relevant indicators of industrial design collaboration optimization, and to issue corresponding warnings and optimizations. The user collaboration terminal is used to receive early warnings and optimization suggestions, and to provide feedback on manual operation data; The central control module is used to output control commands to the user's collaborative terminal.

[0018] Through the above technical solution, this invention proposes a cloud computing-based collaborative optimization system for industrial design. This system achieves efficient management and risk control of the industrial design process through multi-module collaborative work. The data acquisition module collects key parameters in real time during the industrial design process, including: computing resource parameters (CPU / GPU utilization, memory usage, storage space utilization); collaborative efficiency parameters (file synchronization latency, real-time communication latency, data download speed); and design quality parameters (parallel task progress, simulation accuracy error). The data processing module uses distributed storage technology to manage massive amounts of data and normalizes the collected parameters to ensure data comparability. The data analysis module constructs three core indicator models: server status indicators (reflecting computing resource load), collaborative efficiency indicators (evaluating collaborative communication and data synchronization efficiency), and design quality indicators (quantifying task progress and accuracy error). The status assessment module uses dynamic threshold comparisons to determine if the system has risks, calculates potential risk coefficients, and provides comprehensive early warnings based on reliability indicators.

[0019] As a further description of the technical solution of the present invention, the various parameters in the industrial design process include: computational resource parameters, collaborative efficiency parameters, and design quality parameters; The computing resource parameters include: CPU utilization, GPU utilization, memory usage, and storage space utilization. The collaborative efficiency parameters include: file synchronization delay time, real-time communication delay time, and data download speed; The design quality parameters include: parallel task progress and accuracy.

[0020] As a further description of the technical solution of the present invention, the working process of the data analysis module includes: To obtain real-time CPU utilization, GPU utilization, memory usage, and storage utilization of a cloud computing server, a server status indicator calculation model is constructed, expressed as: ; In the formula, It is the server resource utilization rate. , , and These are server CPU utilization, GPU utilization, memory usage, and storage space utilization, respectively. It is the server resource availability rate. These are the weighting coefficients for various server resources. This refers to server status indicators.

[0021] Through the above technical solution, this implementation dynamically evaluates the server load status of an industrial design collaboration system in a cloud computing environment. By comprehensively analyzing the resource utilization and remaining rate of CPU, GPU, memory, and storage, the overall health of the server is quantified using the formula. Calculate server status metrics. A smaller H value indicates higher server load and greater risk, while a larger value indicates a healthier server. In the formula, the numerator... The product reflects the resource surplus rate. The larger the numerator (the more idle the resource), the larger the denominator. The logarithmic product that reflects resource utilization rate The higher the value (the busier the resource), the larger the denominator. The logarithmic function is used to smooth out numerical explosions under extreme high loads.

[0022] Implementation method: Data acquisition and real-time monitoring of server CPU, GPU, memory, and storage usage (via cloud platform APIs or monitoring tools such as Prometheus). Parameter normalization converts the raw data into a uniform dimension. Weight configuration, set according to business needs. ; Dynamic calculation and early warning: The H value is calculated periodically (e.g., every 5 minutes) and compared with a preset threshold to trigger server expansion or task migration.

[0023] As a further description of the technical solution of the present invention, the working process of the data analysis module also includes: To calculate the collaborative efficiency index, we obtain the current server file synchronization delay, real-time communication delay, and data download speed, and construct the collaborative efficiency index calculation model, expressed as: ; In the formula, For data download speed, For file synchronization delay time, For real-time communication delay time, , and These are the weighting coefficients corresponding to server file synchronization latency, real-time communication latency, and data download speed, respectively. This is a collaborative efficiency indicator.

[0024] Based on the above technical solution, this embodiment proposes a collaborative efficiency index calculation model to evaluate the efficiency of data transmission and communication in a cloud-based industrial design collaborative system. This model quantifies the smoothness and responsiveness of the collaborative process by comprehensively analyzing file synchronization latency, real-time communication latency, and data download speed, using a formula... The synergy efficiency index is calculated as follows: a higher C value indicates higher synergy efficiency, and vice versa. In the formula, the numerator... The positive contribution of data download speed is reflected in the numerator; the faster the speed, the larger the denominator. , This is a delay penalty term; the higher the delay, the larger the denominator value. This is a low-speed penalty term; the lower the download speed, the larger this term becomes. The logarithmic function prevents the denominator from growing explosively due to high latency or low speed.

[0025] Implementation method: Data acquisition involves using cloud platform monitoring tools (such as AWS CloudWatch) to obtain real-time data on file synchronization latency (e.g., Git version control synchronization time), real-time communication latency (e.g., PING value of WebRTC or WebSocket), and data download speed (e.g., the speed of downloading design files from cloud storage). Parameter normalization converts the raw data into a uniform dimension. Weight configuration, adjusting weights according to business needs: If file synchronization is a critical bottleneck (such as large 3D model collaboration), then set... > .

[0026] Dynamic calculation and early warning: The C value is calculated periodically (e.g., every minute). If the C value is set too low, the system will automatically switch to a better network node or compress the transmitted data.

[0027] As a further description of the technical solution of the present invention, the working process of the data analysis module also includes: Obtain the number of tasks currently running in parallel and number them sequentially as 1, 2, ..., n. Obtain the progress and accuracy error of each task, and construct a design quality index calculation model, expressed as: ; In the formula, Let be the real-time accuracy error of the j-th task. This is a quantified value representing the cumulative change in the work progress of the j-th task. Quantify the cumulative change in the work progress of the j-th task as defined by the system. Let be the weight coefficient corresponding to the j-th task, where j belongs to n. To design quality indicators.

[0028] As a further description of the technical solution of the present invention, the quantified value of the cumulative change in the work progress of the j-th task is... The acquisition process includes: Obtain the progress of the j-th task over time, and generate a real-time curve of the progress of the j-th task over time in the coordinate system xoy; The area enclosed by the real-time curve of the progress of task j over time and the x-axis is the result. ; The quantitative standard value for the cumulative change in the progress of the j-th task is the area enclosed by the standard curve of the progress of the j-th task over time set by the washing liquid and the x-axis.

[0029] Based on the above technical solution, this embodiment proposes a design quality index calculation model to quantify the design accuracy and schedule deviation of parallel tasks in the industrial design collaboration process, using the formula... Calculate the design quality index, where, A logarithmic function is used to penalize precision errors. Dividing the deviation value by the allowable deviation threshold enables dynamic quality monitoring in a multi-task collaborative state.

[0030] Implementation method: Data acquisition, including precision data, is achieved by obtaining real-time error and progress data through simulation software and by collecting data through the task management system. Parameter configuration: set the allowable deviation threshold and define task weights (critical task weight > ordinary task weight). Parameter normalization converts the raw data into a uniform dimension. Dynamic calculation and early warning: when the value is below the target threshold, the problematic task (the one with the largest negative contribution) is identified, triggering an accuracy review or progress adjustment.

[0031] As a further description of the technical solution of the present invention, the working process of the state assessment module includes: The server status indicators, collaboration efficiency indicators, and design quality indicators are compared with the corresponding standards set by the system. If any of the server status indicators, collaboration efficiency indicators, and design quality indicators fails to meet the corresponding standards, it indicates that there is a risk in the current industrial design collaboration process, and an early warning is immediately issued for the indicators that do not meet the standards. If the server status indicators, collaboration efficiency indicators, and design quality indicators all meet the corresponding standards, then assess whether there are potential risks in the industrial design collaboration process.

[0032] As a further description of the technical solution of the present invention, the process of assessing whether there are potential risks in the industrial design collaboration process includes: A model for calculating the potential risk coefficient in the collaborative industrial design process is constructed, and its expression is as follows: ; In the formula, As a system reliability indicator, , and The weighting coefficients for server status indicators, collaboration efficiency indicators, and design quality indicators are, in order. The potential risk coefficient of the industrial design collaboration process is compared with the threshold set by the system. If the potential risk coefficient of the industrial design collaboration process is greater than or equal to the threshold set by the system, it indicates that there is a potential risk in the industrial design collaboration process.

[0033] As a further description of the technical solution of the present invention, the process of obtaining the system reliability index includes: Obtain historical reliability parameters of the system, including: task completion rate, average fault recovery time, and user operation error rate; The system reliability coefficient calculation model is constructed, and the expression is: ; In the formula, For task completion rate, Mean time to recovery from failure, To account for user operation error rate , and These are the weighting coefficients corresponding to task completion rate, average fault recovery time, and user operation error rate, respectively.

[0034] The above technical solution achieves a three-tiered risk warning system by comparing server status indicators, collaboration efficiency indicators, and design quality indicators with preset thresholds in real time. When any indicator exceeds its limit, a targeted alarm is triggered (e.g., server expansion, network optimization). When multiple indicators simultaneously meet the thresholds, a potential risk coefficient calculation is initiated using a formula. Calculate the potential risk coefficient of the industrial design collaboration process, where, The exponential function amplifies the risk contribution of high load (H↑). When C→0 (extremely inefficient), this term approaches... Significantly reduces X value. This means that the lower the quality, the larger the X value, multiplied by (The reciprocal of the system reliability index) The lower the reliability, the larger the X value.

[0035] Among them, through the formula Calculate the system reliability coefficient, where, This indicates the task completion rate, which directly contributes to reliability. express (Fault recovery time) and (Operational error rate) negatively impacts reliability.

[0036] It should be noted that the formulas in this application are all dimensionless and numerical calculations. The formulas are obtained by software simulation based on a large amount of data and are the closest to the real situation. The thresholds and coefficients involved in this application are all empirical values ​​and are selected by those skilled in the art according to the actual situation.

[0037] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A cloud-based collaborative optimization system for industrial design, characterized in that, The system includes a data acquisition module, a data processing module, a data analysis module, a status assessment module, a user collaboration terminal, and a central control module. The data acquisition module is used to collect various parameters during the industrial design process; The data processing module, connected to the data acquisition module, is used to store and process the acquired data, employing distributed storage technology to store massive amounts of data. Furthermore, various parameters in the industrial design process are normalized to ensure that different parameters are under the same standard. The data analysis module is used to analyze the processed parameters and extract relevant indicators for collaborative optimization of industrial design. The status assessment module is used to assess whether there are risks in the industrial design collaboration process based on relevant indicators of industrial design collaboration optimization, and to issue corresponding warnings and optimizations. The user collaboration terminal is used to receive early warnings and optimization suggestions, and to provide feedback on manual operation data; The central control module is used to output control commands to the user's collaborative terminal.

2. The cloud-based industrial design collaborative optimization system according to claim 1, characterized in that, The various parameters in the industrial design process include: computational resource parameters, collaborative efficiency parameters, and design quality parameters; The computing resource parameters include: CPU utilization, GPU utilization, memory usage, and storage space utilization. The collaborative efficiency parameters include: file synchronization delay time, real-time communication delay time, and data download speed; The design quality parameters include: parallel task progress and accuracy.

3. The cloud-based industrial design collaborative optimization system according to claim 1, characterized in that, The working process of the data analysis module includes: To obtain real-time CPU utilization, GPU utilization, memory usage, and storage utilization of a cloud computing server, a server status indicator calculation model is constructed, expressed as: ； In the formula, It is the server resource utilization rate. , , and These are server CPU utilization, GPU utilization, memory usage, and storage space utilization, respectively. It is the server resource availability rate. These are the weighting coefficients for various server resources. This refers to server status indicators.

4. The cloud-based industrial design collaborative optimization system according to claim 3, characterized in that, The data analysis module's operation also includes: To calculate the collaborative efficiency index, we obtain the current server file synchronization delay, real-time communication delay, and data download speed, and construct the collaborative efficiency index calculation model, expressed as: ； In the formula, For data download speed, For file synchronization delay time, For real-time communication delay time, , and These are the weighting coefficients corresponding to server file synchronization latency, real-time communication latency, and data download speed, respectively. This is a collaborative efficiency indicator.

5. The cloud-based industrial design collaborative optimization system according to claim 4, characterized in that, The data analysis module's operation also includes: Obtain the number of tasks currently running in parallel and number them sequentially as 1, 2, ..., n. Obtain the progress and accuracy error of each task, and construct a design quality index calculation model, expressed as: ; In the formula, Let be the real-time accuracy error of the j-th task. This is a quantified value representing the cumulative change in the work progress of the j-th task. Quantify the cumulative change in the work progress of the j-th task as defined by the system. Let be the weight coefficient corresponding to the j-th task, where j belongs to n. To design quality indicators.

6. The cloud-based industrial design collaborative optimization system according to claim 5, characterized in that, The quantified value of the cumulative change in the work progress of the j-th task The acquisition process includes: Obtain the progress of the j-th task over time, and generate a real-time curve of the progress of the j-th task over time in the coordinate system xoy; The area enclosed by the real-time curve of the progress of task j over time and the x-axis is the result. ; The quantitative standard value for the cumulative change in the progress of the j-th task is the area enclosed by the standard curve of the progress of the j-th task over time set by the washing liquid and the x-axis.

7. A cloud-based collaborative optimization system for industrial design according to any one of claims 1, characterized in that, The working process of the status assessment module includes: The server status indicators, collaboration efficiency indicators, and design quality indicators are compared with the corresponding standards set by the system. If any of the server status indicators, collaboration efficiency indicators, and design quality indicators fails to meet the corresponding standards, it indicates that there is a risk in the current industrial design collaboration process, and an early warning is immediately issued for the indicators that do not meet the standards. If the server status indicators, collaboration efficiency indicators, and design quality indicators all meet the corresponding standards, then assess whether there are potential risks in the industrial design collaboration process.

8. A cloud-based industrial design collaborative optimization system according to any one of claims 7, characterized in that, The process of assessing whether there are potential risks in the industrial design collaboration process includes: A model for calculating the potential risk coefficient in the collaborative industrial design process is constructed, and its expression is as follows: ； In the formula, As a system reliability indicator, , and The weighting coefficients for server status indicators, collaboration efficiency indicators, and design quality indicators are, in order. The potential risk coefficient of the industrial design collaboration process is compared with the threshold set by the system. If the potential risk coefficient of the industrial design collaboration process is greater than or equal to the threshold set by the system, it indicates that there is a potential risk in the industrial design collaboration process.

9. A cloud-based collaborative optimization system for industrial design according to any one of claims 7, characterized in that, The process of obtaining the system reliability indicators includes: Obtain historical reliability parameters of the system, including: task completion rate, average fault recovery time, and user operation error rate; The system reliability coefficient calculation model is constructed, and the expression is: ； In the formula, For task completion rate, Mean time to recovery from failure, To account for user operation error rate , and These are the weighting coefficients corresponding to task completion rate, average fault recovery time, and user operation error rate, respectively.

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