Intelligent debugging and optimizing system for product development

Abstract

The invention belongs to the technical field of product development, and particularly relates to an intelligent debugging and optimizing system for product development, which comprises a data acquisition module, a data analysis and processing module, a debugging detection module, an optimization algorithm module, a result output module and a feedback module, and is characterized in that the data acquisition module is responsible for collecting various data in a product development process in real time; the data analysis and processing module performs preprocessing and feature extraction on the collected data; the debugging detection module is used for debugging and detecting data in a product development process, the optimization algorithm module is a core part of the system and adopts an optimization algorithm based on a complex formula to optimize the product development process, and the optimization algorithm comprises two main parts, namely an objective function and a constraint condition; and the result output module outputs the optimal solution obtained by the optimization algorithm module to a product developer to optimize product design parameters and production data, and the feedback module generates a report and guides further development and optimization according to debugging and optimization results.

Description

Technical Field

[0001] The invention relates to the technical field of product development, and in particular to an intelligent debugging and optimization system for product development. Background Art

[0002] Product development is the process of improving old products or developing new products to give them new features or uses to meet market needs. Product development should select product styles that can adapt to and meet customer needs, while also being able to be designed and developed. Product development can bring benefits and profits to the company, allowing the company to maintain its competitive advantage in the market.

[0003] In the product development process, debugging and optimization are crucial links. Traditional debugging and optimization methods usually rely on the experience and manual operations of developers, which is not only time-consuming and labor-intensive, but also prone to errors and inefficient. As the complexity of products increases, this method becomes particularly insufficient. Therefore, there is an urgent need for an intelligent debugging and optimization system to improve product development efficiency and quality.

[0004] Therefore, the present invention proposes an intelligent debugging and optimization system for product development, which realizes intelligent debugging and optimization of the product development process by integrating advanced optimization algorithms and complex mathematical models.

[0005] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0006] To solve the above problems, the present invention provides the following technical solutions:

[0007] An intelligent debugging and optimization system for product development includes a data acquisition module, a data analysis and processing module, a debugging and detection module, an optimization algorithm module, a result output module and a feedback module. The data acquisition module is responsible for collecting various types of data in the product development process in real time; the data analysis and processing module performs preprocessing and feature extraction on the collected data; the debugging and detection module debugs and detects the data in the product development process. The optimization algorithm module is the core part of the present invention, which adopts an optimization algorithm based on a complex formula to optimize the product development process. The optimization algorithm includes two main parts: an objective function and constraint conditions; the result output module outputs the optimal solution obtained by the optimization algorithm module to product developers, including optimal product design parameters and production data, etc. The feedback module generates a feedback report based on the debugging and optimization results and guides further development and optimization.

[0008] Furthermore, the data collection module includes a storage device for collecting various data during the development process, such as system logs, performance indicators, error reports, etc., and represents the original data set as X = {x1, x2..., x i}, where x i Represents the i-th data, where i is a positive integer.

[0009] Furthermore, the data analysis and processing module includes a processor. The main task of the data processing module is to standardize, normalize or perform other necessary preprocessing operations on these features. Feature extraction can use principal component analysis (PCA) or other dimensionality reduction techniques to reduce the number of features and improve computational efficiency.

[0010] Furthermore, the specific analysis steps of data standardization and feature dimension reduction in the data analysis and processing module are as follows:

[0011] Step 1: Use the processor to process each feature x of the original data set i To standardize:

[0012]

[0013] where μ i For x i The mean value, σ i For x i The standard deviation of .

[0014] Step 2: (PCA) performs dimensionality reduction on the standardized features:

[0015] X'=WX

[0016] Where W is the projection matrix of PCA, X is the standardized feature matrix, and X' is the feature matrix after dimensionality reduction. The data after dimensionality reduction is transferred to the model training module for training.

[0017] Furthermore, the debugging detection module includes a machine learning model to monitor the system in real time and detect possible problems. The debugging detection module uses the reduced dimension training data (X', y), where y is the target variable (such as error type, performance index, etc.), and the prediction result of the machine learning model f is:

[0018] Where f is the random forest model, Θ is the model parameter, For the prediction results.

[0019] Furthermore, the model training module includes feature importance analysis, which is used to locate the problem of model training and locate the problem module or the line where the code is located. The feature importance FI is calculated as follows:

[0020]

[0021] Where n is the number of samples, X ij is the jth feature of the ith sample.

[0022] Furthermore, the optimization module optimizes the system parameters, and the specific steps are as follows:

[0023] Step 1: Initialize the population: randomly generate the initial population, each individual represents a set of system parameters;

[0024] Step 2: Fitness evaluation: Use the fitness function to evaluate the fitness of each individual;

[0025] Step 3: Select parent individuals based on fitness;

[0026] Step 4: Generate new individuals through crossover operation;

[0027] Step 5: Perform mutation operation on the new individual;

[0028] Step 6: Replace old individuals with new ones to form a new generation of population;

[0029] Step 7: Repeat the above steps until the stop condition is met.

[0030] Furthermore, the optimization module includes an evaluation of the data. The optimization module introduces a fitness function to evaluate the quality of each individual. The formula is as follows:

[0031] F(x)=-(αRT(x)+βTP(x)+γRU(x)+δER(x)+∈C(x)), where x is a system parameter, and δ and ∈ are weight parameters used to balance the impact of different complexity metrics.

[0032] Furthermore, the fitness function is specifically analyzed as follows:

[0033] F(x)=-(αRT(x)+βTP(x)+γRU(x)+δER(x)+∈C(x))

[0034] RT(x)≤RT(x) max ;

[0035] TP(x)≥TP(x) min ;

[0036] RU(x)≤RU(x) max ;

[0037] ER(x)≤ER(x) max ;

[0038] C(x)≤C(x)max .

[0039] Where X is the parameter space, RT(x) max TP(x) min RU(x) max RT(x) max 、C(x) max are the performance and complexity constraints of the system. α, β, γ, δ, ∈ are the weights of each evaluation indicator, indicating the importance of each indicator in the total fitness calculation. By adjusting these weights, the relative importance of different indicators can be balanced, thereby optimizing the data development system.

[0040] Furthermore, the result output module outputs the optimal solution obtained by the optimization algorithm module to the product developers, including the optimal product design parameters and production data.

[0041] Furthermore, the feedback module generates a feedback report based on the debugging and optimization results and guides further development and optimization.

[0042] Furthermore, an electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements an intelligent debugging and optimization system for product development as described in any one of claims 1 to 10 when executing the computer program.

[0043] Further, an intelligent debugging and optimization system for product development includes the following steps:

[0044] Step 1: Use the data acquisition module to collect various data in the product development process in real time;

[0045] Step 2: The data analysis and processing module performs preprocessing and feature extraction on the collected data;

[0046] Step 3: Debug and test the data of the acquisition analysis and processing modules;

[0047] Step 4: Optimizing the algorithm module, optimizing the errors or imperfections in the data in the debugging detection module. The optimization algorithm module is the core part of the present invention. It uses an optimization algorithm based on a complex formula to optimize the product development process. The optimization algorithm includes two main parts: the objective function and the constraint conditions.

[0048] Step 5: The optimized data is output through the result output module;

[0049] Step 6: Debug and optimize the results according to the output module results, generate a feedback report and guide further development and optimization.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] In the present invention, firstly, the data acquisition module is responsible for collecting various types of data in the product development process in real time; the data analysis and processing module pre-processes and extracts features of the collected data; the optimization algorithm module is the core part of the present invention, which adopts an optimization algorithm based on a complex formula to optimize the product development process, and the optimization algorithm includes two main parts: the objective function and the constraint conditions; the result output module outputs the optimal solution obtained by the optimization algorithm module to the product developers, including the optimal product design parameters and production data, etc. The present invention realizes intelligent debugging and optimization of the product development process by integrating advanced optimization algorithms and complex mathematical models. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0053] Figure 1 The present invention is a schematic diagram of the steps of an intelligent debugging and optimization system for product development. DETAILED DESCRIPTION

[0054] 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.

[0055] like Figure 1 As shown, an intelligent debugging and optimization system for product development includes the following steps:

[0056] It includes a data acquisition module, a data analysis and processing module, a debugging and detection module, an optimization algorithm module, a result output module and a feedback module. The data acquisition module is responsible for collecting various types of data in the product development process in real time; the data analysis and processing module preprocesses and extracts features of the collected data; the debugging and detection module debugs and detects the data in the product development process. The optimization algorithm module is the core part of the present invention. It adopts an optimization algorithm based on a complex formula to optimize the product development process. The optimization algorithm includes two main parts: an objective function and constraint conditions; the result output module outputs the optimal solution obtained by the optimization algorithm module to the product developers, including the optimal product design parameters and production data, etc. The feedback module generates a feedback report based on the debugging and optimization results and guides further development and optimization.

[0057] The data collection module includes a storage device that collects various data during the development process, such as system logs, performance indicators, error reports, etc., and represents the original data set as X = {x1, x2..., x i}, where x i Represents the i-th data, where i is a positive integer.

[0058] The data analysis and processing module includes a processor. The main task of the data processing module is to standardize, normalize or perform other necessary preprocessing operations on these features. Feature extraction can use principal component analysis (PCA) or other dimensionality reduction techniques to reduce the number of features and improve computational efficiency.

[0059] The specific analysis steps of data standardization and feature dimension reduction in the data analysis and processing module are as follows:

[0060] Step 1: Use the processor to process each feature x of the original data set i To standardize:

[0061]

[0062] where μ i For x i The mean value, σ i For x i The standard deviation of .

[0063] Step 2: (PCA) performs dimensionality reduction on the standardized features:

[0064] X'=WX

[0065] Where W is the projection matrix of PCA, X is the standardized feature matrix, and X' is the feature matrix after dimensionality reduction. The data after dimensionality reduction is transferred to the model training module for training.

[0066] The debugging and detection module includes a machine learning model to monitor the system in real time and detect possible problems. The debugging and detection module uses the reduced dimension training data (X', y), where y is the target variable (such as error type, performance indicator, etc.). The prediction result of the machine learning model f is:

[0067] Where f is the random forest model, Θ is the model parameter, For the prediction results.

[0068] The model training module includes feature importance analysis, which is used to locate model training problems and locate the problem module or line of code. The feature importance FI is calculated as follows:

[0069]

[0070] Where n is the number of samples, X ij is the jth feature of the ith sample.

[0071] The optimization module optimizes the system parameters. The specific steps are as follows:

[0072] Step 1: Initialize the population: randomly generate the initial population, each individual represents a set of system parameters;

[0073] Step 2: Fitness evaluation: Use the fitness function to evaluate the fitness of each individual;

[0074] Step 3: Select parent individuals based on fitness;

[0075] Step 4: Generate new individuals through crossover operation;

[0076] Step 5: Perform mutation operation on the new individual;

[0077] Step 6: Replace old individuals with new ones to form a new generation of population;

[0078] Step 7: Repeat the above steps until the stop condition is met.

[0079] The optimization module includes the evaluation of data. The optimization module introduces a fitness function to evaluate the quality of each individual. The formula is as follows:

[0080] F(x)=-(αRT(x)+βTP(x)+γRU(x)+δER(x)+∈C(x)), where x is a system parameter, and δ and ∈ are weight parameters used to balance the impact of different complexity metrics.

[0081] The specific analysis of the fitness function is as follows:

[0082] F(x)=-(αRT(x)+βTP(x)+γRU(x)+δER(x)+∈C(x))

[0083] RT(x)≤RT(x) max ;

[0084] TP(x)≥TP(x) min ;

[0085] RU(x)≤RU(x) max ;

[0086] ER(x)≤ER(x) max ;

[0087] C(x)≤C(x) max .

[0088] Where X is the parameter space, RT(x)max TP(x) min RU(x) max RT(x) max 、C(x) max are the performance and complexity constraints of the system. α, β, γ, δ, ∈ are the weights of each evaluation indicator, indicating the importance of each indicator in the total fitness calculation. By adjusting these weights, the relative importance of different indicators can be balanced, thereby optimizing the data development system.

[0089] The result output module outputs the optimal solution obtained by the optimization algorithm module to product developers, including the optimal product design parameters and production data.

[0090] Feedback module,The feedback module generates feedback reports and guides further development and optimization based on the debugging and optimization results.

[0091] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements an intelligent debugging and optimization system for product development as described in any one of claims 1 to 10 when executing the computer program.

[0092] An intelligent debugging and optimization system for product development, characterized in that it includes the following steps:

[0093] Step 1: Use the data acquisition module to collect various data in the product development process in real time;

[0094] Step 2: The data analysis and processing module performs preprocessing and feature extraction on the collected data;

[0095] Step 3: Debug and test the data of the acquisition analysis and processing modules;

[0096] Step 4: Optimizing the algorithm module, optimizing the errors or imperfections in the data in the debugging detection module. The optimization algorithm module is the core part of the present invention. It uses an optimization algorithm based on a complex formula to optimize the product development process. The optimization algorithm includes two main parts: the objective function and the constraint conditions.

[0097] Step 5: The optimized data is output through the result output module;

[0098] Step 6: Debug and optimize the results according to the output module results, generate a feedback report and guide further development and optimization.

[0099] The working principle of the present invention is as follows: first, the data acquisition module is used to collect various data in the product development process in real time, and the original data set is represented as X = {x1, x2..., x i}, where x i represents the i-th data, i is a positive integer. The data acquisition module is responsible for collecting various data in the product development process in real time; then the data analysis and processing module is used to pre-process and extract features from the collected data, and the processor is used to process each feature x of the original data set. i To standardize:

[0100]

[0101] After that, the dimension reduction is performed, and (PCA) is used to reduce the dimension of the standardized features:

[0102] X'=WX

[0103] Then, the debugging and detection module is used to debug and detect the data of the acquisition and analysis module; the system performs real-time monitoring and detects possible problems. The debugging and detection module uses the reduced-dimensional training data (X', y), where y is the target variable (such as error type, performance index, etc.), and the prediction result of the machine learning model f is: At the same time, the calculation of feature importance FI is as follows:

[0104]

[0105] The problem modules or lines of code in the system data are accurately located and detected, and then the optimization algorithm module is used to optimize the errors or imperfections in the data in the debugging detection module. The optimization algorithm module is the core part of the present invention. It adopts an optimization algorithm based on a complex formula to optimize the product development process. The optimization algorithm includes two main parts: objective function and constraint conditions, that is, F(x)=-(αRT(x)+βTP(x)+γRU(x)+δER(x)+∈C(x)), where x is a system parameter, and δ and ∈ are weight parameters for balancing the influence of different complexity metrics on data optimization. The optimized data is output through a result output module. According to the debugging and optimization results of the result output module, a feedback report is generated to guide further development and optimization.

[0106] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent debugging and optimization system for product development, characterized in that: It includes a data acquisition module, a data analysis and processing module, a debugging and detection module, an optimization algorithm module, a result output module and a feedback module. The data acquisition module is responsible for collecting various types of data in the product development process in real time; the data analysis and processing module preprocesses and extracts features of the collected data; the debugging and detection module debugs and detects the data in the product development process. The optimization algorithm module is the core part of the present invention. It adopts an optimization algorithm based on a complex formula to optimize the product development process. The optimization algorithm includes two main parts: an objective function and constraint conditions; the result output module outputs the optimal solution obtained by the optimization algorithm module to the product developers, including the optimal product design parameters and production data, etc. The feedback module generates a feedback report based on the debugging and optimization results and guides further development and optimization.

2. The intelligent debugging and optimization system for product development according to claim 1, characterized in that: The data collection module includes a storage device, which collects various data during the development process, such as system logs, performance indicators, error reports, etc., and represents the original data set as X = {x1, x2..., x i }, where x i Represents the i-th data, where i is a positive integer.

3. The intelligent debugging and optimization system for product development according to claim 2, characterized in that: The data analysis and processing module includes a processor. The main task of the data processing module is to standardize, normalize or perform other necessary preprocessing operations on these features. Feature extraction can use principal component analysis (PCA) or other dimensionality reduction techniques to reduce the number of features and improve computing efficiency.

4. The intelligent debugging and optimization system for product development according to claim 3, characterized in that: The specific analysis steps of data standardization and feature dimension reduction in the data analysis and processing module are as follows: Step 1: Use the processor to process each feature x of the original data set i To standardize: where μ i For x i The mean value, σ i For x i The standard deviation of . Step 2: (PCA) performs dimensionality reduction on the standardized features: X'=WX Where W is the projection matrix of PCA, X is the standardized feature matrix, and X' is the feature matrix after dimensionality reduction. The data after dimensionality reduction is transferred to the model training module for training.

5. The intelligent debugging and optimization system for product development according to claim 4, characterized in that: The debugging detection module includes a machine learning model to monitor the system in real time and detect possible problems. The debugging detection module uses the reduced-dimensional training data (X', y), where y is the target variable (such as error type, performance index, etc.), and the prediction result of the machine learning model f is: Where f is the random forest model, Θ is the model parameter, For the prediction results.

6. The intelligent debugging and optimization system for product development according to claim 5, characterized in that: The model training module includes feature importance analysis, which is used to locate the problem of model training and locate the problem module or the line where the code is located. The feature importance FI is calculated as follows: Where n is the number of samples, X ij is the jth feature of the ith sample. The optimization module optimizes the system parameters, and the specific steps are as follows: Step 1: Initialize the population: randomly generate the initial population, each individual represents a set of system parameters; Step 2: Fitness evaluation: Use the fitness function to evaluate the fitness of each individual; Step 3: Select parent individuals based on fitness; Step 4: Generate new individuals through crossover operation; Step 5: Perform mutation operation on the new individual; Step 6: Replace old individuals with new ones to form a new generation of population; Step 7: Repeat the above steps until the stop condition is met.

7. The intelligent debugging and optimization system for product development according to claim 6, characterized in that: The optimization module includes an evaluation of the data. The optimization module introduces a fitness function to evaluate the quality of each individual. The formula is as follows: F(x)=-(αRT(x)+βTP(x)+γRU(x)+δER(x)+∈C(x)): where x is a system parameter, δ and ∈ are weight parameters used to balance the impact of different complexity measures.

8. The intelligent debugging and optimization system for product development according to claim 7, characterized in that: The fitness function is specifically analyzed as follows: F(x)=-(αRT(x)+βTP(x)+γRU(x)+δER(x)+∈C(x)) RT(x)≤RT(x) max ; TP(x)≥TP(x) min ; RU(x)≤RU(x) max ; IS(x)≤IS(x) max ; C(x)≤C(x) max 。 Where X is the parameter space, RT(x) max TP(x) min RU(x) max RT(x) max 、C(x) max are the performance and complexity constraints of the system. α, β, γ, δ, ∈ are the weights of each evaluation indicator, indicating the importance of each indicator in the total fitness calculation. By adjusting these weights, the relative importance of different indicators can be balanced, thereby optimizing the data development system.

9. The intelligent debugging and optimization system for product development according to claim 8, characterized in that: The result output module outputs the optimal solution obtained by the optimization algorithm module to the product developers, including the optimal product design parameters and production data.

10. The intelligent debugging and optimization system for product development according to claim 9, characterized in that: The feedback module generates a feedback report and guides further development and optimization based on the debugging and optimization results.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the intelligent debugging and optimization system for product development described in any one of claims 1 to 10 is implemented.

12. An intelligent debugging and optimization system method for product development, using the intelligent debugging and optimization system for product development described in any one of claims 1 to 11, characterized in that: The following steps are involved: Step 1: Use the data acquisition module to collect various data in the product development process in real time; Step 2: The data analysis and processing module performs preprocessing and feature extraction on the collected data; Step 3: Debug and test the data of the acquisition analysis and processing modules; Step 4: Optimizing the algorithm module, optimizing the errors or imperfections in the data in the debugging detection module. The optimization algorithm module is the core part of the present invention. It uses an optimization algorithm based on a complex formula to optimize the product development process. The optimization algorithm includes two main parts: the objective function and the constraint conditions. Step 5: The optimized data is output through the result output module; Step 6: Debug and optimize the results according to the output module results, generate a feedback report and guide further development and optimization.

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