MOM software quality optimization method and system based on dynamic fuzzy scenario

Abstract

The invention discloses an MOM software quality optimization method and system based on dynamic fuzzy scenario, and the method comprises the steps: collecting heterogeneous data from multiple dimensions of operation monitoring, user interaction and business process tracking, extracting a core feature subset through information measurement after cleaning normalization, and defining an operation scene in combination with the characteristics of system load and the like and core feature expression. A double-layer weight model is constructed and dynamically adjusted, fuzzy mapping of features and preset evaluation levels is established for all scenes to form a judgment matrix, the quality level is determined according to the maximum membership degree after weight and matrix synthesis, software configuration, resource allocation or workflow adjustment instructions are generated based on evaluation results to be issued and executed, and closed-loop optimization is achieved. According to the method, accurate quantitative evaluation and automatic optimization are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial software and artificial intelligence, and particularly relates to a MOM software quality optimization method and system based on dynamic fuzzy scene. BACKGROUND

[0002] As the core platform of digital transformation in manufacturing industry, the software quality of manufacturing operation management system (MOM) is directly related to the reliability of production planning, the accuracy of resource scheduling and the qualification rate of final products. In complex industrial fields such as aerospace and high-end equipment manufacturing, this requirement is particularly stringent. The production activities in these fields present high dynamicity and scene characteristics: for example, in the same set of MOM software, different scenes such as "super-precision part machining", "multi-variety small-batch agile production" and "equipment fault emergency handling" may be alternately run, and the emphasis on quality attributes such as performance, reliability and functional completeness of the software is different in each scene.

[0003] Traditional software quality evaluation methods mostly rely on static index system and fixed weight model, such as the quality model based on ISO / IEC25010 standard. Such methods cannot adapt to the dynamic changes of runtime context, and their evaluation results are often quickly invalidated when the scene is switched, lacking pertinence. Although fuzzy comprehensive evaluation method is introduced to deal with subjectivity and uncertainty in quality evaluation, the membership degree function is used to convert qualitative description into quantitative calculation, but the weight vector is usually subjectively assigned by experts or obtained by offline historical data training. Once determined, this method is fixed and cannot be self-optimized and adjusted according to real-time running data, user feedback and sudden scene changes, resulting in serious lag in evaluation results.

[0004] In the prior art, although some researches have tried to introduce dynamic factors into the evaluation system, they mostly stay at the theoretical level and lack a complete, engineering-based and deeply integrated technical solution with MOM system. Especially under the background of the rise of low-code development platform, how to provide real-time and scene-based quality assurance for the rapid construction and iteration of MOM application, and to realize automatic optimization based on evaluation results, has become a key bottleneck restricting its in-depth application. SUMMARY

[0005] The main purpose of the present application is to overcome the above-mentioned defects in the prior art, and to provide a MOM software quality optimization method and system based on dynamic fuzzy scene, which realizes dynamic weight optimization and fuzzy comprehensive evaluation, and generates optimization strategies based on evaluation results, outputs accurate quantitative results and optimization instructions.

[0006] The present application adopts the following technical solutions:

[0007] A MOM software quality optimization method based on dynamic fuzzy scene, comprising:

[0008] Collecting software quality multi-source heterogeneous data from multiple dimensions of MOM system operation monitoring, user interaction records and business process tracking, obtaining a standardized dataset through cleaning and normalization processing, and then extracting a core feature subset strongly related to software quality from the standardized dataset based on an information measurement method;

[0009] Defining and dividing software running scenes according to system load state, user behavior pattern, business workflow characteristics and different performance modes of the core feature subset, constructing a double-layer weight model containing basic weight and dynamic weight for each software running scene, and completing dynamic weight adjustment combined with historical experience and real-time feedback;

[0010] For each software running scene, a fuzzy mapping relationship between quality features in the core feature subset and preset evaluation grades is established to form a fuzzy judgment matrix, a synthetic operation is performed based on the weight vector after dynamic adjustment and the corresponding fuzzy judgment matrix to obtain a scene-based quality comprehensive evaluation vector, and the final quality grade is determined according to the maximum membership degree principle;

[0011] Based on the quality comprehensive evaluation vector, optimization instructions for software configuration, resource allocation or workflow adjustment are generated and issued to the MOM system for execution to realize software quality closed-loop optimization.

[0012] The cleaning and normalization processing obtains a standardized dataset, which specifically includes the following:

[0013] The multi-source heterogeneous data is cleaned, and then the min-max normalization method is used to linearly map the cleaned data to the [0, 1] interval, and the calculation formula is:

[0014] ;

[0015] Wherein, is the normalized data value, is the cleaned data, and are the minimum and maximum values of the data set, respectively.

[0016] Interaction information is used as a feature correlation measurement index, and based on the standardized dataset, the most relevant features to software quality are selected from all features according to a predetermined selection strategy to form a core feature subset, and the specific expression is as follows:

[0017] ;

[0018] Wherein, is a single feature variable to be screened in the standardized dataset, It refers to the specific values ​​that the feature variable X takes. It is a quality level variable. Representing characteristic variables With quality target variables The amount of information exchanged between them Representing the eigenvector Values And the quality grade variable takes the value of The joint probability distribution, and They represent the characteristic variables respectively. Pick Marginal probability distribution and quality grade variables Pick The marginal probability distribution.

[0019] Based on a core feature subset and the quality levels of expert annotations, a structured training sample library is constructed. A random forest regression algorithm is used, and a feature importance scoring mechanism is employed to calculate the first [value] in the core feature subset. The quality characteristic in the first Initial base weights for each scenario ;

[0020] For each quality feature in the core feature subset, based on the current scenario Based on the real-time evaluation error and scene change signals, the basic weights are adjusted through a weight adaptive update mechanism, the update formula of which is as follows:

[0021] ;

[0022] in, Indicates the adjusted number A quality characteristic in the scene Dynamic weights under; For adaptive learning rate parameters, This represents the evaluation error in the current scenario. This refers to the weight adjustment amount calculated based on real-time scene switching signals and a business priority rule base.

[0023] The basic weights and dynamic weights are combined to obtain the scene. The combined weight of each quality feature in the core feature subset :

[0024] ;

[0025] in, Indicates the total number of quality characteristics, ensuring .

[0026] Adaptive learning rate parameter An annealing strategy is adopted, and the value gradually decreases as the number of evaluation iterations increases. The update rule is as follows:

[0027] ;

[0028] in, Indicates the first Learning rate during iteration The initial learning rate, The attenuation coefficient is... This represents the current iteration number. .

[0029] For the scenario , will be comprehensive weight With fuzzy relation matrix Perform a synthesis operation to obtain the comprehensive evaluation vector for this scenario. ;

[0030] ;

[0031] in, This represents the weighted average composition operator;

[0032] Based on the comprehensive evaluation vector The final quality level is determined using the principle of maximum membership, i.e., a comprehensive evaluation vector is selected. The evaluation level corresponding to the maximum value is used as the software quality assessment result in this scenario.

[0033] Based on quality-driven dynamic optimization decision-making, at least one of the following optimization instructions is generated and executed according to the comprehensive evaluation vector:

[0034] Resource scheduling command: Dynamically adjust the CPU quota or memory allocation of the container where the MOM software is located;

[0035] Business process refactoring instruction: Rearrange the execution sequence of pending work orders according to task priority;

[0036] Parameter configuration commands: Adjust the number of thread pools or database connection parameters related to software performance.

[0037] A MOM software quality optimization system based on dynamic fuzzy scene mapping, including

[0038] The data acquisition, preprocessing, and feature extraction module is used to collect multi-source heterogeneous data on software quality from multiple dimensions, including MOM system operation monitoring, user interaction records, and business process tracking. The module cleans and normalizes the collected heterogeneous data to obtain a standardized dataset, and extracts a subset of core features that are strongly correlated with software quality from the standardized dataset based on information measurement methods.

[0039] The scenario definition and weight adjustment module is used to define and divide software operation scenarios based on system load status, user behavior patterns, business workflow characteristics, and different performance patterns of core feature subsets. For each software operation scenario, a two-layer weight model containing basic weights and dynamic weights is constructed, and the weights are dynamically adjusted by combining historical experience and real-time feedback.

[0040] The fuzzy comprehensive evaluation module is used to establish a fuzzy mapping relationship between quality features in the core feature subset and preset evaluation levels for each software operation scenario to form a fuzzy judgment matrix. Based on the dynamically adjusted weight vector and the corresponding fuzzy judgment matrix, a synthesis operation is performed to obtain a scenario-based comprehensive quality evaluation vector. Finally, the final quality level is determined according to the principle of maximum membership.

[0041] The closed-loop optimization execution module is used to generate optimization instructions for software configuration, resource allocation or workflow adjustment based on the comprehensive quality evaluation vector, and send the optimization instructions to the MOM system for execution to achieve closed-loop optimization of software quality.

[0042] Employing a microservices architecture and deploying it on a Kubernetes cluster using containerization technology, including:

[0043] The data acquisition Pod is used to implement the multi-source heterogeneous data acquisition function in the data acquisition preprocessing and feature extraction module.

[0044] The Feature Processing Pod is used to implement data cleaning, normalization, and core feature subset extraction functions in the data acquisition, preprocessing, and feature extraction modules.

[0045] The Scene Recognition Pod is used to implement the software operation scene definition and classification functions in the Scene Definition and Weight Adjustment module;

[0046] The Weight Optimization Pod is used to implement the two-layer weight model construction and dynamic adjustment function in the scenario definition and weight adjustment module.

[0047] The Comprehensive Evaluation Pod is used to implement the functions of fuzzy judgment matrix construction, quality comprehensive evaluation vector calculation, and final quality level determination in the fuzzy comprehensive evaluation module.

[0048] The strategy execution Pod is used to generate optimization instructions in the closed-loop optimization execution module and send them to the MOM system for execution.

[0049] Each Pod communicates with the others through a service mesh to achieve distributed elastic scaling.

[0050] The closed-loop optimization execution module includes a business optimization strategy library, which maps the final quality level and comprehensive quality evaluation vector into three types of executable instructions:

[0051] Resource scheduling commands are used to dynamically adjust the CPU and memory resource limits of MOM software Pods via the Kubernetes API;

[0052] Process refactoring instructions are used to rearrange the execution priority and path of production work orders through the workflow engine API of the MOM system;

[0053] Parameter configuration commands are used to dynamically adjust key runtime parameters such as database connection pools and thread pools through the configuration management center API.

[0054] As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. This invention, through a dynamic weight adjustment mechanism based on scenario definition and a fuzzy comprehensive evaluation method, can specifically identify quality bottlenecks in different software operation scenarios, providing a reliable basis for subsequent decisions such as resource optimization and process refactoring that is adapted to the scenario, and improving the accuracy of quality problem location.

[0056] 2. This invention relies on real-time data acquisition and preprocessing, dynamic weight adaptive updating, and real-time evaluation error feedback mechanism to enable the quality evaluation model to dynamically adjust according to the system's operating status, effectively avoiding deviations in evaluation results caused by data lag.

[0057] 3. Based on the evaluation results, this invention automatically generates executable optimization instructions such as resource scheduling, process refactoring, and parameter configuration, and sends them to the MOM system for execution, forming a closed-loop mechanism of "data collection-evaluation-optimization-execution" to achieve automated and continuous optimization of software quality.

[0058] 4. This invention generates a quantifiable quality comprehensive evaluation vector and a clear quality level through fuzzy comprehensive evaluation, providing a quantifiable reference for system optimization decisions and improving the objectivity and operability of the decisions.

[0059] 5. This invention adopts a microservice architecture combined with containerization technology and is deployed on a Kubernetes cluster. Each functional module is deployed in a distributed manner and communicates with the service mesh through Pods. It supports dynamic elastic scaling up and down according to system load and adapts to the computing power requirements of different business scenarios. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the overall process of the method of the present invention.

[0061] Figure 2 This is a schematic diagram of the dynamic weight adjustment mechanism in an embodiment of the present invention.

[0062] Figure 3 This is a schematic diagram of the microservice architecture of the system of the present invention.

[0063] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Detailed Implementation

[0064] The present invention will be further described below through specific embodiments.

[0065] See Figure 1 A method for optimizing the quality of MOM software based on dynamic fuzzy scene mapping, comprising:

[0066] S1 multi-source data acquisition, preprocessing, and key feature extraction: collect multi-source heterogeneous data on software quality from multiple dimensions, including MOM system operation monitoring, user interaction records, and business process tracking. After cleaning and normalization, a standardized dataset is obtained. Then, based on information measurement methods, a subset of core features strongly correlated with software quality is extracted from the standardized dataset.

[0067] In this step, CPU load, memory usage, and network I / O metrics are collected using a system performance monitor, user interaction log analyzer, and business workflow tracer. The multi-source heterogeneous data is then cleaned and normalized to obtain a standardized dataset, specifically including the following:

[0068] Multi-source heterogeneous data is cleaned, and then the min-max normalization method is used to linearly map the cleaned data to the [0,1] interval. The calculation formula is as follows:

[0069] ;

[0070] in, These are the normalized data values. For the cleaned data, and These are the minimum and maximum values ​​of the data set, respectively.

[0071] In this step, the amount of interaction information is used as a measure of feature relevance. Based on a standardized dataset, and according to a predetermined selection strategy, the features most relevant to software quality are selected from all features to form a core feature subset. The specific expression is as follows:

[0072] ;

[0073] in, It is a single feature variable to be selected from a standardized dataset. It refers to the specific values ​​that the feature variable X takes. It is a quality level variable. Representing characteristic variables With quality target variables The amount of information exchanged between them This indicates that the eigenvector X takes the value of And the quality grade variable takes the value of The joint probability distribution, and These represent the marginal probability distributions of feature variable X taking the value x and quality grade variable Y taking the value y, respectively.

[0074] S2 defines and classifies operating scenarios and determines adaptive weights. Based on the different performance patterns of system load status, user behavior patterns, business workflow characteristics, and core feature subsets, software operating scenarios are defined and divided. For each software operating scenario, a two-layer weight model containing basic weights and dynamic weights is constructed, and the weights are dynamically adjusted by combining historical experience and real-time feedback.

[0075] S21 constructs a structured training sample library based on a core feature subset and the quality level of expert annotations; it employs a random forest regression algorithm and, through a feature importance scoring mechanism, calculates the first [feature] in the core feature subset. The quality characteristic in the first Initial base weights for each scenario ;

[0076] S22 targets each quality feature in the core feature subset, based on the current scenario. Based on the real-time evaluation error and scene change signals, the basic weights are adjusted through a weight adaptive update mechanism, the update formula of which is as follows:

[0077] ;

[0078] in, Indicates the adjusted number A quality characteristic in the scene Dynamic weights under; Indicates the number before adjustment A quality characteristic in the scene The initial weight or the weight value of the previous period is the basis for weight updates; For adaptive learning rate parameters, This represents the evaluation error in the current scenario. This refers to the weight adjustment amount calculated based on real-time scene switching signals and a business priority rule base.

[0079] S23 combines the basic weights and dynamic weights to obtain the scene. The combined weight of each quality feature in the core feature subset :

[0080] ;

[0081] in, Indicates the total number of quality characteristics, ensuring .

[0082] In this embodiment, the adaptive learning rate parameter An annealing strategy is adopted, and the value gradually decreases as the number of evaluation iterations increases. The update rule is as follows:

[0083] ;

[0084] in, Indicates the first Learning rate during iteration The initial learning rate, The attenuation coefficient is... This represents the current iteration number. .

[0085] S3 multi-scenario fuzzy comprehensive evaluation establishes a fuzzy mapping relationship between quality features in the core feature subset and preset evaluation levels for each software operation scenario to form a fuzzy judgment matrix. Based on the dynamically adjusted weight vector and the corresponding fuzzy judgment matrix, a synthesis operation is performed to obtain a scenario-based quality comprehensive evaluation vector, and the final quality level is determined according to the maximum membership principle.

[0086] The preset evaluation level set V = {Excellent, Good, Average, Poor} is a set of qualitative conclusions jointly set by domain experts before system deployment based on business fault tolerance standards and performance expectations. The quality features used to construct the fuzzy judgment matrix are the subset of core features determined in step S2.

[0087] For the scenario The weighted average synthesis operator is used to combine the weights. With fuzzy relation matrix Perform a synthesis operation to obtain the comprehensive evaluation vector for this scenario. ;

[0088] ;

[0089] in, This represents the weighted average composition operator;

[0090] Based on the comprehensive evaluation vector The final quality level is determined using the principle of maximum membership, i.e., a comprehensive evaluation vector is selected. The evaluation level corresponding to the maximum value is used as the software quality assessment result in this scenario.

[0091] S4 closed-loop optimization execution generates optimization instructions for software configuration, resource allocation, or workflow adjustments based on the final quality level and comprehensive quality evaluation vector, and sends them to the MOM system for execution to achieve closed-loop optimization of software quality.

[0092] Based on quality-driven dynamic optimization decision-making, at least one of the following optimization instructions is generated and executed according to the final quality level and comprehensive evaluation vector:

[0093] Resource scheduling command: Dynamically adjust the CPU quota or memory allocation of the container where the MOM software is located;

[0094] Business process refactoring instruction: Rearrange the execution sequence of pending work orders according to task priority;

[0095] Parameter configuration commands: Adjust the number of thread pools or database connection parameters related to software performance.

[0096] This invention is based on a dynamic fuzzy scenario-based MOM software quality optimization method. It strengthens the data foundation by collecting and preprocessing multi-source data and extracting core features. Combined with the definition of software operation scenarios, it implements dynamic weight adjustment and fuzzy comprehensive evaluation, which can accurately identify quality bottlenecks in different scenarios. It relies on real-time data support to realize dynamic updates of evaluation to avoid lag problems, and can automatically generate optimization instructions based on evaluation results to form a closed-loop optimization. At the same time, it outputs quantitative evaluation results to support decision-making.

[0097] Based on this, embodiments of the present invention also propose a MOM software quality optimization system based on dynamic fuzzy scene mapping, including...

[0098] The data acquisition, preprocessing, and feature extraction module is used to collect multi-source heterogeneous data on software quality from multiple dimensions, including MOM system operation monitoring, user interaction records, and business process tracking. The module cleans and normalizes the collected heterogeneous data to obtain a standardized dataset, and extracts a subset of core features that are strongly correlated with software quality from the standardized dataset based on information measurement methods.

[0099] The scenario definition and weight adjustment module is used to define and divide software operation scenarios based on system load status, user behavior patterns, business workflow characteristics, and different performance patterns of core feature subsets. For each software operation scenario, a two-layer weight model containing basic weights and dynamic weights is constructed, and the weights are dynamically adjusted by combining historical experience and real-time feedback.

[0100] The fuzzy comprehensive evaluation module is used to establish a fuzzy mapping relationship between quality features in the core feature subset and preset evaluation levels for each software operation scenario to form a fuzzy judgment matrix. Based on the dynamically adjusted weight vector and the corresponding fuzzy judgment matrix, a synthesis operation is performed to obtain a scenario-based comprehensive quality evaluation vector. Finally, the final quality level is determined according to the principle of maximum membership.

[0101] The closed-loop optimization execution module is used to generate optimization instructions for software configuration, resource allocation or workflow adjustment based on the final quality level and comprehensive quality evaluation vector, and send the optimization instructions to the MOM system for execution to achieve closed-loop optimization of software quality.

[0102] See Figure 3 In this embodiment, the system adopts a microservice architecture and is deployed on a Kubernetes cluster using containerization technology, including:

[0103] The data acquisition Pod is used to implement the multi-source heterogeneous data acquisition function in the data acquisition preprocessing and feature extraction module.

[0104] The Feature Processing Pod is used to implement data cleaning, normalization, and core feature subset extraction functions in the data acquisition, preprocessing, and feature extraction modules.

[0105] The Scene Recognition Pod is used to implement the software operation scene definition and classification functions in the Scene Definition and Weight Adjustment module;

[0106] The Weight Optimization Pod is used to implement the two-layer weight model construction and dynamic adjustment function in the scenario definition and weight adjustment module.

[0107] The Comprehensive Evaluation Pod is used to implement the functions of fuzzy judgment matrix construction, quality comprehensive evaluation vector calculation, and final quality level determination in the fuzzy comprehensive evaluation module.

[0108] The strategy execution Pod is used to generate optimization instructions in the closed-loop optimization execution module and send them to the MOM system for execution.

[0109] Each Pod communicates with the others through a service mesh to achieve distributed elastic scaling.

[0110] The closed-loop optimization execution module includes a business optimization strategy library, which maps the final quality level and comprehensive quality evaluation vector into three types of executable instructions:

[0111] Resource scheduling commands are used to dynamically adjust the CPU and memory resource limits of MOM software Pods via the Kubernetes API;

[0112] Process refactoring instructions are used to rearrange the execution priority and path of production work orders through the workflow engine API of the MOM system;

[0113] Parameter configuration commands are used to dynamically adjust key operating parameters such as database connection pools and thread pools through the configuration management center API.

[0114] Example: MOM software quality optimization in the scenario of aero-engine blade manufacturing

[0115] This embodiment applies the method of the present invention to perform dynamic quality assessment and optimization of MOM software in the scenarios of ultra-precision aerodynamic forming and machining of aero-engine blades and flexible multi-configuration agile machining. The overall process includes the following steps:

[0116] S1 Multi-source Data Acquisition Preprocessing and Key Feature Extraction

[0117] Operational data for a software module used in engine blade machining was collected from the MOM (Motion Management) system of an aerospace manufacturing company over seven consecutive working days. Data sources included:

[0118] System performance monitor: Collects raw metrics such as average CPU load, memory usage, and network I / O throughput.

[0119] User interaction log analyzer: Records raw logs such as user operation response time, operation error rate, and function call frequency.

[0120] Business workflow tracker: Collects raw business process data such as work order processing time, task queue depth, and device status switching delay.

[0121] First, the aforementioned multi-source heterogeneous raw data is cleaned to remove outliers caused by network interruptions and sensor malfunctions. Then, the min-max normalization method is used to map the data to the [0,1] interval. For example, the original CPU load rate is 0.72, with historical minimum and maximum values ​​of 0.10 and 0.95 respectively. After normalization, it becomes:

[0122]

[0123] We extracted the core features most relevant to the quality of aero-engine blade manufacturing software from massive amounts of data.

[0124] First, based on existing software quality models and the experience of domain experts, a basic quality feature set (20 features in total) covering six dimensions (functional integrity, performance efficiency, compatibility, usability, reliability, and portability) is established, as shown in Table 1:

[0125] Table 1 Software Quality Characteristics

[0126]

[0127] Subsequently, the feature selection training process is initiated. This process relies on a historical data sample library, which is constructed by organizing data collected and preprocessed within the past 3 months according to a basic quality feature set, and labeling each data sample with a quality level (excellent, good, satisfactory, unsatisfactory).

[0128] Based on this "feature-label" sample library, mutual information was used as a metric to select the 8 features most relevant to expert quality judgment from 20 basic features, forming a core feature subset. The final 8 core features with the highest mutual information values ​​are shown in Table 2. This 8-dimensional feature vector F={f1,f2,...,f8} serves as the input for subsequent scenario-based evaluation.

[0129] Table 2 Core Characteristics of Software Quality in the Precision Machining Scenarios of Aero-engine Blades

[0130]

[0131] S2 Operating Scenario Definition, Classification, and Adaptive Weight Determination

[0132] Based on the processing requirements of aero-engine blades and the operating characteristics of MOM software, the following two typical scenarios are defined:

[0133] Ultra-precision pneumatic forming processing scenarios This scenario relates to the core requirement of optimizing engine aerodynamic performance through precise blade profile design, which places extremely high demands on the software's control precision and long-term operational stability. Any slight vibration or calculation error could lead to product scrapping.

[0134] Flexible multi-configuration agile manufacturing scenarios This scenario addresses the various blade configurations resulting from differences in engine models and performance requirements. It demands that the software be able to quickly switch machining tasks, efficiently calculate new toolpaths, and adapt to different materials, placing higher demands on the software's response speed and task adaptability.

[0135] S21 constructs a structured training sample library based on a subset of core features and the quality level of the annotations; it uses a random forest regression algorithm with 80% of the historical samples as the training set to learn the initial basic weight vectors in two scenarios.

[0136] For ultra-precision pneumatic forming processing scenarios, the initial base weights obtained through training are:

[0137] ;

[0138] This weighting indicates that, in this scenario, the integrity of the processing operation (f1) and the stability of long-term operation (f6) are the most important.

[0139] When the system is from the scenario Change to scene Based on the business requirements of this new scenario (emphasizing efficiency and agility), it is necessary to increase the weight of features f2 (toolpath calculation speed), f6 (long-term operational stability), and f7 (accuracy of fault warning).

[0140] Taking feature f2 as an example, the current period's basic weights Set the learning rate Prediction error Weight adjustment amount .

[0141] Calculate the new weights, also known as dynamic weights:

[0142] ;

[0143] Final weight synthesis and normalization: Normalize the weights after adjusting all features to obtain the scene. The final comprehensive weight vector is as follows:

[0144] =[0.24,0.17,0.13,0.13,0.13,0.24,0.06,0.06];

[0145] This is the final comprehensive weight vector in the flexible, multi-configuration, agile manufacturing scenario.

[0146] S3 Multi-Scene Fuzzy Comprehensive Evaluation

[0147] Define a preset evaluation level set V = {v1, v2, v3, v4} = {Excellent, Good, Average, Poor}. For each quality characteristic... and each rating level Define the membership function.

[0148] Repeat this process for all 8 features, and combine the resulting 8 membership vectors to form an 8-row × 4-column fuzzy decision matrix. :

[0149] =[

[0150] [0.60,0.30,0.10,0.00],#f1 Processing operation integrity

[0151] [0.60,0.80,0.20,0.00],#f2 Toolpath calculation speed

[0152] [0.40,0.45,0.10,0.05],#f3 Simulates real-time processing

[0153] [0.20,0.60,0.15,0.05],#f4 Device control compatibility

[0154] [0.60,0.20,0.20,0.00],#f5 User interface friendliness

[0155] [0.80,0.10,0.10,0.00],#f6 Long-term operational stability

[0156] [0.70,0.25,0.05,0.00],#f7 Fault warning accuracy

[0157] [0.30,0.50,0.10,0.10]#f8 Cross-platform deployment convenience ]

[0159] For flexible, multi-configuration, and agile manufacturing scenarios, the overall weight is:

[0160] =[0.24,0.17,0.13,0.13,0.13,0.24,0.06,0.06];

[0161] Using the weighted average synthesis operator, the combined weights are... With the above fuzzy judgment matrix Perform a synthesis operation to obtain the comprehensive evaluation result vector for this scenario. .

[0162] Taking ultra-precision pneumatic forming processing as an example:

[0163] ;

[0164] The result vector indicates that, in the current scenario, the overall membership of the MOM software quality is 63% for "excellent", 33% for "good", 13% for "medium", and 3% for "poor".

[0165] Final quality level determination: Based on the principle of maximum membership, select... The highest value (0.63) corresponds to the "Excellent" rating, which is the final conclusion of this evaluation. Furthermore, by comparing it with the "Ultra-precision pneumatic forming process scenario" (whose evaluation result is [0.55, 0.28, 0.12, 0.03], also "Excellent"), it can be quantitatively analyzed that the software is more suitable for the "flexible multi-configuration agile processing scenario," providing precise data support for subsequent resource allocation and software optimization.

[0166] S4 Closed-Loop Optimization Execution

[0167] Based on the comprehensive quality evaluation vector, a corresponding optimization strategy is generated:

[0168] Resource scheduling optimization: When the membership degree of the performance efficiency dimension is lower than When this happens, the resource elastic expansion strategy will be automatically triggered, and the new CPU quota calculation formula is as follows:

[0169] ;

[0170] in, , This is the resource elasticity coefficient. This represents the membership value of the performance efficiency dimension in the comprehensive evaluation vector.

[0171] Business process refactoring: Dynamically adjust the work order execution sequence based on task priority and equipment status, and optimize the objective function as follows:

[0172] ;

[0173] Constraints:

[0174] ;

[0175] in, This represents the total number of work orders in the queue of pending work orders. Prioritize orders. For decision variables (1 indicates execution, 0 indicates non-execution). For the projected completion rate, For resource consumption, This is to maximize resource constraints.

[0176] Adaptive parameter configuration: Based on the quality assessment results, adjust the operating parameters of the MOM software, including:

[0177] Database connection pool size: ,in To adjust the coefficient, Based on the number of connections, For specific scenarios Below, the membership value of reliability in the comprehensive evaluation vector;

[0178] Number of task processing threads: dynamically adjusted according to system load. At that time, the number of threads increased by 20%;

[0179] Caching strategy: based on Adjust cache size and expiration time. For specific scenarios Below, the membership value of the response speed in the comprehensive evaluation vector.

[0180] After all instructions are executed, their effects are monitored in real time by the data acquisition, preprocessing and feature extraction module and fed back to the fuzzy comprehensive evaluation module, thus forming a complete, self-optimizing closed loop.

[0181] The present invention also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist alone and not assembled into the electronic device.

[0182] The aforementioned computer-readable medium carries one or more programs that, when executed by an electronic device, cause the electronic device to implement the methods described in the above embodiments.

[0183] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0184] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this disclosure.

[0185] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein.

[0186] The above are merely specific embodiments of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept shall be considered as infringing upon the protection scope of the present invention.

Claims

1. A MOM software quality optimization method based on dynamic fuzzy scenario, characterized in that, The method comprises the following steps: Collecting software quality multi-source heterogeneous data from multiple dimensions of MOM system operation monitoring, user interaction records and business process tracking, obtaining a standardized dataset through cleaning and normalization processing, and extracting a core feature subset strongly related to software quality from the standardized dataset based on information measurement method; Defining and dividing software running scenarios according to system load state, user behavior pattern, business workflow characteristics and different performance modes of the core feature subset, constructing a double-layer weight model containing basic weight and dynamic weight for each software running scenario, and completing dynamic adjustment of weight combining historical experience and real-time feedback; For each software running scenario, a fuzzy mapping relationship between quality features in the core feature subset and preset evaluation grades is established to form a fuzzy judgment matrix, a synthesized operation is performed based on the dynamically adjusted weight vector and the corresponding fuzzy judgment matrix to obtain a scenario-based quality comprehensive evaluation vector, and the final quality grade is determined according to the maximum membership degree principle; Based on the quality comprehensive evaluation vector, optimization instructions for software configuration, resource allocation or workflow adjustment are generated and issued to the MOM system for execution to realize software quality closed-loop optimization.

2. The MOM software quality optimization method based on dynamic fuzzy scenerio as claimed in claim 1, wherein, The cleaning and normalization processing to obtain the standardized dataset specifically comprises the following steps: The multi-source heterogeneous data is cleaned, and then the cleaned data is linearly mapped to the [0, 1] interval by using the min-max normalization method, and the calculation formula is: ； wherein, is the normalized data value, is the cleaned data, and are the minimum and maximum values of the data set, respectively.

3. The MOM software quality optimization method based on dynamic fuzzy scenerio as claimed in claim 1, wherein, Interaction information is used as a feature correlation measurement index, and based on the standardized dataset, the most relevant features to software quality are selected from all features according to a predetermined selection strategy to form a core feature subset, and the specific expression is as follows: ； wherein, is a single feature variable to be screened in the standardized dataset, is a specific value of the feature variable X, is a quality grade variable, denotes the interaction information between the feature variable and the quality target variable , denotes the joint probability distribution of the feature vector X taking the value and the quality grade variable Y taking the value , and denote the marginal probability distribution of the feature variable X taking x and the marginal probability distribution of the quality grade variable Y taking y, respectively.

4. The MOM software quality optimization method based on dynamic fuzzy scenerio as claimed in claim 1, wherein, Based on the core feature subset, combined with the quality level labeled by experts, a structured training sample library is constructed; by using a random forest regression algorithm, through a feature importance scoring mechanism, the initial basic weight of the jth quality feature in the core feature subset under the ith scene is calculated. ;​​ For each quality feature in the core feature subset, based on real-time evaluation error and scene change signal under the current scene , the base weight is adjusted through a weight adaptive update mechanism, and its update formula is as follows: ； wherein, represents the adjusted dynamic weight of the i-th quality feature in the scene; is an adaptive learning rate parameter, is an evaluation error in the current scene, is a weight adjustment amount calculated based on a real-time scene switching signal and a service priority rule library.​​ The base weight is combined with the dynamic weight to obtain the scene The comprehensive weight of each quality feature in the lower core feature subset : ； wherein denotes the total number of quality features, ensuring .

5. The MOM software quality optimization method based on dynamic fuzzy scenerio as claimed in claim 4, wherein, the adaptive learning rate parameter With the annealing strategy, the learning rate is gradually reduced with the increase of the number of evaluation iterations, and the update rule is: ； wherein, denotes the learning rate at iteration, is the initial learning rate, is the decay coefficient, is the current iteration number, .

6. The MOM software quality optimization method based on dynamic fuzzy scenerio as claimed in claim 1, wherein, For the scene The comprehensive weight And the fuzzy relation matrix Synthesis operation is carried out to obtain the comprehensive evaluation vector under the scene ; ； wherein denotes a weighted average composition operator; According to the comprehensive evaluation vector , the maximum membership principle is used to determine the final quality level, that is, the maximum value of the comprehensive evaluation vector corresponding to the evaluation level is selected as the software quality evaluation result under this scenario.

7. The MOM software quality optimization method based on dynamic fuzzy scenerio as claimed in claim 1, wherein, Based on the quality-based dynamic optimization decision, at least one of the following optimization instructions is generated and executed according to the comprehensive evaluation vector: Resource scheduling instruction: dynamically adjusting the CPU quota or memory allocation of the container where the MOM software is located; Business process reconstruction instruction: rearranging the execution sequence of the to-be-processed work order according to the task priority; Parameter configuration instruction: adjusting the thread pool quantity or database connection parameter related to the performance inside the software.

8. A MOM software quality optimization system based on dynamic fuzzy scenarioing, characterized in that, The method comprises the following steps: The data acquisition preprocessing and feature extraction module is used for collecting software quality multi-source heterogeneous data from multiple dimensions of MOM system operation monitoring, user interaction records and business process tracking, cleaning and normalizing the collected heterogeneous data to obtain a standardized dataset, and extracting a core feature subset strongly related to software quality from the standardized dataset based on information measurement method; The scenario definition and weight adjustment module is used for defining and dividing software running scenarios according to system load state, user behavior pattern, business workflow characteristics and different performance modes of the core feature subset, and constructing a double-layer weight model containing basic weight and dynamic weight for each software running scenario, and dynamically adjusting the weight combining historical experience and real-time feedback. The fuzzy comprehensive evaluation module is configured to, for each software running scenario, establish a fuzzy mapping relationship between quality features in the core feature subset and preset evaluation levels to form a fuzzy judgment matrix, and perform a synthetic operation based on the dynamically adjusted weight vector and the corresponding fuzzy judgment matrix to obtain a scenario-based quality comprehensive evaluation vector, and then determine a final quality level according to the maximum membership degree principle. The closed-loop optimization execution module is configured to generate an optimization instruction for software configuration, resource allocation or workflow adjustment based on the quality comprehensive evaluation vector, and issue the optimization instruction to the MOM system for execution to realize closed-loop optimization of software quality.

9. A MOM software quality optimization system based on dynamic fuzzy scenarioing as described in claim 8, wherein, The micro-service architecture is deployed in a Kubernetes cluster through containerization technology, including: A data collection pod configured to implement a multi-source heterogeneous data collection function in the data collection preprocessing and feature extraction module; A feature processing pod configured to implement data cleaning, normalization processing and core feature subset extraction functions in the data collection preprocessing and feature extraction module; A scenario recognition pod configured to implement software running scenario definition and classification functions in the scenario definition and weight adjustment module; A weight optimization pod configured to implement a double-layer weight model construction and dynamic adjustment function in the scenario definition and weight adjustment module; An integrated evaluation pod configured to implement fuzzy judgment matrix construction, quality comprehensive evaluation vector calculation and final quality level determination functions in the fuzzy comprehensive evaluation module; A strategy execution pod configured to implement optimization instruction generation and execution function of the closed-loop optimization execution module to the MOM system; The pods communicate with each other through a service mesh to achieve distributed elastic expansion.

10. The MOM software quality optimization system based on dynamic fuzzy scoping of claim 8, wherein, The closed-loop optimization execution module includes a business optimization strategy library configured to map the final quality level and the quality comprehensive evaluation vector into three types of executable instructions: A resource scheduling instruction configured to dynamically adjust CPU and memory resource limits of the MOM software pod through a Kubernetes API; A process reconstruction instruction configured to rearrange execution priority and path of a production work order through a workflow engine API of the MOM system; A parameter configuration instruction configured to dynamically adjust key running parameters such as database connection pool and thread pool through a configuration management center API.

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