Visual device component configuration system

Abstract

The invention relates to the technical field of equipment configuration management, in particular to a visual equipment element configuration system which comprises an operation trend analysis module, a configuration parameter screening module, a parameter conflict detection module, a parameter dynamic adjustment module and a configuration result analysis module. According to the method, through dynamic analysis of the extraction state values of the operation parameters of the equipment elements, accurate identification and induction of the operation change trend of the equipment are realized, clear capture of dynamic change information is ensured, and through screening of the operation parameters in the target range and elimination of configuration combinations which do not accord with the range, the pertinence and accuracy of screening are improved, and the configuration coordination is enhanced; parameter conflict value grading and influence range analysis are combined, the conflict degree and influence thereof are clearly defined, a conflict value range and a logic relation are optimized, configuration flexibility and matching performance are improved, and it is ensured that an optimization result effectively adapts to actual requirements through logic consistency analysis, support degree evaluation and matching performance verification; and the configuration efficiency, the accuracy and the intelligent level are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of device configuration management, and particularly to a visual device component configuration system. Background Art

[0002] The technical field of device configuration management includes technical methods for configuring, managing, and optimizing devices and their related components. The core content of this technical field includes setting the hardware and software parameters of devices, configuring the associations between components, and organizing and managing the overall functional logic. In terms of systematic technology, the technical field of device configuration management covers the definition and storage of device configuration parameters, the interconnection configuration between devices and external systems, the visual presentation of device configurations, and the dynamic adjustment of device operation logic. This technical field focuses on achieving efficient configuration and management of devices through structured and automated means.

[0003] Among them, a visual device component configuration system refers to a method and system for implementing device component configuration through a graphical interface. This system mainly focuses on the component configuration and adjustment matters of devices, covering the definition of device component parameters, the setting of the interaction relationships between components, and the intuitive visualization of component layouts. The system uses means based on computer graphics processing to display the device component structure and configuration options through a graphical interface, combines interactive operation methods to complete the configuration and optimization of component parameters, and simultaneously realizes the dynamic management of component relationships in a graphical manner.

[0004] Existing device configuration management technologies have deficiencies in dynamic change trend analysis. Their operating parameters mostly rely on static settings and lack the ability to capture dynamic changes during device operation. This limitation leads to a lag in the response of devices to environmental changes and internal faults during operation. Existing technologies lack systematicness and pertinence in screening configuration parameters. The screening of parameters is only based on static range determination and fails to fully optimize in combination with the actual operating state of the device, resulting in the screening results deviating from the actual operating requirements. In terms of parameter conflict detection, existing technologies lack fine-grained hierarchical analysis and impact range assessment, and cannot accurately locate conflict parameters and their potential impacts on overall operation, easily leading to problems such as a decrease in device operation efficiency or logical conflicts. In terms of dynamic adjustment, the adjustment process of existing technologies is limited to manual or preset logic and is difficult to flexibly optimize the logical relationships between parameters according to the actual operating situation, resulting in insufficient configuration matching. In terms of configuration result verification, existing technologies pay more attention to the superficial consistency of logic and fail to effectively evaluate the support ability of the configuration for the actual operating state and its matching, and this defect causes functional failures or adaptation problems during operation, restricting the overall efficiency and reliability of device configuration management technologies. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose a visual device component configuration system.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The visualization device component configuration system includes: The operation trend analysis module extracts the state values of parameters at each time point based on the operation parameters of device components, analyzes the change amplitude and direction of the state values, calculates the change rate and summarizes the trend, identifies the operation change trend of device components, extracts the key point information of changes, and generates a trend change relationship data set; The configuration parameter screening module extracts the device operation parameters within the target range based on the trend change relationship data set, screens the component configuration combinations that meet the range, eliminates the configuration parameters outside the target range, classifies and organizes the matching parameter combinations, and generates a screened parameter set; The parameter conflict detection module analyzes the logical relationship of configuration parameters based on the screened parameter set, calculates the parameter conflict value, classifies the conflict value, verifies the interference degree of conflict parameters, analyzes the influence range of conflicts on the operation state, and generates a parameter conflict interference level table; The parameter dynamic adjustment module delimits the value range of conflict parameters based on the parameter conflict interference level table, analyzes the matching situation between the delimited parameters and non-conflict parameters, detects the conflict situation between the adjusted parameters and the configuration combination, optimizes the operation logic of the conflict combination, and obtains an adjusted parameter relationship network; The configuration result analysis module verifies the consistency of the configuration logic, analyzes the support degree of parameters for the component operation state, analyzes the matching degree of the adjusted configuration operation state, and generates a visualization configuration component inspection result based on the adjusted parameter relationship network.

[0007] As a further solution of the present invention, the specific steps for obtaining the operation change trend of the device component are as follows: Based on the operation parameters of device components, capture the operation parameters at each time point and extract the state values, arrange the state values in chronological order and analyze the change amplitude of the state values at each moment, identify the change direction of each state value, judge the trend change of the state value, and generate a state value change amplitude and direction data set; According to the state value change amplitude and direction data set, calculate the change rate at each moment, using the formula: ; Generate a change rate data set; Among them, represents the change rate at time t, and are the state values at the current moment and the previous moment respectively, is the smoothing coefficient; Using the rate-of-change data set, combining the original data and the change direction of the device components, summarizing and identifying the operating change trends of the device components, and adjusting the weights of the trends based on the rate-of-change and direction data in combination with trend factors to obtain the operating change trends of the device components.

[0008] As a further solution of the present invention, the steps for obtaining the trend change relationship data set are specifically as follows: Based on the operating change trends of the device components, perform an operation to eliminate invalid data, eliminate data records that do not meet the standards, process noise data and unqualified data points according to the set time period and sampling rules, screen the qualified data, and obtain the device component operation data set; Based on the device component operation data set, perform data segmentation processing. For different time periods, operating states, and load conditions, analyze the fluctuation trends of each segment of data through numerical interval division and segmented comparison means, identify the positions of the change points, mark each change point, and obtain the key data set of device operation changes; Based on the key data set of device operation changes, perform an analysis of the relationships between the key points, compare the data before and after each key point, analyze the correlation between the key points and each type of operating parameter of the device, and extract the relationships between the trends according to the change plan to generate a trend change relationship data set.

[0009] As a further solution of the present invention, the steps for obtaining the filtered parameter set are specifically as follows: Extract the operating parameters of the device components from the trend change relationship data set, set a target range, screen the device parameters that meet the target range, and judge whether they meet the requirements by comparing the relationship between each parameter and the set target range. The device component configurations that meet the requirements will be retained, while those that do not meet the requirements will be eliminated to generate a device data set that meets the target range; Based on the device data set that meets the target range, in combination with the device's real-time operating environment, and according to the working characteristics, environmental conditions, and original operating data of each device component, perform weighted sorting, adjust the device parameters, and use the formula: ; Generate a list of weighted values of device components; Wherein, is the weighted value of the i-th device component, represents the performance weight of the device component, is the quality score of the configuration parameters of the device component, represents the number of device components; According to the list of device element weighting values, classify and sort the configuration combinations that meet the objectives, analyze the effectiveness of each combination based on the matching degree, select the configuration with the optimal effectiveness, eliminate the parameter combinations that do not meet the requirements, and generate a filtered parameter set.

[0010] As a further solution of the present invention, the specific steps for obtaining the parameter conflict value are as follows: Through the filtered parameter set, analyze the logical relationship between the configuration parameters, extract the direct relationship between the parameters and perform conditional judgment to determine whether there is a conflict between the parameters, and obtain the parameter relationship judgment value; Perform a summary analysis on the parameter relationship judgment value, quantify the conflict, set a threshold and compare it with the judgment value, screen the parameter conflict source, and use logical operations to obtain a preliminary conflict value; According to the preliminary conflict value, combined with the weight parameter, perform an optimization process, using the formula: ; Calculate and obtain the parameter conflict value; Wherein, represents the parameter conflict value, represents the conflict degree value of parameter k with other parameters during the configuration process, is the weight of parameter k, is the coefficient for adjusting the conflict impact, and m is the total number of parameters.

[0011] As a further solution of the present invention, the specific steps for obtaining the parameter conflict interference level table are as follows: Based on the parameter conflict value, perform measurement, recording and comparison of the parameters, record the conflict source information, analyze the real-time conflict impact for each conflict parameter, and perform standardization processing on the data. Classify and sort according to the conflict degree, conflict type and correlation degree to generate a conflict parameter data table; Based on the conflict parameter data table, perform quantitative analysis of the conflict parameters, use a differential standard to stratify, sort and classify the influence range of each parameter, and evaluate the contribution to the operation stability through the fluctuation of the interference parameter to generate a conflict value grading table; Based on the conflict value grading table, analyze the differential conflict values, classify according to the influence range, interference intensity and occurrence frequency, use a predetermined threshold to screen the conflict value influence, and analyze the parameter interference relationship to generate a conflict interference level table.

[0012] As a further solution of the present invention, the specific steps for obtaining the adjusted parameter relationship network are as follows: Classify the associated parameters through the parameter conflict interference level table, extract the interference level of each parameter and delimit the corresponding value range according to the level, and screen the parameters that meet the requirements to obtain the interference parameter interval range; Compare the matching situation between the interference parameter range and the additional parameters, check whether there is a conflict between each adjustment parameter and the non-conflicting parameters, judge the combination of conflicting parameters and non-conflicting parameters, and obtain the configuration conflict situation; Based on the configuration conflict situation, optimize the adjustment parameter configuration combination, readjust the relationship between the conflicting parameters and the non-conflicting parameters, and use the formula: ; Obtain the adjusted parameter relationship network; Wherein, represents the value of the adjusted parameter relationship network, represents the weight of parameter ; represents the weight of parameter ; is the weight coefficient for conflict judgment, is the correction coefficient.

[0013] As a further solution of the present invention, the steps for obtaining the inspection result of the visual configuration element are specifically as follows: Based on the adjusted parameter relationship network, model the relationship between each parameter and the component operation state, record the change of each adjustment parameter and the real-time influence, analyze the support degree of the parameter for the component operation state, and evaluate the stability contribution to obtain the component operation support degree data; Based on the component operation support degree data, verify each adjustment configuration, evaluate the adaptability of the configuration to the component operation state, analyze the reaction influence of the differential configuration, classify according to the configuration support degree and stability, and generate a configuration logic consistency verification table; Based on the configuration logic consistency verification table, combined with the influence of each adjustment configuration on the operation state, analyze the matching performance of the configuration for the component, and evaluate the reaction under different operating conditions to generate the inspection result of the visual configuration element.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through the dynamic analysis of extracting state values based on the operating parameters of device components, the accurate identification and induction of the operating change trends of device components are realized, ensuring that the dynamic change information of the device operating state can be clearly captured, providing data support for subsequent processing. By screening the operating parameters within the target range and eliminating the configuration combinations that do not meet the range, the pertinence and accuracy of the screening process are guaranteed, and at the same time, the overall coordination of the device configuration is improved. Through the analysis of the logical relationships of the parameters, combined with the classification of the parameter conflict values and the verification of the influence range, the conflict degree between the parameters and its influence on the operating state can be clearly defined, making the configuration optimization more scientific and feasible. During the dynamic adjustment process, based on the matching situation of the conflict parameters and non-conflict parameters, the optimization of the conflict value range and the dynamic adjustment of the logical relationship are realized, enhancing the flexibility of the device parameter configuration and the operating matching. The configuration verification realizes the comprehensive verification of the parameter configuration optimization results through logical consistency analysis, support degree evaluation, and operating matching analysis, ensuring that the configuration results can effectively adapt to the actual operating requirements, significantly improving the efficiency, accuracy, and applicability of the device operating parameter configuration and optimization. At the same time, through the combination of dynamic adjustment and trend analysis, the device configuration system is given a higher level of intelligence and dynamic matching ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the system flow chart of the present invention; Figure 2 is the flowchart for obtaining the operating change trend of the device components in the present invention; Figure 3 is the flowchart for obtaining the trend change relationship data set in the present invention; Figure 4 is the flowchart for obtaining the filtered parameter set in the present invention; Figure 5 is the flowchart for obtaining the parameter conflict value in the present invention; Figure 6 is the flowchart for obtaining the parameter conflict interference level table in the present invention; Figure 7 is the flowchart for obtaining the adjusted parameter relationship network in the present invention; Figure 8 is the flowchart for obtaining the visualization configuration component inspection result in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0018] Please refer to Figure 1 , the visualization device element configuration system includes: The operation trend analysis module extracts the state values of the parameters at each time point based on the device element operation parameters, analyzes the change amplitude and direction of the state values, calculates the change rate and summarizes the trend, identifies the operation change trend of the device element, extracts the key point information of the change, and generates a trend change relationship data set; The configuration parameter screening module extracts the device operation parameters within the target range based on the trend change relationship data set, screens the component configuration combinations that meet the range, eliminates the configuration parameters outside the target range, classifies and organizes the matching parameter combinations, and generates a filtered parameter set; The parameter conflict detection module analyzes the logical relationship of the configuration parameters based on the filtered parameter set, calculates the parameter conflict value, classifies the conflict value, verifies the interference degree of the conflict parameters, analyzes the influence range of the conflict on the operation state, and generates a parameter conflict interference level table; The parameter dynamic adjustment module delimits the value range of the conflict parameters based on the parameter conflict interference level table, analyzes the matching situation between the delimited parameters and the non-conflict parameters, detects the conflict situation between the adjusted parameters and the configuration combination, optimizes the operation logic of the conflict combination, and obtains an adjusted parameter relationship network; The configuration result analysis module verifies the consistency of the configuration logic based on the adjusted parameter relationship network, analyzes the support degree of the parameters for the component operation state, analyzes the matching degree of the adjusted configuration operation state, and generates a visualization configuration component inspection result.

[0019] The trend change relationship dataset includes data on the change amplitude of parameter status values, data on the change direction of parameter status values, data on the change rate of parameter status values, and information on key change points. The filtered parameter set includes device operation parameters within the target range, component configuration combinations within the range, configuration parameters excluded outside the target range, and classified and organized matching parameter combinations. The parameter conflict interference level table includes parameter conflict values, classification information on parameter conflict values, information on the interference degree of conflicting parameters, and information on the impact range of conflicts on the operating state. The adjusted parameter relationship network includes the value range of conflicting parameters, the matching situation between defined parameters and non-conflicting parameters, the conflict situation between adjusted parameters and configuration combinations, and the optimized operating logic. The visual configuration component inspection results include the analysis results of configuration logic consistency, the analysis results of the support degree of parameters for the operating state of components, and the analysis results of the matching degree of adjusted configuration operating states.

[0020] Please refer to Figure 2 , and the specific steps for obtaining the operation change trend of device components are as follows: Based on the device component operation parameters, capture the operation parameters at each time point and extract the status values. Arrange the status values in chronological order and analyze the change amplitude of the status values at each moment, identify the change direction of each status value, judge the trend change of the status value, and generate a dataset of status value change amplitude and direction. The status values are obtained in real time through sensors and arranged in chronological order to ensure the timeliness and continuity of the data. The change amplitude of the status values is calculated by the absolute value of the difference between the status values at two moments. The change amplitude reflects the performance fluctuation of the device in the time process. The change direction of the status value is determined based on the increasing and decreasing trends of adjacent moment status values. If the current status value is greater than the previous moment, the direction is positive, otherwise it is negative. Further summarize the change directions at each time point through a trend analysis tool. In the process of trend change analysis, judge the change trajectory of each status value within a set time period to facilitate the optimization of the device's operation and maintenance strategy, and finally form a dataset of status value change amplitude and direction.

[0021] According to the dataset of status value change amplitude and direction, calculate the change rate at each moment, using the formula: ; Generate a change rate dataset; Among them, represents the change rate at time t, and are the status values at the current moment and the previous moment respectively, is the smoothing coefficient; The benefit of the formula is that by introducing the smoothing coefficient and the relative size of the status values, the change rate is precisely adjusted, thereby enhancing the prediction accuracy of the device operation trend; In this formula, is the rate of change at time t, representing the speed of the change in the state of the device component. is the device state value at the current moment. is the state value at the previous moment. is the smoothing coefficient, which avoids overly drastic reactions of the rate of change due to minor fluctuations. Assume that at a certain moment, the state value of the device and the previous moment , and at the same time, the smoothing coefficient is set; Then the calculation of the rate of change is as follows: ; This result indicates that the rate of change of the device component at the moment is 0.0434, indicating that the change in the device state is relatively stable. Through this calculation result, the operation and maintenance personnel can judge the change trend of the device state and formulate a reasonable maintenance strategy based on this data.

[0022] Using the rate of change data set, combining with the original data and change direction of the device component, summarize and identify the operation change trend of the device component. Based on the rate of change and direction data, combined with the trend factor, adjust the weight of the trend to obtain the operation change trend of the device component; Summarize and identify the operation change trend of the device component. The historical data is provided by the device operation and maintenance records, operation logs, and regular inspection results. The historical data needs to be cleaned and preprocessed to ensure the quality and reliability of the data. The change direction data is based on the extracted state change direction. Considering various factors comprehensively, through the weighted average method, combined with actual operation and maintenance experience, calculate the operation trend of the device component in each time period. The weight setting needs to refer to factors such as device workload and environmental conditions, and the weight parameters need to be adjusted regularly according to the device operation state. For example, when the device load is large, the weight of the current state change of the device can be appropriately increased, and the trend factor is used to further adjust the change proportion in different time periods to obtain the operation change trend of the device component.

[0023] Please refer to Figure 3 , and the specific steps for obtaining the trend change relationship data set are as follows: Based on the operation change trend of the device component, perform the operation of eliminating invalid data, eliminate the data records that do not meet the standards, and process the noise data and unqualified data points according to the set time period and sampling rules, and screen the qualified data to obtain the device component operation data set; First, by defining the sampling period and data acquisition frequency, the collected device operation data is segmented by time to ensure that the sampled data in each time period is representative. Using pre-set standards, the collected data is compared with the standard values. Data that is greater than the set error range is excluded. For noise data, by designing noise detection rules, such as checking whether the change amplitude of the data points exceeds a certain threshold, and identifying abnormal data points according to the rules. For the excluded data points, re-acquisition will be carried out according to the set time period and rules until the data meets the standards. The excluded data will be re-organized according to its qualified characteristics by time period to ensure that only data meeting the specified standards enters further analysis in subsequent processing. In this way, a device component operation data set is obtained, which will ensure the quality of the data and provide an accurate and effective data basis for subsequent analysis.

[0024] Based on the device component operation data set, data segmentation processing is carried out. For different time periods, operating states, and load conditions, through numerical interval division and segmented comparison means, the fluctuation trend of each segment of data is analyzed, the position of the change points is identified, each change point is marked, and a data set of key points of device operation change is obtained; First, the operation data of the device is segmented according to different time periods. For the operation conditions of the device under different loads and different operating states, different numerical intervals are set respectively. The data in each time period will be analyzed within the interval, and the data points with large fluctuations within the interval are identified, especially the points with fluctuations exceeding the predetermined amplitude. These points are marked as change points. By comparing the change amplitudes of the data within each time period, combined with the load conditions and operating states of the device, the key change points in the data can be effectively identified. When the load is in a high-load state, the fluctuation amplitudes of some data points are large, indicating that there are potential change trends in the device under high-load conditions. Between the data points, by comparing the operation data of the intervals, the position of the change point is determined and marked. The marking of the change points provides key clues for subsequent analysis of the device state changes, and a data set of key points of device operation change is constructed.

[0025] Based on the data set of key points of device operation change, the relationship analysis between key points is carried out. The data before and after each key point is compared, the correlation between the key points and each type of device operation parameter is analyzed, and according to the change plan, the relationship between the trends is extracted to generate a data set of trend change relationships; By comparing the data before and after the key points of equipment changes, first analyze the equipment operation parameters before and after each key point, such as temperature, rotation speed, pressure, etc., to understand the impact of the parameter change trend on the equipment change points. For each key point, collect multiple operation parameter data in the front and back time periods, calculate its correlation with the change point, and further identify the role of each operation parameter in the key point change. For example, if the equipment temperature continues to rise before a certain key point and highly coincides with the time node of equipment failure, it can be speculated that there is a certain correlation between the temperature increase and the equipment failure. Deeply analyze the relationship between the fluctuation trends of various equipment operation parameters and the change points, extract the change rules of the equipment under different operation states, and finally generate a data set of trend change relationships of equipment operation through systematic analysis and comparison. This data set can comprehensively reflect the mutual relationship and change trend among various equipment parameters, providing a scientific basis for subsequent equipment status prediction and maintenance.

[0026] Please refer to Figure 4 , the steps for obtaining the filtered parameter set are specifically as follows: Extract the operation parameters of equipment components from the data set of trend change relationships, set a target range, filter the equipment parameters that meet the target range, and judge whether they meet the requirements by comparing the relationship between each parameter and the set target range. The equipment component configurations that meet the requirements will be retained, while those that do not will be excluded to generate an equipment data set that meets the target range; First, it is necessary to organize and preprocess the data, arrange it in a time series, and ensure that the parameter values of each equipment component meet the predetermined standard range to ensure the timeliness and integrity of the data. For example, set the working temperature range of the equipment to be between 20°C and 80°C, the vibration amplitude should be between 0.1 mm and 1.0 mm, and the power consumption does not exceed a certain value. For parameter values that do not meet the standards, data cleaning or exclusion processing is required. Parameter screening will be based on the state change characteristics of equipment components, mainly by comparing the data change trends of the equipment at the front and back time points to determine its effectiveness. For example, whether the vibration amplitude and temperature change exceed the normal fluctuation range and whether there are abnormalities. When screening the equipment parameters, the historical operation data of the equipment will be used for matching, and the parameter combinations of equipment components that meet the target range will be selected to ensure the stable operation of the equipment in the future working state. After a series of screening, comparison, and verification, an equipment data set that meets the target range will be finally obtained, which will be used as the basis for subsequent optimization configuration and performance evaluation to ensure that the final configuration can improve the overall performance of the equipment, reduce the failure rate, and accurately screen out the most suitable configuration parameters, providing reliable data support for equipment optimization.

[0027] Based on the device dataset that meets the target range, combined with the real-time operating environment of the device, according to the working characteristics, environmental conditions, and original operating data of each device component, perform weighted sorting, adjust the device parameters, and use the formula: ; Generate a list of weighted values for device components; Among them, is the weighted value of the i-th device component, represents the performance weight of the device component, is the quality score of the configuration parameters of the device component, represents the number of device components; The advantage of the formula is that by weighted calculation of the performance weight and the quality score of the configuration parameters of the device component, it can comprehensively consider the working characteristics, environmental conditions, and actual operating data of the device, so as to screen out the most suitable device component configuration. Formula details and formula calculation derivation process: In this formula, represents the weighted value of the i-th device component, is the performance weight of the device component, which reflects the importance of the component under specific working conditions and is set based on factors such as the load and usage frequency of the device. For example, the performance weight of a certain device component indicates that it occupies a relatively high proportion in the current device configuration; is the quality score of the configuration parameters of the device component, and this score can be calculated through the device operating data, reflecting the performance in aspects such as the working stability and service life of the component. Assuming the configuration score of this component is , it indicates a relatively high quality score. By calculating to obtain the sum of the weighted values of all device components, and finally generating the weighted value of each component. Assuming there are two components in this configuration, namely and ; Then the weighted value calculation is as follows: ; ; Generate a list of weighted values for device components, and the device components can be sorted by priority according to this list, so as to select the best-performing configuration combination in practical applications. This result shows that using the weighted calculation method can accurately consider the influence of multiple factors on the device component configuration, ensure that the selected device components can effectively improve the comprehensive performance of the system during actual use, and help optimize the device configuration to meet the requirements of specific working conditions.

[0028] According to the weighted value list of equipment components, classify and organize the configuration combinations that meet the target, analyze the performance of each combination based on the matching degree, select the configuration with the best performance, eliminate the parameter combinations that do not meet the requirements, and generate the filtered parameter set; First, select components with higher weighted values. Components that perform better in the current environment are classified and organized according to the type, purpose and required operating conditions of the equipment. For example, if some components are specifically used for high-load operation, they are used for low-load operation and are grouped according to the load requirements of the equipment. During the matching process, the performance score of each component and component combination is calculated. The performance score of the combination is based on actual operating data and simulation test results. For example, the performance score of a combination is 0.85, indicating that the performance of the combination is superior and can operate stably in actual work. The performance evaluation process is based on a set of standard parameters, such as the temperature, load capacity, vibration amplitude, etc. of the equipment. All configuration combinations that meet the requirements will be retained in the calculation, and those that do not meet the conditions will be eliminated. The component combination with the highest performance score is selected. Through a series of optimization algorithms and actual tests, the optimal configuration is determined and provides a basis for subsequent operations. The final screened parameter set is the most suitable equipment configuration. The configuration can not only ensure the efficient operation of the equipment, but also reduce the failure rate, improve the system stability, and ensure reliability in various working environments.

[0029] See also Figure 5 , the specific steps for obtaining the parameter conflict value are: Through the filtered parameter set, the logical relationship between the configuration parameters is analyzed, the direct relationship between the parameters is extracted and conditional judgment is performed to determine whether the parameters conflict and obtain the parameter relationship judgment value; First, extract and analyze the collected parameters, obtain the parameter set, and screen the validity of each parameter to ensure the accuracy and relevance of the data. By comparing each pair of parameters, use the logical relationship to determine whether there is a conflict. For example, when the value of some parameters is outside the preset range, it can be regarded as a conflict. If parameter 1 and parameter 2 are interdependent and conflicting, a clear judgment should be given during data processing. If there is a conflict, assign an initial value to the conflict according to the preset rules. The conflict value can be set by real-time calculation in the system. During the generation of the conflict value, the severity of each parameter conflict is weighted by the proportional method, specifically based on the weight factor related to the goal of the current task. For example, assuming that the current task is time-sensitive, then the time-related parameter conflict weight factor is larger, and finally the parameter relationship judgment value is generated, and the current system parameters are updated. It can be judged whether the data meets the predetermined operating rules and adjust the relevant configuration accordingly.

[0030] Summarize and analyze the parameter relationship judgment values, quantify the conflicts, set thresholds and compare them with the judgment values, screen the parameter conflict sources, and use logical operations to obtain preliminary conflict values; Gradually screen the sources of parameter conflicts, use logical operations to obtain preliminary conflict values, summarize all the obtained conflict parameter relationships, and standardize the relationships so that they can be uniformly compared. The standardization process includes weighting and normalization of each conflict relationship. Through the standardized data, a set of standardized conflict values is formed. By comparing this set, identify the parameter conflicts with significantly higher values. Based on the data screening, select the parameter items with higher conflicts, perform logical operations on them and output the preliminary conflict values. Suppose in past data analysis, when the values of certain parameters were high, it would cause system instability. At this time, by comparing and screening the relevant data, confirm which parameters cause large fluctuations in the system, and use logical rules to set the conflict value to be high to obtain the preliminary conflict value.

[0031] According to the preliminary conflict value, combined with the weight parameters, perform optimization processing, using the formula: ; Calculate and obtain the parameter conflict value; Among them, represents the parameter conflict value, represents the conflict degree value of parameter k with other parameters during the configuration process, is the weight of parameter k, is the coefficient for adjusting the conflict impact, and m is the total number of parameters; Set the preliminary conflict value as , combined with the weight parameter of parameter k and the adjustment coefficient , perform further calculations through the above formula. By calculating the product of each conflict value and the weight, then adjusting the conflict value, use to adjust the influence of different conflict values on the final conflict value, sum up the results of all parameters, and thus obtain the final conflict value. For example, assume that for a specific parameter k, the preliminary conflict value is 0.2, the weight parameter , and the adjustment coefficient ; Then according to the formula calculation, it can be obtained: ; This result indicates that the optimized parameter conflict value is 0.2727, meaning that the conflict impact of this parameter is relatively large and further optimization or adjustment is required, thus affecting the overall system configuration and ultimately ensuring that the system can reach the optimal operating state.

[0032] Please refer to Figure 6 , and the specific steps for obtaining the parameter conflict interference level table are as follows: Based on the conflicting parameter values, measure, record, and compare the parameters, record the conflict source information, for each conflicting parameter, analyze the real-time conflict impact, and standardize the data, classify and sort them according to the conflict degree, conflict type, and correlation degree to generate a conflict parameter data table; First, for each parameter conflict value, measure it by collecting the relevant operation data of the device components in different working states. The measurement results of each conflicting parameter will be recorded one by one, and a detailed record file will be established in the device monitoring system. When recording the conflict source information, special attention should be paid to recording key background data such as the time of conflict occurrence, device operation status, and load conditions. Analyze the real-time impact of the conflicting parameters on the device performance, judge the interference degree on the device in different operation stages, and further understand its potential impact on the device by comparing the deviation of each conflicting parameter from the standard value. During the data standardization process, classify the conflicting parameters according to different conflict degrees, types, and the correlation degree between conflicting parameters, and sort them by classification for subsequent in-depth analysis. Finally, generate a conflict parameter data table, which contains detailed information of each conflicting parameter and can reflect the change trend of the parameters under different conflict degrees, providing basic data for the next conflict value analysis.

[0033] Based on the conflict parameter data table, conduct a quantitative analysis of the conflict parameters, use a differential standard to stratify, sort, and classify the influence range of each parameter, and evaluate the contribution to operation stability through the fluctuation of interference parameters to generate a conflict value grading table; First, conduct a quantitative analysis of each parameter in the conflict parameter data table to clarify the influence range of each conflicting parameter, stratify the influence range of each parameter, mark the parameters with a greater influence degree as high conflict levels, and mark the parameters with a smaller influence as low conflict levels. Evaluate the impact of each conflicting parameter on the device operation stability according to its fluctuation situation. Especially for the parameters that frequently fluctuate and are closely related to the decline of device performance, they need to be marked as high-priority conflict parameters in the conflict value grading. Through evaluation, classify various conflicting parameters according to their fluctuation amplitude, influence range, and interference degree, and assign corresponding conflict values to each category of parameters to determine the sorting and priority, generating a conflict value grading table, which can not only clearly show the influence level of each conflicting parameter but also help identify the most influential conflicting parameters, providing a decision-making basis for the adjustment and optimization of the device.

[0034] Based on the conflict value grading table, analyze the differential conflict values, classify them according to the influence range, interference intensity, and occurrence frequency, use a predetermined threshold to screen the influence of the conflict values, analyze the interference relationship of the parameters to generate a conflict interference level table; By deeply analyzing each parameter conflict value in the conflict value grading table, first classify according to the influence range, interference intensity, and occurrence frequency of each conflict parameter. For conflict parameters with a high influence range, high interference intensity, and high occurrence frequency, give the highest interference level and mark them as key parameters. For parameters with low frequency and low interference, they can be classified as low-level conflicts. By setting reasonable predetermined thresholds, screen out the parameter conflict values that have a greater impact on the equipment operation, especially those that repeatedly appear during long-term monitoring and have a greater impact on the equipment performance. Analyze the interference relationships between conflict parameters and reveal their interactions during the equipment operation. For example, when multiple conflict parameters are simultaneously in a high-interference state, their combined effect will cause the equipment operation to be unstable or abnormal. Through this comprehensive analysis, finally generate a conflict interference level table, which can reflect the comprehensive influence of different conflict parameters during the equipment operation and provide a basis for adjusting the equipment settings and optimizing the parameter configuration.

[0035] Please refer to Figure 7 , and the steps for obtaining the adjusted parameter relationship network are specifically as follows: Classify the associated parameters through the parameter conflict interference level table, extract the interference level of each parameter and delimit the corresponding value range according to the level, screen out the parameters that meet the requirements, and obtain the interference parameter interval range; First, it is necessary to obtain the interference levels of all parameters and classify them into the corresponding value intervals. Analyze whether there are conflicts among the parameters within the intervals. By comparing the maximum and minimum values of each interval, determine the value range of the parameters. Through conditional judgment expressions, determine which parameters belong to the conflict group and which belong to the non-conflict group, and screen out the appropriate parameter set. Attention should also be paid to distinguishing the categories of each parameter. For example, some parameters are continuous while others are discrete, and appropriate matching should be made between different types of parameters. Based on the judgment, generate the parameter interval range that meets the requirements and integrate it into the interference parameter interval range of each parameter, which is completed by comparing and analyzing the relationship between the value of different parameters and their interference levels, ensuring that there will be no conflicts in the final interval values, and obtain the interference parameter interval range.

[0036] Compare the matching situation between the interference parameter interval range and the additional parameters, check whether there are conflicts between each adjusted parameter and the non-conflict parameters, and judge the combination situation of the conflict parameters and the non-conflict parameters to obtain the configuration conflict situation; Analyze potential conflict issues by comparing the values of different parameters with the matching of configuration combinations. Use conditional judgment expressions. By comparing the values of conflicting parameters with non-conflicting parameters, identify which configurations cause conflicts. For example, if the value of a certain conflicting parameter is within a specific range and the value of another non-conflicting parameter overlaps with it, mark this configuration as a conflicting configuration and eliminate it. For each conflicting configuration, calculate its conflict level and sort them to prioritize handling cases with higher conflict levels, generate and output the configuration conflict situation, by comparing the overlapping parts between the ranges of different parameters and the dependency relationships between parameters.

[0037] Based on the configuration conflict situation, optimize and adjust the parameter configuration combination, readjust the relationship between conflicting parameters and non-conflicting parameters, using the formula: ; Obtain the adjusted parameter relationship network; Among them, represents the value of the adjusted parameter relationship network, represents the weight of parameter ; represents the weight of parameter ; is the weight coefficient for conflict judgment, is the correction coefficient; The relationship between parameters x and y: In the calculation of the formula, x and y represent the operating parameters of the device components at different states or time points. Specifically, in the calculation formula of the change rate, t represents the current moment, and t - 1 represents the previous moment. The state values (x and y) reflect the operating conditions of the device at different moments. According to the description in the text, the calculation of the change rate can measure the amplitude and direction of the device state change through the difference in state values between two moments. Therefore, the two parameters x and y should be the operating states of the device at consecutive moments, indirectly describing the trend and amplitude of the device change; The weight coefficient for conflict judgment: The "weight coefficient for conflict judgment" mentioned in the formula is a coefficient used to weight the conflict influence degrees of different parameters in the parameter conflict analysis. In the parameter conflict detection, the parameters of multiple device components may have conflicts, and the conflicts will affect the stability and performance of the device. The weight coefficient for conflict judgment determines the weight of each parameter during conflicts by quantifying the influence degree of the conflicting parameters, thus effectively managing conflicts. This coefficient helps to judge which parameter conflicts are more critical and which parameter conflicts have a greater impact on the system operation during the conflict detection process through the correction of the conflict degree. Through a coefficient, the influence of conflicts can be systematically evaluated, and then the parameter values can be adjusted so that the final system configuration can be optimized with the least conflicts; The logical operation process in the formula reflects the calculation method of conflict values. Analyze the filtered parameter set, extract the logical relationships between various parameters, make conditional judgments to identify parameter conflicts, calculate preliminary conflict values through logical operations, and adjust them according to weight parameters. The "weight" and "adjustment coefficient" in the formula are used to quantify the conflict influence degree and adjustment value of each parameter, so as to obtain the final conflict value. The process involves weighted calculation, threshold setting, and the use of optimized correction coefficients, so as to achieve the optimization of parameter configuration and conflict minimization. The obtained adjusted parameter relationship network helps to more accurately define conflict parameters and ensure that the device configuration can be effectively matched and operated under different parameter conditions; The benefit of the formula is that by introducing weight parameters and correction coefficients, the interaction between parameters is optimized, so that the finally adjusted parameter relationship network can more accurately reflect the parameter conflict situation in the actual configuration, thus improving the accuracy and stability of the system; a represents the weight of parameter x, x represents the value of the adjusted parameter x, b represents the weight of parameter y, y represents the value of the adjusted parameter y, L is the weight coefficient for conflict judgment, d is the correction coefficient, representing the correction effect of adjusting conflict parameters. The core of the calculation formula lies in weighting the mutual relationships between various parameters, and at the same time correcting the uncertainty brought by conflict parameters through square root operations. To better understand this formula, assume that through data acquisition, parameter x = 10, parameter y = 5, weight a = 2, weight b = 3, weight coefficient L = 1, and correction coefficient d = 4; After substituting into the formula, we get: ; This result shows that the value of the adjusted parameter relationship network is 37, indicating that the optimization effect of the adjusted parameter relationship network has been confirmed, which is obtained by combining weighted calculation and correction coefficient.

[0038] Please refer to Figure 8 , and the specific steps for obtaining the inspection results of the visual configuration components are as follows: Based on the adjusted parameter relationship network, model the relationship between each parameter and the component operation state, record the changes of each adjusted parameter and the real-time influence, analyze the support degree of the parameters for the component operation state, evaluate the stability contribution, and obtain the component operation support degree data; First, based on the adjusted parameter relationship network, establish the correlation between each adjustment parameter and the operating state of the component. Each adjustment parameter is matched with the actual operating state of the equipment component, and record the real-time impact brought by the parameter change during each adjustment process. Through the real-time monitoring and data acquisition system, compare the changes of each adjustment parameter under different conditions with the state changes of the component, record the key data points, and analyze its support degree according to the component operating state changes caused by each parameter change. A parameter with a higher support degree indicates that its change has a positive impact on the equipment operation, while a parameter with a lower support degree leads to unstable equipment operation. Through the support degree evaluation of each adjustment parameter, further calculate the overall stability contribution, reflecting the actual role of each parameter in the equipment stability. In this way, obtain the component operation support degree data, which provides data support for subsequent equipment adjustment and optimization, and provides an effective reference for the control of the equipment operating state.

[0039] Based on the component operation support degree data, verify each adjustment configuration, evaluate the adaptability of the configuration to the component operating state, analyze the reaction impact of the differential configuration, and classify according to the configuration support degree and stability to generate a configuration logic consistency verification table; Through the analysis of the component operation support degree data, further verify each adjustment configuration. Each adjustment configuration will be evaluated according to its impact on the adaptability of the equipment component operating state. During the evaluation process, consider the matching performance of the equipment operation under different configurations, especially conduct a detailed analysis of the response time, load change, operation efficiency, etc. of the equipment under various different configurations. By tracking the reaction of the equipment under each configuration, analyze the differences in its performance. For example, a certain adjustment configuration performs well under low load conditions, but causes equipment response lag or efficiency reduction under high load. According to the reaction impact of this differential configuration, combined with the configuration support degree and stability, conduct classification processing, classify each configuration into an appropriate category, and generate a configuration logic consistency verification table, which can clearly show the consistency and differences between each configuration and the equipment operating state, providing a specific basis for further configuration optimization and adjustment.

[0040] Based on the configuration logic consistency verification table, combined with the impact of each adjustment configuration on the operating state, analyze the matching performance of the configuration to the component, and evaluate the reaction under differential operating conditions to generate a visual configuration component inspection result; Combined with the configuration logic consistency verification table, further deeply analyze the specific impact of each adjustment configuration on the operating state of device components. By real-time monitoring the performance of the device under different operating conditions, record the differences in the device matching performance after configuration adjustment, especially for the matching performance of the device under different working conditions. The change of each adjustment configuration will reflect different matching performances under different loads, working environments, and temperatures, etc. Especially under high load or extreme conditions of the device, some configurations show better matching performance, while some configurations lead to a decline or instability in device performance. Through the evaluation of the differential performance, finally generate the visual configuration component inspection results, which will be able to intuitively display the matching performance of the device under different configurations, help device managers intuitively understand the impact of each configuration on components during actual operation, and provide effective data support and decision-making basis for subsequent device optimization and adjustment.

[0041] The above is only the preferred embodiment of the present invention and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A visual device component configuration system, characterized in that: The system comprises: The operation trend analysis module extracts the state value of the parameter at each time point based on the operation parameters of the equipment components, analyzes the change amplitude and direction of the state value, calculates the change rate and summarizes the trend, identifies the operation change trend of the equipment components, extracts the key point information of the change, and generates a trend change relationship data set; The configuration parameter screening module extracts the equipment operating parameters within the target range based on the trend change relationship data set, screens the component configuration combinations that meet the range, removes the configuration parameters outside the target range, classifies and sorts the matching parameter combinations, and generates a screened parameter set; The parameter conflict detection module analyzes the logical relationship of the configuration parameters based on the filtered parameter set, calculates the parameter conflict value, and grades the conflict value, verifies the interference degree of the conflicting parameters, analyzes the impact range of the conflict on the running state, and generates a parameter conflict interference level table; The parameter dynamic adjustment module defines the value range of the conflicting parameters based on the parameter conflict interference level table, analyzes the matching between the defined parameters and the non-conflicting parameters, detects the conflict between the adjustment parameters and the configuration combination, optimizes the conflict combination operation logic, and obtains the adjusted parameter relationship network; The configuration result analysis module verifies the configuration logic consistency based on the adjusted parameter relationship network, analyzes the parameter support for the component operation status, analyzes the matching of the adjusted configuration operation status, and generates a visual configuration component inspection result.

2. The visualization device component configuration system according to claim 1, characterized in that: The steps for obtaining the operation change trend of the equipment components are specifically as follows: Based on the operating parameters of equipment components, the operating parameters at each time point are captured and the status values ​​are extracted. The status values ​​are arranged in chronological order and the change amplitude of the status values ​​at each moment is analyzed. The change direction of each status value is identified, the trend change of the status value is determined, and a data set of the change amplitude and direction of the status value is generated. According to the state value change amplitude and direction data set, the change rate at each moment is calculated using the formula: ； Generate a rate of change data set; in, represents the rate of change at time t, and are the state values ​​at the current moment and the previous moment respectively, is the smoothing coefficient; The change rate data set is used in combination with the original data and change direction of the equipment components to summarize and identify the operation change trend of the equipment components. According to the change rate and direction data, combined with the trend factor, the weight of the trend is adjusted to obtain the operation change trend of the equipment components.

3. The visualization device component configuration system according to claim 2, characterized in that: The steps for acquiring the trend change relationship data set are specifically as follows: Based on the operation change trend of the equipment components, perform invalid data elimination operations, eliminate data records that do not meet the standards, process noise data and unqualified data points according to the set time period and sampling rules, screen qualified data, and obtain equipment component operation data sets; Based on the equipment component operation data set, data segmentation processing is performed, and for differentiated time periods, operation states, and load conditions, the fluctuation trend of each segment of data is analyzed by means of numerical interval division and segment comparison, the location of the change point is identified, each change point is marked, and a data set of key points of equipment operation change is obtained; Based on the key point data set of equipment operation changes, the relationship between key points is analyzed, the data before and after each key point are compared, the correlation between the key points and each type of equipment operation parameters is analyzed, and according to the change plan, the relationship between trends is extracted to generate a trend change relationship data set.

4. The visualization device component configuration system according to claim 3, characterized in that: The steps for obtaining the filtered parameter set are specifically as follows: Extracting the operating parameters of the equipment components from the trend change relationship data set, setting a target range, screening the equipment parameters that meet the target range, and judging whether the requirements are met by comparing the relationship between each parameter and the set target range. The equipment component configurations that meet the requirements will be retained, while those that do not meet the requirements will be eliminated, thereby generating an equipment data set that meets the target range; Based on the equipment data set that meets the target range, combined with the real-time operating environment of the equipment, weighted sorting is performed according to the working characteristics, environmental conditions and original operating data of each equipment component, and the equipment parameters are adjusted using the formula: ； Generate a list of weighted values ​​of equipment components; in, is the weighted value of the i-th equipment component, represents the performance weight of the equipment component, Score the quality of the configuration parameters of the equipment components, Indicates the number of equipment components; According to the weighted value list of the equipment components, the configuration combinations that meet the target are classified and sorted, the performance of each combination is analyzed according to the matching degree, the combination configuration with the best performance is selected, the parameter combinations that do not meet the requirements are eliminated, and the filtered parameter set is generated.

5. The visualization device component configuration system according to claim 4, characterized in that: The steps for obtaining the parameter conflict value are specifically as follows: Analyze the logical relationship between the configuration parameters through the filtered parameter set, extract the direct relationship between the parameters and perform conditional judgment to determine whether the parameters conflict and obtain the parameter relationship judgment value; Summarize and analyze the parameter relationship judgment values, quantify the conflicts, set thresholds and compare them with the judgment values, screen the parameter conflict sources, and use logical operations to obtain preliminary conflict values; According to the preliminary conflict value, combined with the weight parameter, optimization processing is performed using the formula: ； Calculate and obtain parameter conflict values; in, Represents parameter conflict values, Represents the degree of conflict between parameter k and other parameters during the configuration process. is the weight of parameter k, To adjust the coefficient of conflict impact, m is the total number of parameters.

6. The visualization device component configuration system according to claim 5, characterized in that: The steps for obtaining the parameter conflict interference level table are specifically as follows: Based on the parameter conflict values, the parameters are measured, recorded and compared, the conflict source information is recorded, and for each conflict parameter, the real-time conflict impact is analyzed, and the data is standardized and classified and sorted according to the conflict degree, conflict type and mutual correlation, to generate a conflict parameter data table; Based on the conflict parameter data table, a quantitative analysis of the conflict parameters is performed, and the influence range of each parameter is layered, sorted and classified using differentiated standards, and the contribution to operation stability is evaluated through the fluctuation of the interference parameters to generate a conflict value classification table; Based on the conflict value classification table, the differentiated conflict values ​​are analyzed, classified according to the impact range, interference intensity, and occurrence frequency, the impact of the conflict values ​​is screened using a predetermined threshold, the parameter interference relationship is analyzed, and a conflict interference level table is generated.

7. The visualization device component configuration system according to claim 6, characterized in that: The steps for obtaining the adjusted parameter relationship network are specifically as follows: The associated parameters are classified by the parameter conflict interference level table, the interference level of each parameter is extracted and the corresponding value range is defined according to the level, parameters that meet the requirements are screened, and the interference parameter interval range is obtained; Compare the matching situation between the interference parameter interval range and the additional parameters, check whether each adjustment parameter conflicts with the non-conflicting parameters, determine the combination of conflicting parameters and non-conflicting parameters, and obtain the configuration conflict situation; Based on the configuration conflict, optimize and adjust the parameter configuration combination, readjust the relationship between the conflicting parameters and the non-conflicting parameters, and use the formula: ； Obtain the adjusted parameter relationship network; in, Represents the adjusted parameter relationship network value, Representative parameters The weight of Representative parameters The weight of is the weight coefficient of conflict judgment, is the correction factor.

8. The visualization device component configuration system according to claim 7, characterized in that: The steps for obtaining the inspection result of the visualization configuration element are specifically as follows: Based on the adjusted parameter relationship network, model the relationship between each parameter and the component operation status, record the change and real-time impact of each adjusted parameter, analyze the support of the parameter to the component operation status, evaluate the contribution to stability, and obtain component operation support data; Based on the component operation support data, each adjustment configuration is verified, the adaptability of the configuration to the component operation state is evaluated, the reaction impact of the differentiated configuration is analyzed, the configuration is classified according to the configuration support and stability, and a configuration logic consistency verification table is generated; Based on the configuration logic consistency verification table, combined with the impact of each adjustment configuration on the operating status, the configuration matching performance of the components is analyzed, the response under differentiated operating conditions is evaluated, and a visual configuration component inspection result is generated.

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