Intelligent evaluation and measurement integrated system of measurement and control equipment based on digital twinborn body

Abstract

The invention discloses an intelligent evaluation and measurement integrated system of measurement and control equipment based on a digital twinborn body, and relates to the technical field of industrial automation and digital twinborn. The technical key points are as follows: the system comprises a data acquisition module, a digital twin modeling module, an intelligent index evaluation module, a weight fusion calculation module, a grade scoring reasoning module and a feedback regulation module. The system collects operation parameters of measurement and control equipment through a multi-source heterogeneous sensor, constructs digital twin models in one-to-one correspondence with the operation parameters, and achieves virtual synchronous mapping of equipment states; an index grade evaluation system is constructed based on a fuzzy membership function, subjective and objective weight fusion is carried out in combination with an analytic hierarchy process and an entropy weight method, a comprehensive weight vector is generated, and quantitative evaluation of the equipment intelligence grade is completed through a fuzzy reasoning algorithm. According to the system, parameter adjustment suggestions are further output through an error feedback mechanism, and model closed-loop correction and performance self-adaptive optimization are achieved.

Description

Technical Field

[0001] This invention relates to the fields of industrial automation and digital twin technology, and more specifically, to an integrated intelligent evaluation and measurement system for measurement and control equipment based on digital twins. Background Technology

[0002] With the continuous improvement of intelligent manufacturing and industrial automation, various measurement and control equipment plays an increasingly crucial role in the production process. Their performance directly affects equipment operational safety, product quality stability, and system energy efficiency. As the nerve center of the industrial field, measurement and control equipment encompasses multiple key subsystems, including sensors, signal processing modules, control logic modules, actuators, and communication interfaces, and is widely used in machine tools, robots, power generation equipment, chemical plants, and rail transit systems. However, current assessments of the health status, intelligence level, and functional adaptability of measurement and control equipment mainly rely on manual routine inspections or qualitative experience-based judgments, lacking a systematic, quantitative, and automated evaluation mechanism.

[0003] Traditional equipment evaluation methods mostly focus on local performance testing or static parameter verification, failing to reflect in real time the dynamic characteristic degradation caused by environmental disturbances, changes in operating conditions, or aging wear during system operation. For example, some high-end CNC machine tools may experience drive circuit oscillations, power fluctuations, or thermal drift during short-term operation, but due to the lack of high-frequency data monitoring and intelligent evaluation mechanisms, these problems are often masked until system failure occurs. Furthermore, there is a widespread problem of "emphasizing control while neglecting evaluation," with many measurement and control equipment lacking performance perception, intelligent diagnostics, and status feedback capabilities throughout their entire lifecycle.

[0004] In recent years, the development of new-generation information technologies such as digital twins, artificial intelligence, and edge computing has provided new pathways for achieving intelligent, dynamic, and refined equipment evaluation. Digital twins, as a bridge integrating physical entities and virtual models, can synchronously map and predict the state changes of physical objects in virtual space, and support data-driven dynamic analysis and optimal control strategy deduction. Especially in the field of measurement and control equipment, if a twin model can be built based on real-time data, and a comprehensive evaluation system can be established by combining multi-source information fusion, fuzzy comprehensive evaluation, hierarchical analysis, entropy weight calculation, and other methods, it is expected to break through the limitations of existing evaluation methods and achieve a leap from "static inspection" to "dynamic intelligent evaluation."

[0005] Currently, while some research has attempted to apply digital twins to manufacturing processes, workflow simulation, or maintenance optimization, systematic technologies specifically designed for "integrated assessment of the intelligent level of measurement and control equipment and measurement feedback" remain scarce. In particular, there is a lack of unified frameworks and mature methods for evaluating heterogeneous features of multiple indicators, integrating subjective and objective weights, intelligent reasoning based on hierarchical distribution, and quantitative feedback control. Furthermore, practical engineering challenges such as inconsistent sensor accuracy, diverse communication protocols, and difficulties in constructing indicator systems also pose challenges to the robustness and adaptability of the system.

[0006] Therefore, there is an urgent need to develop an intelligent integrated system for testing and measurement of control equipment based on digital twins to solve these problems. Summary of the Invention

[0007] The purpose of this invention is to solve the technical problems mentioned in the background section and to provide an integrated intelligent evaluation and measurement system for measurement and control equipment based on digital twins.

[0008] The above-mentioned objective of the present invention is achieved as follows:

[0009] A digital twin-based intelligent evaluation and measurement integrated system for measurement and control equipment includes:

[0010] Data acquisition module: used to collect operating parameter data x of the measurement and control equipment;

[0011] Digital Twin Modeling Module: Used to build virtual twin models of measurement and control equipment. equip And generate twin prediction states.

[0012] Intelligent indicator evaluation module: used to construct an evaluation system containing n intelligent indicators I = {I1, I2, ..., I...} n The indicator system of};

[0013] Weight calculation module: Used to calculate the final fusion weight w for each indicator. i ;

[0014] Fuzzy comprehensive evaluation module: used to calculate the membership degree μ of the equipment's intelligence level. ij And the final intelligent score S int ;

[0015] Intelligent measurement feedback module: used to compare the test results with the reference standard values. Compare and output adjustment suggestions; where each indicator I i membership degree μ ij Calculated using the trapezoidal membership function:

[0016]

[0017] Where x represents the original data collected; μ ij Let a be the membership degree of the i-th indicator to the j-th level; j ,b j ,c j ,d j The four node values ​​of the trapezoidal fuzzy function for level j represent the level boundary; j = 1, 2, 3, ... k, where k is the total number of levels.

[0018] Furthermore, the weight calculation module includes both subjective weighting and objective weighting methods:

[0019] Subjective weighting method: Constructing a judgment matrix C = [c ij ] Calculate the largest eigenvalue λ max , corresponding to the normalized eigenvector As a subjective weight;

[0020] When calculating subjective weights using the analytic hierarchy process (AHP), experts first score the importance of each evaluation indicator using a pairwise comparison method, constructing a judgment matrix C = [c ij ], where c ij This indicates the importance of the i-th indicator relative to the j-th indicator; if c ij >1 indicates that indicator I i Compared to I j More importantly; c ij =1 indicates equal importance, c ji =1 / c ij All c ij ∈{1,3,5,7,9} and its reciprocals, following the Saaty scaling method, this matrix is ​​a positively reciprocal matrix, and its largest eigenvalue λ max It can be used for consistency testing; the normalized eigenvector is the subjective weight vector W. s ;

[0021] Objective weighting method: Based on the entropy weighting method, the index value is defined and normalized to... Calculate the information entropy H of the i-th indicator. i ,Right now: And derive the objective weights: Where, x ij H represents the value of the j-th object on the i-th metric. i Let m be the information entropy of the i-th indicator; m be the number of evaluation objects. Let be the subjective and objective weights, respectively, and c be the information entropy constant.

[0022] Furthermore, the final fusion weight w i By integrating subjective and objective weights through the minimum deviation J optimization function, the optimization objective is:

[0023]

[0024] Finally, the final fusion weight w is obtained. i :

[0025]

[0026] Among them, w i Let J be the final weight of the i-th indicator; α∈[0,1], where α is the fusion coefficient, determined by minimizing J.

[0027] Furthermore, the fuzzy comprehensive evaluation module adopts the following model: Constructing an index membership matrix R = [μ ij ] n×k Combined with the weight vector W = {w1,...,w n}, thus obtaining the overall membership degree B for each level:

[0028] B = W·R = {B1, B2, ..., B} k};

[0029] Based on the level value L j The final intelligent score S of the measurement and control equipment is calculated. int :

[0030]

[0031] Among them, B j L represents the membership degree of the system to level j. j S is the quantitative score value corresponding to level j. int For the final intelligent scoring, k represents the number of level divisions.

[0032] Furthermore, the digital twin modeling module is based on model M. equip Generate the predicted state using the currently collected data D(t):

[0033]

[0034] in, P represents the predicted state of the twin model at time t; P represents the model parameters; and D(t) represents the sensor data collected at time t.

[0035] Furthermore, the twin model optimizes the objective inversion model parameters through the following steps:

[0036]

[0037] Where S(t) represents the actual state of the measurement and control equipment; For twin prediction state; P * To fit the optimal model parameters; P represents the model parameters.

[0038] Furthermore, the intelligent measurement feedback module is used to compare the actual value with the standard value and calculate the index error:

[0039]

[0040] And output feedback adjustment amount:

[0041] ΔP i =-K·ε i ;

[0042] Where, x i This is the i-th actual collected value; ΔP is the reference standard value for the i-th index; K is the adjustment gain coefficient; ΔP i Suggestions for correcting parameters for the model or system.

[0043] Furthermore, the data acquisition module supports multi-protocol access and is compatible with Modbus, OPCUA, Profinet, IEC104 and HostLink industrial communication protocols. The connected sensors include: Hall effect voltage sensors, current transformers, piezoelectric pressure sensors, platinum resistance temperature sensors, triaxial accelerometers, acoustic emission sensors and laser displacement sensors.

[0044] Furthermore, the system features a front-end and back-end separated web architecture. The front-end uses the Vue and ECharts frameworks to build the interactive interface, while the back-end is based on the JavaSSM architecture to implement indicator management, data uploading, scoring algorithms, and report generation. The database used is MySQL.

[0045] Furthermore, the system is applicable to measurement and control equipment such as PLCs, robots, frequency converters, instruments, CNC machine tools, and valve positioners, enabling quantitative assessment, dynamic monitoring, virtual-real linkage, and closed-loop regulation of the level of intelligence.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. This invention constructs an integrated intelligent evaluation and measurement system for measurement and control equipment based on digital twins, achieving real-time mapping and accurate prediction of the operating status of measurement and control equipment. Furthermore, this system collects operating data based on multi-source heterogeneous sensors, integrates digital modeling, real-time simulation, and optimal parameter optimization algorithms to construct a highly consistent and highly responsive twin model, which can dynamically simulate the operating behavior of equipment, filling the gaps in timeliness and completeness of traditional evaluation methods, and effectively improving the status identification and virtual visualization capabilities of measurement and control equipment.

[0048] 2. The present invention introduces an intelligent grade evaluation method that integrates subjective and objective weighting. It uses fuzzy membership functions to construct an index grade matrix, combines the analytic hierarchy process (AHP) and entropy weighting to calculate the comprehensive weight, and obtains the grade distribution and quantitative score through a fuzzy inference model. This algorithm structure retains the subjective judgment of expert experience while incorporating data-driven objective analysis, ensuring the scientific nature and fairness of the evaluation results. Compared with single weighting or fixed rule evaluation methods, this system can adapt to the performance index system and grade classification requirements of diverse measurement and control equipment.

[0049] 3. The solution of this invention further realizes closed-loop coupling between evaluation results and system feedback. It generates parameter adjustment suggestions and dynamically corrects the twin model through an error feedback mechanism, thereby forming an intelligent adaptive mechanism of "measurement-evaluation-feedback-optimization". This mechanism can be widely applied to condition assessment tasks of typical equipment such as CNC machine tools, industrial robots, and power control cabinets, possessing good generalization ability and deployment flexibility, significantly improving the efficiency of equipment lifecycle management and the level of intelligent operation and maintenance. Attached Figure Description

[0050] Figure 1 This is a system block diagram of an intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twins.

[0051] Figure 2 This is a diagram showing the composition of the weight calculation module in an integrated intelligent evaluation and measurement system for measurement and control equipment based on digital twins.

[0052] Figure 3 This diagram shows the composition of the data acquisition module in an integrated intelligent evaluation and measurement system for measurement and control equipment based on digital twins.

[0053] Figure 4 Partial flowchart of an intelligent evaluation and measurement integration system for measurement and control equipment based on digital twins. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0055] The implementation of the present invention will be described in detail below with reference to specific embodiments.

[0056] Example: See Figures 1-4As shown in the figure, the present invention proposes an integrated intelligent evaluation and measurement system for measurement and control equipment based on digital twins. This system integrates multiple functional modules, including data acquisition, virtual modeling, weight calculation, intelligent evaluation, and feedback adjustment, constructing a complete intelligent evaluation closed loop. Deployed in a measurement and control scenario, the system, through real-time acquisition of multi-source heterogeneous data and digital twin simulation, can achieve quantitative perception, level determination, and model correction of equipment operating status, thereby improving the adaptive capability and measurement intelligence level of the measurement and control system.

[0057] When the system is running, such as Figure 4 First, the data acquisition module collects the operating parameters of the measurement and control equipment in real time, resulting in operating parameter data x. The acquisition module is compatible with industrial communication protocols such as Modbus, OPCUA, Profinet, IEC104, and HostLink, and can uniformly process the outputs of sensors such as voltage, current, pressure, temperature, vibration, displacement, acceleration, acoustic emission, and laser ranging.

[0058] The digital twin modeling module is based on model M equip Based on the current sensor data D(t), output the current predicted state. The mathematical relationship is as follows:

[0059]

[0060] in, Let P be the predicted state of the twin model at time t; P be the model parameters; and D(t) be the sensor data collected at time t. To ensure a high fit between the model and the real system, the system adaptively adjusts the parameters using the following objective function:

[0061]

[0062] Where S(t) represents the actual state of the measurement and control equipment; For twin prediction state; P * To fit the optimal model parameters.

[0063] The system's intelligent evaluation index set is I = {I1, I2, ..., I...} n Each indicator is divided into k levels, and each level corresponds to a trapezoidal membership function. The membership degree μ of the i-th indicator at the j-th level is calculated. ij ,Right now:

[0064]

[0065] Where x represents the original data collected; μ ij Let a be the membership degree of the i-th indicator to the j-th level; j ,b j ,cj ,d j The four node values ​​of the trapezoidal fuzzy function for level j represent the level boundary; j = 1, 2, 3, ... k, where k is the total number of levels.

[0066] To achieve comprehensive evaluation of multiple indicators, the system has set up a weight calculation module, which adopts a combination of subjective and objective weighting.

[0067] Subjective weighting employs the analytic hierarchy process (AHP) to construct a judgment matrix C = [c ij The largest eigenvalue λ is obtained. max Extract normalized feature vectors For subjective weights, when calculating subjective weights using the analytic hierarchy process (AHP), experts first score the importance of each evaluation indicator using a pairwise comparison method, constructing a judgment matrix C = [c ij ], where c ij This indicates the importance of the i-th indicator relative to the j-th indicator; if c ij >1 indicates that indicator I i Compared to I j More importantly; c ij =1 indicates equal importance, c ji =1 / c ij All c ij ∈{1,3,5,7,9} and its reciprocals, following the Saaty scaling method, this matrix is ​​a positively reciprocal matrix, and its largest eigenvalue λ max It can be used for consistency testing; the normalized eigenvector is the subjective weight vector W. s .

[0068] Objective weighting is based on the entropy weighting method. Let the value of the i-th indicator in the j-th sample be x. ij Calculate the normalized value: Then calculate the information entropy H i ,Right now:

[0069] Objective weight Expressed as: The final weights are fused using the weighted minimum deviation combination method to construct the optimization objective function: Where α∈[0,1] are the fusion coefficients, the objective is to minimize J, and the fusion weights are obtained by solving:

[0070]

[0071] The membership matrix R = [μ] ij ] n×k With the fusion weight vector W = {w1,...,w n Multiplying these results in the hierarchical comprehensive membership vector B = W·R = {B1, B2, ..., B}.k}, and then combine the score L for each level j The final intelligent evaluation score is obtained as follows: The system compares the evaluation score with historical reference standard values. Comparison, calculation error: Based on this, the feedback adjustment amount is output: ΔP i =-K·ε i Where K is the adjustment gain coefficient, used to guide the correction of model parameters and form an intelligent adaptive adjustment closed loop.

[0072] The system also supports web architecture deployment. The front end uses Vue + ECharts for visualization and human-computer interaction, while the back end uses the JavaSSM framework for data uploading, evaluation calculation and report generation. The database uses MySQL for storage.

[0073] The system is applicable to various measurement and control objects such as PLCs, robots, frequency converters, instruments, CNC machine tools, and valves. It has high adaptability and scalability, and can realize quantitative evaluation of equipment operating status, virtual and real integrated monitoring, and environmental control optimization and adjustment, and has good prospects for industrial applications.

[0074] This embodiment takes a six-axis industrial robot control cabinet as an example and applies the intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twin provided by this invention to model, evaluate and optimize its operating status.

[0075] At the operational site, Hall effect voltage sensors, current transformers, PT100 platinum resistance temperature sensors, IEPE triaxial accelerometers, and acoustic emission sensors were deployed to collect real-time data on the voltage, current, temperature rise rate, vibration amplitude, and noise intensity of the robot control cabinet during a continuous 30-minute operation. The sampling period was 1 second. The average values ​​of the five indicators were obtained after processing: power supply voltage 220.5V, drive current 3.1A, control board temperature rise rate 1.8℃ / min, cabinet vibration amplitude 6.4mm / s, and fan noise intensity 62dB.

[0076] Subsequently, using the digital twin modeling module, a virtual twin model M was constructed based on the electromechanical structure and historical operating data of the control cabinet. equip Using the currently collected data D(t) as input, the simulation prediction state is generated. Where P is the model parameter vector. This is achieved by minimizing the objective function. Obtain the optimal model parameters P * This enables the virtual twin to synchronously predict and approximate the state of physical objects.

[0077] The system defines five categories of intelligent evaluation indicators: power supply stability, drive current fluctuation, control board temperature rise rate, enclosure vibration amplitude, and fan noise intensity. Each indicator is assigned a four-level standard: excellent, good, average, and poor. Taking the enclosure vibration amplitude indicator as an example, its corresponding trapezoidal membership function nodes are set as follows: Level 1 (excellent): a1=0, b1=1, c1=2, d1=3; Level 2 (good): a2=2, b2=3, c2=4, d2=5; Level 3 (average): a3=4, b3=5, c3=6, d3=7; Level 4 (poor): a4=6, b4=7, c4=8, d4=9.

[0078] When the measured value of this indicator is 6.4 mm / s, the membership degree for the fourth level is calculated as follows:

[0079]

[0080] The membership degrees of the other four indicators were also obtained using this method, forming a complete membership degree matrix R = [μij]. 5×4 .

[0081] Subjective weights are used to establish a judgment matrix C = [c ij The importance of each indicator is compared pairwise based on expert scores. The largest eigenvalue λ is calculated. max =5.12, consistency ratio CR = 0.03, meeting the consistency requirement, and the normalized eigenvector W is obtained. s ={0.37,0.25,0.16,0.13,0.09}.

[0082] The objective weighting method employs entropy weighting, calculating the information entropy H based on the normalized data of each indicator. i =-k∑ j p ij lnp ij Then W is calculated. o ={0.35,0.27,0.14,0.11,0.13}.

[0083] The subjective and objective weights are combined using a fusion coefficient α = 0.6 to obtain the final weight:

[0084]

[0085] The calculated result is: W = {0.364, 0.256, 0.154, 0.124, 0.102}.

[0086] In the calculation of the overall score, the weight vector W is multiplied by the membership matrix R to obtain the overall membership degree of the level B = W·R = {0.18, 0.31, 0.37, 0.14}.

[0087] With quantitative values ​​of 100, 80, 60, and 40 for the four levels, the final intelligent scoring result is:

[0088]

[0089] The system determined that the control cabinet's operating status was between "good" and "medium," indicating a certain risk of abnormal vibration and noise, requiring further adjustments.

[0090] Based on the above scoring results, the system enters the feedback adjustment module, which adjusts the measured values ​​of each indicator by x. i Compared with reference standard value The comparison yields the error vector ε = {+0.5,+0.6,+0.6,+2.9,+7}. The feedback gain coefficient K is set to 0.1, and the feedback adjustment is:

[0091] ΔP=-K·ε={-0.05,-0.06,-0.06,-0.29,-0.7};

[0092] Based on this, the system suggests adjusting the control cabinet fan speed, PID response threshold, and electrical wiring vibration damping structure parameters, and feeds these parameters back to the digital twin for the next round of model reconstruction and control strategy iteration.

[0093] This embodiment demonstrates the complete technical closed-loop process of the system of the present invention in terms of real-time data acquisition, multi-source fusion modeling, fuzzy evaluation of indicators, subjective and objective weight calculation, grade scoring reasoning and error feedback control, and verifies the feasibility and intelligence level of the system in industrial settings.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An integrated intelligent evaluation and measurement system for measurement and control equipment based on digital twins, characterized in that, include: Data acquisition module: used to collect operating parameter data x of the measurement and control equipment; Digital Twin Modeling Module: Used to build virtual twin models of measurement and control equipment. equip And generate twin prediction states. Intelligent indicator evaluation module: used to construct an evaluation system containing n intelligent indicators I = {I1, I2, ..., I...} n The indicator system of}; Weight calculation module: Used to calculate the final fusion weight w for each indicator. i ; Fuzzy comprehensive evaluation module: used to calculate the membership degree μ of the equipment's intelligence level. ij And the final intelligent score S int ; Intelligent measurement feedback module: used to compare the test results with the reference standard values. Compare and output adjustment suggestions; where each indicator I i membership degree μ ij Calculated using the trapezoidal membership function: Where x represents the original data collected; μ ij Let a be the membership degree of the i-th indicator to the j-th level; j ,b j ,c j ,d j The four node values ​​of the trapezoidal fuzzy function for level j represent the level boundary; j = 1, 2, 3, ... k, where k is the total number of levels.

2. The intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twins as described in claim 1, characterized in that, The weight calculation module includes subjective weighting and objective weighting methods: Subjective weighting method: Constructing a judgment matrix C = [c ij ] Calculate the largest eigenvalue λ max , corresponding to the normalized eigenvector As a subjective weight; When calculating subjective weights using the analytic hierarchy process (AHP), experts first score the importance of each evaluation indicator using a pairwise comparison method, constructing a judgment matrix C = [c ij ], where c ij This indicates the importance of the i-th indicator relative to the j-th indicator; if c ij >1 indicates that indicator I i Compared to I j More importantly; c ij =1 indicates equal importance, c ji =1 / c ij All c ij ∈{1,3,5,7,9} and its reciprocals, following the Saaty scaling method, this matrix is ​​a positively reciprocal matrix, and its largest eigenvalue λ max It can be used for consistency testing; the normalized eigenvector is the subjective weight vector W. s ; Objective weighting method: Based on the entropy weighting method, the index value is defined and normalized to... Calculate the information entropy H of the i-th indicator. i ,Right now: And derive the objective weights: Where, x ij H represents the value of the j-th object on the i-th metric. i Let m be the information entropy of the i-th indicator; m be the number of evaluation objects. Let be the subjective and objective weights, respectively, and c be the information entropy constant.

3. The intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twins as described in claim 2, characterized in that, The final fusion weight w i By integrating subjective and objective weights through the minimum deviation J optimization function, the optimization objective is: Finally, the final fusion weight w is obtained. i : Among them, w i Let J be the final weight of the i-th indicator; α∈[0,1], where α is the fusion coefficient, determined by minimizing J.

4. The intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twins as described in claim 1, characterized in that, The fuzzy comprehensive evaluation module adopts the following model: Constructing an index membership matrix R = μ ij ] n×k Combined with the weight vector W = {w1,...,w n }, thus obtaining the overall membership degree B for each level: B=W·R={B1,B2,...,B k }; Based on the level value L j The final intelligent score S of the measurement and control equipment is calculated. int : Among them, B j L represents the membership degree of the system to level j. j S is the quantitative score value corresponding to level j. int For the final intelligent scoring, k represents the number of level divisions.

5. The intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twins as described in claim 1, characterized in that, The digital twin modeling module is based on model M. equip Generate the predicted state using the currently collected data D(t): in, P represents the predicted state of the twin model at time t; P represents the model parameters; and D(t) represents the sensor data collected at time t.

6. The intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twins as described in claim 5, characterized in that, The twin model optimizes the target inversion model parameters through the following steps: Where S(t) represents the actual state of the measurement and control equipment; For twin prediction state; P * To fit the optimal model parameters; P represents the model parameters.

7. The intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twins as described in claim 1, characterized in that, The intelligent measurement feedback module is used to compare the actual value with the standard value and calculate the index error: And output feedback adjustment amount: ΔP i =-K·e i ; Where, x i This is the i-th actual collected value; ΔP is the reference standard value for the i-th index; K is the adjustment gain coefficient; ΔP i Suggestions for correcting parameters for the model or system.

8. The intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twins as described in claim 1, characterized in that, The data acquisition module supports multiple protocol access and is compatible with Modbus, OPCUA, Profinet, IEC104 and HostLink industrial communication protocols. The connected sensors include: Hall effect voltage sensors, current transformers, piezoelectric pressure sensors, platinum resistance temperature sensors, triaxial accelerometers, acoustic emission sensors and laser displacement sensors.

9. The intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twins according to claim 1, characterized in that, The system features a front-end and back-end separated web architecture. The front-end uses the Vue and ECharts frameworks to build the interactive interface, while the back-end is based on the JavaSSM architecture to implement indicator management, data uploading, scoring algorithms and report generation. The database used is MySQL.

10. The intelligent evaluation and measurement integrated system for measurement and control equipment based on digital twins according to claim 1, characterized in that, The system is applicable to measurement and control equipment such as PLCs, robots, frequency converters, instruments, CNC machine tools, and valve positioners, enabling quantitative assessment, dynamic monitoring, virtual-real linkage, and closed-loop regulation of the level of intelligence.

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