An automated and intelligent scheduling system for the measurement process of a gear measurement center

Abstract

This invention relates to the field of gear precision measurement and intelligent manufacturing automation control technology, specifically to an automated and intelligent scheduling system for the measurement process of a gear measurement center. The system includes: a data acquisition module for acquiring the processing state characteristic parameters of the gear under test during machining; a first processing module for marking the physical index of abnormal gear tooth units as high-risk areas; a second processing module for decomposing the measurement program into a set of discrete measurement micro-tasks; a third processing module for calculating the process capability confidence index of the gear under test; and a scheduling execution module for generating hierarchical scheduling instructions: if the process capability confidence index is higher than a preset exemption threshold, a direct release instruction is generated and the remaining measurement tasks are skipped; otherwise, after the optimal cooling waiting time, a focused scanning task is triggered. This invention effectively eliminates the measurement uncertainty caused by residual heat during machining, ensuring the consistency of precision geometric measurement results.

Description

Technical Field

[0001] This invention relates to the field of precision gear measurement and intelligent manufacturing automation control technology, specifically to an automated and intelligent scheduling system for the measurement process of a gear measurement center. Background Technology

[0002] Gear measurement centers are a crucial link in precision machinery manufacturing to ensure product quality. Their measurement efficiency and accuracy directly affect the overall throughput and processing quality stability of the production line. Gear measurement systems mainly include data acquisition units, coordinate measurement actuators, and scheduling control modules. They achieve error assessment through precise measurement of workpiece geometric parameters. Optimizing the scheduling parameters of the measurement process to improve detection efficiency and reduce measurement uncertainty has become a key research direction in the field of precision machining.

[0003] Existing technologies mainly rely on fixed full-inspection rules or simple feedback control, which cannot fully utilize multi-source state information such as cutting load and ambient temperature during the machining process for comprehensive decision-making. This makes it difficult to adapt to complex and ever-changing machining conditions and production loads. At the same time, the passive response mode makes it difficult to implement regulation at the optimal thermal stability time, and cannot effectively deal with the impact of residual heat and load fluctuations during machining. In addition, the thermodynamic evolution of gears has obvious nonlinear characteristics and time-delay effects. Existing technologies cannot capture the dynamic changes in the workpiece cooling process, and the regulation accuracy and response speed are limited. Especially when there are small changes in the machining state, it is difficult to achieve targeted detection, resulting in blind allocation of measurement resources. Therefore, there is an urgent need for a control scheme that can integrate machining fingerprint recognition, thermodynamic modeling, and intelligent task scheduling to solve the problems of disconnect between measurement process and machining state, low resource utilization, and insufficient control of thermal stability. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an automated and intelligent scheduling system for the measurement process of a gear measurement center. Specifically, the technical solution of this invention includes:

[0005] The data acquisition module is used to acquire the machining status characteristic parameters of the gear under test during the machining process. The machining status characteristic parameters include: main drive load data, cutting medium temperature, ambient temperature, and machining timestamp.

[0006] The first processing module is used to identify abnormal gear tooth units on the gear under test that have experienced sudden load changes based on the main drive load data, and to mark the physical index of the abnormal gear tooth units as high-risk areas.

[0007] The second processing module is used to decompose the complete measurement program for the gear under test into a set of discrete measurement micro-tasks based on the high-risk area. The set of discrete measurement micro-tasks includes: a thermal scanning task for the overall thermal distribution, a coarse scanning task for the global coordinate system, and a high-precision focusing scanning task that is only performed for the high-risk area.

[0008] The third processing module is used to calculate the process capability confidence index of the gear under test based on the execution results of the thermal scanning task and the coarse scanning task, combined with the processing state characteristic parameters. The process capability confidence index is expressed as the probability value that the gear under test meets the preset quality standard.

[0009] The scheduling execution module is used to generate hierarchical scheduling instructions based on the process capability confidence index.

[0010] If the process capability confidence index is higher than the preset exemption threshold, a direct release instruction is generated and the remaining measurement tasks are skipped.

[0011] If the process capability confidence index is lower than or equal to the exemption threshold, the optimal cooling waiting time is calculated based on the data from the thermal scanning task, and the focused scanning task is triggered after the optimal cooling waiting time has elapsed.

[0012] Preferably, the operation of the first processing module to identify abnormal gear tooth units includes:

[0013] The main drive load data is used to construct the spindle power spectrum curve;

[0014] Monitor the micro-abrupt signals of the spindle power spectrum curve when machining different gear units;

[0015] Establish a mapping relationship between the micro-mutation signal and the machining index number of the gear under test, lock the k-th gear tooth unit that generates the micro-mutation signal, and define the k-th gear tooth unit and its symmetrically distributed gear tooth units as the high-risk area.

[0016] Preferably, the tasks in the discrete measurement microtask set are defined as follows:

[0017] The thermal scanning task is used to acquire surface temperature field distribution data of the gear under test using an infrared thermal imaging device.

[0018] The coarse scanning task is used to collect dimensional data of a preset number of discontinuous gear tooth units on the gear under test, so as to establish a measurement coordinate system and obtain basic machining errors;

[0019] The focused scanning task is used to perform geometric measurements on the high-risk region at a higher sampling density than the coarse scanning task.

[0020] Preferably, the operation of the third processing module in calculating the process capability confidence index includes:

[0021] Call upon the basic machining error data output by the coarse scan task;

[0022] The basic machining error data and the machining state characteristic parameters are used as prior information and input into the Bayesian inference model to calculate the posterior probability distribution of the process capability index of the gear under test.

[0023] The integral probability value of the process capability index in the posterior probability distribution that is greater than a preset lower threshold is determined as the process capability confidence index.

[0024] Preferably, the operation of the scheduling execution module to calculate the optimal cooling waiting time includes:

[0025] The surface temperature field distribution data is retrieved, and the thermal decay curve of the gear under test at the current ambient temperature is calculated based on the non-uniform heat field transfer model.

[0026] The rate of temperature change over time on the thermal decay curve is calculated, and the time required to reduce the rate of change to a preset rate of change threshold is determined as the optimal cooling waiting time.

[0027] Preferably, the scheduling execution module is also used to perform time-slice interleaving scheduling:

[0028] During the period when the gear under test is in the optimal cooling waiting time, the control measuring device performs a thermal scan task or a coarse scan task on another gear under test.

[0029] When the optimal cooling waiting time has ended and the measuring device is idle, the focusing scan task of the gear under test is inserted.

[0030] Preferably, the main drive load data is the real-time spindle power data of the gear hobbing machine or gear grinding machine.

[0031] Preferably, when generating the direct release instruction, the scheduling execution module also performs the following operations:

[0032] Record the mapping relationship between the measurement data of the coarse scanning task and the processing state characteristic parameters to generate an associated feature dataset;

[0033] The associated feature dataset is fed back to the upstream processing control unit to compensate for the processing parameters of the gear to be tested later.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. This system can accurately locate abnormal gear teeth with sudden load changes by monitoring spindle load fluctuations during the machining process and define them as high-risk areas. This recognition mechanism based on machining fingerprints changes the traditional blind scanning mode and realizes targeted high-precision detection of potential quality risk points. This not only significantly enhances the recognition accuracy of local small machining defects, but also avoids risk omissions caused by blind allocation of measurement resources, ensuring the reliability of gear inspection under complex working conditions.

[0036] 2. This system introduces a non-uniform heat field transfer model, which dynamically calculates the optimal cooling waiting time by acquiring the temperature field data of the gear surface in real time. Compared with the traditional fixed constant temperature waiting, this solution can accurately capture the nonlinear change law of the workpiece cooling process, ensuring that the high-precision measurement task is initiated under the optimal thermal stability state. This active thermal stability control method effectively eliminates the measurement uncertainty caused by residual heat from processing and ensures the consistency of precision geometric measurement results.

[0037] 3. This system employs measurement task discretization technology and time-slice staggered scheduling logic, performing hierarchical scheduling based on process capability confidence. During idle periods when the workpiece to be tested is cooling and waiting, the control equipment executes pre-inspection tasks for other workpieces, achieving in-depth utilization of measurement resources. This intelligent concurrent processing mode solves the problem of the measurement center serving as a bottleneck in the production line, significantly shortening the workpiece turnaround time and improving the overall throughput of the inspection system while ensuring the quality interception rate.

[0038] 4. This system constructs a complete closed loop from processing monitoring to measurement decision-making and parameter compensation; by calculating the process capability confidence index, it enables the direct release of high-quality products and feeds back the correlation characteristics of measurement data and processing status to the upstream processing unit in real time; this data-driven feedback mechanism not only realizes the intelligent leap of the measurement process, but also automatically optimizes the processing parameters of subsequent workpieces, realizing the evolution from post-interception to pre-interception prevention, and effectively improving the process stability of the entire production line. Attached Figure Description

[0039] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0040] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0042] Example 1:

[0043] Please see Figure 1An automated and intelligent scheduling system for the measurement process of a gear measurement center, comprising:

[0044] The data acquisition module is used to acquire the machining status characteristic parameters of the gear under test during the machining process. The machining status characteristic parameters include: main drive load data, cutting medium temperature, ambient temperature, and machining timestamp.

[0045] The first processing module is used to identify abnormal gear tooth units on the gear under test that have experienced sudden load changes based on the main drive load data, and to mark the physical index of the abnormal gear tooth units as high-risk areas.

[0046] The second processing module is used to decompose the complete measurement program for the gear under test into a set of discrete measurement micro-tasks based on the high-risk area. The set of discrete measurement micro-tasks includes: a thermal scanning task for the overall thermal distribution, a coarse scanning task for the global coordinate system, and a high-precision focusing scanning task that is only performed for the high-risk area.

[0047] The third processing module is used to calculate the process capability confidence index of the gear under test based on the execution results of the thermal scanning task and the coarse scanning task, combined with the processing state characteristic parameters. The process capability confidence index is expressed as the probability value that the gear under test meets the preset quality standard.

[0048] The scheduling execution module is used to generate hierarchical scheduling instructions based on the process capability confidence index.

[0049] If the process capability confidence index is higher than the preset exemption threshold, a direct release instruction is generated and the remaining measurement tasks are skipped.

[0050] If the process capability confidence index is lower than or equal to the exemption threshold, the optimal cooling waiting time is calculated based on the data from the thermal scanning task, and the focused scanning task is triggered after the optimal cooling waiting time has elapsed.

[0051] An automated and intelligent scheduling system for the measurement process of a gear measurement center, as the core overall solution of this invention, constructs a complete closed loop from processing monitoring to measurement decision-making through the synergistic effect of a data acquisition module, a first processing module, a second processing module, a third processing module, and a scheduling execution module.

[0052] The data acquisition module acquires machining status characteristic parameters from the machine tool side in real time through an industrial communication interface. These parameters include: main drive load data, cutting medium temperature and ambient temperature, machining timestamp; and spindle position data: derived from a high-precision encoder on the machine tool's rotary axis, used to record the real-time phase angle of the gear teeth relative to the initial zero position at every moment during machining. Furthermore, its acquisition frequency is synchronized with the main drive load data to ensure that load fluctuations correspond to the hard physical relationship between mechanical position and mechanical position.

[0053] Main drive load data: Current / power signals output from the frequency converter of the gear hobbing or grinding machine, with a sampling frequency not lower than [missing information]. ;

[0054] Cutting medium temperature and ambient temperature: collected by sensors deployed in the machine tool spray head and measuring station space;

[0055] Processing timestamp: A time reference used to align all subsequent measurement data;

[0056] The first processing module is connected to the data acquisition module, and its core purpose is to identify potential quality risk points through processing fingerprints. This module monitors the fluctuations of the main drive load in real time. When the spindle power experiences a slight change that exceeds the normal reference value, the system extracts the absolute phase angle at the moment of the load change based on the spindle position data. Combined with the total number of teeth of the gear under test The number of abnormal events can be calculated using the following formula. Individual gear tooth index:

[0057] ;

[0058] in, The absolute phase angle at the moment of load change is expressed in degrees. The total number of teeth on the gear under test is used to accurately locate the tooth groove index corresponding to the anomaly and mark it as a high-risk area.

[0059] The second processing module dynamically generates a set of discrete measurement micro-tasks based on the location of the high-risk areas mentioned above. This module breaks the traditional standardized measurement path generation logic and decomposes the tasks into: thermal scanning tasks for thermal gradients, coarse scanning tasks for location, and focused scanning tasks for risk points. This design enables the system to flexibly configure measurement resources according to the real-time risk level.

[0060] The third processing module is used to calculate the process capability confidence index; it uses the environmental and processing parameters provided by the data acquisition module as external constraints, combined with preliminary feedback from thermal scanning and coarse scanning, to assess the stability of the current batch processing process; this index is a [missing information - likely a value or indicator]. In this embodiment, the preset exemption threshold is set to 0.95. This value is based on the quality level (AQL) required by the production line for this batch of workpieces and the statistical results of the historical return rate, which are preset in the memory of the scheduling execution module. It directly represents the probability that the gear meets the preset quality standard without full inspection.

[0061] The scheduling and execution module acts as the decision-making center, executing a hierarchical scheduling strategy based on this indicator. If the process capability confidence index is higher than the preset inspection exemption threshold, the system determines that the gear is minimally affected by processing fluctuations and generates a direct release instruction. Otherwise, by calculating the optimal cooling waiting time, the system ensures that subsequent focusing scanning tasks are executed under controlled thermal stability.

[0062] This embodiment solves the contradiction between measurement efficiency and accuracy uncertainty by introducing a measurement task discretization technology driven by processing fingerprints. Compared with the traditional fixed-cycle full inspection mode, this system significantly improves the overall throughput while ensuring the same interception rate, and realizes the intelligent leap of on-demand measurement.

[0063] Example 2:

[0064] The first processing module's operations for identifying abnormal gear tooth units include:

[0065] The main drive load data is used to construct the spindle power spectrum curve;

[0066] Monitor the micro-abrupt signals of the spindle power spectrum curve when machining different gear units;

[0067] Establish a mapping relationship between the micro-mutation signal and the machining index number of the gear under test, lock the k-th gear tooth unit that generates the micro-mutation signal, and define the k-th gear tooth unit and its symmetrically distributed gear tooth units as the high-risk area.

[0068] In the specific implementation of the first processing module to identify abnormal gear tooth units, this embodiment introduces frequency domain analysis. The module calls the main drive load data and constructs the main shaft power spectrum curve through fast Fourier transform.

[0069] To accurately detect anomalies, this embodiment employs a sliding window algorithm to monitor micro-abrupt signals in the spindle power spectrum curve. These micro-abrupt signals are determined by the statistical deviation threshold of the real-time power spectrum energy value from its historical stable period. This signal typically manifests as an anomalous energy transition at a specific characteristic frequency. Specifically, the system calculates the energy value of the power spectrum within the current window in real time. and call the previous memory Average energy of a stable processing cycle and standard deviation When the judgment condition is met At that time, a micro-mutation signal is determined to be triggered;

[0070] The system establishes a mapping relationship between the time series signal and the machining index number of the gear under test, and locks the generation number of the signal. A gear tooth unit; wherein, if the total number of teeth of the gear to be tested is The index numbers of symmetrically distributed gear tooth units are determined by the formula. Calculations show that; if It is an odd number.

[0071] Then take respectively as well as

[0072] Calculations show that, considering the symmetrical load distribution in gear machining, the system will... Each gear tooth unit, and all symmetrically distributed gear tooth units calculated according to the above formula ( or , These are collectively defined as high-risk areas to address potential systematic processing errors;

[0073] Through power spectrum analysis and index mapping, this feature enables precise location of machining anomalies, avoiding the waste of machine time caused by blind scanning, and providing accurate navigation basis for subsequent targeted measurement.

[0074] Example 3:

[0075] The tasks in the discrete measurement microtask set are defined as follows:

[0076] The thermal scanning task is used to acquire surface temperature field distribution data of the gear under test using an infrared thermal imaging device.

[0077] The coarse scanning task is used to collect dimensional data of a preset number of discontinuous gear tooth units on the gear under test, so as to establish a measurement coordinate system and obtain basic machining errors;

[0078] The focused scanning task is used to perform geometric measurements on the high-risk region at a higher sampling density than the coarse scanning task.

[0079] This embodiment provides a specific definition of task execution within the discrete measurement microtask set:

[0080] Thermal scanning task: Acquire surface temperature field distribution data of the gear under test using infrared thermal imaging equipment to analyze non-uniform thermal fields;

[0081] Rough scanning task: Control the probe to extract a preset number of discontinuous gear tooth units at extremely high speed for touch testing, in order to establish a measurement coordinate system and obtain the basic machining error that reflects the overall trend;

[0082] Focused scanning task: For high-risk areas, geometric measurements are performed using a higher sampling density than in the coarse scanning task; during the execution of the focused scanning task, the scheduling execution module uses the index number locked by the first processing module. Automatically retrieve the first CAD model The theoretical coordinates of each tooth groove are used as a reference to determine its left and right sides. The probe automatically generates an encrypted motion path within the range, enabling targeted and high-precision touch detection of risk points;

[0083] By configuring task attributes differently, a hierarchical measurement architecture of coarse measurement positioning, thermal measurement compensation, and fine measurement risk was realized, which optimized the efficiency of information collection.

[0084] Example 4:

[0085] The third processing module performs the following operations to calculate the process capability confidence index:

[0086] Call upon the basic machining error data output by the coarse scan task;

[0087] The basic machining error data and the machining state characteristic parameters are used as prior information and input into the Bayesian inference model to calculate the posterior probability distribution of the process capability index of the gear under test.

[0088] The integral probability value of the process capability index in the posterior probability distribution that is greater than a preset lower threshold is determined as the process capability confidence index.

[0089] When the third processing module calculates the confidence index of the process capability, Bayesian posterior inference is introduced.

[0090] To dynamically assess quality risks, this embodiment introduces a process capability posterior probability distribution calculation model, the calculation formula of which is as follows:

[0091] ;

[0092] in, It is a proportional sign; The Gaussian likelihood function is constructed based on the basic machining error data; It is the prior probability density function determined based on the processing state characteristic parameters;

[0093] The gear process capability index to be solved is used as an intermediate variable for solving the process capability confidence index.

[0094] The basic processing error data derived from the output of the coarse scanning task is used as the observation likelihood term.

[0095] The processing status characteristic parameters provided by the data acquisition module are used as prior information input;

[0096] The specific solution logic of Bayesian inference is as follows: based on historical processed data, a pre-defined... prior distribution It follows a normal distribution; using basic machining error data Constructing the Gaussian likelihood function The posterior probability density function is calculated according to the following formula:

[0097] ;

[0098] The specific calculation logic is as follows: the system calculates the posterior probability density function... In the interval The cumulative probability value obtained by definite integral is determined as the process capability confidence index. In this way, the system transforms random measurement point information into a statistically significant quality probability distribution, enabling the system to make highly reliable decisions even when data is incomplete.

[0099] This method transforms isolated measurement data into probabilistic decision support, enabling the system to make highly reliable decisions even when data is incomplete.

[0100] Example 5:

[0101] The scheduling execution module calculates the optimal cooldown waiting time by performing the following operations:

[0102] The surface temperature field distribution data is retrieved, and the thermal decay curve of the gear under test at the current ambient temperature is calculated based on the non-uniform heat field transfer model.

[0103] The rate of temperature change over time on the thermal decay curve is calculated, and the time required to reduce the rate of change to a preset rate of change threshold is determined as the optimal cooling waiting time.

[0104] For the operation of the scheduling execution module to calculate the optimal cooling waiting time, this embodiment replaces empirical waiting with physical modeling; it calls the surface temperature field distribution data and calculates the heat decay curve based on the non-uniform heat field transfer model; the heat decay curve is constructed using the discretized form of Newton's law of cooling, and its calculation formula is as follows:

[0105] ;

[0106] in, For ambient temperature, The initial highest surface temperature obtained for the thermal scanning mission. Cooling time, The thermal diffusivity is a coefficient that is pre-determined based on the gear's module, material specific heat capacity, and the ambient convective heat transfer coefficient. Specifically, the thermal diffusivity... The pre-set process includes: before the system goes live, conducting static cooling experiments on gears of specific specifications, recording the cooling curves, and using the least squares method for exponential fitting to calibrate the physical constants applicable to that gear model. Physical constants Periodically based on the ambient temperature obtained by the data acquisition module Dynamic compensation and correction are performed to eliminate the impact of environmental convection heat transfer fluctuations on the cooling model;

[0107] To determine the optimal timing for measurement intervention, this embodiment introduces the rate of temperature change over time. The calculation method is as follows:

[0108] ;

[0109] in, This represents the first derivative of temperature with respect to time. For the thermal decay curve at The instantaneous temperature of the gear surface at a given moment, in units of This is derived from the real-time solution of the non-uniform heat field transfer model; Cooling duration, in units of The system continuously calculates ,when The time required for the temperature to drop to a preset rate of change threshold is determined as the optimal cooling waiting time.

[0110] By using dynamic thermal modeling, the static temperature-controlled time is transformed into a controlled cooling time, thus shortening the cycle time while ensuring accuracy.

[0111] Example 6:

[0112] The scheduling and execution module is also used to perform time-slice interleaving scheduling:

[0113] During the period when the gear under test is in the optimal cooling waiting time, the control measuring device performs a thermal scan task or a coarse scan task on another gear under test.

[0114] When the optimal cooling waiting time ends and the measuring device is idle, the focusing scan task of the gear under test is inserted.

[0115] The main drive load data is the real-time spindle power data of the gear hobbing machine or gear grinding machine.

[0116] The time-slice interleaving scheduling executed by the scheduling and execution module is key to maximizing equipment utilization.

[0117] During the optimal cooling waiting time of gear A, the measuring device is controlled to perform a thermal scan or coarse scan of another gear B; when the cooling time ends and the device is idle, a focused scan of gear A is inserted.

[0118] In this embodiment, the main drive load data specifically refers to the real-time spindle power data of the gear hobbing machine or gear grinding machine;

[0119] This parallel and interleaved scheduling logic eliminates the passive waiting gaps in traditional processes and significantly improves concurrent processing capabilities.

[0120] Example 7:

[0121] When generating the direct release instruction, the scheduling execution module also performs the following operations:

[0122] Record the mapping relationship between the measurement data of the coarse scanning task and the processing state characteristic parameters to generate an associated feature dataset;

[0123] The associated feature dataset is fed back to the upstream processing control unit to compensate for the processing parameters of the gear to be tested later.

[0124] When generating a direct release instruction, the system performs a closed-loop feedback operation; the scheduling execution module records the mapping relationship between measurement data and processing parameters, and generates an associated feature dataset;

[0125] This dataset is fed back to the upstream machining control unit to compensate for the machining parameters of subsequent gears; the specific compensation operation is as follows: extracting the average dimensional deviation from the associated feature dataset. The upstream processing control unit is based on the formula Adjust the tool radial feed compensation value ,in The current compensation benchmark value, The corrected compensation target value, The preset feedback gain operator has a value range of 100%. ;

[0126] This has enabled the evolution from post-interception to feedforward compensation, improving the process stability of the entire processing line.

[0127] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An automated and intelligent scheduling system for the measurement process of a gear measurement center, characterized in that, include: The data acquisition module is used to acquire the machining status characteristic parameters of the gear under test during the machining process. The machining status characteristic parameters include: main drive load data, cutting medium temperature, ambient temperature, and machining timestamp. The first processing module is used to identify abnormal gear tooth units on the gear under test that have experienced sudden load changes based on the main drive load data, and to mark the physical index of the abnormal gear tooth units as high-risk areas. The second processing module is used to decompose the complete measurement program for the gear under test into a set of discrete measurement micro-tasks based on the high-risk area. The set of discrete measurement micro-tasks includes: a thermal scanning task for the overall thermal distribution, a coarse scanning task for the global coordinate system, and a high-precision focusing scanning task that is only performed for the high-risk area. The third processing module is used to calculate the process capability confidence index of the gear under test based on the execution results of the thermal scanning task and the coarse scanning task, combined with the processing state characteristic parameters. The process capability confidence index is expressed as the probability value that the gear under test meets the preset quality standard. The scheduling execution module is used to generate hierarchical scheduling instructions based on the process capability confidence index. If the process capability confidence index is higher than the preset exemption threshold, a direct release instruction is generated and the remaining measurement tasks are skipped. If the process capability confidence index is lower than or equal to the exemption threshold, the optimal cooling waiting time is calculated based on the data from the thermal scanning task, and the focused scanning task is triggered after the optimal cooling waiting time has elapsed.

2. The automated and intelligent scheduling system for the measurement process of a gear measurement center according to claim 1, characterized in that, The operation of the first processing module to identify abnormal gear tooth units includes: The main drive load data is used to construct the spindle power spectrum curve; Monitor the micro-abrupt signals of the spindle power spectrum curve when machining different gear units; Establish a mapping relationship between the micro-mutation signal and the machining index number of the gear under test, lock the k-th gear tooth unit that generates the micro-mutation signal, and define the k-th gear tooth unit and its symmetrically distributed gear tooth units as the high-risk area.

3. The automated and intelligent scheduling system for the measurement process of a gear measurement center according to claim 1, characterized in that, The tasks in the discrete measurement microtask set are defined as follows: The thermal scanning task is used to acquire surface temperature field distribution data of the gear under test using an infrared thermal imaging device. The coarse scanning task is used to collect dimensional data of a preset number of discontinuous gear tooth units on the gear under test, so as to establish a measurement coordinate system and obtain basic machining errors; The focused scanning task is used to perform geometric measurements on the high-risk region at a higher sampling density than the coarse scanning task.

4. The automated and intelligent scheduling system for the measurement process of a gear measurement center according to claim 1, characterized in that, The third processing module's operation for calculating the process capability confidence index includes: Call upon the basic machining error data output by the coarse scan task; The basic machining error data and the machining state characteristic parameters are used as prior information and input into the Bayesian inference model to calculate the posterior probability distribution of the process capability index of the gear under test. The integral probability value of the process capability index in the posterior probability distribution that is greater than a preset lower threshold is determined as the process capability confidence index.

5. The automated and intelligent scheduling system for the measurement process of a gear measurement center according to claim 3, characterized in that, The operation of the scheduling execution module to calculate the optimal cooling waiting time includes: The surface temperature field distribution data is retrieved, and the thermal decay curve of the gear under test at the current ambient temperature is calculated based on the non-uniform heat field transfer model. The rate of temperature change over time on the thermal decay curve is calculated, and the time required to reduce the rate of change to a preset rate of change threshold is determined as the optimal cooling waiting time.

6. The automated and intelligent scheduling system for the measurement process of a gear measurement center according to claim 1, characterized in that, The scheduling execution module is also used to perform time-slice interleaving scheduling: During the period when the gear under test is in the optimal cooling waiting time, the control measuring device performs a thermal scan task or a coarse scan task on another gear under test. When the optimal cooling waiting time has ended and the measuring device is idle, the focusing scan task of the gear under test is inserted.

7. The automated and intelligent scheduling system for the measurement process of a gear measurement center according to claim 6, characterized in that, The main drive load data is the real-time spindle power data of the gear hobbing machine or gear grinding machine.

8. The automated and intelligent scheduling system for the measurement process of a gear measurement center according to claim 1, characterized in that, When generating the direct release instruction, the scheduling execution module also performs the following operations: Record the mapping relationship between the measurement data of the coarse scanning task and the processing state characteristic parameters to generate an associated feature dataset; The associated feature dataset is fed back to the upstream processing control unit to compensate for the processing parameters of the gear to be tested later.

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