Industrial valve workpiece plane grinding intelligent control method and system

Abstract

The present application relates to the technical field of industrial monitoring, in particular to an intelligent control method and system for industrial valve workpiece plane grinding, comprising the following steps: collecting spindle idling current and no-load pressure sequence comparison wave to generate monitoring archives, collecting contact pressure and displacement sequence based on the archives to calculate stability and generate monitoring criteria, calling archives to collect disengagement travel to construct release and unloading boundaries, comparing the electric cylinder back-off output disengagement process based on the boundaries to generate a trajectory, judging the pressure release and unloading end point to establish a control scheme. In the present application, the smooth baseline is established by digging the spindle idling and no-load pressure characteristics to filter mechanical noise, the stability is continuously sequenced and kernel calibrated in multiple dimensions, the adaptive multi-dimensional unloading boundary is constructed by abandoning the fixed boundary, the distortion trajectory is captured by fitting and comparing the back-off parameters and boundary depth, the cutting full-cycle stress evolution trend is accurately controlled, the scratch risk caused by the lagging judgment is eliminated, the overload impact is smoothed, and the grinding quality is exactly guaranteed.

Description

Technical Field

[0001] This invention relates to the field of industrial monitoring technology, and in particular to an intelligent control method and system for planar grinding of industrial valve workpieces. Background Technology

[0002] Industrial monitoring technology involves core aspects such as industrial equipment operation status acquisition, process parameter monitoring, control command issuance, abnormal alarm recording, human-machine interface display, and production process data retention. It typically relies on pressure sensors, displacement sensors, speed sensors, current sensors, PLCs, industrial computers, and human-machine interfaces to continuously monitor and manage the clamping status, spindle speed, feed position, load current, coolant supply, and processing time of machining equipment. Among these, the traditional intelligent control method for surface grinding of industrial valve workpieces refers to the control method for the surface grinding process of industrial valve bodies, valve covers, valve seats, and other workpieces. Typically, the operator inputs grinding pressure, grinding wheel speed, feed rate, grinding time, and coolant flow rate according to the process card. The workpiece is positioned using a fixture, and the spindle, electric cylinder, worktable, and coolant pump are started. Pressure sensors read the grinding contact pressure, displacement gauges read the feed position, and ammeters read the spindle load. Based on preset upper and lower limits, the spindle start / stop, feed pause, pressure release, and alarm prompts are controlled.

[0003] Traditional grinding control relies heavily on manual experience to set monotonous and rigid upper and lower limit thresholds for processing. During operation, it only extracts and compares the instantaneous discrete pressure and current indicators on the surface, mechanically and rigidly blocking the spindle process. Because the grinding condition has extremely nonlinear coupling characteristics, the solidification boundary cannot match the ever-changing contact state and cutting force. Isolated scattered point acquisition seriously misses the deep-seated extrusion distortion and load shock pulses, which can easily induce misjudgment during calm periods, causing the end face structure to suffer transient strong impacts, resulting in scratches and damage. The overall control is rough and greatly reduces the reliability of the finished product. Summary of the Invention

[0004] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent control method for planar grinding of industrial valve workpieces, comprising the following steps: S1: The grinding station detects the clamping signal of the fixture, collects the spindle idle current sequence and the no-load pressure sequence, calculates the baseline of the spindle idle current sequence, compares the fluctuation of the no-load pressure sequence, and generates a no-load monitoring file. S2: Based on the no-load monitoring file, collect the valve seat end face contact pressure sequence, displacement sequence and load current sequence, calculate the stability of the valve seat end face contact pressure sequence, displacement sequence and load current sequence, and generate contact monitoring criteria; S3: Based on the contact monitoring criteria, call the no-load monitoring file, collect the process card release ratio and process card disengagement stroke, construct the pressure release boundary, the retraction displacement boundary and the spindle unloading boundary, and generate the release monitoring boundary; S4: Based on the release monitoring boundary, collect the electric cylinder retraction pressure sequence, retraction displacement sequence and output current sequence, compare the electric cylinder retraction pressure sequence, retraction displacement sequence and output current sequence to separate the process, and generate the separation monitoring trajectory. S5: Based on the departure monitoring trajectory, call the release monitoring boundary and the contact monitoring criterion to determine the pressure release, retraction displacement and spindle unloading endpoint, and generate an intelligent control scheme for industrial valve workpiece plane grinding.

[0005] As a further aspect of the present invention, the no-load monitoring file includes a locking confirmation code, a no-load current reference value, a current drift tolerance, a no-load average pressure, and a pressure fluctuation level; the contact monitoring criteria include contact stability, pressure-position coupling coefficient, load response coefficient, stable duration, and contact anomaly level; the release monitoring boundary includes a pressure release upper limit, a pressure release lower limit, a return stroke threshold, an unloading current threshold, and a boundary margin coefficient; the departure monitoring trajectory includes pressure attenuation characteristics, displacement return characteristics, current unloading characteristics, departure stage label, and trajectory deviation; and the intelligent control scheme for surface grinding of industrial valve workpieces includes a start-stop control strategy, a feed correction strategy, a grinding wheel avoidance strategy, an anomaly alarm strategy, and a process traceability strategy.

[0006] As a further aspect of the present invention, the pressure release boundary refers to the safety threshold and limit range of the pressure drop process between the tooling and the valve seat, which is constructed, limited, and monitored based on the release ratio of the process card during the separation stage after grinding.

[0007] As a further aspect of the present invention, the stability of the displacement sequence and load current sequence refers to the smoothness and fluctuation range of the data sequence of the grinding feed displacement and the spindle working current over time during the end face contact grinding process.

[0008] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Detect the workpiece clamping signal, collect the spindle idle current sequence and pressure sensor idle pressure sequence during the effective clamping period, align the current sampling value and pressure sampling value according to the sampling time, and establish an idle sampling matrix; S102: Based on the no-load sampling matrix, extract the spindle no-load current sequence, calculate the sampling mean value in the current stable section, verify the consistency of current dimensions, and obtain the spindle current baseline value. S103: Call the spindle current baseline value and the no-load sampling matrix, extract the no-load pressure sequence of the pressure sensor, compare the adjacent pressure difference with the preset no-load pressure fluctuation limit, associate the locking signal, the spindle current baseline value and the pressure fluctuation status, and establish a no-load monitoring file.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the aforementioned no-load monitoring file, collect the pressure sequence, displacement sequence, and load current sequence during the contact period between the valve seat end face and the grinding wheel, align the three types of sequences according to the sampling time, remove missing sampling points, and establish a contact sampling matrix; S202: Call the contact sampling matrix to calculate the adjacent pressure difference, adjacent displacement difference, and adjacent load current difference respectively, verify the consistency of the dimensions of similar parameters, associate the three types of differences with the sampling time index, and generate contact fluctuation feature groups; S203: Based on the contact fluctuation feature group, compare the adjacent pressure difference with the contact pressure fluctuation limit, the adjacent displacement difference with the contact displacement fluctuation limit, and the adjacent load current difference with the load current fluctuation limit, associate the three comparison states, and generate contact monitoring criteria.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the contact monitoring criteria, call the no-load monitoring file, collect the process card release ratio and process card disengagement stroke, verify the ratio dimension and stroke unit, map the release ratio, disengagement stroke, no-load pressure state, spindle current baseline value, and establish a release parameter matrix. S302: For the release parameter matrix, calculate the pressure release amplitude according to the release ratio of the process card, compare the pressure release amplitude with the no-load pressure state, and calculate the retraction displacement amplitude according to the process card release stroke to obtain the release boundary parameter set; S303: Based on the release boundary parameter group, associate the pressure release amplitude, retraction displacement amplitude, spindle current baseline value and contact monitoring criterion to construct the pressure release boundary, retraction displacement boundary and spindle unloading boundary respectively, and generate the release monitoring boundary.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the release monitoring boundary, collect the pressure sequence, displacement sequence and main shaft current acquisition device output current sequence during the electric cylinder retraction, align the three types of sequences according to the sampling time, remove missing sampling points, and establish a retraction sampling matrix; S402: Call the backsliding sampling matrix to calculate the adjacent pressure difference, adjacent displacement difference, and adjacent spindle current difference respectively, verify the consistency of the dimensions of similar parameters, associate the three types of differences with the sampling time index, and generate the departure change feature group; S403: Based on the set-off change feature group, compare the adjacent pressure difference with the pressure release boundary, the adjacent displacement difference with the retraction displacement boundary, and the adjacent spindle current difference with the spindle unloading boundary, map the three comparison states and sampling time index, and generate the set-off monitoring trajectory.

[0012] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the departure monitoring trajectory, call the release monitoring boundary and the contact monitoring criterion to extract the pressure release state, retraction displacement state and spindle unloading state, align the three states according to the sampling time index, and establish the endpoint state matrix. S502: For the endpoint state matrix, compare the pressure release state with the pressure release boundary, the retraction displacement state with the retraction displacement boundary, and the spindle unloading state with the spindle unloading boundary, and associate the three boundary comparison values ​​to obtain the endpoint determination identifier. S503: Based on the endpoint determination identifier, associate the contact monitoring criteria, release monitoring boundary and departure monitoring trajectory, determine the grinding stop node and electric cylinder retraction node, map the node time with the control state, and generate an intelligent control scheme for the planar grinding of industrial valve workpieces.

[0013] An intelligent control system for planar grinding of industrial valve workpieces, comprising: The no-load status monitoring module is used to realize S1: the grinding station detects the clamping signal of the fixture, collects the spindle no-load current sequence and no-load pressure sequence, calculates the baseline of the spindle no-load current sequence, compares the fluctuation of the no-load pressure sequence, and generates no-load monitoring files; The contact stability analysis module is used to implement S2: based on the no-load monitoring file, collect the valve seat end face contact pressure sequence, displacement sequence and load current sequence, calculate the stability of the valve seat end face contact pressure sequence, displacement sequence and load current sequence, and generate contact monitoring criteria; The unloading boundary planning module is used to implement S3: based on the contact monitoring criteria, it calls the no-load monitoring file, collects the process card release ratio and process card disengagement stroke, constructs the pressure release boundary, the retraction displacement boundary and the spindle unloading boundary, and generates the release monitoring boundary; The retraction and disengagement tracking module is used to implement S4: based on the release monitoring boundary, it collects the electric cylinder retraction pressure sequence, retraction displacement sequence and output current sequence, compares the electric cylinder retraction pressure sequence, retraction displacement sequence and output current sequence to disengagement process, and generates disengagement monitoring trajectory; The grinding intelligent control module is used to implement S5: based on the departure monitoring trajectory, it calls the release monitoring boundary and the contact monitoring criterion to determine the pressure release, retraction displacement and spindle unloading endpoint, and generates an intelligent control scheme for the planar grinding of industrial valve workpieces.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the timing characteristics of spindle idling and no-load pressure are deeply explored to establish a stable baseline to filter inherent mechanical noise. The grinding stability is continuously verified by the continuous sequence of multi-source heterogeneous end-face contact pressure and displacement. The fixed boundary is abandoned and an adaptive pressure release and unloading multi-dimensional boundary is constructed. The continuous contact timing is fully replicated to fill the blind spots of discrete breakpoints. During the disengagement period, the backtracking parameters and multi-dimensional boundaries are deeply fitted and compared to capture subtle distortion trajectories. The force evolution of the entire cutting and unloading cycle is precisely controlled, completely eliminating the risk of end-face scratches caused by hysteresis judgment and effectively suppressing instantaneous strong impacts. The workpiece grinding shape is absolutely stable and the industrial equipment can operate safely and efficiently for a long time. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Please see Figure 1 This invention provides an intelligent control method for planar grinding of industrial valve workpieces, comprising the following steps: S1: Based on the industrial valve workpiece plane grinding station, detect the workpiece clamping signal, collect the spindle idle current sequence and the pressure sensor no-load pressure sequence, perform current baseline calculation on the spindle idle current sequence, compare pressure fluctuations on the pressure sensor no-load pressure sequence, and generate no-load monitoring files. S2: Based on the no-load monitoring archive, collect the pressure sequence, displacement sequence and load current sequence during the contact between the valve seat end face and the grinding wheel, perform contact stability calculation on the pressure sequence, displacement sequence and load current sequence, and generate contact monitoring criteria; S3: Based on the contact monitoring criteria, call the no-load monitoring file, collect the process card release ratio and process card disengagement stroke, construct the boundary for the pressure release boundary, the retraction displacement boundary and the spindle unloading boundary, and generate the release monitoring boundary; S4: Based on the release monitoring boundary, collect the pressure sequence, displacement sequence and spindle current acquisition device output current sequence during the electric cylinder retraction, compare the pressure sequence, displacement sequence and spindle current acquisition device output current sequence to generate the separation monitoring trajectory; S5: Based on the departure from the monitoring trajectory, it calls the release monitoring boundary and contact monitoring criteria to perform endpoint judgment for pressure release, retraction displacement and spindle unloading, and generates an intelligent control scheme for the planar grinding of industrial valve workpieces.

[0020] The no-load monitoring file includes locking confirmation code, no-load current reference value, current drift tolerance, no-load average pressure, and pressure fluctuation level. The contact monitoring criteria include contact stability, pressure-position coupling coefficient, load response coefficient, stable duration, and contact anomaly level. The release monitoring boundaries include upper pressure release limit, lower pressure release limit, return stroke threshold, unloading current threshold, and boundary margin coefficient. The departure monitoring trajectory includes pressure decay characteristics, displacement return characteristics, current unloading characteristics, departure stage label, and trajectory deviation. The intelligent control scheme for surface grinding of industrial valve workpieces includes start-stop control strategy, feed correction strategy, grinding wheel avoidance strategy, abnormal alarm strategy, and process traceability strategy.

[0021] Please see Figure 2 The specific steps of S1 are as follows: S101: Detect the workpiece clamping signal, collect the spindle idle current sequence and pressure sensor idle pressure sequence during the effective clamping period, align the current sampling value and pressure sampling value according to the sampling time, and establish an idle sampling matrix; A signal acquisition command is sent to the industrial programmable logic controller (PLC) to continuously monitor the potential status fed back from the solenoid valve drive port of the control fixture's closed air circuit. The timestamp of the potential status transitioning from a low to a high level is acquired, and an internal timing loop is initiated. When the high-level state is maintained continuously for more than a 1500-millisecond threshold, the absolute timestamps of the start and end of this duration are extracted as the effective locking period. Simultaneously with acquiring the start of the effective period, a synchronization trigger message is sent to the underlying control chip of the spindle servo driver, activating the Hall current sensing coil built into the three-phase power supply circuit. The analog-to-digital converter in the hardware data channel is configured to perform continuous sampling at a fixed polling frequency of 1200 Hz. The current intensity value of the spindle when not in contact with the workpiece is acquired within each sampling cycle. These values ​​are appended to a one-dimensional linear data list in chronological order of generation, constructing a continuous spindle idling current sequence. Simultaneously, an activation pulse is sent to the thin-film piezoresistive sensing component deployed inside the flange at the end of the grinding contact head. The frequency of the signal sampling circuit of this component is also locked at 1200 Hz. The front-end pneumatic back pressure value under the condition of no physical compression is continuously extracted and written sequentially into another independent linear data list to construct the corresponding no-load pressure sequence. For these two independently acquired sequences, an independent data cleaning process is initiated. A motion detection window with a length limited to 9 data nodes is created and placed at the head of the sequence, with each backward movement step set to 1 data node. In each area where the motion detection window stops, 9 sampled values ​​covered within the window are extracted, and the bubble sort rule is used to perform an ascending sort operation on these 9 values ​​from smallest to largest. The smaller values ​​in the 1st to 3rd positions are removed, and the larger values ​​in the 7th to 9th positions are also removed. The remaining 3 values ​​in the middle positions (4th, 5th, and 6th positions) are extracted. The three centered values ​​are summed, and the sum is divided by a constant 3 to obtain a locally smoothed replacement value for the window coverage area. The original initial value at the center of the motion detection window is then forcibly overwritten with this locally smoothed replacement value. This process is repeated throughout the sequence until the motion detection window reaches its end, eliminating transient spike noise. Next, a cross-alignment of the dual-channel data is performed, extracting each recorded timestamp from the smoothed spindle idle current sequence as a time anchor reference. A comparison is then performed in the smoothed idle pressure sequence, subtracting the timestamp of each data node from this time anchor reference and extracting the absolute value of the subtraction. When this absolute value is less than or equal to the time tolerance limit of 0.4 milliseconds, the corresponding pressure sampling data is directly extracted and bound to the current sampling data under this time anchor reference.When a search reveals that the absolute value of the difference between the timestamps of all pressure data nodes and a certain time anchor point is greater than the time tolerance limit of 0.4 milliseconds, a pressure data loss event is determined at the current time anchor point. The idle pressure sequence is retrieved, and the nearest preceding pressure data item and its corresponding preceding timestamp are extracted from the time anchor point. Simultaneously, the nearest subsequent pressure data item and its corresponding subsequent timestamp are extracted from the time anchor point. The difference between the subsequent and preceding pressure data items is obtained by subtracting the preceding pressure data item, and the total time span is obtained by subtracting the preceding timestamp from the subsequent timestamp. The difference is divided by the total time span to obtain the linear change slope per unit time. The deviation time difference between the time anchor point corresponding to the missing point and the preceding timestamp is extracted, and this deviation time difference is multiplied by the linear change slope to obtain the compensation increment. The compensation increment is added to the preceding pressure data item to obtain the interpolated simulated pressure value at the corresponding time anchor point, and this value is forcibly bound to the current sampling data at that time anchor point. For example, in a certain detection process, the preceding pressure data is 0.15 MPa, generated at the 10th millisecond, and the subsequent pressure data is 0.25 MPa, generated at the 15th millisecond. The missing anchor point is located at the 12th millisecond. Subtracting the preceding and subsequent pressures yields a difference of 0.10 MPa, with a time span of 5 milliseconds. Division results in a linear change slope of 0.02 MPa per millisecond. The time difference between the missing anchor point and the preceding time is calculated to be 2 milliseconds. Multiplying 2 milliseconds by 0.02 MPa per millisecond yields a compensation increment of 0.04 MPa. Adding 0.04 MPa to the preceding value of 0.15 MPa yields an interpolated simulated pressure value of 0.19 MPa for filling the gap at the 12th millisecond. Based on the aforementioned forced binding relationship, a contiguous two-dimensional computer memory block is allocated. The time anchor reference value is used as the primary key index for the row index direction. The corresponding current sampling data is written to the first attribute column field of the corresponding row in this memory block, and the pressure sampling data simulated by binding or interpolation is written to the second attribute column field of the same row. After traversing and processing all valid aligned data, the write permission of this memory block is sealed, completing the initialization and construction of the unloaded sampling matrix.

[0022] S102: Based on the no-load sampling matrix, extract the spindle no-load current sequence, calculate the sampling mean value in the current stable section, verify the consistency of current dimensions, and obtain the spindle current baseline value. Retrieve the unloaded sampling matrix with sealed write permissions, and perform a top-down row-by-row data extraction operation on the first attribute column field within the matrix. Extract all current value sets independent of the matrix structure, and arrange them linearly according to the growth direction of the associated time anchor reference to generate a single-dimensional main axis idle current sequence. Set a data frame with a length of 40 consecutive sampling nodes and place this data frame at the beginning of the sequence. Within the area covered by this data frame, perform a sequential summation operation on the 40 current values ​​to obtain the arithmetic sum of the current range covered by the current frame. Then, divide this arithmetic sum by the total number of nodes (40) to calculate the arithmetic mean of the current in that range. For each sampling node within the data frame, subtract the calculated arithmetic mean of the current from its actual recorded current value to obtain the absolute deviation value. Then, multiply this absolute deviation value by itself to obtain the squared difference term. The system performs a summation operation on all 40 squared differences within the window, then divides the total sum of squares by a constant 39 to obtain a local variance statistic describing the dispersion of the window. This calculated local variance statistic is logically compared numerically with a pre-written upper limit of variance tolerance in the configuration file; this upper limit is hard-coded as 0.06. When the local variance statistic is greater than or equal to 0.06, the control frame moves backward by one node, and the above calculation logic, including mean and variance extraction, is repeated. After 25 consecutive window sliding operations, if all 25 extracted consecutive local variance statistics are strictly less than the 0.06 boundary limit, the last sampling node covered by the data frame after the 25th sliding operation is recorded as the termination index, and the first sampling node in the data frame corresponding to the first occurrence of a local variance less than 0.06 is used as the starting index. Extract all spindle idling current sequence segments enclosed between the start and end indices, and mark the corresponding region on the time axis as the current stable segment. Extract all valid current values ​​within the boundary range of this current stable segment, and perform a uniform summation on these current values ​​to obtain the arithmetic sum of the stable segment. Read the total number of current values ​​involved in the summation operation, divide the arithmetic sum of the stable segment by the total number of current values, and obtain the sampled mean within the current stable segment. Enter the current dimension consistency verification and conversion logic branch, send a communication frame containing a hardware query command to the device main control board, receive the returned hexadecimal encoded response message, and extract the sensor unit identification code located in the 4th byte of the message data payload. Configure the verification comparison dictionary, containing the code 0X0A for the corresponding milliampere level and the code 0X0B for the corresponding ampere level.The extracted sensor unit identifier code is compared with the code entries in the verification dictionary. If the identifier code matches 0X0A, the previously calculated sample mean is multiplied by the span conversion constant 0.001 to perform a dimension reduction transformation. If the identifier code matches 0X0B, the dimension reduction transformation is skipped and the sample mean remains unchanged. After completing the numerical conversion and confirmation at this dimensional level, the established numerical parameters are written into a static variable named "main shaft current baseline value" for subsequent extraction and use. A set of real idle sequences with a total length of 300 nodes is set as a calculation example. When the data frame covers the 1st to 40th nodes in the sequence, the sum is 84 amperes. Dividing by 40 yields an arithmetic mean of the interval current of 2.1 amperes. Further, the summation of the squared differences between each point and 2.1 amperes and the division operation are performed, resulting in a local variance statistic of 0.18 for this interval, which significantly exceeds the comparison limit of 0.06, triggering frame sliding. As the frame continues to move backward, when the front end of the frame is positioned at the 150th node of the sequence, the same operation is performed on nodes 150 to 189, resulting in a sum of 56 amperes. The mean is calculated to be 1.4 amperes, and the variance statistic is sharply reduced to 0.02. The system continues to slide the frame and record, finding that from the data frame starting at node 150 to the data frame corresponding to the 174th position (i.e., containing nodes 174 to 213), the generated 25 consecutive local variances randomly fluctuate between 0.02 and 0.04, without touching or exceeding the hard-coded boundary of 0.06. At this point, the starting node 150 and the ending node 213 are extracted, and these 64 consecutive current values ​​are defined as valid analysis data within the current stable range. Summing these 64 current values ​​yields a sum of 89.6 amperes, which is divided by 64 to calculate the sample mean of 1.4 amperes. Analysis of the hexadecimal response message revealed that its fourth byte was 0x0B, confirming that the current data had not undergone milliampere amplification. The scaling operation of multiplying by 0.001 was skipped, and the value of 1.4 amperes was bound to the parameter label of the spindle current baseline. The aforementioned variance tolerance upper limit boundary value of 0.06 was based on a 100-hour idle data acquisition test conducted on 50 spindles of the same model with different wear conditions under no external force intervention.

[0023] S103: Call the spindle current baseline value and no-load sampling matrix, extract the no-load pressure sequence of the pressure sensor, compare the adjacent pressure difference with the preset no-load pressure fluctuation limit, associate the locking signal, spindle current baseline value and pressure fluctuation status, and establish a no-load monitoring file; A read request command is issued to the spindle current baseline value stored in the static variable memory, and the returned value is loaded into the memory register. Simultaneously, the complete two-dimensional no-load sampling matrix residing in the system memory of the preceding stage is invoked. Within the structure of this two-dimensional no-load sampling matrix, bypassing the time anchor reference and current value contained in the first attribute column field, the code directly points to the second attribute column field. Following the ascending order of row indices, the pressure value elements within the second attribute column field are extracted row by row, and these pressure value elements are continuously spliced ​​together to form a one-dimensional no-load pressure sequence. A differential calculation step size limited to two consecutive data items is established, and the traversal operation loop starts from the second data item of the no-load pressure sequence. The subsequent data item pointed to by the current position is extracted, and the preceding data item immediately adjacent to this subsequent data item at the time node is called. A subtraction arithmetic operation is performed, subtracting the preceding data item from the subsequent data item, and the absolutely positive value of the subtraction result is extracted to generate an adjacent pressure difference independent of the original sequence. After a complete traversal of the entire no-load pressure sequence, a set of differential results consisting of the pressure differences between adjacent values ​​is obtained. Each numerical item in this differential result set is retrieved one by one and compared with the preset no-load pressure fluctuation limit written at the underlying level using a scalar-level comparison operation. This preset no-load pressure fluctuation limit is established by back-calculation from 2000 sets of no-load air pressure test data recorded during the R&D phase under closed operating conditions without environmental interference. The maximum peak value of the difference between adjacent time points in these test data is extracted as 0.015 MPa. This maximum peak value is multiplied by twice the safety redundancy factor to obtain the fixed comparison limit constant of 0.03 MPa. In the cyclic comparison operation, a Boolean flag variable with an initial value of 0 is set to represent the pressure fluctuation state. If any adjacent pressure difference is found to be strictly greater than 0.03 MPa during the comparison process, the comparison loop for the remaining differences is immediately terminated, and the aforementioned Boolean flag variable is forcibly overwritten to the value 1. If all adjacent pressure differences in the difference result set are less than or equal to the limit of 0.03 MPa, the value of the Boolean flag variable remains unchanged at 0. Based on the above collection and judgment results, a hierarchical structure organized using JavaScript object notation is created. A batch unique identifier is set as the root node at the outermost edge of this structure. Under this root node, three independent child key-value pair mapping relationships are attached: the locking signal attribute name with the timestamp start and end point values ​​is mapped to the first key-value pair position, the main shaft current baseline value attribute name storing the specific ampere value is mapped to the second key-value pair position, and the pressure fluctuation state attribute name overwritten with 0 or 1 values ​​is mapped to the third key-value pair position. The object serialization operation packages the above hierarchical structure into a string format data body and writes it to the specified offset address sector in the non-volatile flash memory chip, thus completing the physical creation of the idle monitoring file.A sequence of six consecutively sampled no-load pressure values ​​is introduced as a simulation example. The values ​​of these six nodes are arranged sequentially as follows: 0.41 MPa, 0.42 MPa, 0.40 MPa, 0.45 MPa, 0.44 MPa, and 0.44 MPa. The process involves iterative subtraction and absolute value conversion operations. The absolute value of subtracting the first node from the second node is calculated as follows: 0.01 MPa; 0.02 MPa; 0.05 MPa; 0.01 MPa; and 0 MPa. The absolute value of subtracting the fifth node from the sixth node is also calculated. The resulting set of difference results, containing five values, is then compared one by one with a preset value of 0.03 MPa. When the comparison reached the third difference value of 0.05 MPa, it was found to be greater than the limit condition of 0.03 MPa. The comparison process was immediately terminated, the default pressure fluctuation state value of 0 was erased and assigned a value of 1. Subsequently, the serialization construction process was started, with the serial code 20260525A generated by the device's internal clock as the root node. Under it, the lock start and end time variables [100, 1600], the spindle baseline value of 1.4 calculated in the previous step, and the fluctuation state scalar 1 generated in this step were used to form a structured text that was directly written into the sector unit starting at 0XF000.

[0024] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the no-load monitoring archive, collect the pressure sequence, displacement sequence and load current sequence during the contact between the valve seat end face and the grinding wheel, align the three types of sequences according to the sampling time, remove missing sampling points, and establish a contact sampling matrix; A listening polling message is sent to the industrial bus node to listen for the physical press level inversion signal transmitted by the external trigger module. When the falling edge level transition timestamp generated by the end face contact sensor is captured, it is marked as the start point of the contact stage; listening continues until the timestamp of the action command to reverse the energization of the control electric actuator is captured, and this is marked as the end point of the contact stage. Using the two time boundary nodes obtained above, the time filtering window parameters required for the read command are constructed. A read command is sent to the triaxial force measuring ring deployed at the end of the flange along with the time filtering window parameters, extracting all force measuring signals generated within this window and generating a pressure sequence with a sampling rate of 2000 Hz; a read command with the same window parameters is sent to the grating ruler displacement sensor fixed on the side of the guide rail to obtain the specific physical length of the main shaft extending downward in this stage, generating a displacement sequence with a sampling rate of 1000 Hz; a command is sent to the three-phase drive frequency converter to retrieve the actual feedback current of the stator coil doing work against friction within the same time window, generating a load current sequence with a sampling rate of 2000 Hz. Faced with three time series sets with different sampling frequencies, a strict timestamp reconstruction and alignment algorithm based on downsampling is executed. The millisecond-level timestamp nodes inherent in the 1000 Hz displacement sequence are used as globally unique time scale anchors. In the reconstruction operation for the pressure sequence, using a given time scale anchor as the center, the nearest pressure sequence's built-in timestamp is retrieved along both sides of the time axis, and the pressure values ​​bound to the timestamps of the preceding and following pressure sequences are extracted. The extracted pressure values ​​are summed, and the sum is divided by 2 to obtain the mean, which is then used to replace the pressure sampling result at the time scale anchor. In the reconstruction operation for the load current sequence, the same retrieval, extraction, summation, and division by 2 operations are performed, and the resulting mean current is filled into the time scale anchor. After the above interpolation and frequency downsampling operations, the time axis spacing of all sequences is forcibly unified to a 1000 Hz granularity. A missing scan probe is established, and the probe moves sequentially through the realigned time scale anchor sequence for inspection. If, when searching for the nearest original sequence timestamps to both sides of a given timescale anchor point, no pressure sequence timestamps or load current sequence timestamps can be found within an absolute time span exceeding 3 milliseconds to either side, then the current timescale anchor point is determined to have suffered an irreversible communication frame loss failure. A removal action is triggered, forcibly deleting the faulty timescale anchor point. Simultaneously, all displacement values, average pressure values, and average load current values ​​attached to that timescale anchor point are discarded, ensuring that each sequence retains all three types of physical attributes on the remaining timescale. A contiguous storage space is allocated to create an empty matrix carrier containing four attribute fields. The remaining timescale anchor points, after cleaning and removal operations, are filled into the first column of the empty matrix carrier in chronological order from top to bottom.The reconstructed average pressure value corresponding to each time scale anchor point is filled into column 2, the corresponding original grating ruler displacement value is filled into column 3, and the corresponding reconstructed average load current value is filled into column 4, thus completing the loading process of the contact sampling matrix. A small-span aligned reconstruction example containing multi-frequency mixed signals is constructed. The grating ruler generates a scale anchor point at 4 milliseconds, with a recorded displacement value of 25.6 mm. Searching in the 2000 Hz pressure sequence, values ​​of 2.8 MPa at 3.5 milliseconds and 3.2 MPa at 4.5 milliseconds are extracted. Adding 2.8 and 3.2 gives 6.0, dividing by 2 gives an average of 3.0 MPa, which is assigned to the 4-millisecond anchor point. Searching in the 2000 Hz load current sequence, network jitter causes the nearest signal remnants to be located at 0.5 milliseconds and 8.5 milliseconds. The time distances for extending the search to both sides are calculated to be 3.5 milliseconds and 4.5 milliseconds respectively, both greater than the 3-millisecond decision distance. This triggered a forced removal action, directly erasing the 4-millisecond anchor point from the global timeline, discarding the corresponding 25.6 mm and the calculated 3.0 MPa. When the timeline reached the 5-millisecond anchor point, the displacement record was 25.8 mm. The pressure sequence points before and after it were found to be 3.3 and 3.5 MPa, with a calculated mean of 3.4 MPa; the current sequence points before and after it were found to be 10.5 and 10.7 amperes, with a calculated mean of 10.6 amperes. The search distance in each direction was only 0.5 milliseconds, which was within the limit, so the data was retained.

[0025] S202: Call the contact sampling matrix to calculate the adjacent pressure difference, adjacent displacement difference, and adjacent load current difference respectively, verify the consistency of the dimensions of similar parameters, associate the three types of differences with the sampling time index, and generate contact fluctuation feature groups; A memory cross-region access request is issued to retrieve the filled contact sampling matrix from the previously processed storage block. A circular cursor mechanism is constructed within the matrix's row and column structure, establishing a current row cursor and a lag row cursor. This cursor is controlled to slide forward in time from the second row of the matrix to the bottom. During each sliding hover, the pressure value region is reconstructed in the second column of the matrix. The pressure data element pointed to by the current row cursor is extracted and subtracted from the pressure data element pointed to by the lag row cursor, and the absolute value of the difference is extracted to generate adjacent pressure differences. Similarly, the original displacement value region is reconstructed in the third column of the matrix, and the absolute value conversion operation of subtracting the lag row element from the current row element is performed to generate adjacent displacement differences. The load current region is reconstructed in the fourth column of the matrix, and the absolute value of subtracting the lag row element from the current row element is extracted to generate adjacent load current differences. For the three sets of generated differences, a similar parameter dimension consistency verification process is initiated. The dimension dictionary mapping table in the sensor's factory configuration file is extracted, and the physical measurement unit registers corresponding to the contact sampling matrix are scanned. When the unit code in the pressure register is displayed as kilopascal (kPa), all calculated adjacent pressure differences are uniformly divided by 1000 to force alignment with the megapascal (MPa) standard. When the displacement register is displayed as micrometer (µm), all calculated adjacent displacement differences are uniformly divided by 1000 to convert to the millimeter (m) standard. When the current register is displayed as milliampere (mA), it is divided by 1000 to convert to the ampere standard. If the corresponding register code directly matches the international standard literals of MPa, millimeter, and ampere, no division scaling operation is performed, and the original value is retained. The time scale anchor point value corresponding to the lag row cursor is extracted and used as the sampling time index for data tracking. A wide table structure is created in the relational database management system, and the sampling time index is specified as the primary key column of the wide table. Within the same row record, the adjacent pressure differences, adjacent displacement differences, and adjacent load current differences, after dimension unification processing, are inserted into independent column attribute spaces, and they are forcibly bound together at the same horizontal row level. After traversing all cursor movement trajectories and completing the database insertion, the full data table structure consisting of the primary key and three differences is directly named the Contact Fluctuation Feature Group. A matrix extraction example is set up containing rows 11 to 13 of the matrix. Row 11 contains timestamp 15, pressure 2.5, displacement 28.5, and current 12.2; row 12 contains timestamp 16, pressure 2.6, displacement 28.6, and current 12.5; row 13 contains timestamp 17, pressure 2.4, displacement 28.7, and current 11.8. When the cursor slides between rows 12 and 11, subtraction and absolute value processing are performed: the pressure difference is 2.6 minus 2.5, resulting in 0.1; the displacement difference is 28.6 minus 28.5, resulting in 0.1; and the current difference is 12.5 minus 12.2, resulting in 0.3.Scanning the configuration file revealed that the displacement measurement register code 0X02 represents micrometers, triggering a unit scaling rule. The 0.1 is multiplied by the original base and then divided by 1000 (assuming the original record value is read directly in micrometers; if the record itself already displays a decimal point in the software layer, the scaling depends on the actual underlying hexadecimal message). Assuming the underlying layer directly transmits micrometer values ​​of 28600 and 28500, the difference is actually 100 micrometers, which, after dividing by 1000, generates 0.1 millimeters. The timestamp 15, pointed to by the cursor's lag line, is used as the sampling time index. A binding record is generated consisting of timestamp 15, pressure difference 0.1, displacement difference 0.1, and current difference 0.3. The cursor continues to slide between lines 13 and 12, calculating the pressure difference as 2.4 minus 2.6 (absolute value 0.2), the displacement difference as 0.1, and the current difference as 11.8 minus 12.5 (absolute value 0.7). Extract the lagging timestamp 16. Generate a second binding record containing timestamp 16, pressure difference 0.2, displacement difference 0.1, and current difference 0.7. Write these binding records consecutively into the database to create a wide table. The contents of this wide table form the contact fluctuation characteristic group structure that reflects the dynamic change rate of multiple physical quantities.

[0026] S203: Based on the contact fluctuation characteristic group, compare the adjacent pressure difference with the contact pressure fluctuation limit, the adjacent displacement difference with the contact displacement fluctuation limit, and the adjacent load current difference with the load current fluctuation limit, associate the three comparison states, and generate contact monitoring criteria. A command to read data packets is sent to the relational database to fully retrieve the contact fluctuation feature group dataset, which consists of a sampling time index and three difference columns. For each record in the contact fluctuation feature group, the adjacent pressure differences within that row are extracted and introduced into the first independent comparison calculation channel for scalar-level numerical comparison with the contact pressure fluctuation limits hard-coded in the configuration file. The adjacent displacement differences within that row are extracted and introduced into the second independent comparison calculation channel for comparison with the corresponding hard-coded contact displacement fluctuation limits. The adjacent load current differences within that row are extracted and introduced into the third independent comparison calculation channel for comparison with the corresponding load current fluctuation limits. Each of the three comparison channels outputs an independent Boolean state determination variable based on whether the input difference is greater than the corresponding limit. When the input difference is greater than the fluctuation limit, the corresponding comparison channel outputs a state of 1, indicating that the amplitude limit has been exceeded; when the input difference is less than or equal to the fluctuation limit, the corresponding comparison channel outputs a state of 0, indicating that the fluctuation is stable and controllable. The three fluctuation limits mentioned above were derived from 3,000 standard metallographic polishing process reports collected during the equipment rigid debugging phase in the production workshop. Data binning and statistical processing were performed on the dynamic fluctuation rate data recorded in these 3,000 scratch-free perfect process reports to identify the upper limit of the fluctuation peak that could cover 95% of the statistical samples. Ultimately, the contact pressure fluctuation limit was locked at 0.05 MPa, the contact displacement fluctuation limit at 0.02 mm, and the load current fluctuation limit at 0.8 Amperes. After completing the comparison and judgment for each channel, the three output Boolean state judgment variables were extracted. The state judgment variable representing pressure was shifted left by 2 bits, the state judgment variable representing displacement was shifted left by 1 bit, and the state judgment variable representing current was kept in the lowest bit. A binary bitwise OR logical addition operation was performed on the three shifted state judgment variables, merging them into a three-bit binary combination code with three significant bits. This three-bit binary combination encoding, formed by fusing the three judgment states, along with the sampling time index of the row that generated it, is packaged and encapsulated to directly constitute a new feature flag structure for the contact monitoring criterion. The second binding record generated in the previous example is called for deduction to obtain the pressure difference value of 0.2, displacement difference value of 0.1, and current difference value of 0.7 within that record. First, the pressure difference value of 0.2 is pushed into the first channel and compared with the contact pressure fluctuation limit of 0.05. It is found that 0.2 is much greater than 0.05, so the first channel outputs a state value of 1. The displacement difference value of 0.1 is pushed into the second channel and compared with the contact displacement fluctuation limit of 0.02. It is determined that 0.1 is greater than 0.02, so the second channel outputs a state value of 1. The current difference value of 0.7 is pushed into the third channel and compared with the load current fluctuation limit of 0.8. It is determined that 0.7 is less than 0.8, so the third channel outputs a state value of 0. The three states obtained are 1, 1, and 0.The state 1 generated by the pressure channel is converted to binary and shifted left by 2 bits to obtain 0100 (decimal 4); the state 1 generated by the displacement channel is shifted left by 1 bit to obtain 0010 (decimal 2); the current state 0 remains unchanged at 0000. A bitwise OR logical addition is performed on these three, resulting in 0110 (corresponding to a decimal value of 6). This three-bit binary combination code 0110 is paired and bound to the sampling time index 16 that generated this series of differences, forming a dictionary mapping structure of [time index 16: state code 0110]. This independently generated mapping structure instance formally becomes the contact monitoring criterion for whether the device is operating smoothly at that moment. Through this strictly parallel channel comparison and displacement code merging mechanism, the interference of numerical magnitude on the final logical judgment is completely eliminated, allowing the complex three-state physical limit-breaking state to be expressed in a refined manner.

[0027] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the contact monitoring criteria, call the no-load monitoring file, collect the process card release ratio and process card disengagement stroke, verify the proportional dimension and stroke unit, map the release ratio, disengagement stroke, no-load pressure state, spindle current baseline value, and establish a release parameter matrix. Initiate data dependency tracing for contact monitoring criteria. Once it is confirmed that the criterion dataset exists in the main memory resident area, immediately start a downlink read thread for the non-volatile flash memory chip to accurately retrieve the idle monitoring file stored in the specified sector. Parse the serialized and packaged text content of the file string, accurately separate and extract the previously stored internal mapping items, including the idle pressure status value and the spindle current baseline value. Trigger a transmission control protocol connection with the enterprise manufacturing execution system platform, access the process card distribution interface according to the predetermined product batch number, and locate the relevant parameter tag nodes from the received extended markup language file. Extract the text payload of the process card release ratio from the nodes marked with the release rate attribute, and extract the text payload of the process card detachment stroke from the nodes marked with the backlash attribute. Perform a strict dimensionality verification process on the extracted text payloads, check the attached unit symbol of the process card release ratio, and confirm that the symbol must be a percentage representing a relative reduction ratio. If there is no symbol, force a format error command to be thrown and the process terminated. The length identifier of the string accompanying the process card's release stroke is checked by comparing it with a reference dictionary containing specific string feature sequences such as "mm" or "millimeters". When it is detected that the incoming release stroke dimension is incorrectly marked as "cm", the background processing unit is driven to perform a 10-fold numerical expansion operation on the carried value to force it to be normalized to the millimeter dimension. If the incoming identifier naturally matches "mm", the original value is directly output and left unchanged. An array-style memory data block with dimensions divided into single rows and four columns is created and named the release parameter matrix. On the horizontal axis of this array-style memory data block, the process card release ratio with verified units is placed in the first dimension, the normalized release stroke is placed in the second dimension, the no-load pressure status extracted from the no-load monitoring file is placed in the third dimension, and the retrieved spindle current baseline value is placed in the fourth dimension. A full-process calculation example covering the entire process from system acquisition to matrix generation is established, assuming that the process file node string parsed from the Ethernet interface is "reduction rate: 8%, retraction: 0.5 cm". The process card release ratio text load containing "8" and a percentage sign is extracted. The presence and correctness of the percentage sign are verified, and the pure numerical parameter 8 is directly extracted. The takeoff stroke text load containing "0.5" and "centimeter" is extracted. No millimeter identifier is found compared with the reference dictionary, triggering a centimeter identifier detection branch. The value 0.5 is multiplied by a fixed constant 10, outputting a converted uniform value of 5, with the additional unit forcibly changed to millimeters. The no-load pressure state parameter value of 0 is unpacked and read from flash memory, and the spindle current baseline value parameter value of 1.4 is extracted. Mapping and combination instructions are executed on these four purely digital parameters from different sources and with distinct attributes.The value 8 is filled into the first storage cell of the pre-planned array-style memory data block, the value 5 is filled into the second storage cell, the value 0 is filled into the third storage cell, and the value 1.4 is filled into the fourth storage cell. The physical quantities of the parameters are aggregated in such a compact logical address space, which forms the basic foundation for the subsequent tool release boundary calculation and forms a complete release parameter matrix.

[0028] S302: For the release parameter matrix, calculate the pressure release amplitude according to the release ratio of the process card, compare the pressure release amplitude with the no-load pressure state, calculate the retraction displacement amplitude according to the process card release stroke, and obtain the release boundary parameter set; An access traversal operation is initiated on the release parameter matrix residing in memory. The process card release ratio parameter stored in the first column of the matrix is ​​extracted, and the no-load pressure state parameter stored in the third column is extracted. For the extracted process card release ratio parameter, the standard gas source baseline pressure constant built into the internal system is used as the calculation basis. The standard gas source baseline pressure constant and the process card release ratio parameter are multiplied numerically, and the product is divided by 100 to complete the conversion from percentage ratio to the difference in actual gas pressure physical variables. The calculated difference is directly defined and named the pressure release amplitude parameter. A logical condition comparison mechanism based on the above calculation results is executed to retrieve the no-load pressure state parameter extracted in the previous steps. When the value corresponding to the no-load pressure state parameter is detected as 1, indicating that there is inherent instability during the no-load period, an additive gain compensation operation is performed on the just calculated pressure release amplitude parameter. Ten percent of the pressure release amplitude parameter's value is taken as a gain factor. This gain factor is added to the pressure release amplitude parameter to obtain a reinforced new value, which overwrites the original pressure release amplitude parameter. When the no-load pressure state parameter is detected to be 0, indicating extremely stable background, the gain factor compensation step is skipped, and the calculated pressure release amplitude parameter remains unchanged. Then, the process card disengagement stroke parameter stored in the second column of the release parameter matrix is ​​extracted. This process card disengagement stroke parameter is multiplied by the reciprocal of the physical transmission ratio of the lead screw. The lead distance constant corresponding to one revolution of the main control servo axis and the backlash compensation constant of the reducer are extracted. The process card disengagement stroke parameter is divided by this lead distance constant to calculate the total number of reverse rotations required by the motor. This total number of reverse rotations is then multiplied by the lead distance constant and added to the backlash compensation constant. The final result is confirmed as the retraction displacement amplitude parameter. A dedicated data dictionary container is allocated in the operating system's memory allocation space to hold the generated new parameters. The pressure release amplitude parameter, corrected by state comparison, is pushed into the data dictionary container as the first object. Simultaneously, the backoff displacement amplitude parameter, obtained through a series of transformations, is also pushed into the data dictionary container as the second object. A new variable call interface name, called the release boundary parameter group, is broadcast to the global space. An instantiated scenario containing the matrix's built-in data is imported and used for deduction. The value 8 stored in the first column is extracted as the process card release ratio. The standard gas source base pressure constant hard-coded in the main control layer is extracted as 0.5 MPa. 0.5 multiplied by 8 yields 4, which is divided by 100 to obtain 0.04 MPa. This 0.04 is designated as the base pressure release amplitude parameter. The value of the no-load pressure state parameter in the third column of the matrix is ​​found to be 1. A gain conditional branch is triggered, extracting 10% of 0.04 to obtain the value 0.004.Adding 0.004 to the original parameter 0.04 yields a reinforced 0.044 MPa, which is then locked as the pressure release amplitude parameter. The value 5 in the second column is then extracted as the release stroke parameter. The factory-installed lead distance constant is 10 mm, and the backlash compensation constant is 0.2 mm. Dividing the release stroke 5 by the lead 10 calculates the required motor reverse rotation of 0.5 revolutions. Multiplying 0.5 by 10 gives 5, and finally adding the compensation constant of 0.2 yields the final result of 5.2 mm. This 5.2 mm is the established retraction displacement amplitude parameter. The variable with a value of 0.044 MPa and the variable with a value of 5.2 mm are packaged together. Both are stored in a separate, indexed storage area. This combined storage, containing both critical calculation results, constructs the complete structure of the release boundary parameter set. This conversion mechanism corrects the ideal command data based on actual operating condition feedback.

[0029] S303: Based on the release boundary parameter group, associate the pressure release amplitude, retraction displacement amplitude, spindle current baseline value and contact monitoring criteria to construct the pressure release boundary, retraction displacement boundary and spindle unloading boundary respectively, and generate the release monitoring boundary; The system requests exclusive access to read the encapsulated release boundary parameter group data dictionary structure container. From this container, it extracts the pressure release amplitude variable (assigned a value of 0.044 MPa) and the retraction displacement amplitude variable (assigned a value of 5.2 mm). Subsequently, it sends a read command to the underlying flash memory or reserved memory area to retrieve the spindle current baseline value variable, which was initially stored as the spindle idling current's original state. It sends a request to the temporary database to extract the contact monitoring criterion feature set composed of binary code. It extracts the real-time operating pressure monitoring value of the system air source from the aforementioned process, subtracts the extracted pressure release amplitude variable from this value, and calculates the absolute difference baseline output by the subtraction operation. This absolute difference baseline is assigned an independent attribute identifier and named the pressure release boundary. It retrieves the mechanical absolute coordinate value corresponding to the current tool entry minimum position from the encoder bus of the robotic arm drive control system in real time, subtracts the previously extracted retraction displacement amplitude variable from this absolute coordinate value, and constructs and names the spatial position baseline value as the retraction displacement boundary. For the extracted spindle current baseline value, a multiplier factor with a specific percentage discount rate is set, and this discount rate is defined as a 120% amplification ratio. The spindle current baseline value is multiplied by this 120% amplification ratio to obtain the amplified warning value. Further, the binary combination codes within the retrieved contact monitoring criterion feature set are traversed and read, and a bitmasking operation is performed to determine whether the value of the corresponding binary bit of the current state is 1. If the determination result shows an alarm state 1, an addition operation is performed on the amplified warning value, adding a rated over-limit tolerance value of 0.5 amperes; if the determination bit is 0, this addition tolerance step is skipped. The ampere-level parameter obtained after this calculation is defined as the spindle unloading boundary. A tree-like directory structure object is generated to coordinate these three boundary parameters. The name of this tree-like directory structure is used as the root node and named the release monitoring boundary. Under this main root node, three parallel data leaf nodes are attached. The result value corresponding to the absolute difference baseline is attached to the first leaf node and marked with a pressure release boundary; the spatial position baseline result is attached to the second leaf node and marked with a back displacement boundary; the current parameter after amplification and additive tolerance processing is attached to the third leaf node and marked with a spindle unloading boundary. A data calculation flow example with coherence and continuity is constructed. The pressure release amplitude variable of 0.044 MPa and the back displacement amplitude variable of 5.2 mm are extracted from the dictionary structure container. The real-time pressure displayed in the current air source working pressure gauge register is captured as 0.600 MPa. The subtraction operation 0.600 minus 0.044 yields a result of 0.556 MPa. This 0.556 MPa is designated as the pressure release boundary parameter and stored in memory. The absolute Z-axis coordinate value of the current deepest machining depth is captured and displayed as 150.0 mm.Subtracting 5.2 from 150.0 yields a subtraction result of 144.8 mm. This 144.8 mm is designated as the retraction displacement boundary parameter and stored in the buffer. The baseline value of the spindle current, which has been continuously transmitted, is obtained as 1.40 Amperes. A 120% scale is extracted, and a multiplication operation of 1.40 by 1.20 is performed to obtain the amplified warning value of 1.68 Amperes. The contact monitoring criterion combination code corresponding to the current moment is parsed as 0110. The least significant bit representing the Boolean judgment result of the current state is extracted as 0. Based on the above conditions, the addition step of the rated tolerance value is skipped. Finally, 1.68 Amperes is directly locked as the spindle unloading boundary value. The values ​​of 0.556 MPa, 144.8 mm, and 1.68 Amperes are packaged into a set variable named "Release Monitoring Boundary" containing three element references, which is then output as the judgment benchmark for the entire tool preparation retraction stage.

[0030] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the release monitoring boundary, collect the pressure sequence, displacement sequence and main shaft current acquisition device output current sequence during the electric cylinder retraction, align the three types of sequences according to the sampling time, remove missing sampling points, and establish a retraction sampling matrix; The system continuously monitors the control signal for the electromagnetic relay activation of the electric cylinder's reverse drive, uploaded from the digital output board of the device. When this control signal changes to true, the timestamp of this action is established as the start time of the reverse motion. Upon detecting the start time of the reverse motion, the central processing unit is immediately driven to initiate a high-speed concurrent polling mechanism. Read frames are sent to the pressure sensor channel to acquire dynamic gas resistance changes caused by the change in pipeline space due to cylinder contraction, and these changes are recorded in time sequence as a pressure sequence. Position pull pulses are sent to the displacement measurement grating ruler of the guide rail to acquire the real-time pitch change value caused by the screw reversal, and these changes are stacked in time to construct a displacement sequence. A voltage capture action is applied to the power monitoring pin of the spindle inverter, and the voltage value is converted into a real-time stator operating current feedback parameter flowing through the coil using a current conversion algorithm, generating the output current sequence of the spindle current acquisition unit. Because the clock oscillators of the internal bus topology of each subsystem have slight frequency drift, strong timing alignment processing based on a reference time anchor is required. The built-in time axis of the displacement sequence is selected as the reference. The time tolerance capture frame is set to ±2 milliseconds. Using each time point in the displacement sequence as the search origin, data falling within the time tolerance capture frame is searched in the pressure sequence and the output current sequence of the spindle current acquisition unit. All captured sequence data within ±2 milliseconds of the origin are extracted, and a multi-factor arithmetic average is performed to obtain a smoothed composite value representing the search origin for alignment. If the system detects that for a specific displacement time point, no data items are returned within the capture frame when searching the pressure or current channel, it indicates a missed sampling / interruption phenomenon in some external hardware. A direct drive array deletion command is used to completely remove the displacement time point and its associated displacement values, avoiding approximate interpolation to prevent numerical misjudgments during high-speed reverse operation. A two-dimensional table area structure with row and column cross-addressing capability is allocated and established. The remaining time points after alignment and removal are arranged in the row coordinate system, and the corresponding air pressure, dimension position, and current ampere values ​​are filled into the intersecting column areas, named the backsampling matrix. The data reconstruction and extrapolation are set up for a very short time axis during the back-reaming period. When the grating ruler records a leadscrew displacement of 149.5 mm at 80 milliseconds. Around the center of 80 milliseconds, a search in the pressure sequence list reveals a value of 0.58 MPa at 79 milliseconds and 0.56 MPa at 81 milliseconds. Adding 0.58 to 0.56 equals 1.14, dividing by 2 yields a smoothed composite pressure of 0.57 MPa, which is then bound to point 80. A search in the spindle current sequence reveals 1.6 Amps at 78 milliseconds and 1.5 Amps at 80 milliseconds. Since both times are within the frame, adding 1.6 to 1.5 equals 3.1, dividing by 2 yields 1.55 Amps, which is also bound. Extrapolation continues to 85 milliseconds, where the grating ruler has a given displacement of 149.2 mm.The pressure sequence revealed that data was only returned at milliseconds 82 and 88. Calculations showed that the timeframe at 85 milliseconds and the spans before and after it both reached 3 milliseconds, exceeding the 2-millisecond allowable limit. At this point, the CPU issued a direct erase command, forcibly destroying the timeline containing 149.2 millimeters of data without any interpolation. The time value of the assembly preserved at 80 milliseconds, along with its corresponding three physical parameters, was written into a two-dimensional table in the application. This multi-row, three-column effectively aligned set was then packaged and saved as a backsampling matrix.

[0031] S402: Call the backsampling matrix to calculate the adjacent pressure difference, adjacent displacement difference, and adjacent spindle current difference respectively, verify the consistency of the dimensions of similar parameters, associate the three types of differences with the sampling time index, and generate the departure change feature group; The system issues memory control block operation commands to locate and extract the content of the two-dimensional form in the backsampling matrix deep in memory. It constructs a pair of scan executors containing a primary traversal index cursor and a secondary following index cursor. These two cursors are instructed to move step-by-step downwards from the second row of the table data layer. When the cursor combination enters and suspends at the specified two-row data layer position, it extracts the air pressure value of the row pointed to by the primary cursor and the row pointed to by the secondary following cursor. The former is subtracted from the latter to perform difference stripping, retaining its mathematical positive and negative sign attributes to generate adjacent pressure differences. Following the same mechanism, it extracts the size and position values ​​sandwiched between the current two rows of data layers and performs subtraction to generate adjacent displacement differences including directional signals. It extracts the ampere parameters of the current two rows and performs equivalent subtraction to obtain adjacent spindle current differences including positive and negative feedback. For these batches of calculated differences in directional attributes, the system performs mandatory verification and comparison of the same parameter dimensions using the unified data dictionary standard service at the underlying device. When the dictionary key-value pairing table is consulted and it is determined that the pressure symbol registered in the matrix header is not in megapascals but in bar units, the driving calculation process performs a uniform multiplication of 0.1 on all pressure differences in this batch for a downgrade conversion. If the registered size unit in the header is decimeters, a multiplication of 100 is enforced to convert it to millimeters. If the registered current unit is standard amperes, the shielding multiplication remains unchanged. A key-value index array model is established and associated with the corresponding timestamp. The millisecond timestamp, which is fixed in the row next to the cursor when the corresponding numerical subtraction operation is performed, is used as the primary addressing key name. The pressure difference parameter with a direction symbol, which has undergone unit normalization, is placed in the first attribute slot under this name node; the displacement difference parameter with a direction symbol is placed in the second attribute slot; and the current difference parameter is placed in the third attribute slot. This one-to-three aggregated array is collectively assigned a new identifier header, which is referred to as the decoupled change feature group. A truncated two-row example extracted from the matrix is ​​used to illustrate the specific change parameter acquisition process. The main cursor moves to the 90th millisecond of the timestamp and finds the stored pressure value as 0.54 MPa, displacement value as 148.8 mm, and current value as 1.52 amperes. The secondary cursor then stops at the 80th millisecond of the timestamp and extracts the stored pressure value as 0.57 MPa, displacement value as 149.5 mm, and current value as 1.55 amperes. Strict subtraction operations are performed on the three dimensions: 0.54 minus 0.57 for pressure yields a negative result of -0.03; 148.8 minus 149.5 for displacement yields a negative result of -0.7; and 1.52 minus 1.55 for current yields a negative result of -0.03. After communication between the system backend and the configuration file, the units declared in the matrix header are found to be MPa, mm, and amperes, which perfectly match the data and do not require any multiplication by conversion constants for verification or compensation. Therefore, the time node 80 recorded by the secondary cursor is directly extracted as the unique identifier.Using this unique identifier, a composite data item is constructed containing four discrete parameters: time 80, pressure change rate -0.03, displacement change rate -0.7, and current change rate -0.03. As the cursor continuously probes and scans, dozens of such data structures are generated, which are then aggregated to form an entity that is detached from the change feature group, objectively quantifying the instantaneous acceleration and force dynamic information of the actuator during the reverse tearing and detachment process.

[0032] S403: Based on the detachment change characteristic group, compare the adjacent pressure difference with the pressure release boundary, the adjacent displacement difference with the retraction displacement boundary, and the adjacent spindle current difference with the spindle unloading boundary, map the three comparison states and sampling time index, and generate the detachment monitoring trajectory. The full data package containing dynamic fluctuation parameters for each time period of the detachment change feature group is loaded into the internal workbench of the system's arithmetic logic unit. Simultaneously, a memory access handle is established pointing to the previously generated release monitoring boundary tree object containing three threshold thresholds. A three-channel synchronous comparison and discrimination loop engine is started. In the loop traversing each independent time-series data item within the detachment change feature group: adjacent pressure differences carrying positive and negative attributes are extracted and fed into the first comparison channel for numerical comparison with the static lower limit threshold of the pressure release boundary obtained using the access handle; adjacent displacement differences are extracted and fed into the second channel for comparison with the static threshold of the backlash displacement boundary; adjacent spindle current differences are extracted and fed into the third channel for comparison with the static threshold of the spindle unloading boundary. Channel status feedback output rules are defined: regardless of the channel, if the absolute order of magnitude of the current difference fed in is lower than the absolute lower limit set by the static boundary, a safety judgment code flag of 0 is output for that channel; otherwise, if the order of magnitude is equal to or exceeds the boundary threshold value, an exception judgment code flag of 1 is thrown. The Boolean scalar numerical identifiers generated in parallel from the three channels are collected together to establish a set of literal values ​​encompassing these three state values. The unique time-address key name corresponding to the occurrence moment of this round of processing is extracted as a shell, and the judgment set constructed from the three comparison numbers is tightly embedded within this time shell, building a dictionary mapping body for time-driven state display. This is ultimately given the name "Detached from Monitoring Trajectory," which can display the global tool withdrawal safety situation. A multi-parameter comparison and judgment calculation process with real physical meaning is then substituted into a calculation example. The operation is initiated using the data item generated above, which contains time index 80, pressure difference -0.03, displacement difference -0.7, and current difference -0.03. The handle callback is used to recall the three corresponding constant lower limit boundary parameters generated in the previous deduction: pressure boundary value 0.556, displacement boundary value 144.8, and current unloading boundary value 1.68. The first channel operation compares the absolute value of the pressure difference (0.03) with the static lower limit of pressure (0.556). It determines that the pressure difference has not exceeded the 0.556 limit, and the channel outputs a scalar value of 0. The second channel analysis examines the displacement decrease (0.7), comparing it to the absolute value of 144.8, finding no exceedance, and outputs a safety flag of 0. The third current channel verification compares the current change (0.03) with the limit range of 1.68, again showing no threshold breach, and outputs a flag of 0. The three extracted zero values ​​are combined into a judgment flag array [0, 0, 0] according to the order of pressure, displacement, and current. Using the time-unique address key code 80 that initially guided this data analysis as the root name, a sequence containing three zeros is tightly linked to this name, creating a mapping information pair structure: [Time 80: State (0, 0, 0)].When the entire scanning cycle runs to the end, it generates a series of Boolean triple structures with time sequence. By organizing and binding these structures in ascending order of occurrence time, a panoramic view of the escape monitoring trajectory data stream showing whether all channels are stable and safe during the grinding retreat is obtained.

[0033] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the departure from the monitoring trajectory, call the release monitoring boundary and contact monitoring criteria to extract the pressure release state, retraction displacement state and spindle unloading state, align the three states according to the sampling time index, and establish the endpoint state matrix. The data stream object of the detachment trajectory, composed of time-series Boolean tuples, is pushed into the central scheduling register area as the main input data source. The corresponding memory address segment is activated to read the release monitoring boundary object, which is composed of three static lower limit thresholds. The contact monitoring criterion feature dictionary set, which presents the limit-crossing feedback through bitmask fusion, is extracted from the previous storage stage output. A deep data mining filtering and stripping algorithm framework is established. First, within this framework, each element in the detachment trajectory is traversed and analyzed. Based on the pressure comparison output status record, continuous time intervals that meet the stable characteristic of continuous zeroing are selected. The pressure value at the last time point of this safe zeroing period, along with its state, is extracted and labeled as the pressure release state parameter. Similarly, the final state identifier at the end of the cycle where the internal displacement comparison safety flag shows a continuous zero is retrieved and separately designated as the retraction displacement state parameter. Then, by filtering and determining the spindle tail current rotation characterization under the condition of continuous 0 without alarm triggering, the final point ampere parameter is selected and named the spindle unloading state parameter. To prevent asynchrony in the acquisition of different states, a sorting and alignment operation is performed based on the occurrence time labels associated with the three endpoint states. Using the spindle unloading occurrence time as a baseline, a search and comparison is applied to the other two items: if the absolute deviation of the occurrence times of the three is within a convergence period of ±5 milliseconds, then each item is considered to fall within the physical departure range of the endpoint and is bundled together. A data storage structure named "Endpoint State Matrix" is created. The bundled occurrence time is used as the first column's unique credential index. The extracted and named pressure release state is placed in the second column cell of the corresponding row, the backtracking displacement state parameter is inserted into the third column cell, and the spindle unloading state is placed in the fourth column cell. An alignment, stripping, and recombination operation calculation example is imported. The monitoring trajectory containing [Time 250: State (0, 0, 0)] to [Time 260: State (1, 0, 0)] is filtered. The pressure indicator was found to remain at a safe level of 0 at 258 milliseconds, followed by an alarm state at 259 milliseconds. The residual actual pressure value at point 258, such as 0.550 MPa, was extracted and set as the pressure release state. The displacement parameter remained at 0 until 255 milliseconds, with a position value of 145.0 mm, which was extracted as the retraction displacement state. The current parameter remained stable at 0 at 258 milliseconds, with an ampere value of 1.50 amperes, which was extracted as the spindle unloading state. The current point at 258 milliseconds was selected as the standard reference point. The time deviation difference at the pressure endpoint at 258 milliseconds was found to be 0; the time deviation difference between the displacement endpoint at 255 milliseconds and the calculated time deviation was 3 milliseconds. After confirming that the differences of both indicators perfectly fell within the 5-millisecond clustering safety threshold, the structural merging command was approved. Place node 258 as the base index row at the beginning of the table row, write pressure 0.550 in the corresponding sequence in the same row, fill in displacement space 145.0 in the next position, and fill in filling current parameter 1.50 in the last position.At this point, a terminal state matrix entity is created, which has a base point location and contains the three key final performance values ​​in a single row and four columns data format.

[0034] S502: For the endpoint state matrix, compare the pressure release state with the pressure release boundary, the retraction displacement state with the retraction displacement boundary, and the spindle unloading state with the spindle unloading boundary, associate the three boundary comparison values, and obtain the endpoint determination identifier. For the completed endpoint state matrix entity stored in the cache space, the central arithmetic unit is invoked to execute the final bottom-line verification logic. A read command is issued to the system constant storage area to extract the three static fixed parameters that serve as the adjudication criteria: the lower limit of the pressure release boundary, the lower limit of the backlash displacement boundary, and the red line value of the spindle unloading boundary. The absolute judgment instruction program channel is activated for each pairing. The actual feedback parameter of the pressure release state in the second cell of the matrix is ​​extracted, and a simple and clear magnitude comparison between it and the extracted lower limit of the pressure release boundary is performed inside the arithmetic register. Simultaneously, the backlash displacement state parameter stored in the third cell is extracted and entered into the next arithmetic group for magnitude comparison of the lower limit of the backlash displacement boundary. The spindle unloading state value in the last cell is obtained and used in the calculation to check whether the spindle unloading boundary red line value has been crossed. The comparison result after each group of comparisons is output as a discretized specific character code. When the comparison operation determines that the current actual release parameter has completely fallen below or reached the set boundary constant number, a true flag is generated and written to the first position. Similarly, if the displacement channel determines that its position number has crossed and exceeded the preset anti-collision space baseline, the true flag is also assigned and filled into the second position. If the ampere comparison result shows that the red line barrier has not been breached, the true flag is also generated and placed in the third position. If any comparison shows a breach of the boundary, the corresponding false flag is generated. The three independent and inseparable true and false flags derived from the above three levels of operation are tightly linked together to form a composite boundary comparison value set string with combined judgment power. This three-digit string result, containing decisive significance, is assigned and labeled with a final name called the endpoint judgment flag. A very simple and clear real-world verification example is used to complete the entire logical comparison derivation. The values ​​stored in the matrix derived in the previous section—pressure 0.550, displacement 145.0, and current 1.50—are taken out for later use. The system synchronously reads the earlier-determined critical boundary lines: pressure boundary 0.556, displacement boundary 144.8, and current boundary 1.68. 0.550 is fed into the pressure test funnel and compared with 0.556 to generate a collision detection. The system's arithmetic logic determines that 0.550 is clearly below the 0.556 defense standard, and directly outputs a true character. The displacement data 145.0 is pushed to the spatial test scale and verified against 144.8. Because the spatial data value of 145.0 still exists within the absolutely safe margin outside the 144.8 collision protection line, a second true character is generated. The current value 1.50 is then subjected to a collision detection against the dam limit of 1.68. It is determined that 1.50 is far from reaching the bottom edge of the trigger ceiling of 1.68, and a third true character is generated. The verification results generated by these three rigorous tests are collected and packaged. They are then concatenated and glued together using a plain text concatenation method to form a complete phrase containing three unit blocks: [true-true-true].This tightly encapsulated phrase was immediately registered and authorized as the sole credential guiding the final decision-making and execution actions, thus serving as the endpoint determination identifier.

[0035] S503: Based on the endpoint determination identifier, associate the contact monitoring criteria, release monitoring boundary and departure monitoring trajectory, determine the grinding stop node and electric cylinder retraction node, map the node time with the control state, and generate an intelligent control scheme for the planar grinding of industrial valve workpieces. Load and dissect the multiple string segments within the final command token (endpoint determination identifier) ​​generated in the previous step. Set a control branch selection tree as the decision guide. Extract the contact monitoring criterion feature dictionary representing severe conditions during grinding from the memory stored earlier, extract the static parameter tree of the release monitoring boundary reflecting the safety setting threshold in the middle section, and perform a full-link situation review and aggregation retrieval of the detachment monitoring trajectory data stream representing the force conditions during the subsequent retraction. When any false character is found in the string content contained within the endpoint determination identifier, it indicates that a physical jamming or material adhesion malfunction has occurred in the detachment stage of the corresponding subsystem. For this type of fault, directly extract the millisecond value of the first sudden change time of the alarm jump within the detachment monitoring trajectory and directly define this time point as the grinding stop node; extract the axis end spatial coordinate position recorded by the main control at the time of triggering the fault and register it as the electric cylinder retraction node. When the entire string within the endpoint determination identifier presents a perfect pass state consisting of three consecutive true characters, execute the extraction strategy according to the normal logic of no jamming. The last effective safe moment within the contact monitoring criteria is identified as the valid grinding stop node; the feedback grating code at the complete termination of the system's given displacement command is confirmed as the safe electric cylinder retraction node. A two-way channel mapping association graph control model is established. The successfully judged and calibrated grinding stop node is directly bound to the spindle servo rotary brake control state at the machine tool's underlying level through a direct action handshake binding mapping; simultaneously, the derived electric cylinder retraction node with coordinates is bound to the deep hardware response of the hydraulic valve reverse push electromagnetic opening and closing control state. After such a complex and interwoven process spanning from front to back, including acquisition, comparison, judgment, and command output, the data set plus the operation action command assembly are jointly forged, solidified, and refined into the execution architecture entity of the intelligent control scheme for industrial valve workpiece planar grinding. The calculation results are imported into the execution tree to generate the substantive control logic. Before inspection, what is obtained is a string of green light endpoint judgment identifiers composed of [true-true-true]. The judgment function triggers a perfect safe disengagement state response branch. The system abandons the search for so-called anomalies within the monitoring trajectory. The coordinates reported by the grating ruler, such as 150.0, indicating that the cylinder has reached the safety limit and remains stationary, are directly confirmed and recorded in the electric cylinder retraction node file. The moment indicated by the last stable, limit-free report before the end of grinding, such as the 1500th millisecond, is recorded as the grinding stop node. The 1500th millisecond data is directly bound and mapped to the inverter's emergency stop, power-off, and reverse braking subroutine action, under the same trigger register address; the position coordinate data 150.0 is linked to the command code for issuing the interlocking air pipe closing and retraction relay, establishing a physical mapping relationship. This large, executable operation execution group code block, which completely integrates the precisely calculated time and location with the hardware closed-loop control, is directly solidified into an intelligent control scheme file system applied to the safe advance and retreat management of valve surface grinding.

[0036] An intelligent control system for planar grinding of industrial valve workpieces, comprising: The no-load status monitoring module is used to realize S1: the grinding station detects the clamping signal of the fixture, collects the spindle no-load current sequence and no-load pressure sequence, calculates the baseline of the spindle no-load current sequence, compares the fluctuation of the no-load pressure sequence, and generates no-load monitoring files; The contact stability analysis module is used to implement S2: based on the no-load monitoring archive, it collects the valve seat end face contact pressure sequence, displacement sequence and load current sequence, calculates the stability of the valve seat end face contact pressure sequence, displacement sequence and load current sequence, and generates contact monitoring criteria; The unloading boundary planning module is used to implement S3: based on the contact monitoring criteria, it calls the no-load monitoring file, collects the process card release ratio and process card disengagement stroke, constructs the pressure release boundary, the retraction displacement boundary and the spindle unloading boundary, and generates the release monitoring boundary. The retraction and disengagement tracking module is used to implement S4: based on the release monitoring boundary, it collects the electric cylinder retraction pressure sequence, retraction displacement sequence and output current sequence, compares the electric cylinder retraction pressure sequence, retraction displacement sequence and output current sequence to disengagement process, and generates disengagement monitoring trajectory. The grinding intelligent control module is used to implement S5: based on the departure from the monitoring trajectory, it calls the release monitoring boundary and contact monitoring criteria to determine the pressure release, retraction displacement and spindle unloading endpoint, and generates an intelligent control scheme for the surface grinding of industrial valve workpieces.

[0037] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the technical solution.

Claims

1. A method for intelligent control of planar grinding of industrial valve workpieces, characterized in that, Includes the following steps: S1: The grinding station detects the clamping signal of the fixture, collects the spindle idle current sequence and the no-load pressure sequence, calculates the baseline of the spindle idle current sequence, compares the fluctuation of the no-load pressure sequence, and generates a no-load monitoring file. S2: Based on the no-load monitoring file, collect the valve seat end face contact pressure sequence, displacement sequence and load current sequence, calculate the stability of the valve seat end face contact pressure sequence, displacement sequence and load current sequence, and generate contact monitoring criteria; S3: Based on the contact monitoring criteria, call the no-load monitoring file, collect the process card release ratio and process card disengagement stroke, construct the pressure release boundary, the retraction displacement boundary and the spindle unloading boundary, and generate the release monitoring boundary; S4: Based on the release monitoring boundary, collect the electric cylinder retraction pressure sequence, retraction displacement sequence and output current sequence, compare the electric cylinder retraction pressure sequence, retraction displacement sequence and output current sequence to separate the process, and generate the separation monitoring trajectory. S5: Based on the departure monitoring trajectory, call the release monitoring boundary and the contact monitoring criterion to determine the pressure release, retraction displacement and spindle unloading endpoint, and generate an intelligent control scheme for industrial valve workpiece plane grinding.

2. The intelligent control method for planar grinding of industrial valve workpieces according to claim 1, characterized in that, The no-load monitoring file includes a locking confirmation code, no-load current reference value, current drift tolerance, no-load average pressure, and pressure fluctuation level. The contact monitoring criteria include contact stability, pressure-position coupling coefficient, load response coefficient, stable duration, and contact anomaly level. The release monitoring boundary includes pressure release upper limit, pressure release lower limit, retraction stroke threshold, unloading current threshold, and boundary margin coefficient. The departure monitoring trajectory includes pressure attenuation characteristics, displacement retraction characteristics, current unloading characteristics, departure stage label, and trajectory deviation. The intelligent control scheme for industrial valve workpiece plane grinding includes start-stop control strategy, feed correction strategy, grinding wheel avoidance strategy, anomaly alarm strategy, and process traceability strategy.

3. The intelligent control method for planar grinding of industrial valve workpieces according to claim 1, characterized in that, The pressure release boundary refers to the safety threshold and limit range of the pressure drop process between the tooling and the valve seat, which is constructed, limited, and monitored based on the release ratio of the process card during the separation stage after grinding.

4. The intelligent control method for planar grinding of industrial valve workpieces according to claim 1, characterized in that, The stability of the displacement sequence and load current sequence refers to the smoothness and fluctuation range of the data sequence of grinding feed displacement and spindle working current over time during the end face contact grinding process.

5. The intelligent control method for planar grinding of industrial valve workpieces according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Detect the workpiece clamping signal, collect the spindle idle current sequence and pressure sensor idle pressure sequence during the effective clamping period, align the current sampling value and pressure sampling value according to the sampling time, and establish an idle sampling matrix; S102: Based on the no-load sampling matrix, extract the spindle no-load current sequence, calculate the sampling mean value in the current stable section, verify the consistency of current dimensions, and obtain the spindle current baseline value. S103: Call the spindle current baseline value and the no-load sampling matrix, extract the no-load pressure sequence of the pressure sensor, compare the adjacent pressure difference with the preset no-load pressure fluctuation limit, associate the locking signal, the spindle current baseline value and the pressure fluctuation status, and establish a no-load monitoring file.

6. The intelligent control method for planar grinding of industrial valve workpieces according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the aforementioned no-load monitoring file, collect the pressure sequence, displacement sequence, and load current sequence during the contact period between the valve seat end face and the grinding wheel, align the three types of sequences according to the sampling time, remove missing sampling points, and establish a contact sampling matrix; S202: Call the contact sampling matrix to calculate the adjacent pressure difference, adjacent displacement difference, and adjacent load current difference respectively, verify the consistency of the dimensions of similar parameters, associate the three types of differences with the sampling time index, and generate contact fluctuation feature groups; S203: Based on the contact fluctuation feature group, compare the adjacent pressure difference with the contact pressure fluctuation limit, the adjacent displacement difference with the contact displacement fluctuation limit, and the adjacent load current difference with the load current fluctuation limit, associate the three comparison states, and generate contact monitoring criteria.

7. The intelligent control method for planar grinding of industrial valve workpieces according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the contact monitoring criteria, call the no-load monitoring file, collect the process card release ratio and process card disengagement stroke, verify the ratio dimension and stroke unit, map the release ratio, disengagement stroke, no-load pressure state, spindle current baseline value, and establish a release parameter matrix. S302: For the release parameter matrix, calculate the pressure release amplitude according to the release ratio of the process card, compare the pressure release amplitude with the no-load pressure state, and calculate the retraction displacement amplitude according to the process card release stroke to obtain the release boundary parameter set; S303: Based on the release boundary parameter group, associate the pressure release amplitude, retraction displacement amplitude, spindle current baseline value and contact monitoring criterion to construct the pressure release boundary, retraction displacement boundary and spindle unloading boundary respectively, and generate the release monitoring boundary.

8. The intelligent control method for planar grinding of industrial valve workpieces according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the release monitoring boundary, collect the pressure sequence, displacement sequence and main shaft current acquisition device output current sequence during the electric cylinder retraction, align the three types of sequences according to the sampling time, remove missing sampling points, and establish a retraction sampling matrix; S402: Call the backsliding sampling matrix to calculate the adjacent pressure difference, adjacent displacement difference, and adjacent spindle current difference respectively, verify the consistency of the dimensions of similar parameters, associate the three types of differences with the sampling time index, and generate the departure change feature group; S403: Based on the set-off change feature group, compare the adjacent pressure difference with the pressure release boundary, the adjacent displacement difference with the retraction displacement boundary, and the adjacent spindle current difference with the spindle unloading boundary, map the three comparison states and sampling time index, and generate the set-off monitoring trajectory.

9. The intelligent control method for planar grinding of industrial valve workpieces according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the departure monitoring trajectory, call the release monitoring boundary and the contact monitoring criterion to extract the pressure release state, retraction displacement state and spindle unloading state, align the three states according to the sampling time index, and establish the endpoint state matrix. S502: For the endpoint state matrix, compare the pressure release state with the pressure release boundary, the retraction displacement state with the retraction displacement boundary, and the spindle unloading state with the spindle unloading boundary, and associate the three boundary comparison values ​​to obtain the endpoint determination identifier. S503: Based on the endpoint determination identifier, associate the contact monitoring criteria, release monitoring boundary and departure monitoring trajectory, determine the grinding stop node and electric cylinder retraction node, map the node time with the control state, and generate an intelligent control scheme for the planar grinding of industrial valve workpieces.

10. An intelligent control system for planar grinding of industrial valve workpieces, characterized in that, The system is used to implement the intelligent control method for planar grinding of industrial valve workpieces as described in any one of claims 1-9, comprising: The no-load status monitoring module is used to realize S1: the grinding station detects the clamping signal of the fixture, collects the spindle no-load current sequence and no-load pressure sequence, calculates the baseline of the spindle no-load current sequence, compares the fluctuation of the no-load pressure sequence, and generates no-load monitoring files; The contact stability analysis module is used to implement S2: based on the no-load monitoring file, collect the valve seat end face contact pressure sequence, displacement sequence and load current sequence, calculate the stability of the valve seat end face contact pressure sequence, displacement sequence and load current sequence, and generate contact monitoring criteria; The unloading boundary planning module is used to implement S3: based on the contact monitoring criteria, it calls the no-load monitoring file, collects the process card release ratio and process card disengagement stroke, constructs the pressure release boundary, the retraction displacement boundary and the spindle unloading boundary, and generates the release monitoring boundary; The retraction and disengagement tracking module is used to implement S4: based on the release monitoring boundary, it collects the electric cylinder retraction pressure sequence, retraction displacement sequence and output current sequence, compares the electric cylinder retraction pressure sequence, retraction displacement sequence and output current sequence to disengagement process, and generates disengagement monitoring trajectory; The grinding intelligent control module is used to implement S5: based on the departure monitoring trajectory, it calls the release monitoring boundary and the contact monitoring criterion to determine the pressure release, retraction displacement and spindle unloading endpoint, and generates an intelligent control scheme for the planar grinding of industrial valve workpieces.

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