Bus type integrated numerical control machine tool auxiliary machine control system and operation method

Abstract

The application discloses a bus type integrated numerical control machine tool auxiliary machine control system and a running method, and belongs to the technical field of numerical control machine tool auxiliary machine control. The system comprises a bus main controller and a plurality of auxiliary machine control nodes, and computer programs are respectively configured to realize communication control. The method generates an instruction frame containing expected action parameters and receives a feedback frame, calculates an error vector between actual parameters and expected parameters, and the vector at least contains a time domain deviation and a power domain deviation. The bus transmission priority is adjusted according to the time domain deviation, the instruction time sequence is translated according to the power domain deviation, and the communication cycle is adjusted according to the combined state of the two. The error vector history sequence is stored for predictive maintenance early warning and quality backtracking. The application unifies scheduling, energy management and health evaluation in different mathematical transformations of the error vector, and does not need independent priority arbitration modules, energy management modules and maintenance monitoring modules, so that the integration and light weight of auxiliary machine control are realized.

Description

Technical Field

[0001] This invention belongs to the field of auxiliary control technology for CNC machine tools, and particularly relates to a bus-type integrated auxiliary control system and operation method for CNC machine tools. Background Technology

[0002] The auxiliary system of CNC machine tools includes auxiliary equipment such as hydraulic stations, coolant pumps, chip conveyors, tool magazine motors, lubrication pumps, and pneumatic devices, and is an important component ensuring the normal operation of the machine tool. Currently, CNC machine tool auxiliary control systems typically use a PLC controller as the core, connecting to various auxiliary devices via hardwiring or fieldbus. Communication with each auxiliary device uses a fixed-priority polling method, with the communication priority of each auxiliary device statically set during system configuration and not changing with the machining status. In some high-end machine tool auxiliary control systems, the bus communication protocol supports isochronous synchronization and clock synchronization functions, ensuring that each node executes instructions under a unified time reference. Regarding energy management, existing systems typically have an independent energy management unit that detects overload by monitoring bus current, cutting off power to some auxiliary devices when overloaded to achieve overload protection. For equipment maintenance, existing systems typically monitor the vibration amplitude and temperature rise of auxiliary devices using external vibration and temperature sensors, with analysis and alarms performed by a separate equipment maintenance system.

[0003] However, the existing technologies still have the following shortcomings: First, the communication priorities of each auxiliary machine are statically set, and cannot be dynamically adjusted according to the actual response status of the auxiliary machine and the current processing cycle. When a high-priority auxiliary machine experiences a response delay, it will lead to an extension of the entire processing cycle. Second, the energy management unit is independent of the control system and only performs simple power-off protection when overloaded, and cannot be linked with the auxiliary machine scheduling strategy to achieve refined energy peak shifting. Third, the equipment maintenance system relies on external sensors to obtain monitoring data and has no data interaction with the control system. It cannot use the response delay, power consumption fluctuation, and other data already present in the bus communication to identify the early trend of auxiliary machine performance degradation, and the additional sensors increase hardware costs. Fourth, when processing quality problems occur, maintenance personnel need to check three independent data sources—PLC logs, energy management logs, and equipment maintenance logs—to conduct comprehensive analysis. The data from the three systems are independent of each other and lack a unified time axis correlation, resulting in low efficiency in root cause analysis. Fifth, the existing system uses uniform control parameters for different types of auxiliary machines (hydraulic, electric, pneumatic) and different processing cycle types (safety-critical, quality-critical, efficiency-critical), lacking the ability to configure differentiated scheduling strategies. Sixth, although existing bus communication protocols support time synchronization, they do not take advantage of communication quality fluctuations, and there is no record of lightweight solutions that can reduce computational complexity when bus jitter is stable. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a bus-type integrated auxiliary machine tool control system and operation method, which solves the problems of fixed priority, fragmented energy management and scheduling, reliance on external sensors for predictive maintenance, and isolated data of each subsystem in the existing auxiliary machine control system, resulting in poor real-time performance, high cost and low fault tracing efficiency.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A bus-based integrated auxiliary control system for CNC machine tools includes a bus master controller and N auxiliary machine control nodes, where N≥1. The bus master controller is communicatively connected to each of the auxiliary machine control nodes via an industrial fieldbus. The bus master controller and each of the auxiliary machine control nodes are each configured with a computer program, which is configured to implement the following communication control architecture: The bus master controller is equipped with an instruction generation unit, an error calculation unit, a scheduling unit, and a storage unit. The instruction generation unit is used to generate instruction frames; The error calculation unit is used to generate an error vector based on the deviation between the actual operating parameters and the expected action parameters in the feedback frame. The error vector includes at least the time domain deviation and the power domain deviation. The scheduling unit is used to adjust the bus transmission priority and instruction issuance timing of each auxiliary machine control node according to the error vector; The storage unit is used to store the error vector; Each of the auxiliary machine control nodes is equipped with a communication unit, an execution unit, a status acquisition unit, and a feedback unit. The communication unit is used to send and receive instruction frames and feedback frames; The execution unit is used to parse and execute instruction frames; The status acquisition unit is used to collect actual operating parameters; The feedback unit is used to generate a feedback frame containing the actual operating parameters and send it to the bus master controller.

[0006] Preferably, the instruction generation unit parses the auxiliary machine action requirements corresponding to each machining cycle from the CNC machining program, queries the process feature database to obtain the expected action duration, rated power consumption range and process weight coefficient of each auxiliary machine, and writes the expected action duration, the rated power consumption range and the process weight coefficient into the instruction frame; The error calculation unit calculates the time domain deviation between the actual action duration and the expected action duration and the power domain deviation between the actual peak power consumption and the rated power consumption range based on the actual action duration and the actual peak power consumption fed back by each auxiliary machine control node, and generates an error vector.

[0007] Preferably, the status acquisition unit acquires the actual peak power consumption and actual action duration during the auxiliary equipment's execution of actions; The feedback frame includes the actual peak power consumption, the actual operation duration, and the actual bus jitter value; the instruction frame includes the expected arrival time window, the rated power consumption range, and the process weight coefficient.

[0008] A method for operating a bus-based integrated auxiliary control system for CNC machine tools includes the following steps: Step 1, Pre-calculation of process features: Generate instruction frames containing the expected motion parameters of each auxiliary machine and the process weight coefficients and send them to the corresponding auxiliary machine control nodes; Step 2, Auxiliary machine execution and real-time perception: Each auxiliary machine control node receives and executes the instruction frame, collects actual operating parameters, generates feedback frames, and sends them to the bus master controller; Step 3, Error Vector Calculation: Generate an error vector based on the deviation between the actual operating parameters fed back by each auxiliary machine control node and the desired action parameters; Step 4, Dynamic scheduling correction: Adjust the bus transmission priority of each auxiliary machine control node and the timing parameters of subsequent instruction frames according to the error vector; Step 5, Health Feature Accumulation: The error vector is appended to the historical sequence of the corresponding auxiliary machine control node, and the statistical change characteristics of the historical sequence are calculated. When the statistical change characteristics meet the preset conditions, an early warning signal is generated. Step 6, Historical Backtracking: Receive the external input time interval query command, retrieve the error vector historical sequence of each auxiliary machine control node within the time interval, and output it after arranging it according to the time axis.

[0009] Preferably, in step one, the desired action parameters include the desired action duration and the rated power consumption range; In step two, the actual operating parameters include the actual operation duration, the actual peak power consumption, and the actual bus jitter value. In step three, the difference between the actual action duration and the expected action duration is taken as the time domain deviation, the difference between the actual peak power consumption and the median of the rated power consumption range is taken as the power domain deviation, and the difference between the actual bus jitter value and the preset reference value is taken as the communication domain deviation. The time domain deviation, the power domain deviation, and the communication domain deviation constitute an error vector.

[0010] Preferably, in step one, the process weight coefficient ranges from 0 to 100, and its value corresponds to the action type of the auxiliary equipment: 100 for tool changing action and emergency stop action, 30 to 50 for cooling action and lubrication action, and 10 to 20 for chip removal action and lighting action.

[0011] Preferably, in step four, when the time domain deviation is greater than 0, the process weight coefficient of the corresponding auxiliary machine control node is increased to improve its bus transmission priority. When the power domain deviation is greater than 0, the expected arrival time window in the subsequent instruction frame will be shifted backward. When both the time domain deviation and the power domain deviation are greater than 0, the communication cycle of the corresponding auxiliary machine control node is extended.

[0012] Preferably, the adjusted value of the process weight coefficient is the product of the current process weight coefficient and a monotonically increasing function with time domain deviation as the independent variable; The value after shifting the expected arrival time window is the sum of the original expected arrival time window and a monotonically increasing function with power domain deviation as the independent variable; The communication period is extended to be greater than the standard communication period.

[0013] Preferably, in step five, the statistical change feature is the curvature of the historical sequence, and the curvature is calculated using the three-point difference method, that is, by subtracting twice the previous error vector from the current error vector and adding the previous two error vectors, and then dividing by the square of the sampling period. When the curvature exceeds a preset threshold consecutively within a predetermined number of times, the warning signal is generated.

[0014] Preferably, in step five, after the warning signal is generated, the bus master controller reduces the upper limit of the maximum allowable process weight coefficient of the corresponding auxiliary machine control node; The storage unit continues to monitor the historical sequence statistical change characteristics of the auxiliary machine control node. When the statistical change characteristics no longer meet the preset conditions, the bus master controller restores the maximum allowable process weight coefficient upper limit of the auxiliary machine control node. In step six, when the historical sequence of error vectors is output in a time-axis arrangement, the time identifier corresponding to each error vector is also output simultaneously.

[0015] The technical effects and advantages of the bus-type integrated auxiliary control system and operation method for CNC machine tools of the present invention are as follows: 1. This invention realizes a unified architecture for control functions based on error vectors, unifying bus transmission priority adjustment, command timing scheduling, energy management and equipment health assessment into different mathematical transformations of the same data object. It eliminates the need for separate energy management modules, separate priority arbitration modules and separate equipment maintenance modules, thereby eliminating data barriers between multiple subsystems and improving system integration and data utilization.

[0016] 2. This invention dynamically adjusts the process weight coefficient by time domain deviation, thereby adjusting the bus transmission priority of each auxiliary machine control node. This allows emergency auxiliary machine actions to have priority access to the bus under the current processing cycle, solving the problem that the fixed priority polling method cannot dynamically adjust the order of instruction issuance according to the actual response status of the auxiliary machine, and reducing the extension of processing cycle caused by auxiliary machine response delay.

[0017] 3. This invention uses power domain deviation to drive the backward shift of the expected arrival time window in subsequent command frames, so that the start-up times of multiple high-power auxiliary machines are automatically staggered. It can suppress instantaneous power peaks without setting up an independent energy management unit, reducing grid impact and equipment overheating risks, while also reducing system hardware costs.

[0018] 4. This invention utilizes existing feedback frame data (actual action duration, actual peak power consumption, and actual bus jitter value) in bus communication to construct an error vector historical sequence and calculate its statistical change characteristics. When the auxiliary machine's performance deterioration trend reaches a preset condition, an early warning signal is triggered. No additional hardware such as external vibration sensors and temperature sensors is required, which reduces the implementation cost and system complexity of predictive maintenance.

[0019] 5. This invention uses historical error vector sequences for event backtracking. When quality issues arise, it is only necessary to retrieve the historical error vector sequences within a specified time interval from the same data source to reconstruct the deviation evolution process between the actual action status of each auxiliary node and the expected action parameters. This eliminates the need to review the log records of multiple independent subsystems, thereby improving the efficiency of root cause localization of quality problems.

[0020] 6. This invention generates a weighted error vector by configuring time domain weighting coefficients, power domain weighting coefficients, and communication domain weighting coefficients. This enables the scheduling strategy to dynamically adjust the sensitivity of each deviation component according to the type of auxiliary equipment (hydraulic, electric, pneumatic) and the processing cycle type (safety critical, quality critical, efficiency critical), thus expanding the applicability of the system under different auxiliary equipment configurations and different processing conditions.

[0021] 7. This invention allows the error vector to consist only of time domain deviation and power domain deviation. In application scenarios where bus communication quality is stable and the impact of communication domain deviation on auxiliary machine control is negligible, the calculation and storage of communication domain deviation are omitted, reducing the consumption of computing resources and storage overhead. It is suitable for embedded control systems with limited computing resources. Attached Figure Description

[0022] Figure 1 This is the overall system architecture diagram of the present invention; Figure 2 This is a data structure diagram of the instruction frame and feedback frame of this invention; Figure 3 This is the overall flowchart of the operation method of the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "includes..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0025] refer to Figure 1-3This invention provides a bus-based integrated auxiliary control system and operating method for CNC machine tools, belonging to the field of auxiliary control technology for CNC machine tools. The system includes a bus master controller and multiple auxiliary control nodes connected to it via an industrial fieldbus. The bus master controller and each auxiliary control node are configured with computer programs to implement a communication control architecture. The bus master controller is equipped with an instruction generation unit, an error calculation unit, a scheduling unit, and a storage unit. Each auxiliary control node is equipped with a communication unit, an execution unit, a status acquisition unit, and a feedback unit. The operation method includes: an instruction generation unit generates an instruction frame containing desired action parameters and process weight coefficients and sends it to the corresponding auxiliary machine control node; each auxiliary machine control node receives and executes the instruction frame; a status acquisition unit acquires actual operating parameters; a feedback unit generates a feedback frame containing actual operating parameters and reports it to the bus master controller; an error calculation unit generates an error vector based on the deviation between the actual operating parameters in the feedback frame and the desired action parameters carried in the instruction frame, the error vector containing at least time domain deviation and power domain deviation, and may also contain communication domain deviation; a scheduling unit increases the process weight coefficient of the corresponding node to improve the bus transmission priority when the time domain deviation is greater than 0, and adjusts the process weight coefficient of subsequent instruction frames based on the power domain deviation being greater than 0. The time window is shifted backward, and the communication cycle of the corresponding node is extended when both the time domain deviation and the power domain deviation are greater than 0. The storage unit appends the error vector to the historical sequence of the corresponding node and calculates its statistical change characteristics. When the statistical change characteristics meet the preset conditions, a predictive maintenance warning signal is generated and the upper limit of the maximum allowable process weight coefficient of the corresponding node is reduced. The storage unit can also retrieve the historical sequence of error vectors within a specified time interval and output it in a time-axis arrangement when receiving external query instructions, while also outputting the corresponding time identifier. In scenarios where the bus communication quality is stable, the communication domain deviation can be omitted to reduce computational complexity. A weighted error vector can be configured, which is formed by multiplying its time domain, power domain, and communication domain components by the corresponding weight coefficients. This invention unifies bus transmission priority adjustment, command timing scheduling, energy management, and equipment health assessment into different mathematical transformations of the error vector. It eliminates the need for separate priority arbitration modules, energy management modules, and maintenance monitoring modules, solving the technical problems in existing technologies such as fixed priorities that cannot be dynamically adjusted, fragmented energy management and scheduling that only involve simple power outages, predictive maintenance that relies on external sensors and increases costs, and isolated data from each subsystem that leads to low efficiency in fault tracing. This invention achieves integrated, lightweight, and intelligent operation of the auxiliary machine control system.

[0026] First embodiment: Purpose of implementation: This embodiment aims to demonstrate the specific application of the present invention in the automatic tool changing process of CNC machine tools, verify the collaborative calculation and dynamic scheduling effect of error vector among multiple auxiliary machine nodes under the same machining cycle, and solve the technical problems of prolonged tool changing cycle and difficulty in tracing the root cause of fault due to auxiliary machine response delay during tool changing.

[0027] Implementation System: The bus-based integrated CNC machine tool auxiliary control system used in this embodiment includes: a bus main controller 100, which is communicatively connected to the tool magazine motor control node, hydraulic station control node, and pneumatic device control node via an industrial fieldbus 300. The bus main controller 100 is equipped with an instruction generation unit 110, an error calculation unit 120, a scheduling unit 130, and a storage unit 140; each auxiliary control node is equipped with a communication unit 210, an execution unit 220, a status acquisition unit 230, and a feedback unit 240. The bus main controller 100 and each auxiliary control node 200 are each configured with a computer program, which is configured to implement the following steps.

[0028] Implementation steps: Step 1, Pre-calculation of Process Features: When the CNC machining program reaches the tool change command, the instruction generation unit 110 receives the command and performs look-ahead analysis to identify the current machining cycle as a tool change cycle. The corresponding auxiliary machine action requirements include the tool magazine motor rotating to the target tool position, the hydraulic station providing tool clamping pressure, and the pneumatic device performing tool air cleaning. The instruction generation unit 110 queries the process feature database to obtain the expected action duration, rated power consumption range, and process weight coefficient of each auxiliary machine device—tool magazine motor: expected action duration 800ms, rated power consumption range 0.8kW to 1.2kW, process weight coefficient 100; hydraulic station: expected action duration 300ms, rated power consumption range 2.0kW to 2.8kW, process weight coefficient 100; pneumatic device: expected action duration 200ms, rated power consumption range 0.3kW to 0.5kW, process weight coefficient 80. The instruction generation unit 110 writes the above parameters into the corresponding instruction frames and sends them to the corresponding auxiliary machine control nodes via the bus 300.

[0029] Step 2, Auxiliary Equipment Execution and Real-time Sensing: After receiving the instruction frame, the communication unit 210 of each auxiliary equipment control node parses and drives the corresponding auxiliary equipment to perform the action. During execution, the status acquisition unit 230 of each node collects the instantaneous current and instantaneous voltage of the auxiliary equipment in real time through current and voltage sensors and calculates the actual peak power consumption. The execution time timer records the time length from the receipt of the instruction frame by the auxiliary equipment control node to the completion of the auxiliary equipment execution as the actual action duration. The communication unit 210 records the time fluctuation value from the issuance of the instruction frame to its receipt by the auxiliary equipment control node as the actual bus jitter value. After execution, the feedback unit 240 of each node generates a feedback frame containing the actual peak power consumption, actual action duration, and actual bus jitter value, and reports it to the bus master controller 100 via the bus 300.

[0030] Step 3, Error Vector Calculation: The error calculation unit 120 receives feedback frames from each node and extracts the actual action duration, actual peak power consumption, and actual bus jitter value. The difference between the actual action duration and the expected action duration is recorded as the time domain deviation, the difference between the actual peak power consumption and the median of the rated power consumption range is recorded as the power domain deviation, and the difference between the actual bus jitter value and the preset reference jitter value is recorded as the communication domain deviation. The time domain deviation, power domain deviation, and communication domain deviation constitute the error vector. Tool magazine motor node: actual action duration 850ms, expected 800ms, time domain deviation +50ms; actual peak power consumption 1.1kW, rated range median 1.0kW, power domain deviation +0.1kW; actual bus jitter value 35μs, preset reference jitter value 30μs, communication domain deviation +5μs. Hydraulic station node: Actual action time 280ms, expected 300ms, time domain deviation -20ms; actual peak power consumption 2.5kW, rated range median 2.4kW, power domain deviation +0.1kW; actual bus jitter 28μs, preset baseline 30μs, communication domain deviation -2μs. Error calculation unit 120 stores each error vector into the storage unit 140 of the corresponding auxiliary machine control node.

[0031] Step 4, Dynamic Scheduling Correction: The scheduling unit 130 obtains the error vector of each node. When the time domain deviation of the tool magazine motor node +50ms is greater than 0, the scheduling unit 130 calculates the process weight coefficient of the next instruction frame of the auxiliary machine control node in the following way: the new process weight coefficient is equal to the current process weight coefficient multiplied by the value of an exponential function with the time domain deviation as the independent variable. Where the current process weight coefficient is 100 and the time deviation amplification factor is 0.02, the new process weight coefficient is 100 multiplied by the value of an exponential function with 50ms as the independent variable. The calculation result is approximately 271.8. Due to the limitation of the maximum allowable upper limit of 100, it is actually taken as 100. The scheduling unit 130 maintains the highest bus transmission priority of this node. When the time domain deviation of the hydraulic station node is -20ms and not greater than 0 and the power domain deviation is +0.1kW and greater than 0, the scheduling unit 130 shifts the expected arrival time window in the subsequent hydraulic station instruction frame backward. The shift amount is the power deviation shift coefficient of 50ms per kilowatt multiplied by the power domain deviation of 0.1kW, that is, a shift of 5ms. The subsequent hydraulic station instruction is issued with a delay of 5ms.

[0032] Step 5, Health Feature Accumulation: Storage unit 140 appends the error vectors of each node to the historical sequence of the corresponding auxiliary control node, forming a health drift trajectory. Storage unit 140 uses the three-point difference method to calculate the curvature of the health drift trajectory of each node, that is, the curvature is equal to the current error vector minus twice the previous error vector plus the previous two error vectors, and the result is divided by the square of the sampling period. The current error vector of the tool magazine motor node is (+50ms, +0.1kW, +5μs), the previous error vector is (+30ms, +0.05kW, +3μs), and the previous two error vectors are (+10ms, +0.02kW, +1μs). The calculated curvature does not exceed the preset threshold, so storage unit 140 does not generate a warning signal.

[0033] Step Six, Historical Review: When a quality issue of incomplete tool clamping is subsequently discovered, the storage unit 140 receives an externally input time interval query command, retrieves the historical sequence of error vectors for each auxiliary machine control node within that time interval, arranges them along the time axis, and outputs them. Simultaneously, it outputs the instruction frame ID and absolute timestamp corresponding to each error vector. Through analysis, the operator discovered that the time domain deviation of the tool magazine motor node gradually increased from +10ms to +50ms in the five operations prior to the tool change, while the power domain deviation of the hydraulic station node increased from +0.02kW to +0.1kW. Based on this, it was determined that the hydraulic station pressure fluctuation caused an increase in the load on the tool magazine motor, thus accurately locating the root cause of the fault.

[0034] Corresponding implementation results: In this embodiment, the error vector unifies the deviations between the actual operating parameters and expected action parameters of the three auxiliary machine nodes—tool magazine motor, hydraulic station, and pneumatic device—during the tool changing process into the same data format. Through the dynamic scheduling correction in step four, although the time domain deviation of the tool magazine motor node is positive (action lag), its process weight coefficient has reached the upper limit of 100, so the system maintains its highest bus priority to ensure that the critical action of tool changing is not delayed. At the same time, the power domain deviation of the hydraulic station node is positive, triggering the expected arrival time window to be shifted backward by 5ms, achieving energy peak avoidance with other high-power auxiliary machines. Through the curvature calculation in step five, the storage unit 140 identifies the increasing trend of the time domain deviation of the tool magazine motor and the increasing trend of the power domain deviation of the hydraulic station before the fault occurs. If no intervention is required, predictive maintenance warnings can be triggered before complete failure. Through the historical backtracking in step six, operators do not need to browse through the three independent systems of PLC log, energy management log, and maintenance log. They can accurately locate the root cause of the fault based solely on the same historical sequence of error vectors in the storage unit 140, realizing the improvement from "fault investigation after occurrence" to "data-driven root cause tracing".

[0035] Second embodiment: Purpose of implementation: This embodiment aims to demonstrate the energy collaborative management function of the present invention in the scenario where multiple high-power auxiliary machines are started simultaneously during heavy cutting machining, verify the effectiveness of the scheduling strategy of shifting the expected arrival time window backward driven by power domain deviation in reducing instantaneous power peaks, and solve the technical problems of grid impact and equipment overheating caused by the simultaneous start of multiple high-power auxiliary machines under traditional control methods.

[0036] Implementation System: The bus-based integrated CNC machine tool auxiliary control system used in this embodiment includes: a bus main controller 100, which is communicatively connected to the coolant pump control node, chip conveyor control node, and hydraulic station control node via an industrial fieldbus 300. The bus main controller 100 is equipped with an instruction generation unit 110, an error calculation unit 120, a scheduling unit 130, and a storage unit 140; each auxiliary control node is equipped with a communication unit 210, an execution unit 220, a status acquisition unit 230, and a feedback unit 240. The bus main controller 100 and each auxiliary control node 200 are each configured with a computer program, which is configured to implement the following steps.

[0037] Implementation steps: Step 1, Process Feature Pre-calculation: The instruction generation unit 110 identifies the current cycle as a heavy cutting cycle, and the corresponding auxiliary machine action requirements include coolant pump start-up, chip conveyor start-up, and hydraulic station start-up. The instruction generation unit 110 queries the process feature database to obtain the expected action duration, rated power consumption range, and process weight coefficient of each auxiliary machine—coolant pump: expected action duration 200ms, rated power consumption range 2.7kW to 3.3kW, process weight coefficient 40; chip conveyor: expected action duration 150ms, rated power consumption range 1.8kW to 2.6kW, process weight coefficient 20; hydraulic station: expected action duration 100ms, rated power consumption range 2.0kW to 3.0kW, process weight coefficient 50. The instruction generation unit 110 writes the above parameters into the corresponding instruction frames and sends them to the corresponding auxiliary machine control nodes via bus 300.

[0038] Step 2, Auxiliary Machine Execution and Real-time Sensing: After receiving the instruction frame, the communication unit 210 of each auxiliary machine control node parses and drives the corresponding auxiliary machine device to perform the action. During execution, the status acquisition unit 230 of each node collects instantaneous current and instantaneous voltage and calculates the actual peak power consumption, the execution timer records the actual action duration, and the communication unit 210 records the actual bus jitter value. After execution, the feedback unit 240 of each node generates a feedback frame containing the actual peak power consumption, the actual action duration, and the actual bus jitter value, and reports it to the bus master controller 100 via the bus 300.

[0039] Step 3, Error Vector Calculation: The error calculation unit 120 receives feedback frames from each node, records the difference between the actual action time and the expected action time as the time domain deviation, the difference between the actual peak power consumption and the median of the rated power consumption range as the power domain deviation, and the difference between the actual bus jitter value and the preset reference jitter value as the communication domain deviation. The time domain deviation, power domain deviation, and communication domain deviation constitute the error vector. Cooling pump node: Actual action time 220ms, time domain deviation +20ms; actual peak power consumption 3.5kW, rated range median 3.0kW, power domain deviation +0.5kW. Chip conveyor node: Actual action time 140ms, time domain deviation -10ms; actual peak power consumption 2.0kW, rated range median 2.2kW, power domain deviation -0.2kW. Hydraulic station node: Actual action time 95ms, time domain deviation -5ms; actual peak power consumption 2.8kW, rated range median 2.5kW, power domain deviation +0.3kW. The error calculation unit 120 stores each error vector into the storage unit 140.

[0040] Step 4, Dynamic Scheduling Correction: Scheduling unit 130 obtains the error vector of each node. When the time domain deviation +20ms of the coolant pump node is greater than 0 and the power domain deviation +0.5kW is greater than 0, scheduling unit 130 determines that the coolant pump is in an overload lag state. Scheduling unit 130 calculates the process weight coefficient of the next instruction frame of the coolant pump according to the new process weight coefficient being equal to the current process weight coefficient multiplied by the value of an exponential function with the time domain deviation as the independent variable, and at the same time extends the communication cycle of the coolant pump to 1.5 times the standard communication cycle. Since the power domain deviation +0.5kW of the coolant pump node is greater than 0, scheduling unit 130 shifts the expected arrival time window in the subsequent chip conveyor instruction frame and hydraulic station instruction frame backward by a shift amount equal to the power deviation shift coefficient of 50ms per kilowatt multiplied by the power domain deviation of 0.5 kilowatt, i.e., a shift of 25ms. The start instructions of the chip conveyor and hydraulic station are each delayed by 25ms.

[0041] Corresponding implementation results: In this embodiment, the scheduling unit 130 shifts the expected arrival time window in step four, delaying the start commands of the chip conveyor and hydraulic station by 25ms, thus staggering the start times of the three high-power auxiliary machines. Actual measurements show that after staggered start-up, the instantaneous peak power is 3.5kW for the cooling pump (starting first), 2.0kW for the chip conveyor after 25ms (peak 5.5kW), and then 2.8kW for the hydraulic station after another 25ms (peak still approximately 5.5kW), a reduction of approximately 28.6% compared to the traditional simultaneous start-up of 7.7kW. The implementation results demonstrate that the power domain deviation not only characterizes the degree of power consumption deviation of auxiliary machines but also serves as a direct basis for energy peak-shifting scheduling. It eliminates the need for a separate energy management module, effectively avoiding grid impact and equipment overheating while ensuring the normal operation of each auxiliary machine.

[0042] Third embodiment: Purpose of implementation: This embodiment aims to demonstrate the specific implementation of the present invention using bus communication data as a "byproduct" for predictive maintenance early warning of auxiliary equipment, verify the ability of the curvature calculation and threshold comparison method of error vector historical sequence to identify the trend of auxiliary equipment performance degradation, and solve the technical problem that traditional predictive maintenance requires external additional sensor hardware and is costly.

[0043] Implementation System: The bus-based integrated CNC machine tool auxiliary control system used in this embodiment includes: a bus main controller 100, which is communicatively connected to the hydraulic station control node via an industrial fieldbus 300. The bus main controller 100 is equipped with an instruction generation unit 110, an error calculation unit 120, a scheduling unit 130, and a storage unit 140; the hydraulic station control node is equipped with a communication unit 210, an execution unit 220, a status acquisition unit 230, and a feedback unit 240. The bus main controller 100 and each auxiliary control node 200 are each configured with a computer program, which is configured to implement the following steps. During 100 consecutive instruction executions, the time-domain deviation of the error vector of the hydraulic station control node exhibits a slowly increasing trend.

[0044] Implementation steps: Steps one through four are the same as in Example 1. Each auxiliary machine control node continuously executes instructions, feeds back actual operating parameters, calculates error vectors, and performs dynamic scheduling corrections.

[0045] Step 5, Health Feature Accumulation and Early Warning: The storage unit 140 continuously adds each error vector to the historical sequence of the hydraulic station control node, forming a health drift trajectory. The storage unit 140 uses the three-point difference method to calculate the curvature of the health drift trajectory successively, that is, the curvature is equal to the current error vector minus twice the previous error vector plus the previous two error vectors, and the result is divided by the square of the sampling period. When the calculation reaches the 95th time, the current error vector is (+65ms, +0.15kW, +8μs), the previous error vector is (+58ms, +0.12kW, +7μs), and the previous two error vectors are (+50ms, +0.10kW, +6μs). The curvature is calculated continuously for the 95th, 96th, and 97th times. When the curvature exceeds the preset threshold three times consecutively in the 95th, 96th, and 97th times, the storage unit 140 generates a predictive maintenance early warning signal and outputs it. At the same time, the scheduling unit 130 automatically reduced the maximum allowable process weight coefficient of the hydraulic station control node from 100 to 60.

[0046] In subsequent steps, storage unit 140 continues to monitor the health drift trajectory curvature of the hydraulic station control node. Upon receiving a warning signal, the operator inspects the hydraulic station and discovers that aging of the internal seals in the hydraulic pump has led to increased internal leakage. After replacing the seals, the error vector of the hydraulic station returns to the normal range. When the curvature falls below 0.8 times the preset threshold three times consecutively, scheduling unit 130 restores the maximum allowable process weight coefficient upper limit of the node to 100.

[0047] Corresponding implementation results: In this embodiment, the time domain deviation of the hydraulic station control node gradually deteriorates from -10ms to +10ms to +50ms to +70ms during 100 consecutive command executions. If a traditional periodic maintenance strategy is adopted, unnecessary maintenance (over-maintenance) may be performed when the time domain deviation is still within the normal range, or an alarm may be triggered only after the hydraulic station has completely failed (under-maintenance). In this embodiment, the storage unit 140 generates a warning signal when the threshold is exceeded three consecutive times from the 95th to the 97th time, based on the calculation of the curvature of the healthy drift trajectory in step five. At this point, the hydraulic station has not completely failed (it can still perform tool changing actions, only the response is slow), providing operators with sufficient maintenance window. Simultaneously, the scheduling unit 130 reduces the maximum allowable process weight coefficient from 100 to 60, preventing the hydraulic station from performing high-priority emergency actions under degraded performance conditions, thus avoiding safety accidents caused by insufficient response. The entire predictive maintenance process utilizes existing feedback frame data (actual action duration, actual peak power consumption, and actual bus jitter value) in bus communication, without adding any additional sensor hardware, achieving predictive maintenance with zero additional hardware cost.

[0048] Fourth embodiment: Purpose of implementation: This embodiment aims to demonstrate the calculation and use method of the weighted error vector in this invention, verify the technical effect of dynamically adjusting the weight coefficients of each deviation component according to the type of auxiliary equipment and the processing cycle type, and solve the technical problem that different types of auxiliary equipment and different cycle types have different sensitivities to each deviation component, and that a uniform weight cannot reflect the differences.

[0049] Implementation System: The bus-based integrated CNC machine tool auxiliary control system used in this embodiment includes: a bus main controller 100, which is communicatively connected to hydraulic auxiliary machine control nodes, motor auxiliary machine control nodes, and pneumatic auxiliary machine control nodes via an industrial fieldbus 300. The bus main controller 100 is equipped with an instruction generation unit 110, an error calculation unit 120, a scheduling unit 130, and a storage unit 140. The error calculation unit 120 is configured with a weighted error vector calculation function, which can read weight coefficients from a process feature database according to the auxiliary machine equipment type and machining cycle time type. Each auxiliary machine control node is equipped with a communication unit 210, an execution unit 220, a status acquisition unit 230, and a feedback unit 240. The bus main controller 100 and each auxiliary machine control node 200 are each equipped with a computer program configured to implement the following steps.

[0050] Implementation steps: Step 1, Pre-calculation of process characteristics: The instruction generation unit 110 identifies the current processing cycle type (safety-critical, quality-critical, or efficiency-critical), determines the type of auxiliary equipment to be executed (hydraulic, electric, or pneumatic), generates an instruction frame containing the expected action duration, rated power consumption range, and process weight coefficient, and sends it to the corresponding auxiliary control node.

[0051] Step 2, Auxiliary machine execution and real-time perception: Same as step 2 in Example 1.

[0052] Step 3, Weighted Error Vector Calculation: The error calculation unit 120 receives the actual action duration, actual peak power consumption, and actual bus jitter value from each auxiliary machine control node. The difference between the actual action duration and the expected action duration is recorded as the time domain deviation; the difference between the actual peak power consumption and the median of the rated power consumption range is recorded as the power domain deviation; and the difference between the actual bus jitter value and the preset reference value is recorded as the communication domain deviation. The error calculation unit 120 reads the time domain weighting coefficient, power domain weighting coefficient, and communication domain weighting coefficient from the process characteristic database according to the type of auxiliary equipment and the type of the current processing cycle. The time domain component of the weighted error vector is equal to the time domain weighting coefficient multiplied by the time domain deviation; the power domain component is equal to the power domain weighting coefficient multiplied by the power domain deviation; and the communication domain component is equal to the communication domain weighting coefficient multiplied by the communication domain deviation. These three weighted components form the weighted error vector, which replaces the original error vector and is stored in the storage unit 140 for use in subsequent steps.

[0053] Taking the hydraulic station performing a safety-critical tool changing action as an example: the measured time domain deviation is +30ms, the power domain deviation is +0.2kW, and the communication domain deviation is +5μs. The weighting coefficients for the time domain are 0.8, the power domain weighting coefficient is 1.2, and the communication domain weighting coefficient is 0.6, which are read from the process feature database. Then the weighted error vector is (0.8×30, 1.2×0.2, 0.6×5) = (24ms, 0.24kW, 3μs).

[0054] Steps four to six are the same as in Example 1, except that the weighted error vector is used instead of the error vector to perform dynamic scheduling correction, health feature accumulation and history backtracking.

[0055] Corresponding implementation results: In this embodiment, the error calculation unit 120 achieves differentiated processing of each deviation component under different auxiliary machine types and cycle time types through weighted calculation in step three. Taking the hydraulic station performing a safety-critical tool change action as an example: the original value of the power domain deviation is +0.2kW, which becomes 0.24kW (amplified) after being weighted by a power domain weighting coefficient of 1.2; the original value of the time domain deviation is +30ms, which becomes 24ms (reduced) after being weighted by a time domain weighting coefficient of 0.8. This reflects the higher focus on the power stability (i.e., pressure stability) of the hydraulic station under safety-critical cycle time. If a uniform weight is used (all three weighting coefficients are equal to 1), the scheduling unit 130 will respond to the time domain deviation and the power domain deviation with the same degree, which cannot reflect the actual requirement that pressure stability takes precedence over response speed under safety-critical cycle time. The introduction of the weighted error vector enables the scheduling strategy of this invention to adapt to different auxiliary machine types (hydraulic types are sensitive to power, electric types are sensitive to time, and pneumatic types have a larger tolerance) and different cycle time types (safety-critical types prioritize stability, and efficiency-critical types prioritize speed), thus expanding the scope of application of this invention.

[0056] Fifth embodiment: Purpose of implementation: This embodiment aims to demonstrate a simplified implementation of the present invention that includes only two components: time domain deviation and power domain deviation. It verifies the feasibility of omitting the communication domain deviation component in application scenarios with highly stable bus communication quality and its technical effect of reducing computational complexity and storage overhead.

[0057] Implementation System: The bus-based integrated CNC machine tool auxiliary control system used in this embodiment includes: a bus master controller 100, which communicates with each auxiliary control node via an industrial fieldbus 300. The bus master controller 100 is equipped with an instruction generation unit 110, an error calculation unit 120, a scheduling unit 130, and a storage unit 140, wherein the error calculation unit 120 is configured in a simplified mode that does not calculate communication domain deviation; each auxiliary control node is equipped with a communication unit 210, an execution unit 220, a status acquisition unit 230, and a feedback unit 240. The bus master controller 100 and each auxiliary control node 200 are each equipped with a computer program configured to implement the following steps. In this embodiment, the industrial fieldbus uses an EtherCAT bus, with a network topology length of 30m, 6 nodes, and a bus jitter value stabilized within 20μs.

[0058] Implementation steps: Step 1, Pre-calculation of process characteristics: Same as Step 1 in Example 1.

[0059] Step 2, Auxiliary machine execution and real-time perception: Same as step 2 in Example 1.

[0060] Step 3, Simplify Error Vector Calculation: The error calculation unit 120 receives the actual action duration and actual peak power consumption from each auxiliary machine control node. The difference between the actual action duration and the expected action duration is recorded as the time domain deviation, and the difference between the actual peak power consumption and the median of the rated power consumption range is recorded as the power domain deviation. The time domain deviation and the power domain deviation constitute the error vector; the communication domain deviation is not calculated. The error calculation unit 120 stores the error vector in the storage unit 140 of the corresponding auxiliary machine control node.

[0061] Step 4, Dynamic Scheduling Correction: Scheduling unit 130 acquires the error vector. When the time domain deviation is greater than 0, scheduling unit 130 increases the process weight coefficient of the corresponding auxiliary machine control node to improve its bus transmission priority. When the power domain deviation is greater than 0, scheduling unit 130 shifts the expected arrival time window in subsequent instruction frames backward. When both the time domain deviation and the power domain deviation are greater than 0, scheduling unit 130 extends the communication cycle of the corresponding auxiliary machine control node.

[0062] Step 5, Health Feature Accumulation: Storage unit 140 appends the error vector to the historical sequence of the corresponding auxiliary control node. This historical sequence contains only data in the time and power domains. Storage unit 140 uses the three-point difference method to calculate the curvature of the health drift trajectory. That is, the curvature is equal to the current error vector minus twice the previous error vector plus the previous two error vectors, and the result is divided by the square of the sampling period. The curvature calculation is based on the two-dimensional error vector.

[0063] Step Six, Historical Review: Same as Step Six in Example 1.

[0064] Corresponding implementation results: In this embodiment, due to the simple EtherCAT bus network topology (bus length 30m, number of nodes 6), the actual bus jitter value is stable within 20μs, which is much smaller than the time scale of auxiliary machine actions (milliseconds). The impact of communication domain deviation on the system is negligible. In step three, the error calculation unit 120 omits the calculation of communication domain deviation, reducing the error vector from three dimensions to two dimensions. In step five, the storage unit 140 reduces the amount of historical sequence data stored by approximately 33%, and the computational complexity of curvature is correspondingly reduced. The implementation results show that in application scenarios with stable bus communication quality, this embodiment achieves lower computational resource consumption and storage overhead at the cost of slightly reducing early warning accuracy (missing the communication domain dimension), making it suitable for cost-sensitive or computationally limited embedded control systems.

[0065] Comparative Example 1 Purpose of implementation: This comparative example aims to demonstrate the superiority of the technical solution of the present invention over the traditional auxiliary machine control system through comparison, and to verify the overall technical improvement of the "one core, multiple uses" architecture of the present invention in terms of priority scheduling, energy management and predictive maintenance.

[0066] Implementation System: This comparative example adopts a traditional auxiliary machine control system, including: (1) an independent PLC controller, which is connected to each auxiliary machine through hard wiring or a simple bus, and communicates with each auxiliary machine in a fixed priority polling manner. The communication priority of each auxiliary machine is statically set during system configuration and does not change with the processing status; (2) an independent energy management unit, which determines whether there is an overload by detecting the bus current, and cuts off the power supply of some auxiliary machines when there is an overload; (3) an independent equipment maintenance system, which monitors the health status of the auxiliary machines through external vibration sensors and temperature sensors, and has no data interaction with the control system.

[0067] Implementation steps: Step 1: Fixed Priority Command Issuance: When the CNC machining program reaches the tool change command, the PLC controller sends start commands to the hydraulic station, tool magazine motor, and pneumatic device in a preset fixed priority order. The preset polling order is: hydraulic station priority 1 (highest), tool magazine motor priority 2, and pneumatic device priority 3.

[0068] Step two, fixed timing execution: Each auxiliary device starts sequentially according to the order issued by the PLC. Due to the response delay of the hydraulic station (actual action time 350ms, far exceeding the expected 300ms), and the fact that the PLC's fixed priority strategy cannot be dynamically adjusted, the commands for the tool magazine motor and pneumatic device must wait for the hydraulic station to complete before they can be issued.

[0069] Step 3, Independent Energy Management: When the energy management unit detects that the bus current is too high when the hydraulic station and tool magazine motor are running simultaneously, it simply cuts off the power supply to the cooling pump to reduce the load.

[0070] Step 4, Independent Equipment Maintenance: The equipment maintenance system monitors the health status of auxiliary machines through external vibration and temperature sensors, but it does not access the error data of the control system. Therefore, it cannot provide early warnings as the response delay of the hydraulic station gradually deteriorates (from 300ms to 320ms and then to 350ms), and only triggers an alarm when the hydraulic station completely fails.

[0071] Corresponding implementation results: In this comparative example, because the PLC uses a fixed-priority polling method, it cannot dynamically adjust the command issuance sequence according to the actual response status of the auxiliary machine, resulting in an overall extended tool change cycle due to the delay of the hydraulic station. The energy management unit operates independently, simply cutting off the power supply to the cooling pump when an overload is detected, leaving the tool without cooling during the tool change process, posing a risk of tool thermal damage. The equipment maintenance system operates independently, failing to utilize the existing response delay data in the control system, thus failing to provide early warnings. An alarm is only triggered when the hydraulic station completely fails, making it reactive rather than predictive maintenance. When tool change quality issues occur, maintenance personnel need to separately check the PLC log (to confirm the command issuance sequence), the energy management log (to confirm whether there was a power outage), and the maintenance system log (to confirm whether the equipment has recorded any abnormalities). The data from these three systems are independent and cannot form a unified temporal correlation, making root cause analysis of the fault time-consuming.

[0072] Compared to Examples 1-5 and Comparative Example 1, Comparative Example 1 uses a traditional auxiliary machine control system, consisting of three separate subsystems: an independent PLC controller, an independent energy management unit, and an independent equipment maintenance system. The PLC controller communicates with each auxiliary machine using a fixed-priority polling strategy. The communication priority of each auxiliary machine is statically set during system configuration and does not change with the processing status. The energy management unit determines overload by detecting bus current, and simply cuts off power to some auxiliary machines when overloaded. The equipment maintenance system monitors the health status of auxiliary machines through external vibration and temperature sensors, without data interaction with the control system. During tool changing, the fixed-priority strategy cannot dynamically adjust the command sequence of the tool magazine motor and pneumatic device when the hydraulic station experiences response delays, leading to extended tool changing cycles. The energy management unit simply cuts off the power supply to the cooling pump when overloaded, posing a risk of thermal damage to the tools. The equipment maintenance system does not access response delay data from the control system, making it unable to provide early warnings during the gradual deterioration of the hydraulic station's performance, only providing post-event alarms. When quality anomalies occur, it is necessary to consult three independent data sources—the PLC log, the energy management log, and the maintenance log—to perform root cause analysis.

[0073] Examples 1 through 5 all employ the unified scheduling architecture based on error vectors of this invention. Example 1 constructs an error vector using time-domain and power-domain deviations, achieving collaborative scheduling and quality backtracking of multiple auxiliary machine nodes in the tool changing process. Example 2 utilizes power-domain deviations to shift the expected arrival time window backward, delaying the chip conveyor and hydraulic station start commands by 25ms each, reducing the instantaneous peak power from 7.7kW to 5.5kW, a reduction of approximately 28.6%, achieving intelligent peak shifting without an independent energy management module. Example 3, through curvature monitoring of the historical sequence of error vectors, triggers a predictive warning and automatically lowers its priority upper limit when the hydraulic station exceeds the threshold three times consecutively before complete failure, achieving predictive maintenance with zero additional hardware cost. Example 4, by introducing a weighted error vector, increases the power-domain deviation weight of the hydraulic station's safety-critical tool changing to 1.2, reflecting the differentiated control requirements of different auxiliary machine types and cycle times. Example 5 simplifies the error vector to two dimensions by omitting communication-domain deviations, reducing storage overhead and computational complexity by approximately 33% in scenarios with stable bus communication quality. All five implementations share the same error vector data pool. Priority scheduling, energy peak shaving, predictive maintenance, and event tracing are different mathematical transformations of this data pool. Operators only need to consult the historical error vector sequence in the storage unit to locate the root cause of a fault, without having to sift through multiple independent system logs. A comparison shows that this invention, through its unified "one core, multiple uses" architecture, significantly outperforms traditional discrete solutions in terms of scheduling flexibility, refined energy management, maintenance costs, and control integration.

[0074] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0075] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A bus-based integrated auxiliary control system for CNC machine tools, characterized in that, The system includes a bus master controller and N auxiliary machine control nodes, where N ≥ 1. The bus master controller communicates with each of the auxiliary machine control nodes via an industrial fieldbus. Each of the bus master controller and each of the auxiliary machine control nodes is configured with a computer program to implement the following communication control architecture: The bus master controller is equipped with an instruction generation unit, an error calculation unit, a scheduling unit, and a storage unit. The instruction generation unit is used to generate instruction frames; The error calculation unit is used to generate an error vector based on the deviation between the actual operating parameters and the expected action parameters in the feedback frame. The error vector includes at least the time domain deviation and the power domain deviation. The scheduling unit is used to adjust the bus transmission priority and instruction issuance timing of each auxiliary machine control node according to the error vector; The storage unit is used to store the error vector; Each of the auxiliary machine control nodes is equipped with a communication unit, an execution unit, a status acquisition unit, and a feedback unit. The communication unit is used to send and receive instruction frames and feedback frames; The execution unit is used to parse and execute instruction frames; The status acquisition unit is used to collect actual operating parameters; The feedback unit is used to generate a feedback frame containing the actual operating parameters and send it to the bus master controller.

2. The bus-type integrated CNC machine tool auxiliary control system according to claim 1, characterized in that, The instruction generation unit parses the auxiliary machine action requirements corresponding to each machining cycle from the CNC machining program, queries the process feature database to obtain the expected action duration, rated power consumption range and process weight coefficient of each auxiliary machine, and writes the expected action duration, the rated power consumption range and the process weight coefficient into the instruction frame; The error calculation unit calculates the time domain deviation between the actual action duration and the expected action duration and the power domain deviation between the actual peak power consumption and the rated power consumption range based on the actual action duration and the actual peak power consumption fed back by each auxiliary machine control node, and generates an error vector.

3. The bus-type integrated auxiliary control system for CNC machine tools according to claim 1, characterized in that, The status acquisition unit acquires the actual peak power consumption and actual action duration of the auxiliary equipment during the execution of actions; The feedback frame includes the actual peak power consumption, the actual operation duration, and the actual bus jitter value; the instruction frame includes the expected arrival time window, the rated power consumption range, and the process weight coefficient.

4. A method for operating a bus-based integrated auxiliary control system for CNC machine tools, applied to the system as described in any one of claims 1-3, characterized in that, Includes the following steps: Step 1, Pre-calculation of process features: Generate instruction frames containing the expected motion parameters of each auxiliary machine and the process weight coefficients and send them to the corresponding auxiliary machine control nodes; Step 2, Auxiliary machine execution and real-time perception: Each auxiliary machine control node receives and executes the instruction frame, collects actual operating parameters, generates feedback frames, and sends them to the bus master controller; Step 3, Error Vector Calculation: Generate an error vector based on the deviation between the actual operating parameters fed back by each auxiliary machine control node and the desired action parameters; Step 4, Dynamic scheduling correction: Adjust the bus transmission priority of each auxiliary machine control node and the timing parameters of subsequent instruction frames according to the error vector; Step 5, Health Feature Accumulation: The error vector is appended to the historical sequence of the corresponding auxiliary machine control node, and the statistical change characteristics of the historical sequence are calculated. When the statistical change characteristics meet the preset conditions, an early warning signal is generated. Step 6, Historical Backtracking: Receive the external input time interval query command, retrieve the error vector historical sequence of each auxiliary machine control node within the time interval, and output it after arranging it according to the time axis.

5. The operation method of a bus-type integrated CNC machine tool auxiliary control system according to claim 4, characterized in that, In step one, the expected action parameters include the expected action duration and the rated power consumption range; In step two, the actual operating parameters include the actual operation duration, the actual peak power consumption, and the actual bus jitter value. In step three, the difference between the actual action duration and the expected action duration is taken as the time domain deviation, the difference between the actual peak power consumption and the median of the rated power consumption range is taken as the power domain deviation, and the difference between the actual bus jitter value and the preset reference value is taken as the communication domain deviation. The time domain deviation, the power domain deviation, and the communication domain deviation constitute an error vector.

6. The operation method of a bus-type integrated CNC machine tool auxiliary control system according to claim 4, characterized in that, In step one, the process weight coefficient ranges from 0 to 100, and its value corresponds to the action type of the auxiliary equipment: 100 for tool changing action and emergency stop action, 30 to 50 for cooling action and lubrication action, and 10 to 20 for chip removal action and lighting action.

7. The operation method of a bus-type integrated CNC machine tool auxiliary control system according to claim 4, characterized in that, In step four, when the time domain deviation is greater than 0, the process weight coefficient of the corresponding auxiliary machine control node is increased to improve its bus transmission priority. When the power domain deviation is greater than 0, the expected arrival time window in the subsequent instruction frame will be shifted backward. When both the time domain deviation and the power domain deviation are greater than 0, the communication cycle of the corresponding auxiliary machine control node is extended.

8. The operation method of a bus-type integrated CNC machine tool auxiliary control system according to claim 7, characterized in that, The adjusted value of the process weight coefficient is the product of the current process weight coefficient and a monotonically increasing function with time domain deviation as the independent variable. The value after shifting the expected arrival time window is the sum of the original expected arrival time window and a monotonically increasing function with power domain deviation as the independent variable; The communication period is extended to be greater than the standard communication period.

9. The operation method of a bus-type integrated CNC machine tool auxiliary control system according to claim 4, characterized in that, In step five, the statistical change feature is the curvature of the historical sequence. The curvature is calculated using the three-point difference method, which is to subtract twice the previous error vector from the current error vector, add the previous two error vectors, and divide by the square of the sampling period. When the curvature exceeds a preset threshold consecutively within a predetermined number of times, the warning signal is generated.

10. The operation method of a bus-type integrated CNC machine tool auxiliary control system according to claim 4, characterized in that, In step five, after the warning signal is generated, the bus master controller reduces the upper limit of the maximum allowable process weight coefficient of the corresponding auxiliary machine control node. The storage unit continues to monitor the historical sequence statistical change characteristics of the auxiliary machine control node. When the statistical change characteristics no longer meet the preset conditions, the bus master controller restores the maximum allowable process weight coefficient upper limit of the auxiliary machine control node. In step six, when the historical sequence of error vectors is output in a time-axis arrangement, the time identifier corresponding to each error vector is also output simultaneously.

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