Control system of numerical control machine tool

Abstract

The invention discloses a control system of a numerical control machine tool, which comprises a control acquisition module, a communication supervision module, a sensitivity evaluation module, a sensitivity grading module and an intervention guiding module, and is characterized in that the control acquisition module calculates a fuzzy control complexity coefficient, the communication supervision module calculates a communication response gain coefficient, and the sensitivity evaluation module constructs a sensitive state model; the precision sensitive control index is output, the sensitive grading module grades the influence degree of the communication delay and the complexity of the control variable on the control system of the numerical control machine tool, and the intervention guiding module intervenes the control system of the numerical control machine tool according to the grading result. The problems of positioning errors and machining surface quality reduction are reduced, the machining precision of the numerical control machine tool is improved, the response capability of a communication system is monitored and evaluated in real time, timely transmission and execution of control instructions are ensured, and response delay and overshoot phenomena are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of numerically controlled machine tools, and more specifically, to a control system for a numerically controlled machine tool. Background Art

[0002] The fuzzy control algorithm used in the control system of CNC machine tools is a control strategy based on fuzzy logic theory. It is used to deal with nonlinear, time-varying and uncertain systems that are difficult to solve with traditional control methods. Fuzzy control simulates human thinking and decision-making processes and uses fuzzy rules to convert complex control problems into rule judgments to achieve control of the system. The fuzzy rules of the fuzzy algorithm are set manually, but the sensitivity of the preset rules is insufficient and it is difficult to modify them according to specific circumstances. The processing difficulty and complexity of the parts processed by CNC machine tools may change. In actual production, the complexity of the preset rules determines the upper limit of the processing capacity of CNC machine tools. However, in low-difficulty processing, overly complex preset fuzzy rules may have a reverse effect on processing efficiency, which is further affected by the communication response level of the system. Therefore, the balanced evaluation of the complexity of fuzzy rules and the delay efficiency of communication responses has become an urgent problem to be solved in the control system of CNC machine tools based on fuzzy rule algorithms.

[0003] In order to solve the above defects, a technical solution is now proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a control system for a numerically controlled machine tool to solve the deficiencies in the background technology.

[0005] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a control system for a numerically controlled machine tool, comprising a control acquisition module, a communication monitoring module, a sensitivity assessment module, a sensitivity grading module, and an intervention guidance module;

[0006] The control acquisition module is used to obtain the control variable abundance information of the control system of the CNC machine tool, and calculate the fuzzy control complexity coefficient in combination with the control variable abundance information, and transmit the fuzzy control complexity coefficient to the sensitive evaluation module;

[0007] The communication monitoring module is used to obtain the communication delay difference information of the control system of the CNC machine tool, calculate the communication response gain coefficient in combination with the communication delay difference information, and transmit the communication response gain coefficient to the sensitive evaluation module;

[0008] The sensitive evaluation module is used to construct a sensitive state model according to the fuzzy control complexity coefficient and the communication response gain coefficient, evaluate the influence of communication delay and the complexity of control variables on the control system of the CNC machine tool, and output the precision sensitive control index;

[0009] The sensitivity rating module is used to compare the precision sensitivity control index with a preset precision sensitivity control index threshold, and to rate the degree of influence of the communication delay and the complexity of the control variables on the control system of the CNC machine tool according to the comparison result;

[0010] The intervention guidance module is used to intervene in the control system of the CNC machine tool according to the classification results.

[0011] Preferably, the method for obtaining the control variable abundance information of the control system of the CNC machine tool is as follows:

[0012] Obtain the number of input variables of the control system of the CNC machine tool within the cycle time T, and integrate the number of input variables of the control system of the CNC machine tool within several cycle times T into a data set, and mark the data set as SR = {sr q}, where q = {1, 2, 3, ..., w}, and w is a positive integer, sr q Represents the number of input variables of the control system of the CNC machine tool within the qth cycle time T.

[0013] Preferably, the method for calculating the fuzzy control complexity coefficient in combination with the control variable abundance information is as follows:

[0014] Calculate the standard deviation of the number of input variables of the control system of the CNC machine tool within a certain cycle time T. The calculation expression is: In the formula, srp is the average value of the number of input variables of the control system of the CNC machine tool within several cycle times T, and the calculation expression is:

[0015] Calculate the complexity coefficient of fuzzy control, the calculation expression is In the formula, represents the complexity of fuzzy control, e is the index of the input control variable, R is the number of input control variables, and R is a positive integer, F e is the number of fuzzy sets of the e-th control variable, D e is the order of the control variable of the e-th input.

[0016] Preferably, the method for obtaining the communication delay difference information of the control system of the CNC machine tool is:

[0017] Obtain the time of the input variable and output variable of the control system of the CNC machine tool within the cycle time T, and integrate the time of the input variable and output variable of the control system of the CNC machine tool within several cycle times T into a data set, and mark the data set of the time of the input variable as EN={en t}, where t = {1, 2, 3, ..., y}, and y is a positive integer, en tThe data set of the input variable of the control system of the t-th CNC machine tool is marked as OUT = {out t}, where t = {1, 2, 3, ..., y}, and y is a positive integer, out t represents the time when the output variable of the control system of the t-th CNC machine tool is output. The time difference between the input variable and the output variable within the cycle time T is DI = {di t}, where di t =out t -en t , calculate the average value of the time difference between the input variable and the output variable within the cycle time T as

[0018] Preferably, the method for calculating the communication response gain coefficient in combination with the communication delay difference information is as follows:

[0019] The expression for calculating the communication response gain coefficient is: In the formula, E s is the steady-state error, T r is the response time, M p is the overshoot, α, β, and γ are the weight coefficients of steady-state error, response time, and overshoot, respectively, and α, β, and γ are all positive numbers. The steady-state error E s The calculation expression is In the formula, v represents the time variable, r(v) is the expected value of the control system, and y(v) is the actual output value of the control system at time v. Indicates that time tends to infinity, that is, the control system reaches a steady state, and the response time T r The calculation expression is T r =min{t|y(v)≥δ*r(v)}, where δ is the set response threshold, and 0<δ≤1, and the overshoot M p The calculation expression is In the formula, y max is the maximum value of y(v).

[0020] Preferably, a sensitive state model is constructed according to the fuzzy control complexity coefficient and the communication response gain coefficient, and the precision sensitive control index is calculated. The calculation expression is: Where P k,nom is the energy consumption under no-disturbance conditions when testing the robustness of the control system, P k,dis is the energy consumption under the kth disturbance condition when the control system is tested for robustness, k is the index of the disturbance condition, n is the number of disturbance conditions, and n is a positive integer. The calculation expression of energy consumption is: Where T is the cycle time, E c(t) is the power consumption at time t.

[0021] Preferably, the logic for grading the degree of influence of the communication delay and the complexity of the control variables on the control system of the numerical control machine tool according to the comparison results is:

[0022] The precision sensitivity control index is compared with the preset precision sensitivity control index threshold, and the preset precision sensitivity control index threshold is marked as P th , when the calculated precision sensitivity control index P sc Greater than or equal to the preset precision sensitivity control index threshold P th When , the input complexity of the control variable exceeds the responsiveness of the communication response, and is rated as the first frequency-out level;

[0023] When the calculated precision sensitivity control index P sc Less than the preset precision sensitivity control index threshold P th When , the responsiveness of the communication response exceeds the input complexity of the control variable and is rated as the second frequency-decoupling level.

[0024] Preferably, the logic of intervening in the control system of the CNC machine tool according to the grading result is:

[0025] When the level is classified as the first frequency-off level, the control system of the CNC machine tool experiences data loss or response delay failure, prompting the management personnel to intervene to control the data volume;

[0026] When it is classified as the second frequency-off level, the control system of the CNC machine tool will experience abnormal oscillation or over-response failure, prompting the management personnel to intervene to expand the communication bandwidth.

[0027] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0028] This application constructs a sensitive state model by calculating the fuzzy control complexity coefficient and the communication response gain coefficient, evaluates the impact of communication delay and control variable complexity on the control system, quantifies the impact of control variable complexity and communication delay on control accuracy, provides a basis for system optimization, compares the accuracy sensitive control index with the preset threshold, and classifies the degree of impact. According to the comparison result, the state of the control system is divided into the first off-frequency level and the second off-frequency level for targeted maintenance management. Through refined control variable processing, the positioning error and the deterioration of machining surface quality are reduced, the machining accuracy of CNC machine tools is improved, the response capability of the communication system is monitored and evaluated in real time, and the timely transmission and execution of control instructions are ensured to avoid response delays and overshoot. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0030] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Example 1: Please refer to Figure 1 As shown, the present invention is a control system for a numerically controlled machine tool, comprising a control acquisition module, a communication monitoring module, a sensitivity assessment module, a sensitivity grading module, and an intervention guidance module;

[0033] The control acquisition module is used to obtain the control variable abundance information of the control system of the CNC machine tool, obtain the fuzzy control complexity coefficient according to the control variable abundance information, and transmit the fuzzy control complexity coefficient to the sensitive evaluation module;

[0034] The communication monitoring module is used to obtain the communication delay difference information of the control system of the CNC machine tool, obtain the communication response gain coefficient according to the communication delay difference information, and transmit the communication response gain coefficient to the sensitive evaluation module;

[0035] The sensitive evaluation module is used to construct a sensitive state model according to the fuzzy control complexity coefficient and the communication response gain coefficient, evaluate the influence of communication delay and the complexity of control variables on the control system of the CNC machine tool, and output the precision sensitive control index;

[0036] The sensitivity rating module is used to compare the precision sensitivity control index with a preset precision sensitivity control index threshold, and to rate the degree of influence of the communication delay and the complexity of the control variables on the control system of the CNC machine tool according to the comparison result;

[0037] The intervention guidance module is used to intervene in the maintenance status of the control system of the CNC machine tool according to the classification result.

[0038] The control acquisition module is used to obtain the control variable abundance information of the control system of the CNC machine tool, obtain the fuzzy control complexity coefficient according to the control variable abundance information, and transmit the fuzzy control complexity coefficient to the sensitive evaluation module;

[0039] Fuzzy control algorithm is a control strategy based on fuzzy logic theory, which is used to deal with nonlinear, time-varying and uncertain systems that are difficult to solve with traditional control methods. Fuzzy control simulates human thinking and decision-making processes, and uses fuzzy rules to transform complex control problems into rule judgments to achieve system control. Fuzzy control algorithm generates control outputs according to the steps of fuzzification, reasoning and defuzzification of input control variables. It converts input control variables into fuzzy sets through fuzzification, performs reasoning according to pre-set rules, and converts fuzzy reasoning results into specific control variables through defuzzification to adjust CNC machine tools.

[0040] Fuzzy controllers can handle uncertainties in nonlinear and complex dynamic systems. For example, in the servo system of a CNC machine tool, fuzzy control adjusts the control strategy according to the error changes of the machine tool to reduce errors caused by parameter changes or external interference. Common control variables include speed error, position error, etc., which can be fine-tuned through fuzzy rules to improve the positioning accuracy of the machine tool.

[0041] In CNC machine tools, response speed is crucial to speed up processing and reduce errors. Fuzzy controllers respond quickly to error changes under different working conditions, avoiding response delays or overshoots caused by parameter incompatibility in traditional controllers. For example, fuzzy control automatically adjusts gain based on error and error change rate to ensure that the system responds promptly under different loads or speeds.

[0042] In the fuzzification process, the input control variables are converted into fuzzy sets and expressed by linguistic variables, such as high, medium, and low. The number of fuzzy sets affects the precision of the input control variables. The more fuzzy sets there are, the more precisely they can describe the different states of the input control variables, thereby improving the accuracy of fuzzy reasoning.

[0043] The derivative order of the input control variable affects the degree of refinement of the control. Taking the control variable error as an example, the first-order derivative of the error is the error change rate, and the second-order derivative of the error is the rate of change of the error change rate. Different orders of derivatives of the control variable are introduced to respond to the changing state of the error, adjust the overshoot, and improve the smoothness of the control system of the CNC machine tool.

[0044] If the complexity of the fuzzy control algorithm used in the control system of the CNC machine tool is insufficient, it will have the following adverse effects on the control system of the CNC machine tool:

[0045] Low-complexity fuzzy control algorithms cannot precisely describe and respond to system inputs due to the small number of fuzzy sets or insufficient rules. When faced with complex machining conditions, it is difficult to make precise adjustments, resulting in insufficient machining accuracy of machine tools. Common manifestations include increased positioning errors and reduced machining surface quality.

[0046] For example, if the positioning error increases, the position of the tool cannot be accurately controlled, resulting in an increase in the machining size deviation of the workpiece; if the machining surface quality decreases, the control accuracy is too low, resulting in an increase in surface roughness, affecting the quality of the workpiece;

[0047] The rules of the fuzzy control algorithm are insufficient and cannot respond quickly and accurately enough to the dynamic changes of the system, which manifests itself in slow response, overshoot and oscillation in CNC machine tools;

[0048] For example, if the response speed is slow, the control parameters cannot be adjusted in time when facing rapidly changing errors or processing conditions, resulting in a delayed response of the system and reduced production efficiency. If there is overshoot and oscillation, when the error is too large, the low-complexity control method cannot accurately handle the error change, which will cause overshoot or system oscillation, making the machine tool unable to operate stably.

[0049] Low-complexity fuzzy control algorithms are not good at processing external interference. CNC machine tools are often disturbed by external factors such as vibration, temperature changes, and load fluctuations during actual processing. When the complexity of the control algorithm is too low, it will have poor anti-interference and recovery capabilities.

[0050] For example, if the anti-interference ability is poor, the control system cannot make effective compensation in the face of external interference, resulting in error accumulation and affecting the processing accuracy; if the recovery ability is poor, it is difficult for the system to recover to a stable state in time after being disturbed, affecting the processing continuity and workpiece quality;

[0051] The working conditions of CNC machine tools often change, including material properties, processing speed, tool wear, etc. If the fuzzy control algorithm is too simple, the adaptive ability of the control system will be weakened, which will be manifested in the inability to adapt to complex processing tasks and poor cutting force control.

[0052] For example, if it cannot adapt to complex processing tasks, the low-complexity fuzzy controller is only suitable for simple and single processing tasks and cannot cope with complex and changeable working conditions; if the cutting force control is poor, the cutting force cannot be accurately adjusted under complex cutting tasks, and the low-complexity fuzzy control cannot accurately handle the changeable cutting force, resulting in accelerated tool wear or excessive cutting force and damage to the workpiece;

[0053] Robustness is the ability of a control system to maintain performance in the face of uncertainty or model errors. If the complexity of the fuzzy control algorithm is too low and the rules are not rich enough, the robustness of the system will decrease. The problem manifests itself in the system being sensitive to parameter changes and insensitive to model errors.

[0054] For example, if the system is sensitive to parameter changes, then during the operation of the machine tool, parameter changes will cause the system performance to drop sharply and the system robustness will be insufficient; if it is insensitive to model errors, the low-complexity algorithm will not be able to effectively handle the nonlinear part of the model, resulting in obviously insufficient control effect.

[0055] Therefore, by obtaining the control variable abundance information of the control system of the CNC machine tool, the control variable complexity of the control system of the CNC machine tool is evaluated, and the fuzzy control complexity coefficient is obtained to measure the degree of influence on the control system of the CNC machine tool;

[0056] The method for obtaining the control variable abundance information of the control system of the CNC machine tool is as follows:

[0057] Obtain the number of input variables of the control system of the CNC machine tool within the cycle time T, and integrate the number of input variables of the control system of the CNC machine tool within several cycle times T into a data set, and mark the data set as SR = {sr q}, where q = {1, 2, 3, ..., w}, and w is a positive integer, sr q represents the number of input variables of the control system of the CNC machine tool within the qth cycle time T;

[0058] It should be noted that the input variables of the control system of the CNC machine tool within the cycle time T are collected by the sensors installed on the CNC machine tool. The input variables collected by the sensors include position, speed, force, temperature, etc. After the input variables are collected by the sensors, the data are transmitted to the control system. The data acquisition software, such as LabVIEW, MATLAB's DataAcquisition Toolbox, or the data acquisition component integrated in the machine tool control system itself, is used to collect and record the number of input variables within each cycle time T;

[0059] The method for calculating the fuzzy control complexity coefficient combined with the control variable abundance information is as follows:

[0060] Calculate the standard deviation of the number of input variables of the control system of the CNC machine tool within a certain cycle time T. The calculation expression is: In the formula, srp is the average value of the number of input variables of the control system of the CNC machine tool within several cycle times T, and the calculation expression is:

[0061] Calculate the complexity coefficient of fuzzy control, the calculation expression is In the formula, represents the complexity of fuzzy control, e is the index of the input control variable, R is the number of input control variables, and R is a positive integer, F e is the number of fuzzy sets of the e-th control variable, D e is the order of the control variable of the e-th input.

[0062] From the above calculation, it can be seen that the larger the fuzzy control complexity coefficient is, the greater the influence of the number of input variables and the complexity of input variables on the control precision of the CNC machine tool control system. Conversely, the smaller the fuzzy control complexity coefficient is, the smaller the influence of the number of input variables and the complexity of input variables on the control precision of the CNC machine tool control system.

[0063] The communication monitoring module is used to obtain the communication delay difference information of the control system of the CNC machine tool, obtain the communication response gain coefficient according to the communication delay difference information, and transmit the communication response gain coefficient to the sensitive evaluation module;

[0064] In the control system of CNC machine tools based on fuzzy control algorithms, the sensitivity of communication response has a direct impact on control accuracy. The sensitivity of communication response determines the reaction speed of the control system to input variables and the timeliness of execution adjustment. Real-time is the system's ability to respond quickly to input signals, including the time from acquiring data from sensors, fuzzy reasoning calculations, to the time when control instructions are sent to the actuators. If the communication delay is large, the control system cannot provide timely feedback and correction for errors that occur during the processing, which may lead to a decrease in the processing accuracy of the machine tool. The fuzzy control algorithm relies on the feedback of sensor input data to perform fuzzy reasoning and make corresponding adjustments. If the sensitivity of the communication system is low, the acquisition and transmission of sensor data will be slow. If it is not timely enough, the fuzzy control system will make wrong decisions based on outdated or incomplete input data; in the fuzzy control algorithm, the control system will generate adjustment instructions based on the error, error change rate and other input variables. If the control instructions are not transmitted in time or are lost in the communication link, the actuator of the CNC machine tool will not be able to make adjustments in time, thus affecting the control accuracy; when the communication response sensitivity is low, the system's adaptability to the dynamic processing environment will also be reduced. The fuzzy control algorithm responds to the uncertainty and changes in the processing process through continuous feedback and adjustment. If the communication system is slow to respond and cannot quickly process data from changing working conditions, the system will not be able to respond in time to sudden changes in processing conditions, affecting accuracy.

[0065] The method for obtaining the communication delay difference information of the control system of the CNC machine tool is:

[0066] Obtain the time of the input variable and output variable of the control system of the CNC machine tool within the cycle time T, and integrate the time of the input variable and output variable of the control system of the CNC machine tool within several cycle times T into a data set, and mark the data set of the time of the input variable as EN={en t}, where t = {1, 2, 3, ..., y}, and y is a positive integer, en t The data set of the input variable of the control system of the t-th CNC machine tool is marked as OUT = {out t}, where t = {1, 2, 3, ..., y}, and y is a positive integer, out t represents the time when the output variable of the control system of the t-th CNC machine tool is output. The time difference between the input variable and the output variable within the cycle time T is DI = {di t}, where di t =out t -en t , calculate the average value of the time difference between the input variable and the output variable within the cycle time T as

[0067] The method for calculating the communication response gain coefficient by combining the communication delay difference information is as follows:

[0068] The expression for calculating the communication response gain coefficient is: In the formula, E s is the steady-state error, T r is the response time, M p is the overshoot, α, β, and γ are the weight coefficients of steady-state error, response time, and overshoot, respectively, and α, β, and γ are all positive numbers. The steady-state error E s The calculation expression is In the formula, v represents the time variable, r(v) is the expected value of the control system, and y(v) is the actual output value of the control system at time v. Indicates that time tends to infinity, that is, the control system reaches a steady state, and the response time T r The calculation expression is T r =min{t|y(v)≥δ*r(v)}, where δ is the set response threshold, and 0<δ≤1, and the overshoot M p The calculation expression is In the formula, y max is the maximum value of y(v).

[0069] From the above calculation, it can be seen that the larger the communication response gain coefficient is, the greater the influence on the control accuracy of the control system of the CNC machine tool is, and the smaller the communication response gain coefficient is, the smaller the influence on the control accuracy of the control system of the CNC machine tool is.

[0070] The sensitive evaluation module is used to construct a sensitive state model according to the fuzzy control complexity coefficient and the communication response gain coefficient, evaluate the influence of communication delay and the complexity of control variables on the control system of the CNC machine tool, and output the precision sensitive control index;

[0071] The fuzzy control complexity coefficient and communication response gain coefficient are used to construct a sensitive state model and calculate the precision sensitive control index. The calculation expression is: Where P k,nom is the energy consumption under no-disturbance conditions when testing the robustness of the control system, P k,dis is the energy consumption under the kth disturbance condition when the control system is tested for robustness, k is the index of the disturbance condition, n is the number of disturbance conditions, and n is a positive integer. The calculation expression of energy consumption is: Where T is the cycle time, E c (t) is the power consumption at time t;

[0072] It should be noted that robustness testing is a test that introduces external or internal interference conditions during system operation and measures the robustness of the system by evaluating the changes in the system's performance under interference conditions. Common interference conditions include external environmental interference, changes in input signals, load changes, noise or sensor errors in the system, and changes in controller parameters. External environmental interference includes changes in temperature, humidity, vibration or noise, changes in input signals such as sudden increases or decreases in input signals, load changes such as sudden changes in machining load on CNC machine tools, noise or sensor errors in the system are random errors or deviations generated when sensors collect data, and changes in controller parameters such as changes in controller gain.

[0073] The sensitivity rating module is used to compare the precision sensitivity control index with a preset precision sensitivity control index threshold, and to rate the degree of influence of the communication delay and the complexity of the control variables on the control system of the CNC machine tool according to the comparison result;

[0074] The precision sensitivity control index is compared with the preset precision sensitivity control index threshold, and the preset precision sensitivity control index threshold is marked as P th , when the calculated precision sensitivity control index P sc Greater than or equal to the preset precision sensitivity control index threshold P th When , the input complexity of the control variable exceeds the responsiveness of the communication response, and is rated as the first frequency-out level;

[0075] When the calculated precision sensitivity control index P sc Less than the preset precision sensitivity control index threshold P th When , the responsiveness of the communication response exceeds the input complexity of the control variable and is rated as the second frequency-decoupling level.

[0076] The intervention guidance module is used to intervene in the control system of the CNC machine tool according to the classification results;

[0077] When the level is classified as the first frequency-off level, the control system of the CNC machine tool experiences data loss or response delay failure, prompting the management personnel to intervene to control the data volume;

[0078] When it is classified as the second frequency-off level, the control system of the CNC machine tool will experience abnormal oscillation or over-response failure, prompting the management personnel to intervene to expand the communication bandwidth.

[0079] This application constructs a sensitive state model by calculating the fuzzy control complexity coefficient and the communication response gain coefficient, evaluates the impact of communication delay and control variable complexity on the control system, quantifies the impact of control variable complexity and communication delay on control accuracy, provides a basis for system optimization, compares the accuracy sensitive control index with the preset threshold, and classifies the degree of impact. According to the comparison result, the state of the control system is divided into the first off-frequency level and the second off-frequency level for targeted maintenance management. Through refined control variable processing, the positioning error and the deterioration of machining surface quality are reduced, the machining accuracy of CNC machine tools is improved, the response capability of the communication system is monitored and evaluated in real time, and the timely transmission and execution of control instructions are ensured to avoid response delays and overshoot.

[0080] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0081] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of computer program goods. The computer program goods include one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0082] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0083] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0084] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0085] If the functions are implemented in the form of software functional units and sold or used as independent goods, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially or in other words, the part that contributes to the prior art or the part of the technical solution can be embodied in the form of software goods, and the computer software goods are stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0086] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A control system for a CNC machine tool, characterized in that: It includes control acquisition module, communication monitoring module, sensitivity assessment module, sensitivity classification module and intervention guidance module; The control acquisition module is used to obtain the control variable abundance information of the control system of the CNC machine tool, and calculate the fuzzy control complexity coefficient in combination with the control variable abundance information, and transmit the fuzzy control complexity coefficient to the sensitive evaluation module; The communication monitoring module is used to obtain the communication delay difference information of the control system of the CNC machine tool, calculate the communication response gain coefficient in combination with the communication delay difference information, and transmit the communication response gain coefficient to the sensitive evaluation module; The sensitive evaluation module is used to construct a sensitive state model according to the fuzzy control complexity coefficient and the communication response gain coefficient, evaluate the influence of communication delay and the complexity of control variables on the control system of the CNC machine tool, and output the precision sensitive control index; The sensitivity rating module is used to compare the precision sensitivity control index with a preset precision sensitivity control index threshold, and to rate the degree of influence of the communication delay and the complexity of the control variables on the control system of the CNC machine tool according to the comparison result; The intervention guidance module is used to intervene in the control system of the CNC machine tool according to the classification results.

2. A control system for a CNC machine tool according to claim 1, characterized in that: The method for obtaining the control variable abundance information of the control system of the CNC machine tool is as follows: Obtain the number of input variables of the control system of the CNC machine tool within the cycle time T, and integrate the number of input variables of the control system of the CNC machine tool within several cycle times T into a data set, and mark the data set as SR = {sr q }, where q = {1, 2, 3, ..., w}, and w is a positive integer, sr q Represents the number of input variables of the control system of the CNC machine tool within the qth cycle time T.

3. A control system for a CNC machine tool according to claim 2, characterized in that: The method for calculating the fuzzy control complexity coefficient combined with the control variable abundance information is as follows: Calculate the standard deviation of the number of input variables of the control system of the CNC machine tool within a certain cycle time T. The calculation expression is: In the formula, srp is the average value of the number of input variables of the control system of the CNC machine tool within several cycle times T, and the calculation expression is: Calculate the complexity coefficient of fuzzy control, the calculation expression is In the formula, represents the complexity of fuzzy control, e is the index of the input control variable, R is the number of input control variables, and R is a positive integer, F e is the number of fuzzy sets of the e-th control variable, D e is the order of the control variable of the e-th input.

4. A control system for a numerically controlled machine tool according to claim 1, characterized in that: The method for obtaining the communication delay difference information of the control system of the CNC machine tool is: Obtain the time of the input variable and output variable of the control system of the CNC machine tool within the cycle time T, and integrate the time of the input variable and output variable of the control system of the CNC machine tool within several cycle times T into a data set, and mark the data set of the time of the input variable as EN={en t }, where t = {1, 2, 3, ..., y}, and y is a positive integer, en t The data set of the input variable of the control system of the t-th CNC machine tool is marked as OUT = {out t }, where t={1,2,3,…,y}, and y is a positive integer, out t represents the time when the output variable of the control system of the t-th CNC machine tool is output. The time difference between the input variable and the output variable within the cycle time T is DI = {di t }, where di t =out t -en t , calculate the average value of the time difference between the input variable and the output variable within the cycle time T as 5. A control system for a numerically controlled machine tool according to claim 4, characterized in that: The method for calculating the communication response gain coefficient by combining the communication delay difference information is as follows: The expression for calculating the communication response gain coefficient is: In the formula, E s is the steady-state error, T r is the response time, M p is the overshoot, α, β, and γ are the weight coefficients of steady-state error, response time, and overshoot, respectively, and α, β, and γ are all positive numbers. The steady-state error E s The calculation expression is In the formula, v represents the time variable, r(v) is the expected value of the control system, and y(v) is the actual output value of the control system at time v. Indicates that time tends to infinity, that is, the control system reaches a steady state, and the response time T r The calculation expression is T r =min{t|y(v)≥δ*r(v)}, where δ is the set response threshold, and 0<δ≤1, and the overshoot M p The calculation expression is In the formula, y max is the maximum value of y(v).

6. A control system for a numerically controlled machine tool according to claim 5, characterized in that: According to the fuzzy control complexity coefficient and communication response gain coefficient, the sensitive state model is constructed and the precision sensitive control index is calculated. The calculation expression is: Where P k,nom is the energy consumption under no-disturbance conditions when testing the robustness of the control system, P k,dis is the energy consumption under the kth disturbance condition when the control system is tested for robustness, k is the index of the disturbance condition, n is the number of disturbance conditions, and n is a positive integer. The calculation expression of energy consumption is: Where T is the cycle time, E c (t) is the power consumption at time t.

7. A control system for a numerically controlled machine tool according to claim 6, characterized in that: The logic for grading the degree of influence of communication delay and complexity of control variables on the control system of CNC machine tools according to the comparison results is as follows: The precision sensitivity control index is compared with the preset precision sensitivity control index threshold, and the preset precision sensitivity control index threshold is marked as P th , when the calculated precision sensitivity control index P sc Greater than or equal to the preset precision sensitivity control index threshold P th When , the input complexity of the control variable exceeds the responsiveness of the communication response, and is rated as the first frequency-out level; When the calculated precision sensitivity control index P sc Less than the preset precision sensitivity control index threshold P th When , the responsiveness of the communication response exceeds the input complexity of the control variable and is rated as the second frequency-out level.

8. A control system for a numerically controlled machine tool according to claim 7, characterized in that: The logic of intervening in the control system of CNC machine tools according to the classification results is: When the level is classified as the first frequency-off level, the control system of the CNC machine tool experiences data loss or response delay failure, prompting the management personnel to intervene to control the data volume; When it is classified as the second frequency-off level, the control system of the CNC machine tool will experience abnormal oscillation or over-response failure, prompting the management personnel to intervene to expand the communication bandwidth.

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