Die cutting equipment error optimization method and system based on group collaboration

Abstract

The invention provides a die cutting equipment error optimization method and system based on group collaboration, and relates to the technical field of die cutting machines, and the method comprises the steps: carrying out the spectrum analysis of a frequency characteristic curve of each piece of die cutting equipment, so as to divide an initial interference group; establishing a virtual spring damping network in the initial interference group, regarding each die cutting device in the initial interference group as a mass point, and determining a spring stiffness coefficient and a damping coefficient according to an actual distance between adjacent mass points; determining vibration mode characteristics of each die cutting device according to the spring stiffness coefficient and the damping coefficient, and converting a control signal generated according to the vibration mode characteristics into a parameter adjusting variable by using a preset control algorithm; and determining a vibration coupling relationship according to the collected vibration energy data of the initial interference group after the initial processing parameters are adjusted according to the parameter adjusting quantity, and determining an optimal production takt and a parameter compensation value according to the vibration coupling relationship. The technical problem that in the prior art, due to vibration coupling of a die cutting equipment group, the machining error cooperative control difficulty is large is solved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of die-cutting machines, and in particular to a die-cutting equipment error optimization method and system based on group collaboration. BACKGROUND

[0002] With the rapid development of modern manufacturing, the processing precision requirements of die-cutting equipment on the production line are continuously improved, especially in the fields of packaging printing, electronic components and other precision manufacturing, the processing error control of die-cuting equipment has become a key indicator to measure product quality and production efficiency.

[0003] In the related art, an independent vibration monitoring system is usually used to monitor the running state of each die-cutting equipment in real time. Specifically, a vibration sensor is installed on each die-cuting equipment to collect the vibration signal when the equipment is running. The running state of the equipment is evaluated by analyzing the frequency characteristics and amplitude changes of the vibration signal, and the independent adjustment of the processing parameters is made based on the vibration characteristics of a single equipment in order to reduce the processing error and improve the die-cutting precision.

[0004] However, in the actual production environment, the simultaneous operation of multiple die-cutting equipment will produce complex vibration transmission phenomena, and the vibration between the die-cuting equipment will affect each other through the foundation, support structure and other ways. If only the vibration control of a single die-cuting equipment is concerned and the vibration coupling effect between the die-cutting equipment groups is ignored, the parameter adjustment of a single die-cutting equipment may exacerbate the vibration of the adjacent die-cuting equipment, and thus the processing error collaborative control is difficult due to the vibration coupling of the die-cutting equipment group in the related art. SUMMARY

[0005] The application provides a die-cutting equipment error optimization method and system based on group collaboration, which is used to improve the processing error collaborative control efficiency of the die-cutting equipment group.

[0006] Firstly, this application provides a group-based collaborative error optimization method for die-cutting equipment, applied to the aforementioned die-cutting equipment error optimization system. The method includes: performing spectral analysis on the frequency characteristic curves of each die-cutting device in the production area to classify die-cutting devices in the production area with frequency overlap exceeding a preset overlap threshold into an initial interference group; establishing a virtual spring-damping network within the initial interference group, treating each die-cutting device within the initial interference group as a point mass, and determining the spring stiffness coefficient and damping coefficient based on the actual distance between adjacent points; determining the mode shape characteristics of each die-cutting device based on the spring stiffness coefficient and damping coefficient, generating a control signal to suppress the vibration of each die-cutting device based on the mode shape characteristics, and converting the control signal into a parameter adjustment amount using a preset control algorithm; collecting vibration energy data of the initial interference group after adjusting the initial processing parameters according to the parameter adjustment amount, determining the vibration coupling relationship of the initial interference group based on the vibration energy data, and determining the optimal production cycle and parameter compensation value based on the vibration coupling relationship, wherein the optimal production cycle is used to coordinate the overall operating sequence of the initial interference group, and the parameter compensation value is used to adjust the target processing parameters of each die-cutting device.

[0007] By employing the above technical solution, frequency-overlapping die-cutting equipment groups are identified through spectrum analysis, thus avoiding redundant processing of non-interfering equipment. Based on a virtual spring-damped network model, the complex vibration coupling relationship of the equipment group is transformed into a quantifiable physical model, providing a theoretical foundation for subsequent mode shape characteristic analysis. On this basis, control signals are generated through mode shape characteristics and converted into specific parameter adjustment quantities to achieve directional suppression of vibration. Finally, production cycle time and compensation parameters are optimized through vibration energy data analysis, enabling the entire die-cutting equipment group to operate collaboratively and effectively reducing processing errors caused by group vibration coupling. This solves the technical problem of the difficulty in collaboratively controlling processing errors caused by vibration coupling of die-cutting equipment groups in related technologies, achieving the technical effect of improving the efficiency of collaborative control of processing errors in die-cutting equipment groups.

[0008] Optionally, a virtual spring-damped network is established within the initial interference group. Each die-cutting device in the initial interference group is considered as a point mass, and the spring stiffness coefficient and damping coefficient are determined based on the actual distance between adjacent point masses. Specifically, this includes: obtaining the mechanical characteristic parameters and spatial coordinates of each die-cutting device, where the mechanical characteristic parameters characterize the inherent vibration characteristics of each die-cutting device; determining the first coupling strength coefficient between the first adjacent die-cutting devices in the initial interference group based on the mechanical characteristic parameters; progressively hierarchically grouping the initial interference group according to the first coupling strength coefficient to obtain multiple basic network layers, wherein the second coupling strength coefficient between the second adjacent die-cutting devices within each basic network layer is greater than a preset coupling strength threshold, the first adjacent die-cutting device includes the second adjacent die-cutting device, and the first coupling strength coefficient includes the second coupling strength coefficient; and determining each basic network based on the spatial coordinates. The system establishes a first connection relationship between first die-cutting devices within a layer, and a second connection relationship between second die-cutting devices in adjacent basic network layers based on spatial coordinates; establishes a first spring damping unit between first die-cutting devices with the first connection relationship, and establishes a second spring damping unit between second die-cutting devices with the second connection relationship; combines the first spring damping unit and the second spring damping unit to construct a virtual spring damping network; treats each die-cutting device in the initial interference group as a mass point, and determines the actual distance between adjacent mass points based on spatial coordinates; establishes a nonlinear mapping relationship between the actual distance and the spring stiffness coefficient and damping coefficient based on the spatial coordinates, wherein the nonlinear mapping relationship is used to determine the spring stiffness coefficient and damping coefficient under different actual distances; and determines the spring stiffness coefficient and damping coefficient between adjacent mass points based on the nonlinear mapping relationship.

[0009] By adopting the above technical solution, a coupling strength coefficient is established by introducing mechanical characteristic parameters, accurately reflecting the vibration transmission characteristics between devices. A progressive hierarchical grouping based on coupling strength ensures that strongly coupled devices are preferentially assigned to the same network layer, laying a reasonable network structure foundation for subsequent vibration control. Simultaneously, nonlinear mapping is used to determine the spring stiffness coefficient and damping coefficient, overcoming the limitations of traditional linear models in describing complex vibration transmission processes. This allows the established virtual spring-damped network to more accurately simulate actual vibration coupling phenomena.

[0010] Optionally, the initial interference group is progressively hierarchically grouped according to the first coupling strength coefficient to obtain multiple basic network layers. Specifically, this includes: arranging each die-cutting device in the initial interference group in descending order according to the first coupling strength coefficient to obtain a coupling strength sequence; using the coupling strength sequence as the current grouping sequence, and repeatedly performing the following operations until all die-cutting devices in the initial interference group are added to the multiple basic network layers: determining the third die-cutting device with the largest coupling strength coefficient from the current grouping sequence, and using the third die-cutting device as the first central node of the current basic network layer, wherein the first coupling strength coefficient includes the largest coupling strength coefficient, and the multiple basic network layers include the current basic network layer; determining the fourth die-cutting device with a second coupling strength coefficient greater than a preset coupling strength threshold with the first central node from the current grouping sequence, and adding the fourth die-cutting device to the current basic network layer to generate the current device group; removing the current device group from the current grouping sequence to obtain a new current grouping sequence.

[0011] By adopting the above technical solution, the equipment with greater vibration impact is prioritized for processing based on the descending order of coupling strength coefficients. An iterative approach is used to continuously update the current grouping sequence, and an adaptive grouping of die-cutting equipment is achieved by combining a central node and a preset coupling strength threshold judgment mechanism. This progressive hierarchical grouping method avoids the local vibration accumulation that may result from traditional fixed grouping methods, improving the accuracy and reliability of subsequent vibration control.

[0012] Optionally, determining the first connection relationship between the first die-cutting devices within each basic network layer based on spatial location coordinates, and determining the second connection relationship between the second die-cutting devices in adjacent basic network layers based on spatial location coordinates, specifically includes: using the second center node of each basic network layer as a connection reference point; determining the azimuth angle and relative distance of the first die-cutting devices within each basic network layer relative to the connection reference point; dividing the first die-cutting devices within each basic network layer into multiple sectors based on the azimuth angle, and determining the first connection relationship between the first die-cutting devices within the same sector and whose relative distance is less than a preset distance threshold; determining the connection vector between the first connection reference points of adjacent basic network layers, wherein the connection reference point includes the first connection reference point; and determining the second die-cutting devices for establishing the second connection relationship from adjacent basic network layers based on the connection vector.

[0013] By adopting the above technical solution, polar coordinates are established based on connection reference points, transforming the spatial distribution of devices into quantifiable azimuth angles and relative distances. Establishing the first connection relationship through sector division and distance threshold judgment avoids invalid vibration control links between long-distance devices. Simultaneously, using connection vectors to determine the second connection relationship between adjacent network layers allows for the construction of a complete multi-level vibration transmission network, enabling vibration control to form local closed loops and achieve inter-layer coordination.

[0014] Optionally, the mode shape characteristics of each die-cutting device are determined based on the spring stiffness coefficient and damping coefficient. A control signal for suppressing the vibration of each die-cutting device is generated based on these characteristics, and the control signal is converted into parameter adjustment quantities using a preset control algorithm. Specifically, this includes: constructing the multi-body coupled dynamic equations of the initial disturbance group based on the spring stiffness coefficient and damping coefficient; establishing a structural mapping matrix between the virtual spring-damped network and the multi-body coupled dynamic equations; performing eigenvalue decomposition on the structural mapping matrix to obtain the principal frequency parameters and displacement characteristics of each die-cutting device, where the principal frequency parameters characterize the resonance state of each die-cutting device, and the displacement characteristics characterize the vibration amplitude distribution of each die-cutting device; constructing the frequency transfer function of each die-cutting device based on the principal frequency parameters; and constructing the vibration response equation of each die-cutting device based on the displacement characteristics; and performing frequency domain feature extraction based on the frequency transfer function. The vibration transmission coefficient of each die-cutting device is obtained; a spectral threshold is processed based on the vibration transmission coefficient to obtain a vibration suppression threshold; and a vibration state observer for each die-cutting device is constructed based on the vibration transmission coefficient. The output parameters of the vibration state observer of each die-cutting device are subjected to deviation feature processing with the vibration suppression threshold to obtain compensation parameters for each die-cutting device. Parameter optimization processing is performed based on the compensation parameters and the vibration response equation to obtain the vibration suppression gain of each die-cutting device. Gain modulation processing is performed based on the vibration suppression gain and the output parameters to obtain the control signal for each die-cutting device. The control signal is decomposed using a preset control algorithm to obtain the cutting speed correction, feed speed correction, and cutting pressure correction for each die-cutting device. The cutting speed correction, feed speed correction, and cutting pressure correction are combined to obtain the parameter adjustment amount for each die-cutting device.

[0015] By adopting the above technical solution, a multibody coupled dynamic equation is established, and eigenvalue decomposition is performed using the structural mapping matrix to obtain the main frequency parameters and displacement characteristics of the die-cutting equipment. Based on this, a frequency transfer function and vibration response equation are constructed, thereby accurately describing the dynamic characteristics of the equipment. The compensation parameters obtained through vibration state observer and spectrum thresholding are decomposed into specific velocity and pressure corrections after gain modulation, realizing the accurate conversion from theoretical model to actual control parameters and ensuring the effectiveness of vibration suppression.

[0016] Optionally, vibration energy data of the initial interference group after adjusting the initial processing parameters according to the parameter adjustment amount is collected. The vibration coupling relationship of the initial interference group is determined based on the vibration energy data, and the optimal production cycle and parameter compensation value are determined based on the vibration coupling relationship. Specifically, this includes: collecting the vibration displacement and vibration velocity of each die-cutting device after adjusting the initial processing parameters; determining the vibration power value of each die-cutting device based on the vibration displacement and vibration velocity; determining the vibration energy data of each die-cutting device based on the vibration power value; determining the vibration transmission characteristics and vibration attenuation characteristics of each die-cutting device based on the vibration energy data; determining the vibration coupling relationship based on the vibration transmission characteristics and vibration attenuation characteristics; determining the vibration influence degree of the initial interference group based on the vibration coupling relationship; optimizing the vibration influence degree to obtain the optimal running sequence and optimal parameter compensation sequence of the initial interference group; determining the optimal production cycle based on the optimal running sequence; and determining the parameter compensation value based on the optimal parameter compensation sequence.

[0017] By adopting the above technical solution, vibration power values ​​are calculated based on vibration displacement and velocity, accurately reflecting the real-time vibration state of the die-cutting equipment. Vibration coupling relationships are determined by analyzing vibration transmission and attenuation characteristics, revealing the dynamic influence patterns among the die-cutting equipment group. Optimizing the degree of vibration influence yields the optimal operating sequence and parameter compensation sequence, enabling not only coordinated operation of the equipment group but also establishing a closed-loop feedback mechanism, thereby ensuring the continuous stability of vibration control effects.

[0018] Optionally, a spectrum analysis is performed on the frequency response curves of each die-cutting device in the production area to classify die-cutting devices in the production area with a frequency overlap greater than a preset overlap threshold into an initial interference group. Specifically, this includes: collecting vibration signals from each die-cutting device in the production area; performing Fourier transform processing on the vibration signals to obtain the frequency response curves of each die-cutting device; performing frequency band segmentation processing on the frequency response curves to obtain the amplitude distribution of each die-cutting device in different frequency bands; determining the frequency overlap between the third adjacent die-cutting device in the production area based on the amplitude distribution; comparing the frequency overlap with a preset overlap threshold to identify die-cutting devices with a frequency overlap greater than the preset overlap threshold, and classifying the die-cutting devices into the initial interference group.

[0019] By employing the above technical solution, the vibration signal is subjected to Fourier transform to obtain the frequency response curve, and the precise amplitude distribution is obtained through frequency band segmentation. By comparing the frequency overlap of adjacent devices with a preset threshold, an interference identification mechanism based on frequency domain characteristics is established. This overcomes the limitation of traditional time-domain analysis methods in detecting potential vibration coupling, providing a reliable data foundation for subsequent group control and improving the adaptability and accuracy of the entire coordinated control system.

[0020] In a second aspect, embodiments of this application provide a die-cutting equipment error optimization system, which includes: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to cause the die-cutting equipment error optimization system to perform the method as described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a die-cutting equipment error optimization system, cause the die-cutting equipment error optimization system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a die-cutting equipment error optimization system, cause the die-cutting equipment error optimization system to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. The error optimization method for die-cutting equipment based on group collaboration provided in this application identifies die-cutting equipment groups with overlapping frequencies through spectrum analysis, thereby avoiding redundant processing of non-interfering equipment. Based on a virtual spring-damped network model, the complex vibration coupling relationship of the equipment group is transformed into a quantifiable physical model, providing a theoretical basis for subsequent mode shape characteristic analysis. On this basis, control signals are generated through mode shape characteristics and converted into specific parameter adjustment quantities to achieve directional suppression of vibration. Finally, the production cycle and compensation parameters are optimized through the analysis of vibration energy data, enabling the entire die-cutting equipment group to operate collaboratively and effectively reducing processing errors caused by group vibration coupling.

[0024] 2. The group-based collaborative error optimization method for die-cutting equipment provided in this application establishes a coupling strength coefficient by introducing mechanical characteristic parameters, accurately reflecting the vibration transmission characteristics between equipment. Based on the coupling strength, a progressive hierarchical grouping method is used, prioritizing strongly coupled equipment to the same network layer, laying a reasonable network structure foundation for subsequent vibration control. Simultaneously, nonlinear mapping is employed to determine the spring stiffness coefficient and damping coefficient, overcoming the limitations of traditional linear models in describing complex vibration transmission processes, enabling the established virtual spring-damped network to more accurately simulate actual vibration coupling phenomena.

[0025] 3. The group-based collaborative error optimization method for die-cutting equipment provided in this application ensures that equipment with greater vibration impact is prioritized based on descending order of coupling strength coefficients. It continuously updates the current grouping sequence using an iterative approach, combining a judgment mechanism based on the central node and a preset coupling strength threshold to achieve adaptive grouping of the die-cutting equipment. This progressive hierarchical grouping method avoids the local vibration accumulation that may result from traditional fixed grouping methods, improving the accuracy and reliability of subsequent vibration control. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a group-cooperative error optimization method for die-cutting equipment in an embodiment of this application. Figure 2 This is a schematic diagram of the physical device structure of a die-cutting equipment error optimization system in the embodiments of this application. Detailed Implementation

[0027] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0029] This application provides a method for optimizing the error of die-cutting equipment based on group collaboration, see reference. Figure 1 , Figure 1 This is a flowchart illustrating a group-cooperative error optimization method for die-cutting equipment in this application, comprising the following steps: Step S101: Perform spectrum analysis on the frequency characteristic curves of each die-cutting device in the production area to classify the die-cutting devices in the production area with a frequency overlap greater than a preset overlap threshold into an initial interference group. Step S102: Establish a virtual spring-damping network within the initial interference group, treat each die-cutting device within the initial interference group as a mass point, and determine the spring stiffness coefficient and damping coefficient based on the actual distance between adjacent mass points. Step S103: Determine the vibration mode characteristics of each die-cutting device based on the spring stiffness coefficient and damping coefficient, generate a control signal to suppress the vibration of each die-cutting device based on the vibration mode characteristics, and convert the control signal into parameter adjustment amount using a preset control algorithm; Step S104: Collect vibration energy data of the initial interference group after adjusting the initial processing parameters according to the parameter adjustment amount, determine the vibration coupling relationship of the initial interference group based on the vibration energy data, and determine the optimal production cycle and parameter compensation value based on the vibration coupling relationship. The optimal production cycle is used to coordinate the overall running sequence of the initial interference group, and the parameter compensation value is used to adjust the target processing parameters of each die-cutting equipment.

[0030] In the above embodiments, the die-cutting equipment refers to processing equipment used for cutting and forming materials such as paper and plastic; the frequency response curve refers to a graph describing the relationship between the vibration frequency and amplitude of the equipment; the initial disturbance group refers to a set of equipment that have mutual vibration influence; the virtual spring damping network is used to represent the theoretical model of the vibration transmission relationship between equipment; a mass point refers to simplifying the equipment into an ideal point with mass but no size; the spring stiffness coefficient represents the ability of the spring to resist deformation; the damping coefficient is used to represent the ability of the system to dissipate vibration energy; the mode shape characteristics refer to the characteristic parameters describing the vibration mode of the equipment; the control signal refers to the command signal used to adjust the operation of the equipment; the parameter adjustment amount is used to represent the correction value of the processing parameters of the equipment; the vibration coupling relationship refers to the degree of correlation between the vibrations of the equipment; the optimal production cycle time represents the best scheme for coordinating the operation sequence of multiple equipment; and the parameter compensation value is used to represent the correction amount of the target processing parameters of the die-cutting equipment.

[0031] In the above embodiment, assuming there are 8 die-cutting machines (denoted as machines 1-8) distributed in a production area, vibration signals are first collected by vibration sensors installed on each die-cutting machine. The sampling frequency is set to 1kHz, and the sampling duration is 60s. The sensors are installed at the radial and axial positions of the main bearing seats of each machine, and triaxial accelerometers are used. After performing spectrum analysis on the collected vibration signals, it was found that: the main frequency components of machines 1, 3, and 4 are concentrated in the range of 20-25Hz, and the frequency distribution curve overlap reaches 85%; the main frequency components of machines 2 and 5 are concentrated in the range of 35-40Hz, and the frequency distribution curve overlap reaches 83%; the frequency components of machines 6, 7, and 8 are relatively dispersed, with a maximum overlap of no more than 40%. By comparing the frequency overlap with a preset overlap threshold (set to 80%), machines 1, 3, and 4 are determined to form the first initial interference group, and machines 2 and 5 form the second initial interference group. Taking the first initial interference group as an example, machines 1, 3, and 4 are regarded as mass points P1, P3, and P4, respectively. Based on actual measurements, the distance between P1 and P3 is 3m, the distance between P3 and P4 is 2.5m, and the distance between P1 and P4 is 5m. Virtual spring-damped connections are established between the devices. The initial spring stiffness coefficient (k) is determined using the inverse distance relationship. 13 =900N / m, k 34 =1080 N / m, k 14 =540N / m) and damping coefficient (c 13 =56 N·s / m, c 34 =67 N·s / m, c 14 =34 N·s / m).

[0032] In the above embodiment, the system motion equation is established based on the established virtual spring-damped network: M(d 2 The formula is: X / dt² + C(dX / dt) + KX = 0, where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, and X is the displacement vector. The mass matrix M is a diagonal matrix, and the masses of the equipment are m1 = 500 kg, m3 = 480 kg, and m4 = 520 kg. 2 X / dt² represents acceleration, and dX / dt represents velocity. The solution yields three main mode shape characteristics: the first mode has a natural frequency of 22.5Hz and mode shapes of devices 1, 3, and 4 in phase; the second mode has a natural frequency of 24.3Hz and mode shapes of devices 1 and 4 out of phase; and the third mode has a natural frequency of 26.1Hz and mode shape of device 3 vibrating independently. Based on these mode shape characteristics, a PID controller is designed: u(t) = K p [e(t)+1 / T i ∫e(t)dt+T d ·de(t) / dt], where K p =0.85, Ti =0.45s, T d =0.09s, e(t) is the deviation between the real-time vibration amplitude and the target value. The controller output is the control signal, which is converted into parameter adjustment quantity through linear mapping. The PID controller also adds a temperature compensation term u'(t)=u(t)[1+α(T-T0)], where α=0.001 / ℃ is the temperature compensation coefficient, T is the real-time temperature, and T0 is the standard operating temperature of 25℃. During the operation of the die-cutting equipment, vibration acceleration data is collected every 0.1s, and vibration displacement and velocity are obtained through integration. Based on these data, the vibration power P=F·v is calculated, where F is the vibration force and v is the vibration velocity. Statistical analysis is performed on 100 consecutive sets of data to obtain the vibration energy transfer function H between each piece of equipment. ij (ω)=P i (ω) / P j (ω), where P i (ω) and P j (ω) represents the vibration power spectrum of devices i and j at frequency ω. A vibration coupling matrix R is constructed using this transfer function: R = [1, 0.65, 0.35][0.65, 1, 0.75][0.35, 0.75, 1]. Based on this matrix, optimization calculations are performed to obtain the optimal operating parameters: startup timing "device 1 as the baseline, device 3 starts with a 0.6s delay, device 4 starts with a 1.2s delay", cutting speed compensation value "device 1 increases by 2%, device 3 decreases by 3%, device 4 increases by 1%", feed speed compensation value "device 1 decreases by 1%, device 3 increases by 3%, device 4 decreases by 2%", cutting pressure compensation value "device 1 remains unchanged, device 3 increases by 1.5%, device 4 decreases by 1.5%". The compensation parameters are automatically updated every 2 hours based on real-time monitoring data, and the rate of change of the compensation value is set to not exceed ±0.5% / h of the original value.

[0033] Through the above steps, frequency overlap in the die-cutting equipment group is identified via spectral analysis, thus avoiding redundant processing of non-interfering equipment. Based on a virtual spring-damped network model, the complex vibration coupling relationship of the equipment group is transformed into a quantifiable physical model, providing a theoretical foundation for subsequent mode shape characteristic analysis. On this basis, control signals are generated from mode shape characteristics and converted into specific parameter adjustment quantities to achieve directional suppression of vibration. Finally, by analyzing vibration energy data, production cycle time and compensation parameters are optimized, enabling the entire die-cutting equipment group to operate collaboratively and effectively reducing processing errors caused by group vibration coupling. This solves the technical problem of the difficulty in collaboratively controlling processing errors caused by vibration coupling in die-cutting equipment groups in related technologies, achieving the technical effect of improving the efficiency of collaborative control of processing errors in die-cutting equipment groups.

[0034] The entity executing the above steps can be a control system with error optimization capabilities, such as a die-cutting equipment error optimization system, or a control device with error optimization capabilities, or a controller or processor in the device or system, or a standalone controller or processor, or other processing devices or processing units with similar processing functions, but is not limited to these.

[0035] In an optional embodiment, a virtual spring-damped network is established within the initial interference group. Each die-cutting device within the initial interference group is considered as a point mass, and the spring stiffness coefficient and damping coefficient are determined based on the actual distance between adjacent point masses. Specifically, this includes: acquiring the mechanical characteristic parameters and spatial coordinates of each die-cutting device, where the mechanical characteristic parameters characterize the inherent vibration characteristics of each die-cutting device; determining the first coupling strength coefficient between the first adjacent die-cutting devices in the initial interference group based on the mechanical characteristic parameters; progressively hierarchically grouping the initial interference group according to the first coupling strength coefficient to obtain multiple basic network layers, wherein the second coupling strength coefficient between the second adjacent die-cutting devices within each basic network layer is greater than a preset coupling strength threshold, the first adjacent die-cutting device includes the second adjacent die-cutting device, and the first coupling strength coefficient includes the second coupling strength coefficient; determining the spring stiffness coefficient and damping coefficient between each basic network layer based on the spatial coordinates. The system establishes a first connection relationship between first die-cutting devices within a base network layer, and a second connection relationship between second die-cutting devices in adjacent base network layers based on spatial coordinates. It also establishes a first spring-damping unit between the first die-cutting devices with the first connection relationship, and a second spring-damping unit between the second die-cutting devices with the second connection relationship. The system combines the first and second spring-damping units to construct a virtual spring-damping network. Each die-cutting device within the initial interference group is considered a point mass, and the actual distance between adjacent points is determined based on spatial coordinates. A nonlinear mapping relationship is established between the actual distance and the spring stiffness coefficient and damping coefficient based on the spatial coordinates, wherein the nonlinear mapping relationship is used to determine the spring stiffness coefficient and damping coefficient at different actual distances. Finally, the system determines the spring stiffness coefficient and damping coefficient between adjacent points based on the nonlinear mapping relationship.

[0036] In the above embodiments, mechanical characteristic parameters represent physical quantities that describe the inherent properties of the equipment, such as natural frequency and mass; spatial position coordinates are used to represent the specific position of the equipment in three-dimensional space; coupling strength coefficient is a quantitative index that characterizes the degree of vibration influence between equipment; basic network layer represents a subgroup of equipment with similar coupling characteristics; connection relationship is used to represent the topological relationship between equipment; spring damping unit is a basic component that simulates the vibration transmission characteristics between equipment; nonlinear mapping relationship represents the mathematical correspondence between spatial distance and spring damping parameters.

[0037] In the above embodiment, a virtual spring-damped network is established within the initial interference group: First, the mechanical characteristic parameters and spatial coordinates of each die-cutting device are obtained. Taking the first initial interference group as an example, the mechanical characteristic parameters of devices 1, 3, and 4 are as follows: Device 1: "Natural frequency f1 = 22Hz, mass m1 = 500kg, damping ratio ξ1 = 0.05"; Device 3: "Natural frequency f3 = 23Hz, mass m3 = 480kg, damping ratio ξ3 = 0.06"; Device 4: "Natural frequency f4 = 21Hz, mass m4 = 520kg, damping ξ4 = 0.05". The stiffness of device 1 is k1 = (2π × 22). 2 ×500=9.5×10 6 N / m, Equipment stiffness k3=(2π×23) 2 ×480=10.0×10 6 N / m, equipment stiffness k4=(2π×21) 2 ×520=9.0×10 6 N / m. Spatial coordinates of each device (with device 1 as the origin): Device 1 is (0m, 0m), device 4 is (3m, 0m), device 5 is (2.5m, 4m). Based on the mechanical characteristic parameters, the improved frequency coupling degree calculation formula γ is used. ij =(1-|f i -f j | / max(f i ,f j ))·exp(-|m i -m j | / (m i +m j This formula considers both frequency and quality differences. The frequency-related term (1-|f i -f j | / max(f i ,f j )), f i f represents the inherent frequency (Hz) of device i. j Let |f| represent the natural frequency (Hz) of device j. i -f j | represents the absolute value of the frequency difference between the two devices, max(f i ,f j The term represents the larger of two frequencies, ranging from [0,1]. The closer the frequencies are, the closer the value is to 1. The mass-related term is exp(-|mi-mj| / (mi+mj)), where m... i The mass (kg) of device i is represented by m. j The mass (kg) of device j is represented by |m i -m j |: The absolute value of the mass difference between the two devices, (mi +m j γ: The sum of the masses of the two devices. This value ranges from (0,1). The closer the masses are, the closer this value is to 1. A specific calculation example is for devices 1 and 3: 13 =0.96×exp(-|500-480| / 980)=0.93, γ between devices 3 and 4 34 =0.91×exp(-|480-520| / 1000)=0.88, γ between devices 1 and 4 14 =0.95×exp(-|500-520| / 1020)=0.92. This formula for calculating frequency coupling has a dual physical meaning; the first term (1-|f i -f j | / max(f i ,f j The term reflects frequency coupling characteristics: the closer the natural frequencies of the two devices are, the closer this term is to 1; the greater the frequency difference, the smaller this term is. This indicates that devices with similar frequencies are more prone to resonant coupling. The second term exp(-|m i -m j | / (m i +m j This reflects the mass coupling characteristics: the closer the masses of the two devices are, the closer this value is to 1; the greater the mass difference, the smaller this value is. This characterizes the physical phenomenon that energy transfer efficiency is higher between devices with similar masses. The product of the two terms reflects the degree of coupling between the devices, more accurately describing the vibration transmission characteristics in actual engineering. When two devices have both similar natural frequencies and similar masses, the coupling strength between them is the greatest, and vibration energy exchange is most likely to occur.

[0038] In the above embodiment, the specific steps for progressive hierarchical grouping are as follows: Assuming a preset coupling strength threshold of 0.90, this threshold indicates that when the coupling strength coefficient between two devices is greater than 0.90, a significant vibration coupling effect is considered to exist between them. Converted to frequency difference, this means that when the natural frequency difference between two devices is less than 10% of their maximum frequency, the two devices are considered to have a strong vibration coupling relationship. The first round of grouping starts from the maximum coupling strength of 0.96, dividing devices 1 and 3 into the first basic network layer. The coupling strength between the remaining device 4 and the devices in this layer is checked; if all are greater than the preset coupling strength threshold, device 4 is added to this layer. The second round of grouping checks the remaining ungrouped devices. In this example, there are no remaining devices. If there are remaining devices and their mutual coupling strength is greater than the preset coupling strength threshold, a new basic network layer is formed. When the coupling strength between the remaining devices is less than the preset coupling strength threshold, each device forms a separate basic network layer.

[0039] In the above embodiment, the first connection relationship (within the same layer): the actual distance between computing devices is d. 13 =3m,d 34 =4.03m, d 14 =4.7m, then equipment pairs with an actual distance ≤3m are established with a strong connection, equipment pairs with an actual distance ≤4m with a medium connection, and equipment pairs with an actual distance ≤5m with a weak connection. Second connection relationship (inter-layer): Calculate the distance between the center points of each layer, select the closest equipment pair to establish an inter-layer connection, and maintain at least one connection point between each pair of adjacent layers. Based on the distance between equipment and the connection relationship, the parameters of the spring damping unit are set as follows: First spring damping unit (strong connection, d... 13 =3m): Equipment rooms 1-3 k 14 =k0·e^(-αd)·(1+βγ) 13 )=1200·e^(-0.2×3)·(1+0.5×0.93)=658N / m, c 13 =c0·e^(-μd)·(1+νγ) 13 = 80·e^(-0.15×3)·(1+0.3×0.93) = 48 N·s / m. Second spring damping unit (medium connection, d) 34 =4.03m): Equipment rooms 3-4 k 34 =k0·e^(-αd)·(1+βγ) 34 )=1200·e^(-0.2×4.03)·(1+0.5×0.88)=442N / m, c 34 =c0·e^(-μd)·(1+νγ) 34 = 80·e^(-0.15×4.03)·(1+0.3×0.88) = 35 N·s / m. Third spring damping unit (weak connection, d) 14 =4.7m): Equipment rooms 1-4 k 14 =k0·e^(-αd)·(1+βγ) 14 )=1200·e^(-0.2×4.7)·(1+0.5×0.92)=366N / m, c 14 =c0·e^(-μd)·(1+νγ) 14 = 80·e^(-0.15×4.7)·(1+0.3×0.92) = 31 N·s / m. Where, k0 = 1200 N / m (reference stiffness), c0 = 80 N·s / m (reference damping), α = 0.2 m^(-1) (distance attenuation coefficient), μ = 0.15 m^(-1) (damping attenuation coefficient), β = 0.5 (coupling strength influence coefficient), ν = 0.3 (damping coupling coefficient), γ ij This is the corrected coupling strength coefficient.

[0040] In the above embodiment, firstly, an intra-layer network is constructed: strong connections form the main transmission path, and weak connections form auxiliary transmission paths. Then, inter-layer connections are established: inter-layer bridging is established through the nearest device pair, and the inter-layer connection strength is taken as the average of the intra-layer connection strengths of the two layers. A nonlinear mapping relationship is established: k = k0·e^(-αd)·(1+βγ) ij )·(1-ηΔT), c=c0·e^(-μd)·(1+νγ ij )·(1-θΔT) where ΔT is the difference between the ambient temperature and the reference temperature, η=0.001 / ℃ is the stiffness temperature coefficient, θ=0.0008 / ℃ is the damping temperature coefficient, k0=1200N / m (reference stiffness), c0=80N·s / m (reference damping), α=0.2m^(-1) (distance attenuation coefficient), μ=0.15m^(-1) (damping attenuation coefficient), β=0.5 (coupling strength influence coefficient), ν=0.3 (damping coupling coefficient), d is the actual distance, γ ij This represents the coupling strength coefficient. This nonlinear mapping relationship not only comprehensively considers the dual effects of spatial distance and coupling strength, but also more accurately describes the vibration transmission characteristics.

[0041] In an optional embodiment, the initial interference group is progressively hierarchically grouped according to a first coupling strength coefficient to obtain multiple basic network layers. Specifically, this includes: arranging each die-cutting device in the initial interference group in descending order according to the first coupling strength coefficient to obtain a coupling strength sequence; using the coupling strength sequence as the current grouping sequence, and repeatedly performing the following operations until all die-cutting devices in the initial interference group are added to the multiple basic network layers: determining the third die-cutting device with the largest coupling strength coefficient from the current grouping sequence, and using the third die-cutting device as the first central node of the current basic network layer, wherein the first coupling strength coefficient includes the largest coupling strength coefficient, and the multiple basic network layers include the current basic network layer; determining the fourth die-cutting device with a second coupling strength coefficient greater than a preset coupling strength threshold with the first central node from the current grouping sequence, and adding the fourth die-cutting device to the current basic network layer to generate the current device group; removing the current device group from the current grouping sequence to obtain a new current grouping sequence.

[0042] In the above embodiments, the coupling strength sequence represents a list of devices sorted according to their degree of coupling; the current sequence to be grouped refers to a temporary sequence of devices that have not yet been grouped; the central node represents a representative core device in a certain basic network layer; the maximum coupling strength coefficient is used to represent the coupling relationship value with the strongest influence in the current sequence; the current device group refers to the set of devices in the basic network layer that is being built; and the new current sequence to be grouped represents the remaining sequence after removing the grouped devices.

[0043] In the above embodiments, the initial interference group is progressively hierarchically grouped as follows: the obtained first coupling strength coefficients are arranged in descending order of their numerical values, resulting in the coupling strength sequence: γ 13 =0.96, γ 14 =0.95, γ 34 =0.91. Set this sequence as the current grouping sequence, and group all the die-cutting devices involved in the sequence in turn until all devices are assigned to the corresponding base network layer. If the grouping sequence is not empty, perform the following loop operation: First, determine the device pair (device 1 and device 3) corresponding to the maximum coupling strength coefficient of 0.96 from the current grouping sequence. Select the center node by calculating the correlation degree of the device (the sum of the coupling strength coefficients of other devices). The correlation degree of device 1 is 1.91 (0.96 + 0.95), and the correlation degree of device 3 is 1.87 (0.96 + 0.91). Therefore, device 1 with higher correlation degree is selected as the first center node of the current base network layer. Then check the coupling strength coefficients of other devices in the grouping sequence with the center node. It is found that the coupling strength coefficient of device 4 with the center node device 1 is 0.95, which is greater than the preset coupling strength threshold of 0.90. Meanwhile, the coupling strength coefficient between device 3 and the central node is 0.96, which is greater than the preset coupling strength threshold of 0.90. Therefore, both device 3 and device 4 are added to the current basic network layer to form the current device group. The current device group (devices 1, 3, and 4) is removed from the grouping sequence to obtain a new grouping sequence. Since the new sequence is empty and all devices have been grouped, the loop terminates. If the new sequence is not empty, the above steps are repeated to continue selecting the device pair corresponding to the maximum coupling strength, determining the central node, and adding relevant devices until all devices are assigned to the network layer. This progressive hierarchical grouping method based on coupling strength ensures that there is a sufficiently strong coupling relationship between devices within the same network layer, providing a good network foundation for subsequent vibration control.

[0044] In an optional embodiment, determining the first connection relationship between first die-cutting devices within each basic network layer based on spatial location coordinates, and determining the second connection relationship between second die-cutting devices in adjacent basic network layers based on spatial location coordinates, specifically includes: using the second center node of each basic network layer as a connection reference point; determining the azimuth angle and relative distance of the first die-cutting devices within each basic network layer relative to the connection reference point; dividing the first die-cutting devices within each basic network layer into multiple sectors based on the azimuth angle, and determining the first connection relationship between first die-cutting devices within the same sector and whose relative distance is less than a preset distance threshold; determining the connection vector between the first connection reference points of adjacent basic network layers, wherein the connection reference point includes the first connection reference point; and determining the second die-cutting devices for establishing the second connection relationship from adjacent basic network layers based on the connection vector.

[0045] In the above embodiments, the connection reference point represents a reference node used to determine the relative spatial position; the azimuth angle refers to the horizontal angle between the device and the reference point; the relative distance represents the straight-line distance between the device and the reference point; the sector is used to represent the division of a sector area centered on the reference point; the preset distance threshold is the distance standard for judging whether a connection is established between devices; the connection vector represents the direction and distance between adjacent network layer reference points; the first connection relationship refers to the connection between devices within the same network layer; and the second connection relationship is used to represent the connection between devices in different network layers.

[0046] In the above embodiments, to ensure the stability of the grouping results, the grouping results are iteratively optimized as follows: the grouping status of devices 1, 3, and 4 in the first initial interference group is used as the initial state for the first iteration. The maximum number of iterations is set to 10, the relative convergence threshold is 0.005, and the minimum step size is limited to 0.001. Vibration signal sampling parameters: sampling frequency 1kHz, sampling duration 15s, number of data points 15000; data preprocessing: using Hanning window function, FFT points 16384, frequency resolution 0.061Hz. In each iteration, the vibration energy transfer efficiency of each device in the current group is first calculated. The vibration energy transfer efficiency η is calculated using the following formula. ij =|H ij (ω)|2·γ ij ·exp(-αd ij ), where H ij (ω) is the frequency response function between devices i and j, γ ij Let be the coupling strength coefficient, and α be the spatial attenuation coefficient, taken as 0.2m. -1 d ijLet be the actual distance between devices i and j. Taking device 1 as an example, collect its vibration acceleration data over 15 seconds. Perform an FFT transform on the original signal X(ω) = FFT[x(t)] to obtain the frequency domain response, where x(t) is the time-domain vibration signal, t is the time variable, ω is the angular frequency (ω = 2πf, f is the frequency), X(ω) is the complex form of the frequency domain response, and FFT represents the Fast Fourier Transform operator. Calculate the self-power spectrum G. xx (ω)=X(ω)·X*(ω), where G xx X*(ω) is the power spectral density of signal x, and X*(ω) is the complex conjugate of X(ω). The value of ω ranges from 0 to f. s / 2(f s (Sampling frequency 1000Hz), power spectral density unit: (m / s) 2 ) 2 / Hz. Calculate the cross-power spectrum G. xy (ω)=X(ω)·Y*(ω), where G xy X(ω) is the cross-power spectral density between signals x and y, X(ω) is the frequency domain response of device i, and Y*(ω) is the conjugate complex number of the frequency domain response of device j. The unit of cross-power spectral density is (m / s). 2 ) 2 / Hz. Calculate the frequency response function H. ij (ω)=G xy (ω) / G xx (ω), where H ij (ω) represents the frequency response function from device i to device j, where i and j represent the device numbers. The amplitude of the frequency response function represents the vibration transmissibility, and the phase angle represents the phase difference. H is calculated through the above steps. 13 (ω) is the frequency response function between devices 1 and 3, H 34 (ω) is the frequency response function between devices 3 and 4, H 14 (ω) is the frequency response function between devices 1 and 4. Calculate the energy transfer efficiency between devices as η. 13 =|H3(ω) / H1(ω)| 2 =0.82, η 14 =|H4(ω) / H1(ω)| 2 =0.75, η 34 =|H4(ω) / H3(ω)| 2 =0.78. Based on energy transfer efficiency, construct the transfer matrix T: T = [1, 0.85, 0.71][0.85, 1, 0.76][0.71, 0.76, 1]. Construct the characteristic equation |T - λI| = 0, which expands to -λ. 3 +3λ 2-2.7475λ + 0.7645 = 0, solving for the eigenvalues ​​of the transfer matrix yields λ1 = 2.52, λ2 = 0.35, and λ3 = 0.13. Solving for (T - λ1I)v1 = 0, and standardizing, we obtain the eigenvector v1 = [0.582, 0.608, 0.540] corresponding to the eigenvalue λ1, representing the relative contribution or participation of each device in the group vibration. Each component of this eigenvector corresponds to the weight of a device; the larger the value, the more significant the influence of that device in the group vibration.

[0047] In the above embodiment, the feature vector obtained in the second iteration is v2 = [0.578, 0.615, 0.536], and the relative error ε = ||v2 - v1|| / ||v1|| = 0.0092. Since it is greater than the set convergence threshold of 0.005, the grouping scheme needs to be adjusted. Analyzing the components of the feature vector v2 = [0.578, 0.615, 0.536], it can be seen that device 3 has the highest contribution (0.615), device 1 has the second highest contribution (0.578), and device 4 has the lowest contribution (0.536). Considering that the contribution of device 4 is significantly lower than that of other devices (the relative deviation from the average value of 0.576 is 6.9%), the transmission efficiency of device 4 with other devices is low (T 14 =0.71, T 34 =0.76), the spatial location of device 4 is relatively independent (d 14 =4.7m, d 34 =4.03m), based on the above analysis, removing device 4 from the current group is a reasonable adjustment plan. After adjustment, a third iteration is performed: devices 1 and 3 form a new basic group, and device 4 is grouped separately. Data is re-acquired to calculate the new transfer matrix: T' = [1, 0.85, 0.35][0.85, 1, 0.34][0.32, 0.34, 1]. The eigenvalues ​​of the new transfer matrix are calculated: λ ' 1 = 2.21, λ ' 2 = 0.71, λ ' 3 = 0.08. To assess the stability of the grouping results, the following evaluation index is introduced: condition number k = λ max / λ min=27.63, Energy distribution ratio η=λ1 / (λ1+λ2+λ3)=0.74, Group efficiency ratio: Average transfer efficiency within a group is 0.85, average transfer efficiency between groups is 0.33, efficiency ratio is 2.58. To ensure the reliability of the iteration process, the following convergence guarantee mechanism is established: Introduce an iteration damping factor α=0.7, v(k+1)=αv(k)+(1-α)v(k-1), Minimum step size limit is 0.001, Anomaly handling strategy: When the iteration number k>7 and the convergence condition is not met (i.e. ε>0.005), the following processing is performed in order: Step 1 Save the feature vectors v(k), v(k-1), v(k-2) of the last three iterations (where k is the current iteration number, v(k) is the feature vector obtained in the current iteration, v(k-1) is the feature vector of the previous iteration, and v(k-2) is the feature vector of the iteration before that). Each feature vector is a 3-dimensional vector, corresponding to three devices); Step 2: Calculate the weighted average feature vector v_final = 0.5·v(k) + 0.3·v(k-1) + 0.2·v(k-2) (where 0.5, 0.3, and 0.2 are weight coefficients, and the sum is 1. Newer iteration results are given greater weights, and the weight allocation is based on the principle of decreasing reliability of the results); Step 3: Standardize v_final = v_final / ||v_final|| (where ||v_final|| represents the Euclidean norm of the vector, ||v_final|| = ​​√(v_final[0]) 2 +v_final[1] 2 +v_final[2] 2), the norm of the standardized vector is 1 to ensure the standardization of the results); Step 4: Calculate the contribution of each device: “device 1 contribution v_final[0]”, “device 3 contribution v_final[1]”, “device 4 contribution v_final[2]”, where v_final[i] represents the i-th component of the standardized feature vector. The larger the component value, the more significant the influence of the device in the group vibration; Step 5: Group judgment: Calculate the average contribution avg_contribution=(v_final[0]+v_final[1]+v_final[2]) / 3 (avg_contribution is the arithmetic mean of the contributions of the three devices), and calculate the deviation of the contribution of each device. deviation_i = |v_final[i] - avg_contribution| / avg_contribution (where deviation_i is the relative deviation of the i-th device, and |v_final[i] - avg_contribution| represents the absolute deviation). Grouping decision: If deviation_i > 20% (where 20% is the deviation threshold, which can be set based on engineering experience; exceeding this threshold indicates that the device's behavior differs significantly from the group behavior), divide device i into an independent group; otherwise, keep device i in the current group. Step 6: Update system parameters: Update the transfer matrix according to the new group, recalculate the eigenvalues ​​and eigenvectors using the new T, and update the relevant control parameters. If the iteration count reaches the set maximum of 10 times and still does not converge, the following processing scheme can be adopted: Calculate the average value of the eigenvectors of the last three iterations as the final result, select the grouping scheme with the highest vibration energy transfer efficiency as the final scheme, and add additional monitoring and adjustment mechanisms for this group in subsequent control. Taking the adjustment process in this example: in the original grouping scheme, the energy transfer efficiency between device 4 and other devices was low (0.75 and 0.78), and the adjusted transfer efficiency further decreased (0.35 and 0.38), verifying the rationality of grouping device 4 separately. The new grouping scheme makes the group vibration mode more stable and the eigenvalue distribution more reasonable (λ). ' 1 / λ ' 2 is approximately equal to 3.16, which is less than the original scheme's λ1 / λ2≈7.72.

[0048] In an optional embodiment, the mode shape characteristics of each die-cutting device are determined based on the spring stiffness coefficient and damping coefficient. A control signal for suppressing the vibration of each die-cutting device is generated based on the mode shape characteristics, and the control signal is converted into parameter adjustment quantities using a preset control algorithm. Specifically, this includes: constructing the multi-body coupled dynamic equations of the initial disturbance group based on the spring stiffness coefficient and damping coefficient; establishing a structural mapping matrix between the virtual spring-damped network and the multi-body coupled dynamic equations; performing eigenvalue decomposition on the structural mapping matrix to obtain the principal frequency parameter and displacement characteristic quantity of each die-cutting device, wherein the principal frequency parameter characterizes the resonance state of each die-cutting device, and the displacement characteristic quantity characterizes the vibration amplitude distribution of each die-cutting device; constructing the frequency transfer function of each die-cutting device based on the principal frequency parameter; and constructing the vibration response equation of each die-cutting device based on the displacement characteristic quantity; and extracting frequency domain features based on the frequency transfer function. The process involves: processing the vibration transmission coefficient of each die-cutting device; performing spectral thresholding based on the vibration transmission coefficient to obtain a vibration suppression threshold; constructing a vibration state observer for each die-cutting device based on the vibration transmission coefficient; performing deviation feature processing on the output parameters of the vibration state observer and the vibration suppression threshold for each die-cutting device to obtain compensation parameters for each die-cutting device; performing parameter optimization based on the compensation parameters and the vibration response equation to obtain the vibration suppression gain for each die-cutting device; performing gain modulation based on the vibration suppression gain and the output parameters to obtain the control signal for each die-cutting device; using a preset control algorithm to perform parameter decomposition processing on the control signal to obtain the cutting speed correction, feed speed correction, and cutting pressure correction for each die-cutting device; and combining the cutting speed correction, feed speed correction, and cutting pressure correction to obtain the parameter adjustment amount for each die-cutting device.

[0049] In the above embodiments, the multibody coupled dynamic equation represents a mathematical model describing the overall vibration characteristics of the equipment group; the structural mapping matrix is ​​used to represent the correspondence between the virtual network and the dynamic equation; the dominant frequency parameter refers to the frequency characteristic value characterizing the resonance state of the equipment; the displacement characteristic quantity represents the spatial distribution characteristics of the vibration amplitude of the equipment; the frequency transfer function is used to describe the transmission characteristics of vibration signals between equipment; the vibration response equation refers to the response characteristics of the equipment to external excitation; the vibration transmission coefficient represents the attenuation degree of vibration transmission between equipment; the vibration state observer is used to estimate the vibration state of the equipment in real time; the vibration suppression gain refers to the control parameter used to suppress vibration; the cutting speed correction, feed speed correction, and cutting pressure correction represent the adjustment values ​​of the equipment process parameters.

[0050] In the above embodiment, the vibration of each die-cutting device is controlled as follows: First, a data acquisition system is configured. The sampling frequency is set to 1000Hz, which is based on a sampling principle of 10 times the system's highest analysis frequency of 100Hz. A dual-channel fourth-order low-pass filter is used for signal preprocessing, and the cutoff frequency is set to 400Hz. A 2-second analysis window length is selected, and FFT analysis is performed using a rectangular window with a data block overlap rate of 50%. Vibration characteristics of each device are measured using the above configuration. The spectrum analysis results of device 1 show that the amplitude at the fundamental frequency of 22Hz (corresponding to a spindle speed of 1320rpm) is 2.5m / s². 2 Its second harmonic amplitude at 44Hz is 0.5m / s. 2 The amplitude of the third harmonic at 66Hz is 0.125m / s. 2 Device 3 has an amplitude of 2.3 m / s at a fundamental frequency of 23 Hz (corresponding to a spindle speed of 1380 rpm). 2 The amplitude of the second harmonic at 46Hz is 0.46m / s. 2 Device 4 has an amplitude of 2.7 m / s at a fundamental frequency of 21 Hz (corresponding to a spindle speed of 1260 rpm). 2 The amplitude of the second harmonic at 42Hz is 0.54m / s. 2 Each piece of equipment is mounted on a base with an electromagnetic vibrator as the actuator. The basic parameters of the selected vibrator are: force constant 12N / A, linear working range ±3mm, working bandwidth 0-100Hz, and maximum output force 300N.

[0051] In the above embodiment, the control force is constructed using a strong connection between device 1 and device 3 as an example. The basic control force adopts a spring-damped form: F 13 =-k 13 (x1-x3)-c 13 (v1-v3)+f 13 Where, assume k 13 =900N / m, c 13 = 134 N·s / m, where x1 and x3 are displacements, and v1 and v3 are velocities. Compensation force f 13 Employing a multi-frequency superposition method: f 13 =Σ[α i ·sin(2πf i t)], where f i Given the characteristic frequency set {22Hz, 23Hz, 44Hz}, the initial values ​​of the fundamental frequency compensation coefficients are set to 40N and 38N, and the harmonic compensation coefficient is set to 15N. The control system is configured with the following basic constraints: modal stiffness constraint α. i ≤0.25k i rate of change constraint |dα i / dt|≤8N / s, total force constraint Σα i≤150N. For weak connections (between devices 1-4 and 3-4), the same control structure is used but with stricter constraints: the modal stiffness constraint is reduced to 0.15k. i The upper limit of the rate of change is reduced to 5 N / s, and the total force limit is reduced to 80 N. Real-time data processing uses a fourth-order low-pass RC filter with a cutoff frequency set to 75 Hz. The controller adopts a standard PID structure; specific control parameters need to be determined through debugging based on the actual system characteristics. The system's basic protection mechanism is set when the vibration amplitude exceeds the normal operating maximum value (the fundamental frequency amplitude of device 4 is 2.7 m / s²). 2 Protection is triggered when the temperature is 1.5 times the rated value. The temperature protection threshold for the actuator needs to be determined based on the specific specifications of the selected actuator.

[0052] In an optional embodiment, vibration energy data of the initial interference group after adjusting the initial processing parameters according to the parameter adjustment amount is collected. The vibration coupling relationship of the initial interference group is determined based on the vibration energy data, and the optimal production cycle and parameter compensation value are determined based on the vibration coupling relationship. Specifically, this includes: collecting the vibration displacement and vibration velocity of each die-cutting device after adjusting the initial processing parameters; determining the vibration power value of each die-cutting device based on the vibration displacement and vibration velocity; determining the vibration energy data of each die-cutting device based on the vibration power value; determining the vibration transmission characteristics and vibration attenuation characteristics of each die-cutting device based on the vibration energy data; determining the vibration coupling relationship based on the vibration transmission characteristics and vibration attenuation characteristics; determining the vibration influence degree of the initial interference group based on the vibration coupling relationship; optimizing the vibration influence degree to obtain the optimal running sequence and optimal parameter compensation sequence of the initial interference group; determining the optimal production cycle based on the optimal running sequence; and determining the parameter compensation value based on the optimal parameter compensation sequence.

[0053] In the above embodiments, vibration displacement represents the distance the equipment deviates from its equilibrium position; vibration velocity refers to the instantaneous speed of the equipment's movement; vibration power value is used to represent the vibration energy per unit time; vibration energy data represents the vibration energy stored and consumed by the equipment; vibration transmission characteristics refer to the law of vibration propagation between equipment; vibration attenuation characteristics are used to describe the law of vibration energy loss; vibration influence degree represents the severity of mutual interference between equipment; optimal operating sequence refers to the optimal start-up and operation sequence of the equipment group; and optimal parameter compensation sequence is used to represent the optimal adjustment scheme for the parameters of each equipment.

[0054] In the above embodiment, the interaction between the various die-cutting devices is determined as follows: First, vibration signals are acquired. Accelerometers (sensitivity 100mV / g) are used to synchronously acquire vibration acceleration signals from die-cutting devices 1, 3, and 4. The sampling frequency is set to 1kHz (considering the device's main frequency is approximately 20-25Hz, it is set to at least 10 times this to ensure sampling accuracy). Each acquisition lasts 30 seconds (containing at least 600 cycles). During analysis, the data is divided into 6 segments, each lasting 5 seconds, with adjacent segments overlapping by 50%, thus obtaining 11 valid data samples for statistical analysis. The acquired acceleration signals are preprocessed. High-frequency noise is filtered out using a 4th-order low-pass filter (cutoff frequency 100Hz). Then, integration is performed to obtain the velocity signal, and a high-pass filter (cutoff frequency 1Hz) is used to eliminate integration drift. Taking device 1 as an example, the measured root mean square value of acceleration is 2.5m / s². 2 The root mean square value of the integrated velocity is 0.018 m / s, which is consistent with the velocity-acceleration relationship of a typical mechanical vibration system. Calculate the instantaneous vibration power for each device. Vibration power P(t) = F(t)·v(t), where F(t) is the inertial force F(t) = m·a(t), m is the mass of the device (approximately 500 kg), and v(t) is the vibration velocity. Taking device 1 as an example, the average vibration power is 22.5 W (=500 kg × 2.5 m / s). 2 ×0.018m / s). Based on the same calculation method, the average vibration power of device 3 is 19.8W (=22.5W×0.88), and that of device 4 is 24.3W (=22.5W×1.08).

[0055] In the above embodiments, to determine the vibration transmission characteristics between devices, the power transfer function H between devices is calculated. ij (f)=P i (f) / P j (f), where H ij (f) is the power transfer function between devices i and j, characterizing the transfer characteristics of vibration energy, P i (f) represents the power spectral density of device i, in W. 2 / Hz, P j (f) represents the power spectral density of device j, in W. 2 / Hz, where f is the frequency in Hz. The calculation process for the power spectral density is as follows: Divide the 30-second time-domain power data P(t) into 11 overlapping data segments, each 5 seconds long. Perform FFT on each segment to obtain the frequency-domain data P(f), and calculate the power spectrum P of each segment. i (f)=|P i(f)|2 / T, where T is the time length (5 seconds), the power spectrum of the 11 data segments is taken as the arithmetic mean to reduce the influence of random errors and improve the reliability of spectrum estimation. Taking devices 1 and 3 as examples: at 22Hz (the main frequency of device 1), P1(22Hz) = 15.6W 2 / Hz, at the same frequency, P3(22Hz)=13.3W 2 / Hz, then H 13 (22Hz) = 13.3 / 15.6 = 0.85, indicating that approximately 85% of the vibration energy is transmitted from device 1 to device 3 within the main frequency range. Similarly, the transmission characteristics between other devices can be calculated. Within the main frequency range (20–25Hz), the amplitude of the transfer function reflects the intensity of vibration transmission. Power spectrum analysis yields an average transmission coefficient of 0.85 between devices 1 and 3, 0.65 between devices 1 and 4, and 0.45 between devices 3 and 4. These coefficients reflect the attenuation of vibration energy. A vibration coupling matrix is ​​constructed based on the transmission coefficients: R = [0, 0.85, 0.65][0.85, 0, 0.45][0.65, 0.45, 0]. Device startup timing is optimized based on the coupling matrix. Considering the distance between devices (3–5m) and the vibration propagation speed in the structure (approximately 3000m / s), the minimum time delay is calculated to be approximately 2ms. To ensure system stability, the actual delay time is set as follows: 0.5s delay for device 3 and 1.0s delay for device 4. This delay is sufficient to avoid vibration superposition. The parameter compensation values ​​are determined based on the vibration power ratio: device 3 / device 1 = 19.8W / 22.5W = 0.88, device 4 / device 1 = 24.3W / 22.5W = 1.08. Based on this, the compensation values ​​are set as follows: cutting speed compensation "device 1 as the baseline (0%), device 3 increases by 2%, device 4 decreases by 3%", feed speed compensation "device 1 as the baseline (0%), device 3 decreases by 1%, device 4 decreases by 2%".

[0056] In an optional embodiment, frequency response curves of each die-cutting device in the production area are analyzed to classify die-cutting devices in the production area with frequency overlap greater than a preset overlap threshold into an initial interference group. Specifically, this includes: collecting vibration signals from each die-cutting device in the production area; performing Fourier transform processing on the vibration signals to obtain the frequency response curves of each die-cutting device; performing frequency band segmentation processing on the frequency response curves to obtain the amplitude distribution of each die-cutting device in different frequency bands; determining the frequency overlap between the third adjacent die-cutting device in the production area based on the amplitude distribution; comparing the frequency overlap with a preset overlap threshold to identify die-cutting devices with frequency overlap greater than the preset overlap threshold, and classifying the die-cutting devices into the initial interference group.

[0057] In the above embodiments, the vibration signal represents a time-domain waveform reflecting the motion state of the equipment; Fourier transform processing is used to convert the time-domain signal into frequency-domain features; the frequency characteristic curve refers to the function curve describing the relationship between vibration frequency and amplitude; frequency band segmentation processing means dividing the frequency range into multiple intervals for analysis; amplitude distribution is used to describe the distribution of vibration energy in each frequency band; frequency overlap degree refers to the degree of overlap of vibration frequencies of different equipment; preset overlap threshold represents the standard value for judging whether there is interference between equipment; the third adjacent die-cutting equipment refers to a combination of equipment that are spatially close in the production area.

[0058] In the above embodiment, the group vibration mode is determined as follows: First, vibration signal acquisition is performed. Vibration data from devices 1, 3, and 4 are simultaneously acquired using a triaxial accelerometer (sensitivity 100mV / g). Based on the device's operating frequency range (20-25Hz), the sampling frequency is set to 1000Hz, satisfying the sampling theorem and facilitating digital processing. Each acquisition lasts 10 seconds (10,000 sampling points), encompassing more than 200 operating cycles to ensure statistical reliability. To reduce the impact of random errors, each device is repeatedly acquired 5 times. The raw vibration signal is preprocessed. First, a fourth-order low-pass filter is used for noise reduction, with a cutoff frequency set to 100Hz (covering the fourth harmonic of the operating frequency). Then, baseline correction is performed to eliminate sensor DC bias. Taking device 1 as an example, the mean value of the raw signal is 0.15m / s². 2 After baseline correction, the mean value decreased to 0.02 m / s. 2 The standard deviation is 2.5 m / s 2 After undergoing the same treatment, the standard deviations of devices 3 and 4 were 2.3 m / s. 2 and 2.6m / s 2 The processed data from the three devices are used to form a state matrix X(t) = [x1(t); x3(t); x4(t)], where x1(t), x3(t), and x4(t) are the vibration acceleration values ​​of devices 1, 3, and 4 at time t, respectively, in m / s². 2 t represents the sampling time. Sampling was performed at 1 / 1000-second intervals, from 0 seconds to 10 seconds, resulting in 10,000 data points. X(t) is a 3×10000 matrix, with each row representing the complete time series of one device. Basic statistical analysis was conducted on the vibration data of each device to calculate the time-domain average of the signal. Taking device 1 as an example, the average value was obtained by summing the 10,000 data points and dividing by the number of data points, resulting in an average of 0.02 m / s². 2 Then calculate the squared difference between each data point and the mean, sum these squared values, divide by the number of data points, and take the square root to obtain the standard deviation of 2.5 m / s. 2The same calculation method was used for devices 3 and 4: the mean value of 10,000 data points for device 3 was calculated to be 0.03 m / s. 2 Sum of standard deviation 2.3 m / s 2 The average value calculated by device 4 is 0.02 m / s. 2 Sum of standard deviation 2.6m / s 2 These statistical values ​​reflect the differences in vibration levels among the devices under stable operating conditions. To eliminate dimensional differences between devices, each signal is standardized: the original signal is subtracted from its calculated mean, and then divided by its standard deviation to obtain the standardized matrix X"(t) = [x"1(t); x"3(t); x"4(t)]. After this processing, the signals of each device are converted into standardized data with a mean of 0 and a standard deviation of 1, facilitating subsequent correlation analysis.

[0059] In the above embodiment, based on the average of 5 collected data, the covariance matrix of the standardized data is calculated: C = [1.00, 0.45, 0.25][0.45, 1.00, 0.30][0.25, 0.30, 1.00]. This matrix exhibits symmetry; diagonal elements of 1 reflect that the data has been standardized, and off-diagonal elements represent the degree of correlation between devices. For example, 0.45 indicates a moderate correlation between devices 1 and 3, while 0.25 indicates a weak correlation between devices 1 and 4. Eigenvalue decomposition of the covariance matrix yields three eigenvalues: λ1 = 1.65 (representing the primary vibration mode), λ2 = 0.85 (representing the secondary vibration mode), and λ... 3 = 0.50 (representing the residual vibration mode), the sum of eigenvalues ​​equals 3, which conforms to the properties of standardized data. The first principal mode accounts for 55% of the total variation (= 1.65 / 3), the second principal mode accounts for 28.3% (= 0.85 / 3), and the third principal mode accounts for 16.7% (= 0.50 / 3). The corresponding eigenvectors are: v1 = [0.63, 0.58, 0.44] (first principal mode), v2 = [-0.42, 0.45, 0.79] (second principal mode), v3 = [0.65, -0.68, 0.34] (third principal mode). These eigenvectors are mutually orthogonal and have been normalized. The square of the components of the first eigenvector v1 represents the contribution of each device to the principal mode: device 1 = 0.63 2 =39.7%", Equipment 3 "0.58" 2 =33.6%", Equipment 4 "0.44" 2 =26.7%", the sum of the contributions is 100%, indicating that the vibration of equipment 1 has the greatest impact on the group mode, which is consistent with the actual observation results. Based on this, the main mode of group vibration is constructed as: y1(t) = 0.63·x”1(t) + 0.58·x”3(t) + 0.44·x”4(t), where x”i (t) represents the standardized vibration signal of each device. FFT analysis was performed on the main modal signal to obtain three main frequency components: 22.5Hz "power share 50% (corresponding to the operating frequency of device 1)", 23.0Hz "power share 35% (corresponding to the operating frequency of device 3)", and 21.5Hz "power share 15% (corresponding to the operating frequency of device 4)". These frequency components correspond to the actual operating speed of each device.

[0060] This application's embodiments identify die-cutting equipment groups with overlapping frequencies based on spectral analysis, thus avoiding redundant processing of non-interfering equipment. Based on a virtual spring-damped network model, the complex vibration coupling relationship of the equipment group is transformed into a quantifiable physical model, providing a theoretical foundation for subsequent mode shape characteristic analysis. On this basis, control signals are generated from mode shape characteristics and converted into specific parameter adjustment values ​​to achieve directional suppression of vibration. Finally, by analyzing vibration energy data, production cycle time and compensation parameters are optimized, enabling the entire die-cutting equipment group to operate collaboratively and effectively reducing processing errors caused by group vibration coupling.

[0061] The following describes the die-cutting equipment error optimization system in the embodiments of this invention from the perspective of hardware processing. (See attached document.) Figure 2 , Figure 2 This is a schematic diagram of the physical device structure of a die-cutting equipment error optimization system in the embodiments of this application.

[0062] It should be noted that, Figure 2 The structure of the die-cutting equipment error optimization system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0063] like Figure 2 As shown, the die-cutting equipment error optimization system includes a Central Processing Unit (CPU) 201, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 202 or a program loaded from storage section 208 into Random Access Memory (RAM) 203, such as executing the methods described in the above embodiments. The RAM 203 also stores various programs and data required for system operation. The CPU 201, ROM 202, and RAM 203 are interconnected via a bus 204. An Input / Output (I / O) interface 205 is also connected to the bus 204.

[0064] The following components are connected to I / O interface 205: input section 206 including audio input devices, push-button switches, etc.; output section 207 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 208 including a hard disk, etc.; and communication section 209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 209 performs communication processing via a network such as the Internet. Drive 210 is also connected to I / O interface 205 as needed. Removable media 211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 210 as needed so that computer programs read from them can be installed into storage section 208 as needed.

[0065] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 209, and / or installed from removable medium 211. When the computer program is executed by central processing unit (CPU) 201, it performs the various functions defined in the present invention.

[0066] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0067] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0068] Specifically, the die-cutting equipment error optimization system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the group-cooperative die-cutting equipment error optimization method provided in the above embodiment.

[0069] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the die-cutting equipment error optimization system described in the above embodiments; or it may exist independently and not assembled into the die-cutting equipment error optimization system. The storage medium carries one or more computer programs, which, when executed by a processor of the die-cutting equipment error optimization system, cause the die-cutting equipment error optimization system to implement the group-cooperative die-cutting equipment error optimization method provided in the above embodiments.

[0070] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0071] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for optimizing the error of die-cutting equipment based on group collaboration, characterized in that, include: Spectrum analysis is performed on the frequency characteristic curves of each die-cutting device in the production area to classify the die-cutting devices in the production area with a frequency overlap greater than a preset overlap threshold into an initial interference group; A virtual spring-damping network is established within the initial interference group. Each die-cutting device within the initial interference group is regarded as a mass point, and the spring stiffness coefficient and damping coefficient are determined based on the actual distance between adjacent mass points. The vibration mode characteristics of each die-cutting device are determined based on the spring stiffness coefficient and the damping coefficient. A control signal for suppressing the vibration of each die-cutting device is generated based on the vibration mode characteristics. The control signal is then converted into a parameter adjustment amount using a preset control algorithm. Vibration energy data of the initial interference group after adjusting the initial processing parameters according to the parameter adjustment amount is collected. The vibration coupling relationship of the initial interference group is determined based on the vibration energy data, and the optimal production cycle and parameter compensation value are determined based on the vibration coupling relationship. The optimal production cycle is used to coordinate the overall running sequence of the initial interference group, and the parameter compensation value is used to adjust the target processing parameters of each die-cutting device.

2. The method according to claim 1, characterized in that, The process of establishing a virtual spring-damping network within the initial interference group, treating each die-cutting device within the initial interference group as a point mass, and determining the spring stiffness coefficient and damping coefficient based on the actual distance between adjacent point masses, specifically includes: The mechanical characteristic parameters and spatial position coordinates of each die-cutting device are obtained, and the mechanical characteristic parameters are used to characterize the inherent vibration characteristics of each die-cutting device; The first coupling strength coefficient between the first adjacent die-cutting devices in the initial interference group is determined based on the mechanical characteristic parameters. The initial interference group is progressively hierarchically grouped according to the first coupling strength coefficient to obtain multiple basic network layers. The second coupling strength coefficient between the second adjacent die-cutting devices within each basic network layer is greater than a preset coupling strength threshold. The first adjacent die-cutting device includes the second adjacent die-cutting device, and the first coupling strength coefficient includes the second coupling strength coefficient. The first connection relationship between the first die-cutting devices within each of the basic network layers is determined based on the spatial location coordinates, and the second connection relationship between the second die-cutting devices of adjacent basic network layers is determined based on the spatial location coordinates. A first spring damping unit is established between first die-cutting devices having the first connection relationship, and a second spring damping unit is established between second die-cutting devices having the second connection relationship; The virtual spring damping network is constructed by combining the first spring damping unit and the second spring damping unit; Each die-cutting device in the initial interference group is considered as a point mass, and the actual distance between adjacent points mass is determined based on the spatial coordinates. A nonlinear mapping relationship is established between the actual distance and the spring stiffness coefficient and the damping coefficient based on the spatial location coordinates, wherein the nonlinear mapping relationship is used to determine the spring stiffness coefficient and the damping coefficient under different actual distances; The spring stiffness coefficient and the damping coefficient between adjacent mass points are determined based on the nonlinear mapping relationship.

3. The method according to claim 2, characterized in that, The step of progressively grouping the initial interference group according to the first coupling strength coefficient to obtain multiple basic network layers specifically includes: The die-cutting devices in the initial interference group are sorted in descending order according to the first coupling strength coefficient to obtain a coupling strength sequence; Using the coupling strength sequence as the current sequence to be grouped, the following operation is performed repeatedly until all die-cutting devices in the initial interference group are added to the multiple basic network layers: The third die-cutting device with the maximum coupling strength coefficient is determined from the current sequence to be grouped, and the third die-cutting device is used as the first central node of the current basic network layer, wherein the first coupling strength coefficient includes the maximum coupling strength coefficient, and the plurality of basic network layers include the current basic network layer; A fourth die-cutting device is identified from the current sequence to be grouped, whose second coupling strength coefficient with the first central node is greater than the preset coupling strength threshold, and the fourth die-cutting device is added to the current basic network layer to generate the current device group; The current device group is removed from the current grouping sequence to obtain a new current grouping sequence.

4. The method according to claim 2, characterized in that, The step of determining the first connection relationship between the first die-cutting devices within each of the basic network layers based on the spatial location coordinates, and determining the second connection relationship between the second die-cutting devices of adjacent basic network layers based on the spatial location coordinates, specifically includes: The second central node of each of the basic network layers is used as the connection reference point; Determine the azimuth and relative distance of the first die-cutting device within each of the basic network layers relative to the connection reference point; The first die-cutting device within each of the basic network layers is divided into multiple sectors based on the azimuth angle, and the first connection relationship between the first die-cutting devices within the same sector and whose relative distance is less than a preset distance threshold is determined. Determine the connection vector between first connection reference points of adjacent basic network layers, wherein the connection reference points include the first connection reference points; The second die-cutting device that establishes the second connection relationship is determined from the adjacent base network layers based on the connection vector.

5. The method according to claim 1, characterized in that, The process of determining the vibration mode characteristics of each die-cutting device based on the spring stiffness coefficient and the damping coefficient, generating a control signal for suppressing the vibration of each die-cutting device based on the vibration mode characteristics, and converting the control signal into a parameter adjustment amount using a preset control algorithm specifically includes: The multibody coupled dynamic equations of the initial disturbance group are constructed based on the spring stiffness coefficient and the damping coefficient. Establish the structural mapping matrix between the virtual spring-damped network and the multibody coupled dynamic equations; The eigenvalue decomposition process is performed based on the structure mapping matrix to obtain the principal frequency parameters and displacement characteristics of each die-cutting device. The principal frequency parameters are used to characterize the resonance state of each die-cutting device, and the displacement characteristics are used to characterize the vibration amplitude distribution of each die-cutting device. The frequency transfer function of each die-cutting device is constructed based on the main frequency parameter, and the vibration response equation of each die-cutting device is constructed based on the displacement characteristic. Frequency domain feature extraction is performed based on the frequency transfer function to obtain the vibration transmission coefficient of each die-cutting device; The vibration transmission coefficient is used to perform spectral thresholding to obtain a vibration suppression threshold, and a vibration state observer for each die-cutting device is constructed based on the vibration transmission coefficient. The output parameters of the vibration state observer of each die-cutting device are subjected to deviation feature processing with the vibration suppression threshold to obtain the compensation parameters of each die-cutting device; The parameters are optimized based on the compensation parameters and the vibration response equation to obtain the vibration suppression gain of each die-cutting device; Gain modulation processing is performed based on the vibration suppression gain and the output parameters to obtain the control signal for each die-cutting device; The preset control algorithm is used to perform parameter decomposition processing on the control signal to obtain the cutting speed correction, feed speed correction and cutting pressure correction of each die-cutting device; The cutting speed correction, the feed speed correction, and the cutting pressure correction are combined to obtain the parameter adjustment amount for each die-cutting device.

6. The method according to claim 1, characterized in that, The process of collecting vibration energy data of the initial interference group after adjusting the initial processing parameters according to the parameter adjustment amount, determining the vibration coupling relationship of the initial interference group based on the vibration energy data, and determining the optimal production cycle and parameter compensation value based on the vibration coupling relationship specifically includes: The vibration displacement and vibration velocity of each die-cutting device were collected after the initial processing parameters were adjusted. The vibration power value of each die-cutting device is determined based on the vibration displacement and the vibration velocity. The vibration energy data of each die-cutting device is determined based on the vibration power value; The vibration transmission characteristics and vibration attenuation characteristics of each die-cutting device are determined based on the vibration energy data. The vibration coupling relationship is determined based on the vibration transmission characteristics and the vibration attenuation characteristics. The degree of vibration influence of the initial disturbance group is determined based on the vibration coupling relationship. The degree of vibration impact is optimized to obtain the optimal runtime sequence and optimal parameter compensation sequence of the initial disturbance group; The optimal production cycle time is determined based on the optimal runtime sequence, and the parameter compensation value is determined based on the optimal parameter compensation sequence.

7. The method according to claim 1, characterized in that, The step of performing spectral analysis on the frequency characteristic curves of each die-cutting device in the production area to classify die-cutting devices in the production area with a frequency overlap greater than a preset overlap threshold into an initial interference group specifically includes: Collect vibration signals from each die-cutting device in the production area; The vibration signal is subjected to Fourier transform processing to obtain the frequency characteristic curves of each die-cutting device; The frequency response curve is segmented into frequency bands to obtain the amplitude distribution of each die-cutting device in different frequency bands; The frequency overlap between the third adjacent die-cutting equipment in the production area is determined based on the amplitude distribution. The frequency overlap degree is compared with the preset overlap threshold to identify the die-cutting device whose frequency overlap degree is greater than the preset overlap threshold, and the die-cutting device is assigned to the initial interference group.

8. A die-cutting equipment error optimization system, characterized in that, The die-cutting equipment error optimization system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the die-cutting equipment error optimization system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the die-cutting equipment error optimization system, the die-cutting equipment error optimization system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the die-cutting equipment error optimization system, the die-cutting equipment error optimization system performs the method as described in any one of claims 1-7.

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