Smart factory intelligent control method and device based on signal monitoring and medium

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a smart factory intelligent control method and device based on signal monitoring and a medium, and the method comprises the steps: carrying out the signal reconstruction of each operation signal in an operation signal set of a smart factory, and obtaining a reconstructed signal; performing signal enhancement according to the reconstructed signal to obtain an enhanced signal, and identifying a signal source according to the enhanced signal; constructing an operation signal image of the reconstructed signal, and calculating a signal distance between the operation signal image and a reference signal image corresponding to the signal source; identifying an equipment operation category according to the signal distance, and performing equipment regulation and control on the signal source based on the equipment operation category to obtain a regulation and control signal; and generating a combined regulation and control signal according to the regulation and control signal, and controlling the smart factory by using the combined regulation and control signal. The accuracy of intelligent control of the intelligent factory can be improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a smart factory intelligent control method, device and medium based on signal monitoring. Background Art

[0002] In a smart factory environment, signal monitoring covers the real-time capture and analysis of various types of signals. This includes operating parameter signals from production equipment (such as temperature, pressure, vibration frequency, etc.), location and status signals of logistics systems (such as the location of material transport vehicles, inventory level change signals), environmental monitoring signals (such as temperature and humidity in the workshop, air quality signals) and personnel operation-related signals (such as employee clock-in records, equipment operation instruction signals), etc. Through sensors, instruments and data acquisition equipment distributed in every corner of the factory, these signals are efficiently collected and transmitted to the central processing system. Based on the massive data obtained by signal monitoring, smart factories can work with control systems to accurately regulate various links such as production processes, equipment operation, and resource allocation.

[0003] Existing signal monitoring methods, such as methods based on basic statistics in the time and frequency domains, require the determination of parameters such as carrier frequency, sampling frequency, and symbol rate. The estimated values ​​are sensitive to additive noise and are not suitable for low signal-to-noise ratios. The estimated variance of high-order cumulative quantities is large, and it is difficult to accurately estimate when the amount of data is small. In non-collaborative BOC (Binary Offset Carrier) signal detection, the cyclic spectrum and its improved algorithm have limited sampling time, and the length of the received data cannot be infinite. This causes the cyclic spectrum of the noise at non-zero cyclic frequencies to be non-constantly zero and the amount of calculation is large. There is also a problem of deviation in the position of the secondary peak of the cyclic spectrum envelope of the cose-BOC signal near twice the carrier frequency, resulting in poor accuracy in signal monitoring. Therefore, how to improve the accuracy of signal monitoring and thus more accurately perform intelligent control of smart factories has become an urgent problem to be solved. Summary of the invention

[0004] The present invention provides a method, device and medium for intelligent control of a smart factory based on signal monitoring, the main purpose of which is to solve the problem of poor accuracy of intelligent control of a smart factory.

[0005] To achieve the above object, the present invention provides a smart factory intelligent control method based on signal monitoring, comprising:

[0006] Acquire an operation signal set of the smart factory, and reconstruct each operation signal in the operation signal set to obtain a reconstructed signal;

[0007] Performing signal enhancement on the operating signal according to the reconstructed signal to obtain an enhanced signal, and identifying a signal source of the operating signal according to the enhanced signal;

[0008] Constructing an operation signal image of the reconstructed signal, and calculating a signal distance between the operation signal image and a reference signal image corresponding to the signal source;

[0009] Identify the device operation category of the operation signal according to the signal distance, and perform device control on the signal source based on the device operation category to obtain a control signal;

[0010] A joint control signal of the smart factory is generated according to the control signal, and the smart factory is controlled by using the joint control signal.

[0011] Optionally, reconstructing each operating signal in the operating signal set to obtain a reconstructed signal includes:

[0012] Sampling each of the operating signals using a preset sampling frequency to obtain a sampling signal;

[0013] Performing Hamming window double interpolation fast Fourier transform processing on the sampling signal to obtain an actual sampling frequency;

[0014] Using the actual sampling frequency to collect the actual sampling signal of each of the operating signals;

[0015] The actual sampled signal is reconstructed by performing cubic spline interpolation to obtain a reconstructed signal.

[0016] 3. The intelligent control method of a smart factory based on signal monitoring according to claim 1, characterized in that the step of enhancing the operating signal according to the reconstructed signal to obtain an enhanced signal comprises:

[0017] Performing complete set empirical mode decomposition on the reconstructed signal to obtain decomposition components;

[0018] Calculating component parameter values ​​of each of the decomposed components respectively;

[0019] The component parameter value of each decomposed component is calculated using the following formula:

[0020]

[0021] M=ρK

[0022] Among them, Cov(·) represents the covariance, Y IMF represents the decomposed component, x represents the reconstructed signal, D(·) represents the variance, E(·) represents the expectation, and M represents the component parameter value;

[0023] Calculating a signal threshold of the reconstructed signal according to the component parameter value, and screening the decomposed components based on the signal threshold to obtain a target component;

[0024] An enhancement signal of the operating signal is constructed using the target component.

[0025] 4. The intelligent control method of a smart factory based on signal monitoring according to claim 1, characterized in that the device operation category of the operation signal is identified according to the signal distance, comprising:

[0026] identifying a signal position of the operating signal according to the signal distance, and determining a target operating signal according to the signal position;

[0027] Performing modal decomposition on the target operation signal to obtain multiple modal components;

[0028] Calculating the correlation coefficient between each of the modal components and the target operating signal;

[0029] Calculating the effective modal component of the target operation signal according to the correlation coefficient, and constructing the Hilbert spectrum of the target operation signal according to the effective modal component;

[0030] The target operation signal is classified according to the Hilbert spectrum to obtain the device operation category of the operation signal.

[0031] 5. The intelligent control method of a smart factory based on signal monitoring according to claim 1, characterized in that the step of identifying the signal source of the operation signal according to the enhanced signal comprises:

[0032] extracting a signal feature of the enhanced signal;

[0033] Extracting dual features of the signal features using a pre-built dual attention network;

[0034] Performing feature fusion on the dual features to obtain fused features;

[0035] The enhanced signal is classified according to the fusion feature to obtain the signal source.

[0036] 6. The intelligent control method for a smart factory based on signal monitoring according to claim 1, characterized in that the step of constructing the operating signal image of the reconstructed signal comprises:

[0037] Performing segmentation and overlapping sampling on the reconstructed signal to obtain segmented signal samples of the reconstructed signal;

[0038] Calculate the pixel value corresponding to each sample point in each of the signal segmentation number samples;

[0039] Image encoding is performed according to the pixel values ​​to obtain a signal image.

[0040] 7. The intelligent control method for a smart factory based on signal monitoring according to claim 1, characterized in that the step of calculating the signal distance between the operating signal image and the reference signal image corresponding to the signal source comprises:

[0041] respectively calculating the difference between adjacent pixel values ​​of the operating signal image and the reference signal image;

[0042] Constructing a pixel difference sequence matrix according to the adjacent pixel value differences, and calculating a feature hash value according to the pixel difference sequence matrix;

[0043] Calculate the Hamming distance according to the characteristic hash value, and use the Hamming distance as the signal distance;

[0044] The Hamming distance is calculated using the following formula:

[0045]

[0046] Where L represents the Hamming distance, Indicates the g-th feature hash value corresponding to the running signal image, represents element-by-element addition, Represents the g-th feature hash value corresponding to the running signal image.

[0047] 8. The intelligent control method of a smart factory based on signal monitoring according to claim 1, characterized in that the device control of the signal source based on the device operation category to obtain the control signal includes:

[0048] Determining equipment adjustment parameters according to the equipment operation category;

[0049] Perform signal type conversion according to the device adjustment parameters to obtain an analog signal;

[0050] The analog signal is encoded to obtain a control signal.

[0051] In order to solve the above problems, the present invention also provides a smart factory intelligent control device based on signal monitoring, the device comprising:

[0052] A signal reconstruction module, used to obtain an operation signal set of the smart factory, and perform signal reconstruction on each operation signal in the operation signal set to obtain a reconstructed signal;

[0053] a signal source identification module, configured to perform signal enhancement on the operating signal according to the reconstructed signal to obtain an enhanced signal, and identify a signal source of the operating signal according to the enhanced signal;

[0054] A signal distance calculation module, used to construct an operation signal image of the reconstructed signal, and calculate a signal distance between the operation signal image and a reference signal image corresponding to the signal source;

[0055] A control signal construction module, used for identifying the device operation category of the operation signal according to the signal distance, and performing device control on the signal source based on the device operation category to obtain a control signal;

[0056] The smart factory control module is used to generate a joint control signal of the smart factory according to the control signal, and use the joint control signal to control the smart factory.

[0057] In order to solve the above problems, the present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the smart control method of a smart factory based on signal monitoring as described in any one of claims 1 to 8 is implemented.

[0058] The embodiment of the present invention can obtain a more accurate reconstructed signal by reconstructing each operation signal in the operation signal set of the smart factory; enhance the operation signal according to the reconstructed signal to obtain an enhanced signal, identify the signal source according to the enhanced signal, and adjust the factory equipment corresponding to the operation signal in a targeted manner to improve the accuracy of the smart factory control; construct an operation signal image of the reconstructed signal, calculate the signal distance between the operation signal image and the reference signal image, which can reflect the difference between the operation signal in different signal segments and the reference signal, and then reflect the equipment operation status of the signal source, effectively improving the accuracy of the smart factory control; identify the equipment operation category of the operation signal according to the signal distance, and control the equipment of the signal source to obtain a control signal; generate the joint control signal of the smart factory according to the control signal, which can realize the joint control of the smart factory and more accurately perform the intelligent control of the smart factory. Therefore, the smart factory intelligent control method, device and medium based on signal monitoring proposed by the present invention can solve the problem of poor accuracy of smart factory intelligent control. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flow chart of a smart factory intelligent control method based on signal monitoring provided by an embodiment of the present invention;

[0060] Figure 2 A schematic diagram of a process of performing signal reconstruction on each operating signal in an operating signal set provided by an embodiment of the present invention;

[0061] Figure 3 A schematic diagram of a process of performing signal enhancement on the operating signal according to the reconstructed signal provided in one embodiment of the present invention;

[0062] Figure 4 A functional module diagram of a smart factory intelligent control device based on signal monitoring provided by an embodiment of the present invention;

[0063] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0064] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0065] The embodiment of the present application provides a smart factory intelligent control method based on signal monitoring. The execution subject of the smart factory intelligent control method based on signal monitoring includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the smart factory intelligent control method based on signal monitoring can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0066] Reference Figure 1 FIG. 1 is a flow chart of a smart factory intelligent control method based on signal monitoring provided by an embodiment of the present invention. In this embodiment, the smart factory intelligent control method based on signal monitoring includes:

[0067] S1. Obtain an operation signal set of a smart factory, and reconstruct each operation signal in the operation signal set to obtain a reconstructed signal.

[0068] In the embodiment of the present invention, the smart factory is one of the core concepts of Industry 4.0. It realizes the high intelligence, automation and informatization of the production process by deeply integrating information technology, automation technology and communication technology. The smart factory uses advanced automation equipment (such as robots, automated production lines, automated warehousing systems, etc.) to reduce manual intervention and improve production efficiency and product quality. The operation signals of each device in the smart factory are collected through sensors, for example, operation parameter signals (such as temperature, pressure, vibration frequency, etc.), location and status signals of the logistics system (such as the location of material transport vehicles, inventory level change signals), environmental monitoring signals (such as temperature and humidity in the workshop, air quality signals) and personnel operation related signals (such as employee clock-in records, equipment operation instruction signals), etc.

[0069] In the embodiment of the present invention, refer to Figure 2 As shown, the signal reconstruction of each operating signal in the operating signal set to obtain a reconstructed signal includes:

[0070] S21, sampling each of the operating signals using a preset sampling frequency to obtain a sampling signal;

[0071] S22, performing a Hamming window double interpolation fast Fourier transform process on the sampling signal to obtain an actual sampling frequency;

[0072] S23, collecting an actual sampling signal of each of the operating signals using the actual sampling frequency;

[0073] S24, performing cubic spline interpolation reconstruction on the actual sampled signal to obtain a reconstructed signal.

[0074] In an embodiment of the present invention, the number of times the operating signal is sampled per unit time is determined by a preset sampling frequency to obtain a sampling signal. For example, each original operating signal can be sampled at a sampling frequency set at 50 Hz to obtain a sampling signal.

[0075] Furthermore, the sampled signal is processed by a Hamming window double interpolation fast Fourier transform, which is to apply a Hanning window function to the signal, then perform an FFT transform, and finally correct the amplitude and frequency of the spectrum by an interpolation method to obtain the actual fundamental frequency of each running signal. Then, the signal can be sampled more accurately through the actual sampled signal, providing a basis for subsequent signal reconstruction.

[0076] In the embodiment of the present invention, the cubic spline interpolation reconstruction is to fit multiple cubic polynomials between known sampling points, thereby realizing smooth reconstruction of the original signal. It can not only pass through all known sampling points, but also ensure the continuity of the first-order and second-order derivatives of the interpolation function, thereby obtaining a smoother curve.

[0077] In the embodiment of the present invention, by performing signal reconstruction on the running signal, the sampled signal can be effectively reconstructed to restore its original shape while maintaining the smoothness of the reconstructed signal to obtain a more accurate reconstructed signal.

[0078] S2. Perform signal enhancement on the operating signal according to the reconstructed signal to obtain an enhanced signal, and identify a signal source of the operating signal according to the enhanced signal.

[0079] In the embodiment of the present invention, signal enhancement is to further optimize the quality of the reconstructed signal. By enhancing the signal, the signal source of the operating signal can be identified, for example, the device in the smart factory and the specific category of the operating signal.

[0080] In the embodiment of the present invention, refer to Figure 3 As shown, the step of performing signal enhancement on the operating signal according to the reconstructed signal to obtain an enhanced signal includes:

[0081] S31, performing complete set empirical mode decomposition on the reconstructed signal to obtain decomposition components;

[0082] S32, respectively calculating the component parameter value of each of the decomposed components;

[0083] S33, calculating a signal threshold of the reconstructed signal according to the component parameter value, and screening the decomposed components based on the signal threshold to obtain a target component;

[0084] S34. Construct an enhanced signal of the operating signal using the target component.

[0085] In detail, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is an improved empirical mode decomposition (EMD) method for processing nonlinear and non-stationary signals. It introduces adaptive noise and step-by-step averaging processing on the basis of EEMD (Ensemble Empirical Mode Decomposition), which can effectively solve the problems of modal aliasing and noise interference.

[0086] In detail, performing complete set empirical mode decomposition on the reconstructed signal to obtain decomposition components includes:

[0087] Taking the reconstructed signal as the original signal, and adding a preset Gaussian white noise to the original signal to obtain a white noise reconstructed signal;

[0088] Performing modal decomposition on the white noise reconstruction signal to obtain multiple modal decomposition components;

[0089] Performing summation and averaging on the multiple modal decomposition components to obtain an eigenmodal component and a residual modal component;

[0090] The residual mode is used as the original signal, and the step of adding a preset Gaussian white noise to the original signal to obtain a white noise reconstruction signal is returned to the above step until the residual component is a monotonic function, and a plurality of eigenmode components are obtained;

[0091] The multiple eigenmode components are positioned as decomposed components of the reconstructed signal.

[0092] In detail, Gaussian white noises of different amplitudes and opposite to each other are added to the original signal to obtain a white noise reconstructed signal, and the white noise reconstructed signal is subjected to modal decomposition (Empirical Mode Decomposition) to obtain multiple modal decomposition components, and the intrinsic modal components are obtained by summing the average values ​​of the modal decomposition components. After subtracting the intrinsic modal components from the reconstructed signal, the residual modal components are obtained, and the residual modes are used as the original signal. The modal decomposition steps are repeated until the obtained residual modal components are monotonic functions that cannot be further decomposed, and multiple decomposition components after the reconstructed signal is subjected to complete set empirical mode decomposition are obtained.

[0093] In the embodiment of the present invention, the component parameter value of each decomposed component is calculated using the following formula:

[0094]

[0095] M=ρK

[0096] Among them, Cov(·) represents the covariance, Y IMF represents the decomposed component, x represents the reconstructed signal, D(·) represents the variance, E(·) represents the expectation, and M represents the component parameter value.

[0097] Furthermore, the signal threshold of the reconstructed signal is calculated using the following formula:

[0098]

[0099] Where V represents the signal threshold, M n represents the component parameter value of the nth decomposition component, N represents the total number of decomposition components, Represents the mean of the component parameter values.

[0100] In the embodiment of the present invention, the decomposed components below the signal threshold are removed as false components or noises, the decomposed components are screened, the target components are obtained, and the signals of the target components are reconstructed to obtain enhanced signals.

[0101] In an embodiment of the present invention, the step of identifying the signal source of the operation signal according to the enhanced signal includes:

[0102] extracting a signal feature of the enhanced signal;

[0103] Extracting dual features of the signal features using a pre-built dual attention network;

[0104] Performing feature fusion on the dual features to obtain fused features;

[0105] The enhanced signal is classified according to the fusion feature to obtain the signal source.

[0106] In detail, the dual attention network includes a channel attention module and a spatial attention module. The channel attention module performs global maximum pooling and global average pooling on the input signal features in the spatial dimension to extract context features. The spatial attention module generates features representing two different contexts by performing global maximum pooling and global average pooling on the channel dimension, which can increase the feature extraction capability without increasing the amount of calculation. The features are then fused by element-by-element addition to obtain fused features.

[0107] Furthermore, the enhanced signal can be classified using a pre-built classifier to obtain the signal source, for example, a SVM classifier, a pre-trained signal classification network, and the like.

[0108] In an embodiment of the present invention, by performing signal enhancement on the operating signal, the signal noise of the reconstructed signal can be removed to avoid interference from irrelevant signals. Then, by identifying the signal source of the operating signal, the factory equipment corresponding to the operating signal can be adjusted in a targeted manner to improve the accuracy of smart factory control.

[0109] S3. Constructing an operation signal image of the reconstructed signal, and calculating a signal distance between the operation signal image and a reference signal image corresponding to the signal source.

[0110] In the embodiment of the present invention, the signal image is a one-dimensional signal converted into a two-dimensional image, which is conducive to analyzing the time-frequency characteristics, periodicity, trend and other characteristics of the reconstructed signal.

[0111] In an embodiment of the present invention, the step of constructing the operating signal image of the reconstructed signal includes:

[0112] Performing segmentation and overlapping sampling on the reconstructed signal to obtain segmented signal samples of the reconstructed signal;

[0113] Calculate the pixel value corresponding to each sample point in each of the signal segmentation number samples;

[0114] Image encoding is performed according to the pixel values ​​to obtain a signal image.

[0115] In detail, segmented overlapping sampling is to truncate the reconstructed signal, and at the same time, there is a partial overlap between the next truncated signal and the previous truncated signal to improve the diversity of signal samples and ensure the accuracy of signal image construction. By segmenting the reconstructed signal and performing overlapping sampling, the running signal image of the reconstructed signal image in multiple time periods can be obtained.

[0116] In the embodiment of the present invention, the pixel value of each sample point is normalized and converted into a grayscale pixel value ranging from 0 to 255, which provides a basis for subsequent image encoding.

[0117] Furthermore, for a length of D 2 The signal samples (d1, d2…d i …d D2 ), the matrix form of the signal image can be expressed as:

[0118]

[0119] In an embodiment of the present invention, the reference signal image is a signal image of a reference signal during normal operation preset by a signal source. By calculating the signal distance between the operating signal image and the reference signal image corresponding to the signal source, the difference size and difference distribution between the operating signal and the reference signal can be analyzed, thereby determining the equipment operation category corresponding to the operating signal.

[0120] Specifically, the calculating the signal distance between the running signal image and the reference signal image corresponding to the signal source includes:

[0121] respectively calculating the difference between adjacent pixel values ​​of the operating signal image and the reference signal image;

[0122] Constructing a pixel difference sequence matrix according to the adjacent pixel value differences, and calculating a feature hash value according to the pixel difference sequence matrix;

[0123] The Hamming distance is calculated according to the characteristic hash value, and the Hamming distance is used as the signal distance.

[0124] In detail, the embodiment of the present invention calculates the difference between adjacent pixel values ​​from left to right and from top to bottom. If there are n pixels, an n-1 dimensional pixel difference sequence matrix is ​​formed. Each matrix element value in the pixel difference sequence matrix is ​​reset to 1 if it is greater than zero, and reset to zero if it is not greater than zero, to obtain the characteristic hash value of each matrix element.

[0125] The Hamming distance is calculated using the following formula:

[0126]

[0127] Where L represents the Hamming distance, Indicates the g-th feature hash value corresponding to the running signal image, represents element-by-element addition, Represents the g-th feature hash value corresponding to the running signal image.

[0128] In the embodiment of the present invention, the Hamming distance can reflect the difference between the operating signal in different signal segments and the reference signal, and further reflect the operating status of the equipment from which the signal comes, thereby effectively improving the accuracy of smart factory control.

[0129] S4. Identify the device operation category of the operation signal according to the signal distance, and perform device control on the signal source based on the device operation category to obtain a control signal.

[0130] In an embodiment of the present invention, the device operation category is a specific state or mode that the device corresponding to the signal source is in during operation, for example, whether a device failure occurs, whether the device operates according to a set program, etc. The device operation category can be used to accurately control the device.

[0131] In the embodiment of the present invention, the step of identifying the device operation category of the operation signal according to the signal distance includes:

[0132] identifying a signal position of the operating signal according to the signal distance, and determining a target operating signal according to the signal position;

[0133] Performing modal decomposition on the target operation signal to obtain multiple modal components;

[0134] Calculating the correlation coefficient between each of the modal components and the target operating signal;

[0135] Calculating the effective modal component of the target operation signal according to the correlation coefficient, and constructing the Hilbert spectrum of the target operation signal according to the effective modal component;

[0136] The target operation signal is classified according to the Hilbert spectrum to obtain the device operation category of the operation signal.

[0137] In an embodiment of the present invention, segmented signal samples that may be abnormal are identified through signal distance. For example, if the signal distance between the third and fourth signal segmented signal samples has a sudden change and is greater than a preset distance threshold, then the third and fourth signal segmented signal samples are target operation signals, and the corresponding equipment operation category needs to be determined to more accurately monitor the signal of the operation signal.

[0138] Furthermore, the target operation signal is decomposed into a preset number k of modal components through modal decomposition, and then the correlation coefficient between each modal component and the target operation signal is calculated according to the Pearson correlation coefficient to reflect the similarity between the modal component and the target operation signal. The larger the correlation coefficient, the more similar it is to the target operation signal.

[0139] In detail, it is determined whether the minimum correlation coefficient is less than a preset threshold, for example, whether it is less than 0.2. If it is not less than the preset threshold, k is set to k+1, and the target operation signal is modally decomposed again until the minimum correlation coefficient is less than the preset threshold, and the decomposition component corresponding to the minimum correlation coefficient is removed as an invalid component, and the remaining decomposition components are Hilbert transformed as valid modal components to obtain the Hilbert spectrum of the target operation signal. The equipment operation category of the operation signal can be identified according to the change of the characteristic frequency in the Hilbert spectrum. For example, the characteristic similarity between the change of the characteristic frequency in the Hilbert spectrum and the change of the preset category characteristic frequency is calculated to determine the equipment operation category.

[0140] In the embodiment of the present invention, the operating signals in different time periods can be identified respectively through signal distance, so as to more accurately identify the specific state or mode of the device during operation, thereby improving the accuracy of device control.

[0141] In the embodiment of the present invention, device control is to adjust the device corresponding to the signal source to a required state to ensure the normal operation of the device.

[0142] In the embodiment of the present invention, the step of performing device control on the signal source based on the device operation category to obtain a control signal includes:

[0143] Determining equipment adjustment parameters according to the equipment operation category;

[0144] Perform signal type conversion according to the device adjustment parameters to obtain an analog signal;

[0145] The analog signal is encoded to obtain a control signal.

[0146] In an embodiment of the present invention, the device adjustment parameters are to adjust the device operation category of the signal source to a preset target device state, for example, to adjust the power, voltage, frequency and other operating states and various configuration parameters of the device, and to generate a control signal recognizable by the device from the device adjustment parameters, for example, to generate a PWM (pulse width modulation) signal, etc., to achieve control of the device corresponding to the signal source.

[0147] Furthermore, corresponding signal conversion is performed according to the hardware type of each signal source to ensure that the control signal can control the signal source, convert the digital device adjustment parameters into corresponding analog signals, and encode them into transmittable data signals to obtain control signals.

[0148] Specifically, the form of the control limit signal can be determined according to the hardware type of the signal source, for example, analog signal (voltage / current), digital signal (high and low level, PWM), communication protocol (I2C, SPI, UART, CAN, Modbus, etc.). The corresponding control code can be used to obtain the control signal and perform targeted control on the device of the signal source.

[0149] S5. Generate a joint control signal of the smart factory according to the control signal, and use the joint control signal to control the smart factory.

[0150] In the embodiment of the present invention, the joint control signal is an overall signal required for adjusting the equipment in the entire smart factory, and the overall control of the equipment in the smart factory can be achieved through the joint control signal.

[0151] In the embodiment of the present invention, the step of generating the joint control signal of the smart factory according to the control signal includes:

[0152] Calculating the task priority of the control signal;

[0153] Generate a control signal queue according to the task priority;

[0154] A joint control signal of the smart factory is constructed according to the control signal queue.

[0155] In detail, the task priority can be determined based on the category of each control signal, the urgency of the control signal, the resource requirements of the control signal, and other dimensions. For example, priority can be given to control signals related to equipment abnormalities, control signals with the shortest execution time, or control signals that are more relevant to smart factories. Specifically, the priority rules can be set according to the category of the control signal to determine the task priority among multiple control signals.

[0156] For example, a weight may be assigned to the type of each control signal and the urgency of the control signal, and each control signal may be scored in multiple dimensions according to the weight and the weighted sum may be used to determine the task priority.

[0157] In detail, each control signal is sent to the corresponding device for execution according to the control signal queue to achieve collaborative optimization of multiple device systems in the smart factory.

[0158] Furthermore, the control task with the highest task priority is used as the first task in the control signal queue. When executing the joint control signal, the control signal is transmitted in sequence according to the control signal queue, thereby realizing the joint control of the smart factory and more accurately performing intelligent control of the smart factory.

[0159] like Figure 4 , which is a functional module diagram of a smart factory intelligent control device based on signal monitoring provided by one embodiment of the present invention.

[0160] The smart factory intelligent control device 400 based on signal monitoring of the present invention can be installed in an electronic device. According to the functions implemented, the smart factory intelligent control device 400 based on signal monitoring can include a signal reconstruction module 401, a signal source identification module 402, a signal distance calculation module 403, a control signal construction module 404 and a smart factory control module 405. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0161] In this embodiment, the functions of each module / unit are as follows:

[0162] The signal reconstruction module 401 is used to obtain an operation signal set of the smart factory, and perform signal reconstruction on each operation signal in the operation signal set to obtain a reconstructed signal;

[0163] The signal source identification module 402 is used to enhance the operating signal according to the reconstructed signal to obtain an enhanced signal, and identify the signal source of the operating signal according to the enhanced signal;

[0164] The signal distance calculation module 403 is used to construct an operation signal image of the reconstructed signal and calculate the signal distance between the operation signal image and a reference signal image corresponding to the signal source;

[0165] The control signal construction module 404 is used to identify the device operation category of the operation signal according to the signal distance, and perform device control on the signal source based on the device operation category to obtain a control signal;

[0166] The smart factory control module 405 is used to generate a joint control signal of the smart factory according to the control signal, and use the joint control signal to control the smart factory.

[0167] In detail, each module described in the smart factory intelligent control device 400 based on signal monitoring in the embodiment of the present invention is used in the same manner as described above. Figures 1 to 3The smart factory intelligent control method based on signal monitoring described in the invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0168] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 501 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0169] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0170] The present invention also provides an electronic device, which may include a processor, a memory, a communication bus and a communication interface, and may also include a computer program stored in the memory and executable on the processor, such as a method program for improving welding stability of heterogeneous titanium alloy laser welding technology.

[0171] In some embodiments, the processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory (for example, a program for improving the welding stability of heterogeneous titanium alloy laser welding technology, etc.), and calls data stored in the memory to execute various functions of the electronic device and process data.

[0172] The memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory may be an internal storage unit of an electronic device in some embodiments, such as a mobile hard disk of the electronic device. The memory may also be an external storage device of an electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory may also include both an internal storage unit and an external storage device of the electronic device. The memory may be used not only to store application software and various types of data installed in the electronic device, such as the code of the welding stability improvement method program of the heterogeneous titanium alloy laser welding technology, but also to temporarily store data that has been output or is to be output.

[0173] The communication bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory and at least one processor, etc.

[0174] The communication interface is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0175] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0176] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0177] Specifically, the specific implementation method of the processor for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0178] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0179] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0180] Acquire an operation signal set of the smart factory, and reconstruct each operation signal in the operation signal set to obtain a reconstructed signal;

[0181] Performing signal enhancement on the operating signal according to the reconstructed signal to obtain an enhanced signal, and identifying a signal source of the operating signal according to the enhanced signal;

[0182] Constructing an operation signal image of the reconstructed signal, and calculating a signal distance between the operation signal image and a reference signal image corresponding to the signal source;

[0183] Identify the device operation category of the operation signal according to the signal distance, and perform device control on the signal source based on the device operation category to obtain a control signal;

[0184] A joint control signal of the smart factory is generated according to the control signal, and the smart factory is controlled by using the joint control signal.

[0185] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0186] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0188] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0189] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0190] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0191] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A smart factory intelligent control method based on signal monitoring, characterized in that: The method comprises: Acquire an operation signal set of the smart factory, and reconstruct each operation signal in the operation signal set to obtain a reconstructed signal; Performing signal enhancement on the operating signal according to the reconstructed signal to obtain an enhanced signal, and identifying a signal source of the operating signal according to the enhanced signal; Constructing an operation signal image of the reconstructed signal, and calculating a signal distance between the operation signal image and a reference signal image corresponding to the signal source; Identify the device operation category of the operation signal according to the signal distance, and perform device control on the signal source based on the device operation category to obtain a control signal; A joint control signal of the smart factory is generated according to the control signal, and the smart factory is controlled by using the joint control signal.

2. The intelligent control method for a smart factory based on signal monitoring according to claim 1, characterized in that: The performing signal reconstruction on each operating signal in the operating signal set to obtain a reconstructed signal includes: Sampling each of the operating signals using a preset sampling frequency to obtain a sampling signal; Performing Hamming window double interpolation fast Fourier transform processing on the sampling signal to obtain an actual sampling frequency; Using the actual sampling frequency to collect the actual sampling signal of each of the operating signals; The actual sampled signal is reconstructed by performing cubic spline interpolation to obtain a reconstructed signal.

3. The intelligent control method for a smart factory based on signal monitoring according to claim 1, characterized in that: The step of performing signal enhancement on the operating signal according to the reconstructed signal to obtain an enhanced signal includes: Performing complete set empirical mode decomposition on the reconstructed signal to obtain decomposition components; Calculating component parameter values ​​of each of the decomposed components respectively; The component parameter value of each decomposed component is calculated using the following formula: M=ρK Among them, Cov(·) represents the covariance, Y IMF represents the decomposed component, x represents the reconstructed signal, D(·) represents the variance, E(·) represents the expectation, and M represents the component parameter value; Calculating a signal threshold of the reconstructed signal according to the component parameter value, and screening the decomposed components based on the signal threshold to obtain a target component; An enhancement signal of the operating signal is constructed using the target component.

4. The intelligent control method for a smart factory based on signal monitoring according to claim 1, characterized in that: The step of identifying the device operation category of the operation signal according to the signal distance includes: identifying a signal position of the operating signal according to the signal distance, and determining a target operating signal according to the signal position; Performing modal decomposition on the target operation signal to obtain multiple modal components; Calculating the correlation coefficient between each of the modal components and the target operating signal; Calculating the effective modal component of the target operation signal according to the correlation coefficient, and constructing the Hilbert spectrum of the target operation signal according to the effective modal component; The target operation signal is classified according to the Hilbert spectrum to obtain the device operation category of the operation signal.

5. The intelligent control method for a smart factory based on signal monitoring according to claim 1, characterized in that: The step of identifying the signal source of the operation signal according to the enhanced signal comprises: extracting a signal feature of the enhanced signal; Extracting dual features of the signal features using a pre-built dual attention network; Performing feature fusion on the dual features to obtain fused features; The enhanced signal is classified according to the fusion feature to obtain the signal source.

6. The intelligent control method for a smart factory based on signal monitoring according to claim 1, characterized in that: The constructing the running signal image of the reconstructed signal comprises: Performing segmentation and overlapping sampling on the reconstructed signal to obtain segmented signal samples of the reconstructed signal; Calculate the pixel value corresponding to each sample point in each of the signal segmentation number samples; Image encoding is performed according to the pixel values ​​to obtain a signal image.

7. The intelligent control method for a smart factory based on signal monitoring according to claim 1, characterized in that: The calculating the signal distance between the running signal image and the reference signal image corresponding to the signal source includes: respectively calculating the adjacent pixel value differences between the operation signal image and the reference signal image; Constructing a pixel difference sequence matrix according to the adjacent pixel value differences, and calculating a feature hash value according to the pixel difference sequence matrix; Calculate the Hamming distance according to the characteristic hash value, and use the Hamming distance as the signal distance; The Hamming distance is calculated using the following formula: Where L represents the Hamming distance, Indicates the g-th feature hash value corresponding to the running signal image, represents element-by-element addition, Represents the g-th feature hash value corresponding to the running signal image.

8. The intelligent control method for a smart factory based on signal monitoring according to claim 1, characterized in that: The performing device control on the signal source based on the device operation category to obtain a control signal includes: Determining equipment adjustment parameters according to the equipment operation category; Perform signal type conversion according to the device adjustment parameters to obtain an analog signal; The analog signal is encoded to obtain a control signal.

9. A smart factory intelligent control device based on signal monitoring, characterized in that: The device comprises: A signal reconstruction module, used to obtain an operation signal set of the smart factory, and perform signal reconstruction on each operation signal in the operation signal set to obtain a reconstructed signal; a signal source identification module, configured to perform signal enhancement on the operating signal according to the reconstructed signal to obtain an enhanced signal, and identify a signal source of the operating signal according to the enhanced signal; A signal distance calculation module, used to construct an operation signal image of the reconstructed signal, and calculate a signal distance between the operation signal image and a reference signal image corresponding to the signal source; A control signal construction module, used for identifying the device operation category of the operation signal according to the signal distance, and performing device control on the signal source based on the device operation category to obtain a control signal; The smart factory control module is used to generate a joint control signal of the smart factory according to the control signal, and use the joint control signal to control the smart factory.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the intelligent control method of a smart factory based on signal monitoring as described in any one of claims 1 to 8 is implemented.

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