Intelligent data processing method and equipment for laser cutting

Abstract

The embodiment of the invention discloses an intelligent data processing method and equipment for laser cutting, which are applied to the technical field of laser metal cutting and are used for solving the problem that the existing data processing is easy to cause system jamming. The method comprises the following steps: acquiring to-be-analyzed data based on a signal input unit and / or an internet access processing unit; wherein the to-be-analyzed data corresponding to the signal input unit is obtained based on the system interaction unit and a preset sensor, and the to-be-analyzed data corresponding to the network interface processing unit is obtained based on the network interface integration module; processing to-be-analyzed data and setting data corresponding to the to-be-analyzed data through a data processing module to obtain processing result data; and transmitting the processing result data to a preset system board card through a data output module, and analyzing the processing result data through the system board card, so that the system board card performs interface display on the processing result data.

Description

Technical Field

[0001] The present invention relates to the field of laser metal cutting technology, and in particular to a data intelligent processing method and device for laser cutting. Background Art

[0002] With the rapid development of modern manufacturing industry, laser cutting technology has been widely used in many fields such as metal processing, automobile manufacturing, aerospace, etc. due to its high precision and high efficiency, and the application of laser processing in the metal processing market is becoming more and more common. With the continuous development of technology, the current laser processing power is gradually increasing, and the requirements for the number of laser functions are increasing. The increase in functions can also meet the customer's demand for the use of laser cutting machines. Therefore, the demand for laser cutting machine functions is gradually increasing. At this time, how to monitor the data of each function has become an important link to ensure the normal operation of the laser cutting machine.

[0003] However, some functions of the current traditional laser cutting operation do not require the use of the system for processing, such as alarming when exceeding a certain threshold, performing some simple calculations for interface display, etc., and usually these functions are to enter RS485, RS232 or TTL data signals or analog voltage or analog current data signals into the system for conversion, and then open a thread in the system to convert the protocol data signal and perform data processing. However, since one function often corresponds to one thread, this method will cause the software to occupy too much CPU and memory in the system when the more functions there are, the more threads there are, which will easily cause the system to freeze, and the cost of using the whole machine will also increase accordingly. Summary of the invention

[0004] In order to solve the above technical problems, one or more embodiments of this specification provide a data intelligent processing method and device for laser cutting.

[0005] One or more embodiments of this specification adopt the following technical solutions:

[0006] One or more embodiments of the present specification provide a data intelligent processing method for laser cutting, which is applied to an intelligent processing control device, wherein the intelligent processing control device includes a system interaction unit, a signal input module, a data processing module, a data output module, and a network port processing unit connected to the data output module, and a network port integration module connected to the network port processing unit. The method includes:

[0007] The data to be analyzed is obtained based on the signal input unit and / or the network port processing unit; wherein the data to be analyzed corresponding to the signal input unit is obtained based on the system interaction unit and the preset sensor, and the data to be analyzed corresponding to the network port processing unit is obtained based on the network port integration module;

[0008] Processing the data to be analyzed and the setting data corresponding to the data to be analyzed by the data processing module to obtain processing result data;

[0009] The processing result data is transmitted to a preset system board through the data output module, so that the processing result data is parsed by the system board, so that the system board displays the processing result data on an interface.

[0010] Optionally, in one or more embodiments of the present specification, obtaining the data to be analyzed based on the signal input unit and / or the network port processing unit specifically includes:

[0011] Acquiring initial setting parameters of the system board through the system interaction unit, and inputting the initial setting parameters into the signal input unit;

[0012] Integrate the initial setting parameters based on the signal input unit to determine the parameter range of each function of the current laser cutting machine, and use the parameter range as the first data to be analyzed;

[0013] Based on the preset sensor, the collected data of the current laser cutting machine is obtained, and the collected data is integrated based on the signal input unit to obtain the second data to be analyzed; wherein the second data to be analyzed includes: water quality data of the water chiller, pressure data of the differential pressure filter, temperature data on the laser head, temperature data in the electric control cabinet, and current data of each branch in the electric control cabinet;

[0014] and / or,

[0015] Unifying the network segments of the camera and the laser of the current laser cutting machine based on the network port integration module to read the real-time data of the camera and the laser located in the same network segment;

[0016] The real-time data corresponding to each IP address is input into the network port processing unit, so that the network port processing unit saves the real-time data based on a preset time interval to obtain data to be analyzed.

[0017] Optionally, in one or more embodiments of the present specification, the data to be analyzed and the setting data corresponding to the data to be analyzed are processed by the data processing module to obtain processing result data, which specifically includes:

[0018] If the data to be analyzed is obtained based on the signal input unit, the data processing module receives the second data to be analyzed based on a Boolean signal;

[0019] Determine the current laser cutting machine function corresponding to the second data to be analyzed, so as to determine the first data to be analyzed corresponding to the second data to be analyzed as the setting data corresponding to the data to be analyzed;

[0020] According to the alarm strategies corresponding to the current laser cutting machine functions, the setting data corresponding to the second data to be analyzed and the data to be analyzed are compared in sequence to obtain processing result data.

[0021] Optionally, in one or more embodiments of the present specification, the data to be analyzed and the setting data corresponding to the data to be analyzed are processed by the data processing module to obtain processing result data, which specifically includes:

[0022] If the data to be analyzed is obtained based on the network port processing unit, the data processing module performs binarization processing on the data to be analyzed to obtain processed data to be analyzed;

[0023] The processed data to be analyzed are filtered by the data processing module to obtain the filtered data to be analyzed, and the filtered data to be analyzed are low-confidence processed according to the setting data preset by the system board to obtain the processing result data;

[0024] Preferably, if the data to be analyzed is obtained based on the network port processing unit, the data processing module performs binarization processing on the data to be analyzed, and before obtaining the processed data to be analyzed, the method further includes:

[0025] Acquire the real-time data collected by the network port integration unit, and perform grayscale processing on the real-time data based on the network port processing unit to obtain grayscale image data corresponding to the real-time data;

[0026] Filtering the grayscale image data based on a preset Gaussian filter, and dividing the filtered grayscale image data into regions based on a preset size;

[0027] The filtered grayscale image data of each region is detected according to a preset human feature image file to determine the data to be analyzed that contains human features.

[0028] Optionally, in one or more embodiments of the present specification, after the data to be analyzed and the setting data corresponding to the data to be analyzed are processed by the data processing module to obtain processing result data, the method further includes:

[0029] Determine the alarm status value corresponding to each function of the current laser cutting machine based on the system board;

[0030] If it is determined based on the data processing module that the processing result data does not correspond to the preset alarm state value of the data to be analyzed, iteratively detecting the data to be analyzed;

[0031] If it is determined based on the data processing module that the processing result data corresponds to the preset alarm state value of the data to be analyzed, the processing result data is transmitted to the preset system board through the data output module.

[0032] Optionally, in one or more embodiments of the present specification, the preset sensor and the signal processing unit are communicatively connected based on a preset communication protocol; wherein the preset communication protocol includes: RS485 transmission protocol, RS232 transmission protocol, and TTL transmission protocol.

[0033] Optionally, in one or more embodiments of the present specification, transmitting the processing result data to a preset system board through the data output module specifically includes:

[0034] Preprocessing the processing result data based on the data output module to obtain the result data to be transmitted; wherein the preprocessing includes: data cleaning, data formatting, and data calibration;

[0035] Determining the communication protocol between the data output module and the preset system board through the data output module, so as to logically summarize the result data to be transmitted according to the communication protocol to obtain a corresponding result data packet to be transmitted; wherein the communication protocol is an RS485 communication protocol;

[0036] The result data packet to be transmitted is encapsulated into a format recognizable by the preset system board based on the data output module, and the encapsulated result data packet to be transmitted is transmitted to the preset system board based on the preset communication protocol.

[0037] Optionally, in one or more embodiments of the present specification, the data intelligent processing control device further comprises an industrial computer and a display; wherein the industrial computer is connected to the display and the system interaction unit respectively;

[0038] Preferably, before acquiring the data to be analyzed based on the signal input unit and / or the network port processing unit, the method further comprises: transmitting the data to be analyzed acquired by the system interaction unit to the signal input module based on the industrial computer.

[0039] Optionally, in one or more embodiments of the present specification, transmitting the processing result data to a preset system board through the data output module specifically includes:

[0040] If the data processing unit determines that the processing result data does not correspond to the preset alarm state value of the data to be analyzed, the processing result data is given to the industrial computer through the data output module, so that the display is controlled by the industrial computer to display the processing result data;

[0041] If the data processing unit determines that the processing result data corresponds to the preset alarm status value of the data to be analyzed, the display is controlled to display a first prompt message based on the industrial controller, and the data output module is controlled to transmit the processing result data to a preset system board; wherein the first prompt message is used to prompt that the function of the current laser cutter corresponding to the data to be analyzed is abnormal.

[0042] One or more embodiments of the present specification provide a data intelligent processing device for laser cutting, which is applied to an intelligent processing control device, wherein the intelligent processing control device includes a system interaction unit, a signal input module, a data processing module, a data output module, and a network port processing unit connected to the data output module, and a network port integration module connected to the network port processing unit, and the device includes:

[0043] at least one processor; and,

[0044] a memory communicatively connected to the at least one processor; wherein,

[0045] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: execute any of the above-mentioned data intelligent processing methods for laser cutting.

[0046] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0047] By acquiring the two types of data to be analyzed, the signal input unit and the network port processing unit, the intelligent data processing process of laser cutting can be used for functional data processing of conventional cutting operations of laser cutting machines, and is also suitable for complex application scenarios that require real-time monitoring and early warning. In addition, through the intelligent processing control device, the data to be analyzed corresponding to the different functions of the signal input unit and the network port processing unit are integrated, so that subsequent data can be uniformly transmitted to the system board, and the system board only needs to perform simple data analysis to obtain the processing result data processed by the data processing module. The unified data processing process reduces the number of threads opened, avoiding the problem of excessive CPU rate and memory occupancy of the system and system jamming caused by the more existing functions, the more threads are opened. In addition, by integrating ports and unifying data processing processes, this method simplifies the system architecture and reduces unnecessary hardware and software components. This not only reduces the complexity and maintenance cost of the system, but also improves the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:

[0049] Figure 1 A schematic diagram of a data intelligent processing method for laser cutting provided in an embodiment of this specification;

[0050] Figure 2 A schematic diagram of the structure of an intelligent processing control device provided in an embodiment of this specification;

[0051] Figure 3 A logical diagram of a method for intelligent processing of laser cutting data in an application scenario provided by an embodiment of this specification;

[0052] Figure 4 A logical diagram of a method for intelligently processing laser cutting data in another application scenario provided in an embodiment of this specification

[0053] Figure 5 A logical schematic diagram of a network port processing unit in an application scenario provided in an embodiment of this specification;

[0054] Figure 6 A schematic diagram of the structure of a data intelligent processing device for laser cutting provided in an embodiment of this specification. DETAILED DESCRIPTION

[0055] The embodiments of this specification provide a data intelligent processing method and device for laser cutting.

[0056] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0057] like Figure 1 The embodiment of this specification provides a flow chart of a method for intelligent data processing for laser cutting. Figure 1 It can be seen that in one or more embodiments of this specification, a data intelligent processing method for laser cutting is applied to Figure 2 The intelligent processing control device shown in the figure, wherein the intelligent processing control device includes a system interaction unit, a signal input module, a data processing module, a data output module, a network port processing unit connected to the data output module, and a network port integration module connected to the network port processing unit, and the method includes the following steps:

[0058] S101: Acquire the data to be analyzed based on the signal input unit and / or the network port processing unit; wherein the data to be analyzed corresponding to the signal input unit is acquired based on the system interaction unit and the preset sensor, and the data to be analyzed corresponding to the network port processing unit is acquired based on the network port integration module.

[0059] In order to solve the problem in the prior art that one function corresponds to one thread, the more functions the more threads are opened, which in turn causes the software to occupy too much CPU and memory of the system, the embodiments of this specification will unify the functions to process the data together and transmit it to the system. To achieve this purpose, the embodiments of this specification first obtain the data to be analyzed based on the signal input unit and / or the network port processing unit. Figure 2 It can be seen that the data to be analyzed corresponding to the signal input unit is obtained based on the system interaction unit, and the data to be analyzed corresponding to the network port processing unit is obtained based on the network port integration module. The data to be analyzed obtained based on the above method has passed through a unified data processing framework, so that when adding new functions, it can be more easily integrated into the existing process without creating a separate thread for each new function, which helps to maintain the flexibility and scalability of the software, and centralized data processing helps to ensure data consistency and integrity.

[0060] Specifically, in one or more embodiments of the present specification, obtaining the data to be analyzed based on the signal input unit and / or the network port processing unit specifically includes the following process:

[0061] First, the initial setting parameters of the system board are obtained through the system interaction unit, and the initial setting parameters are input into the signal input unit. At the same time, the initial setting parameters are integrated based on the signal input unit to determine the parameter range of each function of the current laser cutting machine, and the parameter range is used as the first data to be analyzed. Among them, it can be understood that the initial setting parameters are the parameter data of each function set by the user on the interface obtained by the system interaction unit. And the integration of the initial setting parameters can be understood as integrating the processed data into the parameter range of each function according to the working principle and cutting requirements of the laser cutting machine. At the same time, the collected data of the current laser cutting machine is obtained based on the preset sensor, so that the collected data is integrated according to the signal input unit to obtain the second data to be analyzed. Among them, the second data to be analyzed includes: water quality data of the water chiller, pressure data of the differential pressure filter, temperature data on the laser head, temperature data in the electric control cabinet, current data of each branch in the electric control cabinet, etc. It should also be noted that the preset sensor and the signal processing unit are connected for communication based on the preset communication protocol, so the second data to be analyzed can be transmitted to the signal processing unit based on the preset sensor. Among them, it can be understood that the preset communication protocols include: RS485 transmission protocol, RS232 transmission protocol, and TTL transmission protocol.

[0062] And as Figure 2 As shown, in addition to being obtained based on the signal processing unit, the data to be analyzed can also be obtained by unifying the network segments of the camera and laser of the current laser cutting machine based on the network port integration module, and then reading the real-time data of the camera and laser in the same network segment through the network port integration module. Then the real-time data corresponding to each IP address is input into the network port processing unit, so that the real-time data is saved based on the preset time interval according to the network port processing unit to obtain the data to be analyzed. For example, in a certain application scenario, the network port integration module is processing the camera in the same network segment, and after the network port processing unit reads the video stream of the camera, it saves a picture for one second.

[0063] In the embodiment of this specification, the initial setting parameters and real-time data are obtained and integrated respectively by the signal input unit and the network port processing unit, so as to realize the centralized management and efficient processing of data. This avoids the decentralized storage and processing of data, and improves the efficiency and accuracy of data processing. The initial setting parameters can be obtained through the system interaction unit, and the user can set them on the interface. This setting method is both intuitive and convenient, so that the user can flexibly adjust the parameters according to the actual cutting requirements, and improves the flexibility and adaptability of cutting. The acquisition data of the current laser cutting machine is obtained by the preset sensor, and integrated into the second data to be analyzed, which is helpful for the subsequent comparison of the functional threshold according to the first data to be analyzed and the second data to be analyzed, so that the alarm result can be directly transmitted to the system board to improve the production efficiency. In addition, the network segment of the camera and the laser of the current laser cutting machine is unified through the network port integration module, which simplifies the network configuration and makes data reading more convenient. At the same time, the network port processing unit saves the real-time data based on the preset time interval, which not only ensures the integrity of the data, but also reduces the storage space occupied. By acquiring two types of data to be analyzed, the intelligent processing of laser cutting data can be used not only for functional data processing of conventional cutting operations of laser cutting machines, but also for complex application scenarios that require real-time monitoring and early warning. For example, in terms of camera monitoring, by regularly saving pictures, a visual record of the cutting process can be achieved to prevent people from entering the laser cutting work scene and being harmed.

[0064] S102: Processing the data to be analyzed and the setting data corresponding to the data to be analyzed by the data processing module to obtain processing result data.

[0065] After obtaining the data to be analyzed based on the above steps, in order to make the system only need to do a simple data analysis and display it on the interface, in the embodiment of this specification, an alarm will be issued when a certain threshold is exceeded, and some simple calculations will be performed to display the interface, etc. The data processing module is used to process the setting data corresponding to the data to be analyzed and the data to be analyzed, so as to obtain the processing result data.

[0066] Specifically, in one or more embodiments of the present specification, the data to be analyzed and the setting data corresponding to the data to be analyzed are processed by the data processing module to obtain the processing result data, which specifically includes the following process:

[0067] Based on the above step S101, it can be known that the data intelligent processing method can be used not only for functional data processing of conventional cutting operations of laser cutting machines, but also for complex application scenarios that require real-time monitoring and early warning. Therefore, for the case where the data to be analyzed is obtained based on the signal input unit, the data processing module in the embodiment of the present specification will receive the second data to be analyzed based on the Boolean signal, that is, receive the preset sensor. Then, by determining the current laser cutting machine function corresponding to the second data to be analyzed, the first data to be analyzed corresponding to the second data to be analyzed is determined as the setting data corresponding to the data to be analyzed. That is, the system interaction unit is used to obtain the parameters of each function set by the user on the interface, and based on the above, it can be known that the first data to be analyzed is the setting data. By determining the first data to be analyzed corresponding to the second data to be analyzed as the setting data corresponding to the data to be analyzed, it is helpful to subsequently determine whether the data to be analyzed collected by the preset sensor needs to be alarmed. Therefore, after determining the first data to be analyzed corresponding to the second data to be analyzed as the setting data corresponding to the data to be analyzed, it can be as follows Figure 3 , Figure 4 As shown, according to the alarm strategies corresponding to the current laser cutting machine functions, the setting data corresponding to the second data to be analyzed and the data to be analyzed are compared in turn to obtain the processing result data.

[0068] In addition, it should be noted that since the data to be analyzed transmitted to the data intelligent processing control device by the sensor through RS485, RS232, and TTL is an analog quantity, the data processing unit actually calculates the actual value corresponding to each sensor through the corresponding ratio of the sensor analog quantity to the digital quantity. Since the sensor value may be inaccurate due to interference problems, in a feasible embodiment, the first five values, the last five values, and the middle value of the data to be analyzed will be judged, and then a deviation threshold will be given. If the difference of the middle value exceeds the threshold, the current value will be filtered out.

[0069] In this process, the data to be analyzed and the corresponding setting data are processed by the data processing module, and the processing result data can be quickly obtained. According to the alarm strategy corresponding to each current laser cutting machine function, the data to be analyzed and the setting data can be compared to determine whether an alarm is needed. This intelligent alarm strategy helps to reduce false alarms and missed alarms and improve the accuracy of alarms. And through the data processing module, the functions are unified for data processing and transmission to the system, so that the system board only needs to do a simple data analysis and display it on the interface, which greatly simplifies the system structure and solves the problem of high CPU rate and memory occupancy of the system, so that the system response speed is accelerated.

[0070] It should be noted that: for the alarm strategy corresponding to each current laser cutting machine function, the setting data corresponding to the second data to be analyzed and the data to be analyzed are compared in turn to obtain the sight process of the processing result data. In one example, after the current value exceeds the functional threshold set by the user on the interface, the data processing unit will not send an alarm signal to the system board through the data output module, but will average the first K data of the data to be analyzed based on the alarm strategy corresponding to the current laser cutting machine function, and then compare it with the functional threshold, that is, the first data to be analyzed. If the value does not exceed the functional threshold corresponding to the first data to be analyzed at this time, the first data is discarded, and the next data is added, and the data comparison is continued by analogy; if the value exceeds the functional threshold at this time, the data processing unit sends it to the system board through the data output module, and the system board analyzes whether the current function needs an alarm through a specific technical protocol. Among them, K is the number of intervals of the user's data on the system board. In another example, suppose the alarm logic of function 1 is: the detection range is 0-3000; the value changes every half a minute, then if it exceeds the function threshold, the alarm strategy is to detect K times, if the K times are all above the function threshold, then an alarm is issued, if there is one time in the middle that is not above the threshold, the alarm number is re-recorded. The alarm strategy of function 2 is: directly detect the value and function threshold, and issue an alarm if it exceeds the alarm value.

[0071] Specifically, in another embodiment of the present specification, the data to be analyzed and the setting data corresponding to the data to be analyzed are processed by the data processing module to obtain the processing result data, which specifically includes the following process:

[0072] If the data to be analyzed is obtained based on the network port processing unit, the data processing module will perform binarization processing on the data to be analyzed to obtain the processed data to be analyzed. Then, the data processing module performs filtering processing on the processed data to be analyzed to obtain the filtered data to be analyzed, and performs low-confidence processing on the filtered data to be analyzed according to the setting data preset by the system board to obtain the processing result data.

[0073] Preferably, if the data to be analyzed is obtained based on the network port processing unit, the data processing module performs binarization processing on the data to be analyzed. Before obtaining the processed data to be analyzed, the method also includes the following process: first, the real-time data collected by the network port integration unit is obtained to grayscale the real-time data based on the network port processing unit to obtain the grayscale image data corresponding to the real-time data. Then, the grayscale image data is filtered based on a preset Gaussian filter, and the filtered grayscale image data is divided into regions based on a preset size. The filtered grayscale image data of each region is detected according to a preset human feature image file to determine the data to be analyzed that contains human features.

[0074] That is, in order to be able to analyze whether there are personnel in the data to be analyzed obtained by the network port processing unit, so as to output an alarm to the system board through the output module, so that the system board can control the laser cutting machine to stop and prevent personnel from being harmed. In one embodiment, the camera is divided into a front camera and a rear camera on the laser cutting machine. The two video heads are in the same network segment, but the IP addresses are different. Taking the camera in the large enclosure of the laser cutting machine as an example, the network port integration module reads the camera of the camera, and the IP address and port number of the camera are required to be 192.168.1.108 and 37777 respectively. If the setting is wrong, it will affect the function use. After setting the IP address and port number of the camera, the network port processing unit reads the auxiliary code stream of the camera. The main code stream is used for the system board to read the camera data. The resolution of the auxiliary code stream is set to 704*576 (D1), the encoding mode is set to H.264H, the frame rate (FPS) is set to 25, the code stream control is set to a fixed code stream, the code stream is set to 512, and the frame interval is set to 50. After the setting is completed, calibration can be performed.

[0075] Before using the network port integration module, Figure 5The following process is also shown: First, the visual detection device needs to be installed on the large surround, and the camera on the large surround needs to be fixed. Then, after the visual detection device is installed on the large surround, the visual device is installed according to the debugging data prompt issued by the data intelligent processing control device. Since the visual device will not use the data intelligent processing control method correctly if it is installed on the top or bottom, the position must be debugged according to the debugging data. After installation and debugging, the photo preprocessing is performed. When the software is opened during the photo shooting, the photo will be automatically taken to the D drive, and the folder attribute is hidden. Specifically, the folders under this folder are all automatically created. First, the Alarmarchinewushanchu folder contains photos saved after the alarm. After the alarm, the current alarm photo and the first ten photos before the alarm and the last ten photos after the alarm are saved. The content in this folder is deleted once a month, and only files older than one month will be deleted, and files older than one month will be saved. The photos in the Savepicture folder are the current alarm photos saved and framed on the photos. The frame selects which part caused the alarm. The content in this folder is deleted once an hour. The Savepicturewushan folder stores photos of every second, and this folder is deleted once a day. Then, open the software on the laser equipment machine to take pictures, perform histogram equalization on the pictures, and then train the pictures to generate a specific XML file. In this step, the human body parts of the laser equipment will be photographed separately, and then a specific XML file will be generated. Convert the original picture to a grayscale picture. This step is to avoid the error caused by color as much as possible. Then filter the converted grayscale picture based on the Gaussian filter to remove some factors that are prone to false alarms. Then divide the captured pictures into regions, use the trained XML file to detect the human features in the photos and parse the positions of the human features.

[0076] In another embodiment, after the camera parameters are set, the system board opens the software, the software communicates with the data intelligent processing control device, and then adjusts the position of the camera according to the area given by the data intelligent processing control device to perform position calibration. Then the network port processing unit analyzes the image through the pre-processed library. Before the analysis, the image is first binarized, and then the image is binarized. After the binarization is completed, the information in the image is read, and then the places where people may be are filtered.

[0077] Through the above process, before acquiring the data to be analyzed, the embodiment of this specification first collects real-time data through the network port integration unit, and performs grayscale processing and regional division, which helps to narrow the processing range and make subsequent human feature detection more efficient and accurate. The use of a preset human feature image file for detection can ensure that only data containing human features are processed, thereby avoiding unnecessary calculations and waste of resources. The filtered data to be analyzed is processed with low confidence according to the preset setting data of the system board, so that the system board can be adjusted and optimized according to different application scenarios and requirements, thereby improving the flexibility and adaptability of the system. Through a series of processing steps, including binarization, filtering, regional division and human feature detection, the accuracy and reliability of data processing can be significantly improved, and an alarm is sent to the system board in the presence of personnel, so that the system board can control the laser cutting machine to stop and prevent personnel from being harmed.

[0078] Further, in one or more embodiments of the present specification, after the data to be analyzed and the setting data corresponding to the data to be analyzed are processed by the data processing module to obtain the processing result data, the method further includes the following process:

[0079] First, the alarm status values ​​corresponding to the current functions of the laser cutting machine are determined based on the system board. If the processing result data is determined based on the data processing module to not correspond to the preset alarm status value of the data to be analyzed, the data to be analyzed is iteratively detected. If the processing result data is determined based on the data processing module to correspond to the preset alarm status value of the data to be analyzed, the processing result data is transmitted to the preset system board through the data output module. That is, during the operation of the laser cutting machine, a large amount of data, such as cutting speed, power output, temperature, etc., will be generated. These data are collected in real time and compared and analyzed with the preset setting data through the data processing module. Assuming that the cutting speed of the laser cutting machine exceeds the preset maximum value at this time, the data processing module will calculate this abnormality and generate processing result data. The system board will determine the alarm status value corresponding to each function according to the current state of the laser cutting machine. Since the cutting speed is abnormal in this example, the system board will identify an alarm status value related to the cutting speed. At this time, the data processing module compares the processing result data with the preset alarm status value. If the processing result data matches the preset alarm status value, it means that an alarm is required.

[0080] In this process, after the data processing module processes the data to be analyzed and the corresponding setting data to obtain the processing data results, the alarm status values ​​of each function of the current laser cutting machine are determined based on the system board, and compared with the processing result data, so as to accurately determine whether the alarm needs to be triggered. This method avoids false alarms or missed alarms caused by data errors or misjudgments, and advancing the judgment to the data processing module also relieves the content pressure of the system board. Once the processing result data corresponds to the preset alarm status value of the data to be analyzed, the data will be immediately transmitted to the preset system board through the data output module. This immediate response mechanism can quickly trigger an alarm, allowing operators to take timely measures to prevent potential equipment damage or production accidents.

[0081] S103: transmitting the processing result data to a preset system board through the data output module, so that the system board parses the processing result data, so that the system board displays the processing result data on an interface.

[0082] Based on the above step S102, the data to be analyzed and the setting data corresponding to the data to be analyzed are processed by the data processing module, and after obtaining the processing result data, in order to achieve the purpose of timely alarm response and data display, in the embodiment of this specification, the processing result data is transmitted to the preset system board through the data output module, so that the processing result data is parsed by the system board, and then the system board displays the processing result data on the interface. In this process, once the processing result data is transmitted to the system board through the data output module, the system board only needs to parse it to obtain a certain abnormality or alarm state indicated by the processing result data, which helps to quickly trigger the alarm mechanism, thereby achieving timely response to potential problems. And in this process, the system board is transmitted to the processing result data after the data processing is unified by the data processing module, which avoids the traditional scheme that one function often corresponds to one thread, resulting in more functions and more threads being opened, which will cause the CPU rate and memory occupancy rate of the software to be too high in the later stage, so it will cause the system to be stuck in use, and the cost of using the whole machine will also increase accordingly, and improve the response rate of the system board.

[0083] Specifically, in one or more embodiments of the present specification, the processing result data is transmitted to the preset system board through the data output module, which specifically includes the following process:

[0084] First, the processing result data is preprocessed based on the data output module to obtain the result data to be transmitted; wherein, it can be understood that the preprocessing includes: data cleaning, data formatting, and data calibration. Then, the communication protocol between the data output module and the preset system board is determined through the data output module, so as to logically summarize the result data to be transmitted according to the communication protocol and obtain the corresponding result data packet to be transmitted; wherein, it should be noted that the communication protocol is the RS485 communication protocol, in which the specific parameters are 9600 baud rate, no check, 8 data bits, and 1 stop bit. Then, according to the data output module, the result data packet to be transmitted is encapsulated into a recognizable format of the preset system board, and the encapsulated result data packet to be transmitted is transmitted to the preset system board based on the preset communication protocol. That is, the video stream of the network port is transmitted through the network cable, and the laser parameters and the data sent by the sensor are integrated and transmitted to the system board through RS485. The system board decompiles and parses the data according to the rules formulated by the RS485 protocol, and transmits the corresponding functional data to the corresponding display screen for display.

[0085] Among them, for the explanation of logically summarizing the transmission result data according to the communication protocol, for example, the following Table 1 shows an example of the format of the data intelligent processing control device corresponding to the two functions sent to the system board through the data output module in an example. The meaning of each position in the following Table 1 is:

[0086] The first bit of the sent data is fixed at 0x81, indicating that this command is a response command;

[0087] The second bit indicates whether the query command is correctly responded to. 0x01 indicates a normal response, and the others indicate an abnormal response.

[0088] The third bit is the function 1 status;

[0089] The 4th and 5th bits are the response value of function 1, which is calculated as 256*Byte1+Byte2;

[0090] Bit 6 is function 2 status;

[0091] The 7th and 8th bits are the response value of function 2, which is calculated as 256*Byte1+Byte2;

[0092] The 9th bit is the status bit of whether the function 1 is connected successfully. 0x01 is a normal response and the others are abnormal.

[0093] The 10th bit is the status bit of whether function 2 is connected successfully, 0x01 is a normal response and the others are abnormal.

[0094] Table 1 The format of the data intelligent processing control device sent to the system board through the data output module

[0095]

[0096] In addition to the data transmission method in which the data intelligent processing control device initiates data transmission to the system board through the data output module, in another embodiment, it can also be sent to the data intelligent processing unit through the system board. The following Table 2 is an example table of the command format sent by the system board to the data intelligent processing unit.

[0097] Table 2 The command format sent by the system board to the data intelligent processing unit

[0098]

[0099] The meaning of each position in the above Table 2 is:

[0100] The first bit of the sent data is fixed at 0x80, indicating that this instruction is a query command;

[0101] The second bit indicates whether function 1 is enabled;

[0102] The 3rd and 4th bits are function 1, and the calculation method is 256*Byte1+Byte2;

[0103] The 5th bit indicates whether function 2 is enabled;

[0104] The 6th and 7th bits are function 2, and the calculation method is 256*Byte1+Byte2;

[0105] The 8th and 9th bits are function 2, and the calculation method is 256*Byte1+Byte2.

[0106] In addition, from the above, it can be seen that the data intelligent processing process can be used not only for functional data processing of conventional cutting operations of laser cutting machines, but also for complex application scenarios that require real-time monitoring and early warning. Therefore, in one embodiment, the data processing module will also analyze the pictures saved after the network port processing unit reads the video stream of the camera to determine whether there are people. If so, the data output module will output an alarm to the system board, and the system board will control the laser cutting machine to stop to prevent people from being harmed.

[0107] Furthermore, in one or more embodiments of the present specification, the data intelligent processing control device further comprises an industrial computer and a display; wherein the industrial computer is connected to the display and the system interaction unit respectively. Preferably, in one embodiment, before obtaining the data to be analyzed based on the signal input unit and / or the network port processing unit, the method further comprises: transmitting the data to be analyzed obtained by the system interaction unit to the signal input module through the industrial computer.

[0108] Furthermore, in one or more embodiments of the present specification, the processing result data is transmitted to the preset system board through the data output module, which specifically includes the following process:

[0109] If the data processing unit determines that the processing result data does not correspond to the preset alarm status value of the data to be analyzed, the processing result data is given to the industrial computer through the data output module, so that the display is controlled by the industrial computer to display the processing result data. If the data processing unit determines that the processing result data corresponds to the preset alarm status value of the data to be analyzed, the display is controlled by the industrial computer to display the first prompt information, and the data output module is controlled to transmit the processing result data to the preset system board. It can be understood that the first prompt information is used to prompt that the function of the current laser cutter corresponding to the data to be analyzed is abnormal.

[0110] For the intelligent processing control device including the industrial computer, the data to be analyzed acquired by the system interaction unit can be transmitted to the signal input module through the industrial computer, realizing real-time monitoring of the data. At the same time, according to whether the processing result data corresponds to the preset alarm status value, the system can give feedback immediately, such as displaying the processing result data or the first prompt information for further processing or alarm. This real-time monitoring and instant feedback mechanism helps to timely discover and handle abnormal situations and ensure the normal operation of the laser cutter. It also reduces manual intervention and improves the automation level of the system.

[0111] like Figure 6 As shown, in one or more embodiments of this specification, a structural schematic diagram of a data intelligent processing device for laser cutting is provided. Figure 6 It can be seen that in one or more embodiments of the present specification, a data intelligent processing device for laser cutting is applied to an intelligent processing control device, the intelligent processing control device includes a system interaction unit, a signal input module, a data processing module, a data output module, and a network port processing unit connected to the data output module, and a network port integration module connected to the network port processing unit, the device includes:

[0112] at least one processor; and,

[0113] a memory communicatively connected to the at least one processor; wherein,

[0114] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: execute any of the above-mentioned data intelligent processing methods for laser cutting.

[0115] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0116] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A data intelligent processing method for laser cutting, characterized in that: Applied to an intelligent processing control device, the intelligent processing control device includes a system interaction unit, a signal input module, a data processing module, a data output module, a network port processing unit connected to the data output module, and a network port integration module connected to the network port processing unit, the method includes: The data to be analyzed is obtained based on the signal input unit and / or the network port processing unit; wherein the data to be analyzed corresponding to the signal input unit is obtained based on the system interaction unit and the preset sensor, and the data to be analyzed corresponding to the network port processing unit is obtained based on the network port integration module; Processing the data to be analyzed and the setting data corresponding to the data to be analyzed by the data processing module to obtain processing result data; The processing result data is transmitted to a preset system board through the data output module, so that the processing result data is parsed by the system board, so that the system board displays the processing result data on an interface.

2. The data intelligent processing method for laser cutting according to claim 1 is characterized in that: Acquiring the data to be analyzed based on the signal input unit and / or the network port processing unit specifically includes: Acquiring initial setting parameters of the system board through the system interaction unit, and inputting the initial setting parameters into the signal input unit; Integrate the initial setting parameters based on the signal input unit to determine the parameter range of each function of the current laser cutting machine, and use the parameter range as the first data to be analyzed; Based on the preset sensor, the collected data of the current laser cutting machine is obtained, and the collected data is integrated based on the signal input unit to obtain the second data to be analyzed; wherein the second data to be analyzed includes: water quality data of the water chiller, pressure data of the differential pressure filter, temperature data on the laser head, temperature data in the electric control cabinet, and current data of each branch in the electric control cabinet; and / or, Unifying the network segments of the camera and the laser of the current laser cutting machine based on the network port integration module to read the real-time data of the camera and the laser located in the same network segment; The real-time data corresponding to each IP address is input into the network port processing unit, so that the network port processing unit saves the real-time data based on a preset time interval to obtain data to be analyzed.

3. The data intelligent processing method for laser cutting according to claim 2 is characterized in that: Processing the data to be analyzed and the setting data corresponding to the data to be analyzed by the data processing module to obtain processing result data specifically includes: If the data to be analyzed is obtained based on the signal input unit, the data processing module receives the second data to be analyzed based on a Boolean signal; Determine the current laser cutting machine function corresponding to the second data to be analyzed, so as to determine the first data to be analyzed corresponding to the second data to be analyzed as the setting data corresponding to the data to be analyzed; According to the alarm strategies corresponding to the current laser cutting machine functions, the setting data corresponding to the second data to be analyzed and the data to be analyzed are compared in sequence to obtain processing result data.

4. The data intelligent processing method for laser cutting according to claim 2 is characterized in that: Processing the data to be analyzed and the setting data corresponding to the data to be analyzed by the data processing module to obtain processing result data specifically includes: If the data to be analyzed is obtained based on the network port processing unit, the data processing module performs binarization processing on the data to be analyzed to obtain processed data to be analyzed; The processed data to be analyzed are filtered by the data processing module to obtain the filtered data to be analyzed, and the filtered data to be analyzed are low-confidence processed according to the setting data preset by the system board to obtain the processing result data; Preferably, if the data to be analyzed is obtained based on the network port processing unit, the data processing module performs binarization processing on the data to be analyzed, and before obtaining the processed data to be analyzed, the method further includes: Acquire the real-time data collected by the network port integration unit, and perform grayscale processing on the real-time data based on the network port processing unit to obtain grayscale image data corresponding to the real-time data; Filtering the grayscale image data based on a preset Gaussian filter, and dividing the filtered grayscale image data into regions based on a preset size; The filtered grayscale image data of each region is detected according to a preset human feature image file to determine the data to be analyzed that contains human features.

5. The data intelligent processing method for laser cutting according to claim 1 is characterized in that: After the data processing module processes the data to be analyzed and the setting data corresponding to the data to be analyzed to obtain the processing result data, the method further includes: Determine the alarm status value corresponding to each function of the current laser cutting machine based on the system board; If it is determined based on the data processing module that the processing result data does not correspond to the preset alarm state value of the data to be analyzed, iteratively detecting the data to be analyzed; If it is determined based on the data processing module that the processing result data corresponds to the preset alarm state value of the data to be analyzed, the processing result data is transmitted to the preset system board through the data output module.

6. The data intelligent processing method for laser cutting according to claim 1, characterized in that: The preset sensor is communicatively connected with the signal processing unit based on a preset communication protocol; wherein the preset communication protocol includes: RS485 transmission protocol, RS232 transmission protocol, and TTL transmission protocol.

7. The data intelligent processing method for laser cutting according to claim 1, characterized in that: Transmitting the processing result data to the preset system board through the data output module specifically includes: Preprocessing the processing result data based on the data output module to obtain the result data to be transmitted; wherein the preprocessing includes: data cleaning, data formatting, and data calibration; Determining the communication protocol between the data output module and the preset system board through the data output module, so as to logically summarize the result data to be transmitted according to the communication protocol to obtain a corresponding result data packet to be transmitted; wherein the communication protocol is an RS485 communication protocol; The result data packet to be transmitted is encapsulated into a format recognizable by the preset system board based on the data output module, and the encapsulated result data packet to be transmitted is transmitted to the preset system board based on the preset communication protocol.

8. The data intelligent processing method for laser cutting according to claim 1 is characterized in that: The data intelligent processing control device also includes an industrial computer and a display; wherein the industrial computer is connected to the display and the system interaction unit respectively; Preferably, before acquiring the data to be analyzed based on the signal input unit and / or the network port processing unit, the method further comprises: transmitting the data to be analyzed acquired by the system interaction unit to the signal input module based on the industrial computer.

9. The data intelligent processing method for laser cutting according to claim 8, characterized in that: Transmitting the processing result data to the preset system board through the data output module specifically includes: If the data processing unit determines that the processing result data does not correspond to the preset alarm state value of the data to be analyzed, the processing result data is given to the industrial computer through the data output module, so that the display is controlled by the industrial computer to display the processing result data; If the data processing unit determines that the processing result data corresponds to the preset alarm status value of the data to be analyzed, the display is controlled to display a first prompt message based on the industrial controller, and the data output module is controlled to transmit the processing result data to a preset system board; wherein the first prompt message is used to prompt that the function of the current laser cutter corresponding to the data to be analyzed is abnormal.

10. A data intelligent processing device for laser cutting, characterized in that: Applied to an intelligent processing control device, the intelligent processing control device includes a system interaction unit, a signal input module, a data processing module, a data output module, a network port processing unit connected to the data output module, and a network port integration module connected to the network port processing unit, the device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: execute the data intelligent processing method for laser cutting described in any one of claims 1-9 above.

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