Intelligent remote management method and terminal for numerical control equipment

Abstract

The invention relates to the technical field of data transmission management, in particular to an intelligent remote management method and terminal for numerical control equipment. According to the invention, a three-layer redundant transmission system comprising RS485, PROFINET and Internet of Things communication lines is constructed, so that the data transmission redundancy and stability are improved. And during real-time data transmission, a verification mode and three-layer redundancy synchronous transmission are adopted, so that the data are ensured to be accurate and complete. For batch data, the method analyzes the historical transmission performance of PROFINET and Internet of Things lines, such as bandwidth fluctuation, size change and packet loss rate, calculates an interference coefficient, predicts and adjusts the packet loss rate, and accurately evaluates the data transmission quality of different lines. Finally, the transmission packet loss rates of all the lines are compared, the optimal line is selected to transmit batch data, the data integrity is guaranteed, the transmission cost is reduced, and the overall efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission management, and in particular to a method and terminal for intelligent remote management of numerical control equipment. Background Art

[0002] In modern manufacturing, CNC equipment is the core component of automated production lines, and its efficient and stable operation is crucial to ensuring production efficiency and product quality. The intelligence and networking of CNC equipment have become a development trend, and remote management has become an important means to improve equipment maintenance efficiency and optimize production processes. However, in actual applications, the amount of data generated by CNC equipment is huge, and this data needs to be transmitted in real time or in batches to a remote management center for analysis and processing.

[0003] CNC equipment generates a large amount of real-time data and batch data during operation. Real-time data usually has high requirements for real-time transmission and needs to be transmitted to the remote management system quickly and accurately; batch data may contain a large amount of historical records or configuration information, and has high requirements for the integrity and reliability of transmission; however, existing remote management methods for CNC equipment often rely on a single communication line, such as RS485, Ethernet, etc. This single communication method is easily affected by external environmental interference, such as electromagnetic interference, physical damage, etc., resulting in unstable and interrupted data transmission, and even serious packet loss affecting the integrity and accuracy of the data. Summary of the invention

[0004] In order to solve the technical problem that the existing remote management methods often do not perform differentiated processing on different types of data, resulting in the difficulty in achieving the optimal data transmission efficiency and reliability, the purpose of the present invention is to provide a method and terminal for intelligent remote management of numerical control equipment. The technical solutions adopted are as follows:

[0005] Construct three-layer redundant transmission lines based on RS485 communication lines, PROFINET communication lines and IoT communication lines;

[0006] When transmitting the real-time data segment generated by the numerical control equipment, setting the data transmission verification mode, and using the three-layer redundant transmission line to transmit the real-time data segment;

[0007] When transmitting batch data generated by CNC equipment, the PROFINET communication line and the Internet of Things communication line in the three-layer redundant transmission line are used as the lines to be tested; under each line to be tested, multiple historical real-time data segments, the bandwidth timing fluctuation sequence and the bandwidth timing change sequence when transmitting each historical real-time data segment, and the packet loss rate of each historical real-time data segment are obtained; the correlation between each historical real-time data segment and the corresponding bandwidth timing fluctuation sequence and bandwidth timing change sequence is analyzed to obtain the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient;

[0008] Under each line to be tested, based on the packet loss rate of the historical real-time data segment, the corresponding bandwidth size interference coefficient and the overall size of the batch data, the predicted packet loss rate of the batch data is determined; according to the size of a single file in the batch data, the bandwidth fluctuation interference coefficient of the historical real-time data segment and the bandwidth size interference coefficient, the predicted packet loss rate is adjusted to obtain the transmission packet loss rate of the batch data under each line to be tested; based on the transmission packet loss rate, a transmission line is selected under two lines to be tested and the batch data is transmitted.

[0009] Furthermore, the data transmission verification method is:

[0010] At the transmitting end, a CRC value of the real-time data segment to be transmitted is generated, and the CRC value is attached to the data packet of the real-time data segment to be transmitted in the PROFINET communication line and the Internet of Things communication line respectively for transmission, and the data packet of the real-time data segment to be transmitted is transmitted by the RS485 communication line;

[0011] At the receiving end, the CRC values ​​of the data packets of the real-time data segment to be transmitted in the PROFINET communication line and the IoT communication line are compared. If they are consistent, the data packets of the real-time data segment are output;

[0012] If they are inconsistent, the data packet of the real-time data segment will be output in the RS485 communication line.

[0013] Furthermore, the method for acquiring the bandwidth timing fluctuation sequence includes:

[0014] Under each line to be tested, for any historical real-time data segment, the standard deviation of the bandwidth within one second is taken as the bandwidth fluctuation value per second, thereby obtaining the bandwidth timing fluctuation sequence.

[0015] Furthermore, the method for acquiring the bandwidth timing change sequence includes:

[0016] Under each line to be tested, for any historical real-time data segment, the average bandwidth within each second is taken as the bandwidth value per second, thereby obtaining a bandwidth time series change sequence.

[0017] Furthermore, the analysis of the correlation between each historical real-time data segment and the corresponding bandwidth timing fluctuation sequence and bandwidth timing change sequence, to obtain the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient, includes:

[0018] Under each line to be tested, the absolute value of the correlation coefficient between each historical real-time data segment and the corresponding bandwidth fluctuation time series is used as the bandwidth fluctuation interference coefficient;

[0019] The absolute value of the correlation coefficient between each historical real-time data segment and the corresponding bandwidth time series change sequence is used as the bandwidth interference coefficient.

[0020] Furthermore, the method for obtaining the predicted packet loss rate includes:

[0021] Under each line to be tested, the product of the value after negative correlation mapping of the bandwidth interference coefficient of the last historical real-time data segment in the time sequence and the preset bandwidth is used as the current bandwidth value;

[0022] The ratio of the size of the batch data to the current bandwidth value is used as the transmission duration;

[0023] In terms of timing, the packet loss rate of the last historical real-time data segment is integrated over time, with the lower limit of the integration being 0 and the upper limit being the transmission duration, and the integrated value obtained is used as the predicted packet loss rate of the batch data under each line to be tested.

[0024] Furthermore, the method for obtaining the transmission packet loss rate includes:

[0025] Obtain the size of a single file in the batch data;

[0026] Under each line to be tested, under the current bandwidth value, according to the packet loss rate corresponding to each historical real-time data segment, the bandwidth fluctuation interference coefficient and the size of a single file in the batch data, obtain a first comprehensive packet loss rate of the batch data under each bandwidth fluctuation interference coefficient;

[0027] Perform straight line fitting on the bandwidth fluctuation interference coefficient corresponding to the historical real-time data segment and the corresponding first comprehensive packet loss rate, and use the slope value of the fitting straight line as the bandwidth fluctuation sensitivity coefficient;

[0028] Under different bandwidth interference coefficients, a second comprehensive packet loss rate of the batch data is obtained, and a change of the second comprehensive packet loss rate with the bandwidth interference coefficient is analyzed to obtain a bandwidth sensitivity coefficient;

[0029] The product of the bandwidth fluctuation interference coefficient of the last historical real-time data segment in time sequence and the bandwidth fluctuation sensitivity coefficient is used as the first adjustment factor, and the product of the bandwidth size interference coefficient of the last historical real-time data segment in time sequence and the bandwidth size sensitivity coefficient is used as the second adjustment factor;

[0030] Determine an adjustment coefficient based on the first adjustment factor and the second adjustment factor, and both the first adjustment factor and the second adjustment factor are positively correlated with the adjustment coefficient;

[0031] The product of the adjustment coefficient for each line to be tested and the predicted packet loss rate of the batch data is used as the transmission packet loss rate of the batch data for each line to be tested.

[0032] Furthermore, the method for obtaining the bandwidth sensitivity coefficient includes:

[0033] Different bandwidth interference coefficients are preset, and the product of the value after negative correlation mapping and normalization of each bandwidth interference coefficient and the preset bandwidth is used as the bandwidth value to be tested, and the packet loss rate of the last historical real-time data segment in the time series is used as the target packet loss rate;

[0034] Under each of the bandwidth values ​​to be measured, according to the target packet loss rate and the size of a single file in the batch data, obtaining a second comprehensive packet loss rate of the batch data;

[0035] A straight line fitting is performed on the bandwidth interference coefficient corresponding to the bandwidth value to be measured and the second comprehensive packet loss rate, and the slope value of the fitting straight line is used as the bandwidth sensitivity coefficient.

[0036] Further, the selecting a transmission line under two lines to be tested and transmitting the batch data based on the transmission packet loss rate includes:

[0037] Under two lines under test, the line under test with the smallest transmission packet loss rate of the batch data is used to transmit the batch data.

[0038] A smart remote management terminal for numerical control equipment comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps of a method for smart remote management of numerical control equipment are implemented.

[0039] The present invention has the following beneficial effects:

[0040] In the present invention, by constructing a three-layer redundant transmission line including an RS485 communication line, a PROFINET communication line and an Internet of Things communication line, the redundancy of data transmission can be effectively improved. When any single line fails, other lines can still continue to undertake the data transmission task, ensuring that the real-time data and batch data generated by the numerical control equipment can be continuously and stably transmitted, reducing the risk of data loss or transmission interruption. When transmitting the real-time data generated by the numerical control equipment, a data transmission verification mode is set, and a three-layer redundant transmission line is used for synchronous transmission, which not only enhances the redundancy of data transmission, but also ensures the accuracy and integrity of the data through a verification mechanism, avoiding the impact of data errors or loss on production control. For the transmission of batch data, the present invention analyzes the historical transmission performance of the PROFINET communication line and the Internet of Things communication line, including key indicators such as bandwidth fluctuation, bandwidth size change and packet loss rate. Specifically, the correlation between the historical real-time data segment and the bandwidth fluctuation and bandwidth size is deeply analyzed, and the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient are calculated, and then the packet loss rate of the batch data is predicted and adjusted, so that the data transmission quality under different lines can be accurately predicted and evaluated. This refined management makes the transmission of batch data more efficient and reliable, reduces unnecessary transmission packet loss, and improves the success rate of data transmission. Finally, the transmission packet loss rates of different lines are compared to select the line that best suits the current transmission needs for batch data transmission, which not only optimizes the transmission path and ensures data integrity, but also reduces transmission costs and improves overall transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A method flow chart of a method for intelligent remote management of numerical control equipment provided by one embodiment of the present invention;

[0043] Figure 2 A flow chart of a data transmission verification method provided by an embodiment of the present invention;

[0044] Figure 3 A method flow chart of a method for obtaining a transmission packet loss rate provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of the structure of an intelligent remote management terminal for numerical control equipment provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a CNC equipment intelligent remote management method and terminal proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0047] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0048] The specific scheme of a method and terminal for intelligent remote management of numerical control equipment provided by the present invention is described in detail below with reference to the accompanying drawings.

[0049] See also Figure 1 , which shows a method flow chart of a method for intelligent remote management of numerical control equipment provided by an embodiment of the present invention, the method comprising the following steps:

[0050] Step S1: construct a three-layer redundant transmission line based on RS485 communication line, PROFINET communication line and Internet of Things communication line.

[0051] With the rapid development of Industry 4.0 and intelligent manufacturing, CNC equipment, as the core technical equipment of modern manufacturing industry, has a direct impact on production quality and economic benefits due to its operating efficiency and stability. At present, the remote management of CNC equipment is facing potential security risks and signal interference problems in the data transmission process, especially in complex and heavy-load industrial environments. Existing remote management methods for CNC equipment often rely on a single communication line, such as RS485, Ethernet, etc. This single communication method is susceptible to interference from the external environment, such as electromagnetic interference, physical damage, etc. However, since CNC equipment generates a large amount of real-time data and batch data during operation, real-time data usually has high requirements for real-time transmission and needs to be transmitted to the remote management system quickly and accurately; while batch data may contain a large amount of historical records or configuration information, and has high requirements for the integrity and reliability of transmission. Therefore, the existing single communication method may cause transmission instability, interruption, or even packet loss when dealing with interference, attacks or failures during transmission.

[0052] In an embodiment of the present invention, in order to ensure the reliability, integrity and real-time performance of the data of the CNC equipment during the transmission process, a three-layer redundant transmission line is constructed using RS485 communication lines, PROFINET communication lines and Internet of Things communication lines. The design of this three-layer redundant transmission line aims to improve the stability and reliability of data transmission.

[0053] First of all, as a commonly used serial communication interface standard, RS485 communication line has the characteristics of long transmission distance and strong anti-noise interference ability, but relatively slow data transmission speed. It is suitable for basic data transmission between CNC equipment and remote management center.

[0054] Secondly, as a high-performance industrial Ethernet standard, PROFINET communication lines provide high-speed data transmission capabilities, which can meet the data transmission needs of CNC equipment in a variety of scenarios, but are highly sensitive to the environment.

[0055] Finally, the IoT communication lines use wireless communication technologies, such as Wi-Fi or Bluetooth, to achieve wireless connection between CNC equipment and remote management centers. This wireless connection method not only improves the flexibility of data transmission, but also reduces wiring costs and maintenance difficulties. It has a higher transmission rate, but is more dependent on the stability of the network.

[0056] By combining these three communication lines to construct a three-layer redundant transmission line, the embodiment of the present invention achieves diversified selection and high reliability guarantee of data transmission.

[0057] Step S2: When transmitting the real-time data segment generated by the numerical control equipment, a data transmission verification mode is set, and the real-time data segment is transmitted using a three-layer redundant transmission line.

[0058] Real-time data usually has high requirements for the real-time transmission, because in CNC equipment, real-time data is directly related to the operating status of CNC equipment and the control of the production process. Therefore, in the production process of CNC equipment, for the equipment temperature data, equipment voltage data, equipment vibration data, etc. collected by the sensor; a data transmission verification method can be set, and the three-layer redundant transmission line in the embodiment of the present invention is used to transmit the real-time data segment, and the data is verified and verified during the data transmission process, so as to ensure the integrity and accuracy of the data while ensuring the real-time performance of data transmission.

[0059] Preferably, in one embodiment of the present invention, the data transmission verification method includes:

[0060] See also Figure 2 , which shows a flow chart of a data transmission verification method in one embodiment of the present invention.

[0061] Since both the PROFINET communication line and the IoT communication line have a high transmission rate, for real-time data segments with high real-time requirements, a CRC value of the real-time data segment to be transmitted is generated at the sending end, and the CRC value is attached to the data packet of the real-time data segment to be transmitted in the PROFINET communication line and the IoT communication line respectively, and the data packet of the real-time data segment to be transmitted is transmitted by the RS485 communication line at the same time.

[0062] It should be noted that cyclic redundancy check (CRC) is a commonly used data verification method, which generates a check code, namely, a CRC value, by performing polynomial calculation on the data and appends it to the data packet.

[0063] At the receiving end, the CRC values ​​of the data packets of the real-time data segment to be transmitted in the PROFINET communication line and the IoT communication line are first compared. If they are consistent, it means that no error occurs in the transmission of the real-time data segment, and the data packet of the real-time data segment is output.

[0064] If they are inconsistent, it means that an error may have occurred in the transmission of the real-time data segment. Therefore, for the PROFINET communication line and the Internet of Things communication line with faster transmission speeds, the credibility of the data packets of the real-time data segment transmitted is reduced. Therefore, the RS485 communication line with strong anti-noise interference ability is selected to output the data packets of the real-time data segment.

[0065] It should be noted that in the embodiment of the present invention, real-time data is sampled once every second, and the length of the real-time data segment is set to 5 seconds. The length of the real-time data segment and the sampling interval can be adjusted according to the implementation scenario and are not limited here.

[0066] Step S3: When transmitting batch data generated by CNC equipment, the PROFINET communication line and the Internet of Things communication line in the three-layer redundant transmission line are used as the lines to be tested; under each line to be tested, multiple historical real-time data segments, the bandwidth timing fluctuation sequence and the bandwidth timing change sequence when transmitting each historical real-time data segment, and the packet loss rate of each historical real-time data segment are obtained; the correlation between each historical real-time data segment and the corresponding bandwidth timing fluctuation sequence and bandwidth timing change sequence is analyzed to obtain the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient.

[0067] In the production activities of numerical control equipment, in addition to real-time data, due to the diverse production working modes of numerical control equipment, large-scale batch data will also be generated, such as the processing task information (task number, processing part type, processing quantity) of numerical control equipment, production progress (number of completed parts, expected completion time), production quality (processing accuracy, scrap rate), etc. For large-scale batch data, when sending, it is necessary to ensure the integrity of data transmission to the greatest extent and to increase the data transmission speed as much as possible. Therefore, in the embodiment of the present invention, when transmitting the batch data generated by the numerical control equipment, the PROFINET communication line and the Internet of Things communication line in the three-layer redundant transmission line are preferentially selected, and these two communication lines are used as the lines to be tested. Further, when the mode in the production process of numerical control equipment changes, the change of some sensor information under the real-time data will have different degrees of influence on the bandwidth of the current transmission line, and the fluctuation and change of the bandwidth on the transmission line will affect the integrity and transmission speed of the transmission data. Therefore, when selecting the final transmission line in the line to be tested, it is necessary to consider the influence of the data change in the real-time data segment transmitted in the historical process on the line bandwidth.

[0068] Under each line to be tested, multiple historical real-time data segments in the historical transmission process, a bandwidth timing fluctuation sequence and a bandwidth timing change sequence when transmitting each historical real-time data segment, and a packet loss rate of each historical real-time data segment are obtained.

[0069] Preferably, in one embodiment of the present invention, the method for acquiring the bandwidth fluctuation sequence and the bandwidth time series change sequence includes:

[0070] The standard deviation is an effective indicator for measuring the degree of data dispersion and is suitable for quantifying data fluctuations. Therefore, for each line to be tested, for any historical real-time data segment, the standard deviation of the bandwidth of the line to be tested within each second when the historical real-time data segment is transmitted is taken as the bandwidth fluctuation value per second. Then, the bandwidth fluctuation values ​​of all seconds are arranged in time series to obtain the bandwidth timing fluctuation sequence of each line to be tested during the period of transmitting the historical real-time data segment.

[0071] The mean is a common indicator for measuring the trend of data concentration and is suitable for revealing the changes in data. Therefore, for each line to be tested, for any historical real-time data segment, the mean of the bandwidth of the line to be tested per second when the historical real-time data segment is transmitted is used as the bandwidth value per second, and the bandwidth values ​​of all seconds are sorted in time series. In this way, the bandwidth time series change sequence of each line to be tested during the period of transmitting the historical real-time data segment can be obtained.

[0072] The packet loss rate of each real-time data segment during the historical transmission process is calculated as: [(input message-output message) / input message]×100%.

[0073] So far, through the above calculations, the packet loss rate, bandwidth timing fluctuation sequence and bandwidth timing change sequence of each historical real-time data segment during the historical transmission process can be obtained under each line to be tested.

[0074] Based on the above analysis, it can be known that the fluctuation and change of bandwidth on the transmission line will affect the integrity and transmission speed of the transmitted data. Therefore, in this embodiment of the present invention, in order to further quantify the bandwidth fluctuation interference and bandwidth change interference, under each line to be tested, for each historical real-time data segment in the historical transmission process, the correlation between it and the corresponding bandwidth timing fluctuation sequence and the correlation with the corresponding bandwidth timing change sequence are analyzed respectively, so as to obtain the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient.

[0075] Preferably, in one embodiment of the present invention, a method for obtaining a bandwidth fluctuation interference coefficient and a bandwidth size interference coefficient includes:

[0076] The correlation coefficient measures the degree of correlation between two sets of data. Its value is between -1 and 1. If it is close to 1, it means that the two sets of data are more positively correlated. The closer it is to -1, the more negatively correlated the two sets of data are. If the absolute value of the correlation coefficient is closer to 1, the stronger the linear relationship between the two sets of data is, which means that changes in one set of data will affect changes in another set of data.

[0077] Therefore, for each line to be tested, the absolute value of the correlation coefficient between each historical real-time data segment and the corresponding bandwidth fluctuation time series is used as the bandwidth fluctuation interference coefficient. At this time, the larger the bandwidth fluctuation interference coefficient is, the more likely the change of data value in the historical real-time data segment is to affect the bandwidth fluctuation of the line to be tested.

[0078] Similarly, the absolute value of the correlation coefficient between each historical real-time data segment and the corresponding bandwidth timing change sequence is used as the bandwidth interference coefficient. At this time, the larger the bandwidth interference coefficient is, the more likely the change in the data value in the historical real-time data segment is to affect the bandwidth of the line to be tested.

[0079] At this point, the impact of each historical real-time data segment on the bandwidth during the historical transmission process can be obtained under each line to be tested, and quantified as a bandwidth fluctuation interference coefficient and a bandwidth size interference coefficient.

[0080] Step S4: Under each line to be tested, based on the packet loss rate of the historical real-time data segment, the corresponding bandwidth size interference coefficient and the overall size of the batch data, determine the predicted packet loss rate of the batch data; according to the size of a single file in the batch data, the bandwidth fluctuation interference coefficient of the historical real-time data segment and the bandwidth size interference coefficient, adjust the predicted packet loss rate to obtain the transmission packet loss rate of the batch data under each line to be tested; based on the transmission packet loss rate, select the transmission line under the two lines to be tested and transmit the batch data.

[0081] Based on the above steps, we can get the packet loss rate of the historical real-time data segment and the corresponding bandwidth interference coefficient. Therefore, we can first calculate the predicted packet loss rate of the overall transmission of batch data under each line to be tested based on these two indicators and the overall size of the batch data, as an indicator to measure each line to be tested as the final selected transmission line. However, since the content of the batch data is composed of a large number of files during transmission, the sizes of these files will vary. For files of different sizes in the batch data, they will have different degrees of sensitivity to the changes in the bandwidth size and fluctuations of the communication line during the transmission process, so the packet loss rate will also vary. And if the files in the same batch data are transmitted in different arrangement orders, in the arrangement mode where large files appear continuously, the bandwidth they occupy will be longer and the fluctuations allocated to each file will be smoothed, so the transmission will be more stable. When small files are arranged continuously, since small files occupy less bandwidth, multiple small files are sent in parallel at the same time. When the bandwidth is allocated to a single file at this time, it will cause relatively large transmission fluctuations, making it more prone to packet loss. Therefore, when batch data is transmitted, the size and arrangement of the files therein will also affect the packet loss rate. Therefore, in this embodiment of the present invention, the size of a single file in the batch data and the sensitivity to bandwidth size changes and fluctuations under the arrangement of the files in the batch data are analyzed, so as to adjust the predicted packet loss rate and obtain a more accurate transmission packet loss rate of the batch data under each line to be tested.

[0082] Therefore, in this embodiment of the present invention, under each line to be tested, the predicted packet loss rate of the batch data can be determined based on the packet loss rate of the historical real-time data segment, the corresponding bandwidth interference coefficient and the overall size of the batch data.

[0083] Preferably, in one embodiment of the present invention, the method for obtaining the predicted packet loss rate includes:

[0084] Since the bandwidth interference coefficient reflects the interference to the bandwidth, it is closely related to the actual size of the bandwidth. The larger the coefficient is, the greater the interference is, and the smaller the actual size of the bandwidth will be.

[0085] Therefore, for each line to be tested, the bandwidth interference coefficient of the last historical real-time data segment in the timing of the historical transmission process is negatively correlated, and the product of the negatively correlated mapped value and the preset bandwidth is used as the current bandwidth value. The current bandwidth value can be regarded as the actual bandwidth of the transmission line when transmitting batch data. For each line to be tested, the reliability of calculating the current bandwidth value using the bandwidth interference coefficient of the historical real-time data segment closest to the timing of the current batch data transmission will be higher. The negative correlation mapping here can use the formula 1-x, where x represents the independent variable.

[0086] Then the ratio of the size of the batch data to the current bandwidth value is used as the transmission duration, which represents the time required to transmit the entire batch data under the current bandwidth value.

[0087] Finally, in terms of timing, the packet loss rate of the last historical real-time data segment is integrated over time. The physical meaning of this integration can be understood as the accumulation of data loss due to packet loss within a given transmission duration, where the lower limit of the integration is 0 and the upper limit is the transmission duration calculated above. The integrated value is used as the predicted packet loss rate of batch data on each line to be tested.

[0088] It should be noted that the preset bandwidth is related to the transmission capacity of the transmission line. In the embodiment of the present invention, it is set to 1000M. The specific size needs to be adjusted according to the rated bandwidth of the transmission line in actual circumstances.

[0089] At this point, the predicted packet loss rate of the batch data under each line to be tested can be obtained. Further, the sensitivity of the arrangement of files in the batch data to the change in bandwidth size and fluctuation can be further analyzed, so as to adjust the predicted packet loss rate and obtain a more accurate transmission packet loss rate of the batch data under each line to be tested.

[0090] Preferably, in one embodiment of the present invention, the method for obtaining the transmission packet loss rate includes:

[0091] See also Figure 3 , which shows a method flow chart of a method for obtaining a transmission packet loss rate in an embodiment of the present invention, wherein the method comprises the following steps:

[0092] Step S401: for any line to be tested, a first comprehensive packet loss rate of batch data is obtained under different bandwidth fluctuation interference coefficients, and a change of the first comprehensive packet loss rate with the bandwidth fluctuation interference coefficient is analyzed to obtain a bandwidth fluctuation sensitivity coefficient.

[0093] Get the size of a single file in the batch data, which can be obtained through the information in the header file of the batch data.

[0094] Then, under the current bandwidth value (calculated in the above process), according to the packet loss rate corresponding to each historical real-time data segment, the bandwidth fluctuation interference coefficient and the size of a single file in the batch data, the first comprehensive packet loss rate of the batch data under each bandwidth fluctuation interference coefficient is obtained. At this time, in a large number of historical real-time data segments of the line to be tested, the first comprehensive packet loss rate of the batch data corresponding to each bandwidth fluctuation interference coefficient can be obtained.

[0095] Finally, a straight line fitting is performed on the bandwidth fluctuation interference coefficient corresponding to the historical real-time data segment and the corresponding first comprehensive packet loss rate. The fitting straight line can intuitively show the relationship between the bandwidth fluctuation interference coefficient and the first comprehensive packet loss rate. Therefore, in this embodiment of the present invention, the slope value of the fitting straight line is used as the bandwidth fluctuation sensitivity coefficient. The larger the bandwidth fluctuation sensitivity coefficient, the greater the bandwidth fluctuation interference. Therefore, the current file arrangement method of the batch data may cause a greater possibility of packet loss for the batch data.

[0096] It should be noted that the method for obtaining the fitting straight line can adopt the least square method, which is a well-known technology and the specific process will not be described here.

[0097] Here, an example is given to illustrate the calculation method of the first comprehensive packet loss rate: Assuming that there are three files in the batch data, the file sizes are 2, 3, and 5 in order of arrangement. If the current bandwidth value is 8, the sizes of the files in the batch data after segmentation are 2, 3, 3, and 2. At this time, if the bandwidth fluctuation interference coefficient is 0.2 and the packet loss rate is 0.02 in a certain historical real-time data segment, the packet loss rate of the batch data is 0.02×4=0.08; if the bandwidth fluctuation interference coefficient is 0.4 and the packet loss rate is 0.06 in a certain historical real-time data segment, the packet loss rate of the batch data is 0.06×4=0.24.

[0098] Step S402: for any line to be tested, under different bandwidth interference coefficients, a second comprehensive packet loss rate of batch data is obtained, and a change of the first comprehensive packet loss rate with the bandwidth interference coefficient is analyzed to obtain a bandwidth sensitivity coefficient.

[0099] Different bandwidth interference coefficients are preset, and the product of the value after negative correlation mapping and normalization of each bandwidth interference coefficient and the preset bandwidth is used as the bandwidth value to be tested, and the packet loss rate of the last historical real-time data segment in the timing of the historical transmission process of the line to be tested is used as the target packet loss rate. The negative correlation mapping here can adopt the formula 1-x, where x represents the independent variable.

[0100] Then similarly, at each bandwidth value to be tested, the second comprehensive packet loss rate of the batch data is obtained according to the target packet loss rate and the size of a single file in the batch data. At this time, at each bandwidth value to be tested, the batch data corresponds to a second comprehensive packet loss rate.

[0101] Finally, a straight line fitting is performed on the preset bandwidth interference coefficient corresponding to the bandwidth value to be tested and the second comprehensive packet loss rate, and the slope value of the fitted straight line is used as the bandwidth sensitivity coefficient. The larger the bandwidth sensitivity coefficient, the greater the bandwidth interference. In this case, the current file arrangement method of the batch data may cause a greater possibility of packet loss for the batch data.

[0102] It should be noted that the method for obtaining the fitting straight line can adopt the least squares method, which is a well-known technology and the specific process will not be repeated here; the interference coefficients of different bandwidth sizes are preset. In this embodiment of the present invention, they are set to 0.2, 0.4, 0.6 and 0.8. The specific values ​​and quantities can be adjusted according to the implementation scenario, but the value must be between 0-1.

[0103] Here, the calculation method of the second comprehensive packet loss rate is illustrated by an example: assuming that there are three files in the batch data, and the file sizes are 2, 3, and 5 in order of arrangement. If the preset bandwidth is 10, then when the preset bandwidth size interference coefficient is 0.2, the bandwidth value to be measured is 8, and the sizes of the files in the batch data after segmentation are 2, 3, 3, and 2. At this time, if the target packet loss rate is 0.02, the packet loss rate of the batch data is 0.02×4=0.08; if the preset bandwidth size interference coefficient is 0.5, then the bandwidth value to be measured is 5, and if the target packet loss rate is 0.02, the packet loss rate of the batch data is 0.02×3=0.06.

[0104] Step S403: Under each line to be tested, the predicted packet loss rate of the batch data is adjusted by comprehensively considering the bandwidth fluctuation sensitivity coefficient, the bandwidth size sensitivity coefficient, the bandwidth fluctuation interference coefficient of the historical real-time data segment, and the bandwidth size interference coefficient to obtain the transmission packet loss rate of the batch data under each line to be tested.

[0105] The product of the bandwidth fluctuation interference coefficient and the bandwidth fluctuation sensitivity coefficient of the last historical real-time data segment in the time series is used as the first adjustment factor, and the product of the bandwidth size interference coefficient and the bandwidth size sensitivity coefficient of the last historical real-time data segment in the time series is used as the second adjustment factor.

[0106] An adjustment coefficient is determined based on the first adjustment factor and the second adjustment factor, and both the first adjustment factor and the second adjustment factor are positively correlated with the adjustment coefficient.

[0107] The product of the adjustment coefficient of each line to be tested and the predicted packet loss rate of the batch data is taken as the transmission packet loss rate of the batch data on each line to be tested. The formula model of the transmission packet loss rate includes:

[0108] CD=YD×(1+DX×DM+DB×DD)

[0109] Among them, CD represents the transmission packet loss rate of batch data on each line to be tested; YD represents the predicted packet loss rate of batch data on each line to be tested; DX represents the bandwidth fluctuation interference coefficient of the last historical real-time data segment on each line to be tested; DM represents the bandwidth fluctuation sensitivity coefficient on each line to be tested; DB represents the bandwidth size interference coefficient of the last historical real-time data segment on each line to be tested; DD represents the bandwidth size sensitivity coefficient on each line to be tested.

[0110] In the formula model of the transmission packet loss rate, DX×DM represents the first adjustment factor under each line to be tested. The larger the value, the higher the sensitivity to bandwidth fluctuation under the arrangement of the current files in the batch data, that is, it is easily affected by the bandwidth fluctuation, and the packet loss rate will increase; similarly, DB×DD represents the second adjustment factor under each line to be tested. The larger the value, the higher the sensitivity to bandwidth size change under the arrangement of the current files in the batch data, that is, it is easily affected by the bandwidth size change, and the packet loss rate will increase; and (1+DX×DM+DB×DD) represents the adjustment coefficient under each line to be tested, indicating the bandwidth fluctuation interference and bandwidth size interference of the line to be tested, respectively, combined with the sensitivity to bandwidth fluctuation and bandwidth size change under the file arrangement of the current batch data, and the degree of adjustment of the predicted packet loss rate of the batch data. The larger the value, the greater the possibility of packet loss. Therefore, under each line to be tested, the adjustment coefficient is multiplied by the predicted packet loss rate of the batch data to obtain the transmission packet loss rate of the batch data under each line to be tested.

[0111] At this point, through the above process, the transmission packet loss rate of the current batch data to be transmitted under the two lines to be tested can be obtained, and based on this indicator, an optimal transmission line can be selected to transmit the batch data.

[0112] Preferably, in one embodiment of the present invention, based on the transmission packet loss rate, selecting a transmission line under two lines to be tested and transmitting the batch data comprises:

[0113] Since the smaller the transmission packet loss rate is, the more reliable the transmission line is, therefore, under the two lines to be tested, the line to be tested with the smallest transmission packet loss rate of the batch data is used to transmit the batch data.

[0114] In summary, in an embodiment of the present invention, by constructing a three-layer redundant transmission line including an RS485 communication line, a PROFINET communication line, and an Internet of Things communication line, the redundancy of data transmission can be effectively improved. When any single line fails, other lines can still continue to undertake the data transmission task, ensuring that the real-time data and batch data generated by the numerical control equipment can be continuously and stably transmitted, reducing the risk of data loss or transmission interruption. When transmitting the real-time data generated by the numerical control equipment, a data transmission verification method is set, and a three-layer redundant transmission line is used for synchronous transmission, which not only enhances the redundancy of data transmission, but also ensures the accuracy and integrity of the data through the verification mechanism, avoiding the impact of data errors or loss on production control. For the transmission of batch data, the present invention analyzes the historical transmission performance of the PROFINET communication line and the Internet of Things communication line, including key indicators such as bandwidth fluctuation, bandwidth size change, and packet loss rate. Specifically, the correlation between the historical real-time data segment and the bandwidth fluctuation and bandwidth size is deeply analyzed, and the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient are calculated, and then the packet loss rate of the batch data is predicted and adjusted, so that the data transmission quality under different lines can be accurately predicted and evaluated. This refined management makes the transmission of batch data more efficient and reliable, reduces unnecessary transmission packet loss, and improves the success rate of data transmission. Finally, the transmission packet loss rates of different lines are compared to select the line that best suits the current transmission needs for batch data transmission, which not only optimizes the transmission path and ensures data integrity, but also reduces transmission costs and improves overall transmission efficiency.

[0115] The embodiment of the present invention also provides a CNC equipment intelligent remote management terminal, see Figure 4 , which shows a schematic diagram of the structure of a smart remote management terminal for numerical control equipment provided by an embodiment of the present invention, including a processor 500, a memory 501, a bus 502 and a communication interface 503, wherein the processor 500, the communication interface 503 and the memory 501 are connected via the bus 502; wherein the memory 501 may include a high-speed random access memory, the bus 502 may be an ISA bus, a PCI bus or an EISA bus, etc., and the processor 500 may be an integrated circuit chip with signal processing capabilities; the memory 501 stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, a step in a method for smart remote management of numerical control equipment is implemented.

[0116] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying 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 various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for intelligent remote management of numerical control equipment, characterized in that: The method comprises: Construct three-layer redundant transmission lines based on RS485 communication lines, PROFINET communication lines and IoT communication lines; When transmitting the real-time data segment generated by the numerical control equipment, setting the data transmission verification mode, and using the three-layer redundant transmission line to transmit the real-time data segment; When transmitting batch data generated by CNC equipment, the PROFINET communication line and the Internet of Things communication line in the three-layer redundant transmission line are used as the lines to be tested; under each line to be tested, multiple historical real-time data segments, the bandwidth timing fluctuation sequence and the bandwidth timing change sequence when transmitting each historical real-time data segment, and the packet loss rate of each historical real-time data segment are obtained; the correlation between each historical real-time data segment and the corresponding bandwidth timing fluctuation sequence and bandwidth timing change sequence is analyzed to obtain the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient; Under each line to be tested, based on the packet loss rate of the historical real-time data segment, the corresponding bandwidth size interference coefficient and the overall size of the batch data, the predicted packet loss rate of the batch data is determined; according to the size of a single file in the batch data, the bandwidth fluctuation interference coefficient of the historical real-time data segment and the bandwidth size interference coefficient, the predicted packet loss rate is adjusted to obtain the transmission packet loss rate of the batch data under each line to be tested; based on the transmission packet loss rate, a transmission line is selected under two lines to be tested and the batch data is transmitted.

2. A method for intelligent remote management of numerical control equipment according to claim 1, characterized in that: The data transmission verification method is: At the transmitting end, a CRC value of the real-time data segment to be transmitted is generated, and the CRC value is attached to the data packet of the real-time data segment to be transmitted in the PROFINET communication line and the Internet of Things communication line respectively for transmission, and the data packet of the real-time data segment to be transmitted is transmitted by the RS485 communication line; At the receiving end, the CRC values ​​of the data packets of the real-time data segment to be transmitted in the PROFINET communication line and the IoT communication line are compared. If they are consistent, the data packets of the real-time data segment are output; If they are inconsistent, the data packet of the real-time data segment will be output in the RS485 communication line.

3. The intelligent remote management method for numerical control equipment according to claim 1, characterized in that: The method for acquiring the bandwidth timing fluctuation sequence includes: Under each line to be tested, for any historical real-time data segment, the standard deviation of the bandwidth within one second is taken as the bandwidth fluctuation value per second, thereby obtaining the bandwidth timing fluctuation sequence.

4. The intelligent remote management method for numerical control equipment according to claim 1, characterized in that: The method for acquiring the bandwidth timing change sequence includes: Under each line to be tested, for any historical real-time data segment, the average bandwidth within each second is taken as the bandwidth value per second, thereby obtaining a bandwidth time series change sequence.

5. The intelligent remote management method for numerical control equipment according to claim 1, characterized in that: The analysis of the correlation between each historical real-time data segment and the corresponding bandwidth timing fluctuation sequence and bandwidth timing change sequence to obtain the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient includes: Under each line to be tested, the absolute value of the correlation coefficient between each historical real-time data segment and the corresponding bandwidth fluctuation time series is used as the bandwidth fluctuation interference coefficient; The absolute value of the correlation coefficient between each historical real-time data segment and the corresponding bandwidth time series change sequence is used as the bandwidth interference coefficient.

6. The intelligent remote management method for numerical control equipment according to claim 1, characterized in that: The method for obtaining the predicted packet loss rate includes: Under each line to be tested, the product of the value after negative correlation mapping of the bandwidth interference coefficient of the last historical real-time data segment in the time sequence and the preset bandwidth is used as the current bandwidth value; The ratio of the size of the batch data to the current bandwidth value is used as the transmission duration; In terms of timing, the packet loss rate of the last historical real-time data segment is integrated over time, with the lower limit of the integration being 0 and the upper limit being the transmission duration, and the integrated value obtained is used as the predicted packet loss rate of the batch data under each line to be tested.

7. A method for intelligent remote management of numerical control equipment according to claim 6, characterized in that: The method for obtaining the transmission packet loss rate includes: Obtain the size of a single file in the batch data; Under each line to be tested, under the current bandwidth value, according to the packet loss rate corresponding to each historical real-time data segment, the bandwidth fluctuation interference coefficient and the size of a single file in the batch data, a first comprehensive packet loss rate of the batch data under each bandwidth fluctuation interference coefficient is obtained; Perform straight line fitting on the bandwidth fluctuation interference coefficient corresponding to the historical real-time data segment and the corresponding first comprehensive packet loss rate, and use the slope value of the fitting straight line as the bandwidth fluctuation sensitivity coefficient; Under different bandwidth interference coefficients, a second comprehensive packet loss rate of the batch data is obtained, and a change of the second comprehensive packet loss rate with the bandwidth interference coefficient is analyzed to obtain a bandwidth sensitivity coefficient; The product of the bandwidth fluctuation interference coefficient of the last historical real-time data segment in time sequence and the bandwidth fluctuation sensitivity coefficient is used as the first adjustment factor, and the product of the bandwidth size interference coefficient of the last historical real-time data segment in time sequence and the bandwidth size sensitivity coefficient is used as the second adjustment factor; Determine an adjustment coefficient based on the first adjustment factor and the second adjustment factor, and both the first adjustment factor and the second adjustment factor are positively correlated with the adjustment coefficient; The product of the adjustment coefficient for each line to be tested and the predicted packet loss rate of the batch data is used as the transmission packet loss rate of the batch data for each line to be tested.

8. The intelligent remote management method for numerical control equipment according to claim 7, characterized in that: The method for obtaining the bandwidth sensitivity coefficient includes: Different bandwidth interference coefficients are preset, and the product of the value after negative correlation mapping and normalization of each bandwidth interference coefficient and the preset bandwidth is used as the bandwidth value to be tested, and the packet loss rate of the last historical real-time data segment in the time series is used as the target packet loss rate; Under each of the bandwidth values ​​to be measured, according to the target packet loss rate and the size of a single file in the batch data, obtaining a second comprehensive packet loss rate of the batch data; A straight line fitting is performed on the bandwidth interference coefficient corresponding to the bandwidth value to be measured and the second comprehensive packet loss rate, and the slope value of the fitting straight line is used as the bandwidth sensitivity coefficient.

9. The intelligent remote management method for numerical control equipment according to claim 1, characterized in that: The selecting a transmission line under two lines to be tested based on the transmission packet loss rate and transmitting the batch data includes: Under two lines under test, the line under test with the smallest transmission packet loss rate of the batch data is used to transmit the batch data.

10. A smart remote management terminal for numerical control equipment, characterized in that: It includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and when the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor, the steps of a method for intelligent remote management of CNC equipment as described in any one of claims 1 to 9 are implemented.

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