A method and terminal for intelligent remote management of CNC equipment

Abstract

This invention relates to the field of data transmission management technology, specifically to a smart remote management method and terminal for CNC equipment. The invention constructs a three-layer redundant transmission system including RS485, PROFINET, and IoT communication lines to improve data transmission redundancy and stability. During real-time data transmission, a verification method is used to synchronize transmission with the three-layer redundancy, ensuring data accuracy and integrity. For batch data, the invention analyzes the historical transmission performance of the PROFINET and IoT lines, such as bandwidth fluctuations, size variations, and packet loss rates, calculates the interference coefficient, predicts and adjusts the packet loss rate, and accurately evaluates the data transmission quality of different lines. Finally, by comparing the packet loss rates of each line, the optimal line is selected for batch data transmission, ensuring data integrity, reducing transmission costs, and improving overall efficiency.

Description

Technical Field

[0001] This invention relates to the field of data transmission management technology, specifically to a smart remote management method and terminal for CNC equipment. Background Technology

[0002] In modern manufacturing, CNC equipment, as a core component of automated production lines, is crucial for ensuring production efficiency and product quality through its efficient and stable operation. The intelligentization and networking of CNC equipment are becoming development trends, and remote management is an important means to improve equipment maintenance efficiency and optimize production processes. However, in practical applications, CNC equipment generates massive amounts of data, which needs to be transmitted to a remote management center in real time or in batches for analysis and processing.

[0003] CNC equipment generates a large amount of real-time 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 data or configuration information, which 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 or Ethernet. This single communication method is susceptible to interference from the external environment, such as electromagnetic interference and physical damage, which can lead to unstable or interrupted data transmission, or even affect the integrity and accuracy of the data due to severe packet loss. Summary of the Invention

[0004] To address the technical problem that existing remote management methods often fail to differentiate data for different data types, resulting in suboptimal data transmission efficiency and reliability, this invention aims to provide a smart remote management method and terminal for CNC equipment. The specific technical solution adopted is as follows:

[0005] A three-layer redundant transmission line is constructed based on RS485 communication lines, PROFINET communication lines, and IoT communication lines.

[0006] When transmitting real-time data segments generated by CNC equipment, a data transmission verification method is set, and the real-time data segments are transmitted using the three-layer redundant transmission line;

[0007] When transmitting batch data generated by CNC equipment, the PROFINET communication line and the IoT communication line in the three-layer redundant transmission line are used as the lines under test. Under each line under test, multiple historical real-time data segments, the bandwidth time-series fluctuation sequence and bandwidth time-series 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 time-series fluctuation sequence and bandwidth time-series change sequence are analyzed to obtain the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient.

[0008] For each line under test, the predicted packet loss rate of the batch data is determined based on the packet loss rate of historical real-time data segments, the corresponding bandwidth size interference coefficient, and the overall size of the batch data. The predicted packet loss rate is adjusted according to the size of individual files in the batch data, the bandwidth fluctuation interference coefficient of historical real-time data segments, and the bandwidth size interference coefficient to obtain the transmission packet loss rate of the batch data under each line under test. Based on the transmission packet loss rate, a transmission line is selected for each of the two lines under test, and the batch data is transmitted.

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

[0010] At the transmitting end, a CRC value for the real-time data segment to be transmitted is generated. The CRC value is appended 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 through 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 are compared in the PROFINET communication line and the IoT communication line. If they match, the data packets of the real-time data segment are output.

[0012] If there is a discrepancy, the data packet of the real-time data segment will be output in the RS485 communication line.

[0013] Furthermore, the method for obtaining the bandwidth time-series fluctuation sequence includes:

[0014] For each line under test, for any historical real-time data segment, the standard deviation of the bandwidth per second is used as the bandwidth fluctuation value per second, thus obtaining the bandwidth time-series fluctuation sequence.

[0015] Furthermore, the method for obtaining the bandwidth time-series change sequence includes:

[0016] For each line under test, for any historical real-time data segment, the average bandwidth per second is taken as the bandwidth value per second, thus obtaining the bandwidth time-series change sequence.

[0017] Furthermore, the analysis of the correlation between each historical real-time data segment and its corresponding bandwidth time-series fluctuation sequence and bandwidth time-series change sequence yields the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient, including:

[0018] For each line under test, 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 size interference coefficient.

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

[0021] For each line under test, the current bandwidth value is obtained by negatively mapping the bandwidth interference coefficient of the last historical real-time data segment in the time sequence and multiplying it by the preset bandwidth.

[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 a lower limit of 0 and an upper limit of the transmission duration. The resulting integrated value is used as the predicted packet loss rate of the batch data under each line under test.

[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] For each line under test, under the current bandwidth value, based on 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.

[0027] A linear fit is performed between the bandwidth fluctuation interference coefficient corresponding to the historical real-time data segment and the corresponding first comprehensive packet loss rate, and the slope value of the fitted line is used as the bandwidth fluctuation sensitivity coefficient.

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

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

[0030] Based on the first adjustment factor and the second adjustment factor, an adjustment coefficient is determined, 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 under test and the predicted packet loss rate of the batch data is taken as the transmission packet loss rate of the batch data for each line under test.

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

[0033] Different bandwidth interference coefficients are preset. The product of the negative correlation mapping and normalization of each bandwidth interference coefficient and the preset bandwidth is used as the bandwidth value to be tested. 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] For each bandwidth value to be measured, a second comprehensive packet loss rate of the batch data is obtained based on the target packet loss rate and the size of a single file in the batch data;

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

[0036] Further, the step of selecting a transmission line and transmitting the batch data based on the packet loss rate among the two lines under test includes:

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

[0038] A smart remote management terminal for CNC equipment includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set, or instruction set, it implements the steps of the smart remote management method for CNC equipment.

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

[0040] In this invention, by constructing a three-layer redundant transmission line including RS485 communication lines, PROFINET communication lines, and IoT communication lines, the redundancy of data transmission can be effectively improved. Even if any single line fails, the other lines can continue to handle data transmission, ensuring the continuous and stable transmission of real-time and batch data generated by CNC equipment, reducing the risk of data loss or transmission interruption. When transmitting real-time data generated by CNC equipment, a data transmission verification method is set, and synchronous transmission is performed using the three-layer redundant transmission line. This 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 batch data transmission, this invention analyzes the historical transmission performance of PROFINET and IoT communication lines, including key indicators such as bandwidth fluctuation, bandwidth size variation, and packet loss rate. Specifically, it deeply analyzes the correlation between historical real-time data segments and bandwidth fluctuation and bandwidth size, calculates the bandwidth fluctuation interference coefficient and bandwidth size interference coefficient, and then predicts and adjusts the packet loss rate of batch data, thereby accurately predicting and evaluating the data transmission quality under different lines. This refined management makes batch data transmission more efficient and reliable, reducing unnecessary packet loss and improving data transmission success rate. Finally, by comparing the packet loss rates of different lines, the most suitable line for the current transmission needs is selected for batch data transmission. This not only optimizes the transmission path and ensures data integrity but also reduces transmission costs and improves overall transmission efficiency. Attached Figure Description

[0041] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating a method for intelligent remote management of CNC equipment according to an embodiment of the present invention;

[0043] Figure 2 A flowchart illustrating a data transmission verification method provided in one embodiment of the present invention;

[0044] Figure 3 A flowchart illustrating a method for obtaining packet loss rate according to an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the structure of a smart remote management terminal for CNC equipment provided in one embodiment of the present invention. Detailed Implementation

[0046] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a smart remote management method and terminal for CNC equipment proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0047] Unless otherwise defined, 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 pertains.

[0048] The following description, in conjunction with the accompanying drawings, details a specific solution for a smart remote management method and terminal for CNC equipment provided by this invention.

[0049] Please see Figure 1 The diagram illustrates a method flowchart for intelligent remote management of CNC equipment according to an embodiment of the present invention. The method includes the following steps:

[0050] Step S1: Construct a three-layer redundant transmission line based on RS485 communication line, PROFINET communication line and IoT communication line.

[0051] With the rapid development of Industry 4.0 and intelligent manufacturing, CNC equipment, as a core technology in modern manufacturing, directly impacts production quality and economic benefits through its operational efficiency and stability. Currently, remote management of CNC equipment faces potential security risks and signal interference issues during data transmission, especially in complex and heavily loaded industrial environments. Existing remote management methods for CNC equipment often rely on a single communication line, such as RS485 or Ethernet. This single communication method is susceptible to external environmental interference, such as electromagnetic interference and physical damage. However, CNC equipment generates a large amount of real-time and batch data during operation. Real-time data typically requires high real-time transmission speed and accuracy to reach the remote management system; while batch data may contain a large amount of historical data or configuration information, demanding high integrity and reliability in transmission. Therefore, existing single communication methods may experience transmission instability, interruptions, or even packet loss when dealing with interference, attacks, or malfunctions during transmission.

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

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

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

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

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

[0057] Step S2: When transmitting real-time data segments generated by CNC equipment, set the data transmission verification method and use three-layer redundant transmission lines to transmit the real-time data segments.

[0058] Real-time data typically requires high real-time transmission performance because in CNC equipment, real-time data directly affects the operating status of the CNC equipment and the control of the production process. Therefore, during the production process of CNC equipment, for equipment temperature data, equipment voltage data, equipment vibration data, etc. collected by sensors, a data transmission verification method can be set. The three-layer redundant transmission line in this embodiment of the invention is used to transmit real-time data segments, and the data is verified and validated during the data transmission process. This ensures the integrity and accuracy of the data while guaranteeing the real-time performance of data transmission.

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

[0060] Please see Figure 2 The diagram illustrates a flowchart of a data transmission verification method in one embodiment of the present invention.

[0061] Since both PROFINET and IoT communication lines have high transmission rates, for real-time data segments with high real-time requirements, the CRC value of the real-time data segment to be transmitted is generated at the sending end. The CRC value is then appended to the data packet of the real-time data segment to be transmitted in both the PROFINET and IoT communication lines before transmission. At the same time, the data packet of the real-time data segment to be transmitted is transmitted via the RS485 communication line.

[0062] It should be noted that Cyclic Redundancy Check (CRC) is a commonly used data verification method. It generates a check code, or CRC value, by performing polynomial calculations on the data and then 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 are first compared in the PROFINET communication line and the IoT communication line. If they match, it means that no error occurred during the transmission of the real-time data segment, and the data packets of the real-time data segment are then output.

[0064] If there is a discrepancy, it indicates that an error may have occurred during the transmission of the real-time data segment. Therefore, for PROFINET communication lines and IoT communication lines with higher transmission speeds, the reliability of the data packets of the real-time data segment is reduced. Thus, RS485 communication lines with strong noise immunity are selected to output the data packets of the real-time data segment.

[0065] It should be noted that in this embodiment of the 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 IoT communication line in the three-layer redundant transmission line are used as the lines under test; under each line under test, multiple historical real-time data segments, the bandwidth time-series fluctuation sequence and bandwidth time-series 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 time-series fluctuation sequence and bandwidth time-series change sequence are analyzed to obtain the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient.

[0067] In CNC equipment production activities, in addition to real-time data, large-scale batch data is generated due to the diverse production modes of CNC equipment. This includes CNC machining task information (task number, type of machined parts, quantity), production progress (number of completed parts, estimated completion time), and production quality (machining accuracy, scrap rate), etc. For large-scale batch data, it is necessary to ensure the integrity of data transmission to the greatest extent possible and to maximize data transmission speed when sending it. Therefore, in this embodiment of the invention, when transmitting batch data generated by CNC equipment, the PROFINET communication line and the IoT communication line in the three-layer redundant transmission line are preferentially selected as the lines to be tested. Furthermore, since changes in the mode of CNC equipment production can affect the bandwidth of the current transmission line to varying degrees, and fluctuations and changes in bandwidth on the transmission line can affect the integrity and speed of transmitted data, the final transmission line selected for testing needs to consider the impact of data changes in historical real-time data segments on the line bandwidth.

[0068] For each line under test, acquire multiple historical real-time data segments during the historical transmission process, the bandwidth timing fluctuation sequence and bandwidth timing change sequence during the transmission of each historical real-time data segment, and the packet loss rate of each historical real-time data segment.

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

[0070] Standard deviation is an effective indicator for measuring the dispersion of data and is suitable for quantifying the fluctuation of data. Therefore, for any historical real-time data segment, the standard deviation of the bandwidth of the line under test per second during the transmission of the historical real-time data segment is taken as the bandwidth fluctuation value per second. Then, the bandwidth fluctuation values ​​of all seconds are arranged in time sequence to obtain the bandwidth time-series fluctuation sequence of each line under test during the transmission of the historical real-time data segment.

[0071] The mean is a commonly used indicator for measuring the central tendency of data and is suitable for revealing the changes in data. Therefore, for any historical real-time data segment, under each line under test, the mean of the bandwidth of the line under test per second during the transmission of the historical real-time data segment is taken as the bandwidth value per second. The bandwidth values ​​of all seconds are sorted according to time sequence to obtain the time sequence change sequence of the bandwidth of each line under test during the transmission of the historical real-time data segment.

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

[0073] Thus, through the above calculations, we can obtain 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 for each line under test.

[0074] Based on the foregoing analysis, it is known that fluctuations and changes in bandwidth on transmission lines can affect the integrity and speed of transmitted data. Therefore, in this embodiment of the present invention, in order to further quantify the interference of bandwidth fluctuations and bandwidth changes, for each historical real-time data segment in the historical transmission process under each line under test, the correlation between the segment and the corresponding bandwidth time-series fluctuation sequence and the corresponding bandwidth time-series change sequence are analyzed to obtain the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient.

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

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

[0077] Therefore, for each line under test, 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 larger the bandwidth fluctuation interference coefficient, the more likely the change in data value in the historical real-time data segment is to affect the bandwidth fluctuation of the line under test.

[0078] Similarly, 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 size interference coefficient. The larger the bandwidth size interference coefficient, the more likely the change in data value in the historical real-time data segment is to affect the bandwidth size of the line under test.

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

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

[0081] Based on the aforementioned steps, the packet loss rate of historical real-time data segments and the corresponding bandwidth interference coefficient can be obtained. Therefore, based on these two indicators and the overall size of the batch data, the predicted packet loss rate for transmitting batch data under each test line can be calculated as an indicator for evaluating each test line as the final transmission line. However, since the content transmitted in batch data consists of a large number of files of varying sizes, the files of different sizes in the batch data will be sensitive to changes and fluctuations in the bandwidth of the communication line during transmission, resulting in different packet loss rates. Furthermore, if the files in the same batch data are transmitted in different arrangements, the bandwidth occupied by the consecutive large files is longer and the fluctuations allocated to each file are smoothed out, thus making the transmission more stable. When small files appear consecutively, they occupy less bandwidth. Multiple small files are sent in parallel at the same time, leading to relatively large transmission fluctuations when bandwidth is allocated to a single file, making packet loss more likely. Therefore, the size and arrangement of files in batch data transmission also affect the packet loss rate. In this embodiment of the invention, the size of individual files in the batch data and the sensitivity of the file arrangement to changes and fluctuations in bandwidth are analyzed, thereby adjusting the predicted packet loss rate and obtaining a more accurate packet loss rate for batch data on each tested line.

[0082] Therefore, in this embodiment of the present invention, for each line under test, 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 received by the bandwidth, it is closely related to the actual bandwidth size. The larger the coefficient, the greater the interference, and the smaller the actual bandwidth size will be.

[0085] Therefore, for each line under test, the bandwidth interference coefficient of the last historical real-time data segment in the historical transmission process is negatively correlated and mapped. The product of the negatively correlated 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 under test, the reliability of calculating the current bandwidth value using the bandwidth interference coefficient of the historical real-time data segment with the closest timing to the current batch data transmission is higher. The negative correlation mapping here can use formula 1-x, where x represents the independent variable.

[0086] The ratio of the size of the batch data to the current bandwidth value is then 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. 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 the batch data under each line under test.

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

[0089] At this point, the predicted packet loss rate of the batch data under each tested line can be obtained. Further analysis can be conducted to examine the sensitivity of the file arrangement in the batch data to changes and fluctuations in bandwidth size, thereby adjusting the predicted packet loss rate to obtain a more accurate transmission packet loss rate for the batch data under each tested line.

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

[0091] Please see Figure 3 The diagram illustrates a method flowchart for obtaining the packet loss rate in one embodiment of the present invention. The method includes the following steps:

[0092] Step S401: For any line under test, under different bandwidth fluctuation interference coefficients, obtain the first comprehensive packet loss rate of batch data, and analyze the change of the first comprehensive packet loss rate with the bandwidth fluctuation interference coefficient, thereby obtaining the bandwidth fluctuation sensitivity coefficient.

[0093] The size of a single file in a batch of data can be obtained from the information in the batch data header file.

[0094] Then, under the current bandwidth value (which has been calculated in the previous process), based on 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 under test, the first comprehensive packet loss rate of the batch data corresponding to each bandwidth fluctuation interference coefficient can be obtained.

[0095] Finally, a linear fit is performed between the bandwidth fluctuation interference coefficient corresponding to the historical real-time data segment and the corresponding first comprehensive packet loss rate. The fitted line can intuitively represent the relationship between the bandwidth fluctuation interference coefficient and the first comprehensive packet loss rate. Therefore, in this embodiment of the invention, the slope value of the fitted line is used as the bandwidth fluctuation sensitivity coefficient. The larger the bandwidth fluctuation sensitivity coefficient, the greater the bandwidth fluctuation interference. In this case, the current file arrangement of the batch data may cause the batch data to have a greater possibility of packet loss.

[0096] It should be noted that the least squares method can be used to obtain the fitted line, which is a well-known technique, and the specific process will not be elaborated here.

[0097] Here's an example to illustrate the calculation method for the first overall packet loss rate: Assume there are three files in the batch data, with file sizes of 2, 3, and 5 in order. If the current bandwidth is 8, then the files in the batch data, after being split, will have sizes of 2, 3, 3, and 2. If, under a certain historical real-time data segment, the bandwidth fluctuation interference coefficient is 0.2 and the packet loss rate is 0.02, then the packet loss rate of the batch data is 0.02 × 4 = 0.08. If, under a certain historical real-time data segment, the bandwidth fluctuation interference coefficient is 0.4 and the packet loss rate is 0.06, then the packet loss rate of the batch data is 0.06 × 4 = 0.24.

[0098] Step S402: For any line under test, under different bandwidth interference coefficients, obtain the second comprehensive packet loss rate of batch data, and analyze the change of the first comprehensive packet loss rate with the bandwidth interference coefficient, thereby obtaining the bandwidth sensitivity coefficient.

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

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

[0101] Finally, a linear fit was performed on the preset bandwidth interference coefficient and the second comprehensive packet loss rate corresponding to the bandwidth value to be tested. The slope of the fitted line was used as the bandwidth sensitivity coefficient. The larger the bandwidth sensitivity coefficient, the greater the bandwidth interference. Therefore, the current file arrangement of the batch data may cause the batch data to have a greater possibility of packet loss.

[0102] It should be noted that the method for obtaining the fitted straight line can be the least squares method, which is a well-known technique, and the specific process will not be described in detail here. Different interference coefficients with different bandwidth sizes are preset. In this embodiment of the 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 values ​​must be between 0 and 1.

[0103] Here is an example illustrating the calculation method for the second comprehensive packet loss rate: Assume there are three files in the batch data, with file sizes of 2, 3, and 5 in order. If the preset bandwidth is 10, then when the preset bandwidth interference coefficient is 0.2, the bandwidth to be tested is 8. After the files in the batch data are split, their sizes are 2, 3, 3, and 2. If the target packet loss rate is 0.02, then the packet loss rate of the batch data is 0.02 × 4 = 0.08. If the preset bandwidth interference coefficient is 0.5, then the bandwidth to be tested is 5. If the target packet loss rate is 0.02, then the packet loss rate of the batch data is 0.02 × 3 = 0.06.

[0104] Step S403: Under each line under test, the predicted packet loss rate of the batch data is adjusted by combining 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, so as to obtain the transmission packet loss rate of the batch data under each line under test.

[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] The 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 for each line under test and the predicted packet loss rate of the batch data is taken as the transmission packet loss rate of the batch data for each line under test. The formula model for the transmission packet loss rate includes:

[0108] CD = YD × (1 + DX × DM + DB × DD)

[0109] Wherein, CD represents the packet loss rate of batch data transmission under each line under test; YD represents the predicted packet loss rate of batch data under each line under test; DX represents the bandwidth fluctuation interference coefficient of the last historical real-time data segment under each line under test; DM represents the bandwidth fluctuation sensitivity coefficient of each line under test; DB represents the bandwidth size interference coefficient of the last historical real-time data segment under each line under test; and DD represents the bandwidth size sensitivity coefficient of each line under test.

[0110] In the formula model for packet loss rate, DX×DM represents the first adjustment factor for each line under test. A larger value indicates higher sensitivity to bandwidth fluctuations under the current file arrangement in the batch data, meaning it is more susceptible to bandwidth fluctuations and the packet loss rate will increase. Similarly, DB×DD represents the second adjustment factor for each line under test. A larger value indicates higher sensitivity to bandwidth changes under the current file arrangement in the batch data, meaning it is more susceptible to bandwidth changes and the packet loss rate will increase. (1+DX×DM+DB×DD) represents the adjustment coefficient for each line under test, indicating the degree to which the predicted packet loss rate of the batch data is adjusted based on the bandwidth fluctuation interference and bandwidth size interference of the line under test, combined with the sensitivity to bandwidth fluctuations and bandwidth size changes under the current file arrangement of the batch data. A larger value indicates a higher probability of packet loss. Therefore, for each line under test, 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 for each line under test.

[0111] Thus, through the above process, we can obtain the packet loss rate of the batch data to be transmitted under the two test lines. Based on this indicator, we can select the optimal transmission line to transmit the batch data.

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

[0113] Since a lower packet loss rate indicates a more reliable transmission line, the batch data is transmitted using the line with the lowest packet loss rate among the two lines under test.

[0114] In summary, in this embodiment of the invention, by constructing a three-layer redundant transmission line including RS485 communication lines, PROFINET communication lines, and IoT communication lines, the redundancy of data transmission can be effectively improved. When any single line fails, the other lines can still continue to handle data transmission, ensuring the continuous and stable transmission of real-time and batch data generated by CNC equipment, reducing the risk of data loss or transmission interruption. When transmitting real-time data generated by CNC equipment, a data transmission verification method is set, and synchronous transmission is performed using the three-layer redundant transmission line. This 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 batch data transmission, this invention analyzes the historical transmission performance of the PROFINET and IoT communication lines, including key indicators such as bandwidth fluctuation, bandwidth size variation, and packet loss rate. Specifically, it deeply analyzes the correlation between historical real-time data segments and bandwidth fluctuation and bandwidth size, calculates the bandwidth fluctuation interference coefficient and bandwidth size interference coefficient, and then predicts and adjusts the packet loss rate of batch data, thereby accurately predicting and evaluating the data transmission quality under different lines. This refined management makes batch data transmission more efficient and reliable, reducing unnecessary packet loss and improving data transmission success rate. Finally, by comparing the packet loss rates of different lines, the most suitable line for the current transmission needs is selected for batch data transmission. This not only optimizes the transmission path and ensures data integrity but also reduces transmission costs and improves overall transmission efficiency.

[0115] This invention also provides a smart remote management terminal for CNC equipment. Please refer to [link / reference]. Figure 4 This diagram illustrates the structure of a smart remote management terminal for CNC equipment according to an embodiment of the present invention. It includes a processor 500, a memory 501, a bus 502, and a communication interface 503. The processor 500, communication interface 503, and memory 501 are connected via the bus 502. The memory 501 may contain a high-speed random access memory, and the bus 502 may be an ISA bus, PCI bus, or EISA bus, etc. 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, code set, or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set, or instruction set, it implements the steps of a smart remote management method for CNC equipment.

[0116] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. 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. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for intelligent remote management of CNC equipment, characterized in that, The method includes: A three-layer redundant transmission line is constructed based on RS485 communication lines, PROFINET communication lines, and IoT communication lines. When transmitting real-time data segments generated by CNC equipment, a data transmission verification method is set, and the real-time data segments are transmitted using the three-layer redundant transmission line; When transmitting batch data generated by CNC equipment, the PROFINET communication line and the IoT communication line in the three-layer redundant transmission line are used as the lines under test. Under each line under test, multiple historical real-time data segments, the bandwidth time-series fluctuation sequence and bandwidth time-series 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 time-series fluctuation sequence and bandwidth time-series change sequence are analyzed to obtain the bandwidth fluctuation interference coefficient and the bandwidth size interference coefficient. For each line under test, the predicted packet loss rate of the batch data is determined based on the packet loss rate of historical real-time data segments, the corresponding bandwidth size interference coefficient, and the overall size of the batch data. The predicted packet loss rate is adjusted according to the size of individual files in the batch data, the bandwidth fluctuation interference coefficient of historical real-time data segments, and the bandwidth size interference coefficient to obtain the transmission packet loss rate of the batch data under each line under test. Based on the transmission packet loss rate, a transmission line is selected for each of the two lines under test, and the batch data is transmitted.

2. The intelligent remote management method for CNC equipment according to claim 1, characterized in that, The data transmission verification method is as follows: At the transmitting end, a CRC value for the real-time data segment to be transmitted is generated. The CRC value is appended 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 through the RS485 communication line. At the receiving end, the CRC values ​​of the data packets of the real-time data segment to be transmitted are compared in the PROFINET communication line and the IoT communication line. If they match, the data packets of the real-time data segment are output. If there is a discrepancy, the data packet of the real-time data segment will be output in the RS485 communication line.

3. The intelligent remote management method for CNC equipment according to claim 1, characterized in that, The method for obtaining the bandwidth time-series fluctuation sequence includes: For each line under test, for any historical real-time data segment, the standard deviation of the bandwidth per second is used as the bandwidth fluctuation value per second, thus obtaining the bandwidth time-series fluctuation sequence.

4. The intelligent remote management method for CNC equipment according to claim 1, characterized in that, The method for obtaining the bandwidth time-series variation sequence includes: For each line under test, for any historical real-time data segment, the average bandwidth per second is taken as the bandwidth value per second, thus obtaining the bandwidth time-series change sequence.

5. The intelligent remote management method for CNC equipment according to claim 1, characterized in that, The analysis of the correlation between each historical real-time data segment and its corresponding bandwidth time-series fluctuation sequence and bandwidth time-series change sequence yields the bandwidth fluctuation interference coefficient and the bandwidth magnitude interference coefficient, including: For each line under test, 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 size interference coefficient.

6. The intelligent remote management method for CNC equipment according to claim 1, characterized in that, The method for obtaining the predicted packet loss rate includes: For each line under test, the current bandwidth value is obtained by negatively mapping the bandwidth interference coefficient of the last historical real-time data segment in the time sequence and multiplying it by the preset bandwidth. 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 a lower limit of 0 and an upper limit of the transmission duration. The resulting integrated value is used as the predicted packet loss rate of the batch data under each line under test.

7. The intelligent remote management method for CNC 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; For each line under test, under the current bandwidth value, based on 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. A linear fit is performed between the bandwidth fluctuation interference coefficient corresponding to the historical real-time data segment and the corresponding first comprehensive packet loss rate, and the slope value of the fitted line is used as the bandwidth fluctuation sensitivity coefficient. Under different bandwidth interference coefficients, the second comprehensive packet loss rate of the batch data is obtained, and the change of the second comprehensive packet loss rate with the bandwidth interference coefficient is analyzed to obtain the bandwidth sensitivity coefficient. The product of the bandwidth fluctuation interference coefficient of the last historical real-time data segment in the time series 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 the time series and the bandwidth size sensitivity coefficient is used as the second adjustment factor. Based on the first adjustment factor and the second adjustment factor, an adjustment coefficient is determined, 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 under test and the predicted packet loss rate of the batch data is taken as the transmission packet loss rate of the batch data for each line under test.

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

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

10. A smart remote management terminal for CNC equipment, characterized in that, It includes a processor and a memory, the memory storing 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, it implements the steps of the intelligent remote management method for CNC equipment as described in any one of claims 1-9.

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