Multi-channel time sequence synchronous communication method for intelligent loudspeaker production line

Abstract

The invention discloses a multi-channel time sequence synchronous communication method for an intelligent loudspeaker production line, and particularly relates to the technical field of synchronous communication. The method comprises the following steps of: broadcasting a production instruction carrying a timestamp label through a master control node, acquiring phase drift data of each production channel link layer, and analyzing the phase drift data by adopting a sliding window algorithm to calculate an asymmetric load disturbance factor; a drift accumulation trend of transmission delay of each production channel is fitted through a least square method, a dynamic compensation strategy of load weight and phase drift is generated based on cross-layer correlation analysis, a sending time sequence is further remapped and corrected through an instruction queue, and finally response consistency evaluation and bidirectional convergence verification are performed. The closed-loop optimization of the communication strategy is dynamically realized, and the synchronization accuracy and the control consistency of the automatic cooperation of the intelligent loudspeaker production line are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of synchronous communication technology, and more specifically, to a multi-channel timing synchronous communication method for a smart speaker production line. Background Art

[0002] Smart speaker production lines usually use multi-channel communication to achieve collaborative operations between multiple workstations to ensure the precise connection of each production process.

[0003] In real-world production environments, network link loads are asymmetric and dynamically fluctuate, resulting in inconsistent command responses between workstations and severely disrupting the precise connection and stable collaboration of automated processes. Existing communication protocols lack effective and precise cross-channel link phase drift suppression and dynamic compensation. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a multi-channel timing synchronization communication method for a smart speaker production line to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A multi-channel timing synchronization communication method for a smart speaker production line includes the following steps: 1. A multi-channel timing synchronization communication method for a smart speaker production line, characterized by comprising the following steps: S1: The master control node broadcasts production instructions with timestamp tags to each production channel and obtains the phase drift data of the link layer of each production channel; S2: Based on the phase drift data, a sliding window is used to calculate the asymmetric load disturbance factor between production channels; S3: Based on the timestamp tags carried by the production instructions, the drift accumulation trend of the production instruction transmission delay of each production channel is fitted by the least squares method; S4: Perform cross-layer correlation analysis on asymmetric load disturbance factors and drift accumulation trends to generate dynamic compensation strategies; S5: Inject the dynamic compensation strategy into the production instruction scheduling queue and correct the sending timing of the production instructions on the target production channel through the remapping mechanism; S6: Collect the response time difference of the terminal execution node to the revised production instruction and generate the response consistency evaluation result; S7: Perform bidirectional convergence verification on the sending timing and response consistency evaluation results of the corrected production instructions in the target production channel, dynamically trigger the dynamic compensation strategy iteration or output a synchronization confirmation signal to the controller.

[0006] In a preferred embodiment, S1 is specifically: The master control node broadcasts production instructions with timestamp tags to each production channel through the network communication interface in each communication cycle; The phase information field in the link layer data frame returned by each production channel is parsed to obtain the phase drift data of each production channel.

[0007] In a preferred embodiment, S2 is specifically: The phase drift data is continuously intercepted according to a preset fixed window length to obtain multiple sets of sliding window data sequences; Calculate the variance of each sliding window data sequence one by one and generate the variance eigenvalue corresponding to each sliding window; Calculate the phase difference between adjacent production channels based on the variance eigenvalues ​​to obtain the phase drift difference between each adjacent production channel; Calculating the deviation ratio of each production channel relative to a common reference value based on the phase drift difference between adjacent production channel pairs; The asymmetric load disturbance factor of each production channel is calculated based on the deviation ratio.

[0008] In a preferred embodiment, S3 is specifically: Record the delay data between the production instruction sending and the receiving end timestamp of each production channel to generate a delay trend sequence; Based on the delay trend sequence, the delay trend data scatter distribution of each production channel is generated with the theoretical sending time marked by the timestamp label as the horizontal axis and the actual transmission delay value of the production instruction as the vertical axis; The least squares method is used to perform linear fitting on the scattered distribution of delay trend data to obtain a fitting straight line showing the change of production instruction transmission delay over time in each production channel. According to the fitted straight line, the transmission delay change slope of each production channel is calculated and used as the drift accumulation trend of the production instruction transmission delay of each production channel.

[0009] In a preferred embodiment, S4 is specifically: Based on the asymmetric load disturbance factor and drift accumulation trend, a correlation matrix between production channel load disturbance and transmission delay is constructed; The correlation strength between the asymmetric load disturbance factor and the drift accumulation trend is calculated through the correlation matrix; Based on the correlation strength between the symmetric load disturbance factor and the drift accumulation trend, a dynamic compensation strategy including load weight distribution parameters and phase drift correction parameters is generated.

[0010] In a preferred embodiment, S5 is specifically: The production instruction scheduling queue calculates instruction sending time correction based on load weight distribution parameters; Adjust the original timestamp tag carried by the production instruction according to the phase drift correction parameter; Remap the adjusted timestamp label according to the instruction sending time correction amount to obtain a new timestamp label for the production instruction; Rearrange the sending order of production instructions according to the new timestamp labels to form a production instruction sending queue; According to the production instruction sending queue, a production instruction with a new timestamp tag is sent to the target production channel.

[0011] In a preferred embodiment, S6 is specifically: The terminal execution node records the actual receiving time corresponding to each received production instruction and extracts the marked theoretical sending time; Calculate the production instruction response time difference of each terminal execution node based on the actual receiving time and the theoretical sending time; Summarize the production instruction response time difference of each terminal execution node and calculate the difference between the maximum response time difference and the minimum response time difference among all terminal execution nodes; The response consistency evaluation result is generated according to the difference between the maximum response time difference and the minimum response time difference.

[0012] In a preferred embodiment, S7 is specifically: Taking the production instruction carrying the new timestamp tag as the first input data; The response consistency assessment result is used as the second input data; Performing bidirectional convergence verification on the sending timing of the first input data and the difference between the maximum value and the minimum value of the response time difference in the second input data; Pre-set the response time difference tolerance threshold for determining whether synchronization meets the requirements; When the result of the bidirectional convergence verification does not exceed the response time difference allowable threshold, a synchronization confirmation signal indicating successful production instruction timing synchronization is sent to the production line controller; When the result of the bidirectional convergence verification exceeds the response time difference allowable threshold, the dynamic compensation strategy iterative update is triggered.

[0013] The technical effects and advantages of the multi-channel timing synchronization communication method for smart speaker production lines of the present invention are as follows: By collecting and analyzing the phase drift data of the production channel link layer, the timing offset status caused by asymmetric load disturbances can be grasped in real time; the load disturbance factor of each channel is calculated through the sliding window method to accurately characterize the asymmetric load characteristics; the least squares fitting is used to reveal the timing drift trend and accurately predict the degree of offset accumulation; dynamic compensation strategies are generated through cross-layer correlation analysis to significantly improve the synchronization accuracy and reliability of command sending; at the same time, through response consistency evaluation and bidirectional convergence verification, the timing control accuracy of multi-channel collaboration of the smart speaker production line is achieved, effectively ensuring the consistency and stability of automated collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a schematic diagram of the multi-channel timing synchronization communication method for smart speaker production lines of the present invention. DETAILED DESCRIPTION

[0015] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention. Example

[0016] Figure 1 The present invention provides a multi-channel timing synchronization communication method for a smart speaker production line, which includes the following steps: 1. A multi-channel timing synchronization communication method for a smart speaker production line, characterized by comprising the following steps: S1: The master control node broadcasts production instructions with timestamp tags to each production channel and obtains the phase drift data of the link layer of each production channel; S2: Based on the phase drift data, a sliding window is used to calculate the asymmetric load disturbance factor between production channels; S3: Based on the timestamp tags carried by the production instructions, the drift accumulation trend of the production instruction transmission delay of each production channel is fitted by the least squares method; S4: Perform cross-layer correlation analysis on asymmetric load disturbance factors and drift accumulation trends to generate dynamic compensation strategies; S5: Inject the dynamic compensation strategy into the production instruction scheduling queue and correct the sending timing of the production instructions on the target production channel through the remapping mechanism; S6: Collect the response time difference of the terminal execution node to the revised production instruction and generate the response consistency evaluation result; S7: Perform bidirectional convergence verification on the sending timing and response consistency evaluation results of the corrected production instructions in the target production channel, dynamically trigger the dynamic compensation strategy iteration or output a synchronization confirmation signal to the controller.

[0017] S1: The master node broadcasts a production instruction with a timestamp tag to each production channel and obtains the phase drift data of the link layer of each production channel, including: The master control node broadcasts production instructions with timestamp tags to each production channel through the network communication interface in each communication cycle; The master control node refers to the central control device used for unified control and command issuance on a smart speaker production line, such as an industrial computer, PLC controller, or embedded control unit. An industrial computer is used as an example for this description. A network communication interface, such as an industrial Ethernet interface, connects the master control node to multiple production channels for network communication. Options include standard RJ45 or fiber optic communication interfaces. An RJ45 industrial Ethernet interface is used for this description. The master control node's industrial computer has a pre-set communication cycle, each of which is a fixed duration, such as 100 milliseconds. At the beginning of each 100-millisecond communication cycle, the industrial computer sends unified production instructions via the industrial Ethernet interface to all production channels connected to it, effectively broadcasting the production instructions simultaneously. Production instructions are command data used to guide speaker production stations to complete specific process actions, such as automated welding or automated assembly of speaker units. Broadcasting means that the industrial computer simultaneously broadcasts the exact same production instruction data to each production channel, rather than sending it one by one. This ensures that each channel receives the exact same production instruction data simultaneously. A timestamp tag is a precise time stamp attached to production instruction data, with microsecond accuracy. For example, the theoretical transmission time of a command broadcast is accurate to 0.1 microseconds, such as: July 16, 2025, 10:00:00.0001 seconds. After receiving a timestamp-tagged production instruction, each production channel can determine the execution sequence based on the precise transmission time marked on the tag, ensuring the coordination and synchronization of production actions across channels. A production channel is a network communication channel connecting the master control node industrial computer and the end-of-line execution nodes of a loudspeaker production line, for example, a one-to-one network communication link between an industrial Ethernet switch port and a loudspeaker assembly station.

[0018] Parse the phase information field in the link layer data frame returned by each production channel to obtain the phase drift data of each production channel; After receiving a production instruction with a timestamp, each production channel returns a link layer data frame to the master control node according to a pre-agreed communication protocol. A link layer data frame is a data frame defined by the production channel at the communication link layer, such as the Ethernet data frame structure defined by the IEEE 802.3 standard. The link layer data frame contains a phase information field, which records the time difference between the actual reception time of the production instruction broadcast by the master control node and the theoretical transmission time carried by the production instruction. This is called the phase difference, or phase drift data. The data in the phase information field can be an integer or a floating point number. For example, an integer such as "+25" indicates that the actual reception time is 25 microseconds later than the theoretical transmission time, or "-10" indicates that the actual reception time is 10 microseconds earlier than the theoretical transmission time. If expressed as a floating point number, it might be "+25.350" microseconds, indicating that the phase drift is accurate to sub-microseconds. After receiving the link layer data frames returned by each production channel, the master node industrial computer first calls the built-in data frame parser to parse the fields in the link layer data frame one by one, extract the phase information field, and read the corresponding phase drift value. By parsing the link layer data frame, it can reflect the impact of link load changes between production channels on the reception of synchronization commands, thereby providing data support for precise calibration and compensation of production line timing.

[0019] S2: Based on the phase drift data, a sliding window is used to calculate the asymmetric load disturbance factor between production channels, including: The phase drift data is continuously intercepted according to a preset fixed window length to obtain multiple sets of sliding window data sequences; Phase drift data is the time difference between the time that multiple production channels receive production instructions and the time that the master control node theoretically sends them. A fixed sliding window length is set within the master control node's industrial computer. For example, the window length is set to 10 consecutive communication cycles. The phase drift data for each production channel over 10 consecutive communication cycles is then considered a sliding window data sequence. For example, the first sliding window sequence consists of 10 consecutive data points recorded from communication cycles 1 to 10, the second sliding window sequence consists of data from communication cycles 2 to 11, and so on. Each time a sliding window data sequence is intercepted and shifted back one communication cycle, a series of sliding window data sequences containing 10 phase drift data points is obtained.

[0020] Calculate the variance of each sliding window data sequence one by one and generate the variance eigenvalue corresponding to each sliding window; The master node industrial computer calculates the variance eigenvalue of the sliding window data sequence using the following method: The data contained in the sliding window data sequence is defined as X1, X2, X3, ..., X10, totaling 10 phase drift data points. The variance eigenvalue of the sliding window data sequence is calculated as: Variance = [(X1 - X average)^2 + (X2 - average)^2 + ... + (X10 - average)^2] / 10, where the average is the arithmetic mean of the 10 phase drift values. The master node industrial computer calculates the variance eigenvalues ​​of all sliding window data sequences one by one, with each sliding window sequence corresponding to one variance eigenvalue.

[0021] Calculate the phase difference between adjacent production channels based on the variance eigenvalues ​​to obtain the phase drift difference between each adjacent production channel; Based on the calculated variance eigenvalues, we analyze the differences between adjacent production channels, obtaining data values ​​reflecting the timing differences between them, called phase drift differences. For example, consider three adjacent production channels, A, B, and C, on a production line. Each channel obtains its own sliding window variance eigenvalue. For example, if the variance eigenvalue of channel A is 0.8, that of channel B is 1.5, and that of channel C is 0.6, then the phase drift differences between adjacent pairs of production channels are calculated as: |1.5 - 0.8| = 0.7, and |0.6 - 1.5| = 0.9. Thus, the phase drift differences between all adjacent pairs of production channels are obtained, reflecting the synchronization performance differences caused by load disturbances between adjacent channels.

[0022] Calculating the deviation ratio of each production channel relative to a common reference value based on the phase drift difference between adjacent production channel pairs; Calculate the average of the phase drift differences of all adjacent production channel pairs as the common reference value: Public benchmark value = (0.7 + 0.9) / 2 = 0.8; Calculate the degree of deviation of each production channel relative to the common reference value, that is, calculate the deviation ratio of each production channel as: For production channel A, the deviation ratio is the ratio of the phase drift difference between production channels A and B to the common reference value, that is, 0.7÷0.8=0.875; For production channel B, since it is adjacent to production channels A and C, the average of the differences between the two adjacent sides of production channel B is taken as the reference difference of channel B: (0.7 + 0.9) ÷ 2 = 0.8, and the deviation ratio is 0.8 ÷ 0.8 = 1.0; For production channel C, the deviation ratio is the ratio of the phase drift difference between production channels B and C to the common reference value, that is, 0.9÷0.8=1.125.

[0023] Calculate the asymmetric load disturbance factor of each production channel based on the deviation ratio; The master node's industrial computer linearly maps the deviation ratios into a quantitative value that directly reflects the magnitude of the load disturbance in each production channel. This value is defined as the asymmetric load disturbance factor. For example, by multiplying the deviation ratios by a fixed proportional coefficient, such as 10, the asymmetric load disturbance factor for production channel A is 0.875 × 10 = 8.75; the asymmetric load disturbance factor for production channel B is 1.0 × 10 = 10.0; and the asymmetric load disturbance factor for production channel C is 1.125 × 10 = 11.25, thus obtaining the asymmetric load disturbance factors for all production channels.

[0024] S3: Based on the timestamp tags carried by the production instructions, the drift accumulation trend of the production instruction transmission delay of each production channel is fitted using the least squares method, including: Record the delay data between the production instruction sending and the receiving end timestamp of each production channel to generate a delay trend sequence; Production instructions broadcast to each production channel carry a timestamp, recording the theoretical transmission time, with an accuracy of, for example, 0.1 microseconds, using July 16, 2024, at 10:00:00.0001 seconds. After receiving the production instruction, the execution node at the end of each production channel uses its own high-precision clock chip or dedicated timing module to record the precise moment of receipt. For example, the execution node in the first production channel actually received the production instruction at 10:00:00.0250 seconds on July 16, 2024, which is 24.9 microseconds later than the theoretical transmission time. The execution node in the second production channel actually received the instruction at 10:00:00.0150 seconds on July 16, 2024, which is a delay of 14.9 microseconds. The execution node in the third production channel actually received the instruction at 10:00:00.0300 seconds on July 16, 2024, which is a delay of 29.9 microseconds. The difference between the theoretical send timestamp and the actual receive timestamp of each production channel's production instruction, known as the specific delay data, is recorded and organized one by one for each consecutive communication cycle to generate a continuous delay trend sequence. For example, for the first production channel, the delay data recorded over 10 consecutive communication cycles is 24.9 μs, 25.5 μs, 26.0 μs, 26.8 μs, 27.2 μs, 28.0 μs, 28.5 μs, 29.1 μs, 30.0 μs, and 30.6 μs, forming the delay trend sequence: {24.9, 25.5, 26.0, 26.8, 27.2, 28.0, 28.5, 29.1, 30.0, 30.6} μs. A complete delay trend sequence is generated for all production channels.

[0025] Based on the delay trend sequence, the delay trend data scatter distribution of each production channel is generated with the theoretical sending time marked by the timestamp label as the horizontal axis and the actual transmission delay value of the production instruction as the vertical axis; Using the delay trend sequence, an intuitive data scatter plot is generated for each production channel. For example, using the first production channel as an example, the data scatter plot uses the theoretical sending time as the horizontal axis, specifically the theoretical sending time of each broadcast production instruction as the horizontal axis data point, such as communication cycle 1 is 10:00:00.0001 seconds, communication cycle 2 is 10:00:00.1001 seconds, and so on, with each interval of 100 milliseconds. Each delay data point in the recorded delay trend sequence is then used as the vertical axis data point, such as the horizontal axis point corresponding to the delay data of 24.9 microseconds in communication cycle 1 at 10:00:00.0001 seconds; the horizontal axis point corresponding to the delay data of 25.5 microseconds in communication cycle 2 at 10:00:00.1001 seconds. These data points are marked one by one on the coordinate graph, forming a series of scatter plots, called the delay trend data scatter plot. Similarly, this operation is performed on all other production channels, such as the second production channel and the third production channel, and each production channel generates its own independent delay trend data scatter plot.

[0026] The least squares method is used to perform linear fitting on the scattered distribution of delay trend data to obtain a fitting straight line showing the change of production instruction transmission delay over time in each production channel. The least squares method is used to perform a linear fitting analysis on the scatter distribution of delay trend data generated by each production channel. Specifically, taking the 10 delay scatter points of the first production channel as an example, a linear fitting line is determined using the least squares formula. In other words, the best straight line is found so that the sum of the squares of the longitudinal distances from the straight line to all scatter points is minimized. Specifically: Assume that the delay data scatter points are (0, 24.9), (1, 25.5), (2, 26.0) ..., (9, 30.6), and the horizontal axis values ​​corresponding to 10 consecutive communication cycles are 0 to 9, and the vertical axis is the delay value. The equation of the fitting line is calculated using the least squares method, for example: y = 0.64x + 24.7, where y represents the delay value and x represents the communication cycle number. Similarly, the data scatter distribution of the second and third production channels were linearly fitted using the least squares method to obtain their respective fitting line equations. For example, the fitting equation for the second production channel is: y=0.40x+14.8, and the fitting equation for the third production channel is: y=0.72x+29.5. Linear fitting lines were obtained for all production channels in this way, reflecting the change pattern of delay over time.

[0027] According to the fitted straight line, the transmission delay change slope of each production channel is calculated and used as the drift accumulation trend of the production instruction transmission delay of each production channel; Based on the equation of the linear fit line, calculate the delay change slope for each production channel. For example, in the linear fit equation for the first production channel (y=0.64x+24.7), the 0.64 in the equation represents the transmission delay change slope, indicating an average increase of 0.64 microseconds in the transmission delay per communication cycle for the first production channel. The slope of 0.40 in the linear fit equation for the second production channel (y=0.40x+14.8) indicates an average increase of 0.40 microseconds in the transmission delay per communication cycle. The slope of 0.72 in the linear fit equation for the third production channel (y=0.72x+29.5) indicates an average increase of 0.72 microseconds in the transmission delay per communication cycle. The calculated slopes are recorded and defined as the cumulative drift trend of the production instruction transmission delay. This cumulative drift trend indicates that the transmission delay of the production instruction shows a clear cumulative increase or decrease over the communication cycle, fully reflecting the cumulative effect of timing offset caused by long-term link load changes in each production channel.

[0028] S4: Perform cross-layer correlation analysis on asymmetric load disturbance factors and drift accumulation trends to generate dynamic compensation strategies, including: Based on the asymmetric load disturbance factor and drift accumulation trend, a correlation matrix between production channel load disturbance and transmission delay is constructed; The asymmetric load disturbance factor and the cumulative drift trend are mapped to each production channel, creating a two-dimensional correlation matrix to reflect the relationship between load disturbance and transmission delay. Specifically, the production line includes production channels A, B, and C. The corresponding asymmetric load disturbance factors are 8.75, 10.0, and 11.25, respectively, and the cumulative drift trends are 0.64, 0.40, and 0.72 microseconds per cycle, respectively.

[0029] The correlation strength between the asymmetric load disturbance factor and the drift accumulation trend is calculated through the correlation matrix; Based on the correlation matrix, the Pearson correlation coefficient calculation method was used to calculate the correlation strength between the asymmetric load disturbance factor and the drift accumulation trend.

[0030] Define the asymmetric load disturbance factor as variable X and the drift accumulation trend as variable Y, and calculate the average value of the two variables. For example: The average value of the asymmetric load disturbance factor X is: (8.75 + 10.0 + 11.25) ÷ 3 = 10.0; The average value of the drift cumulative trend Y is: (0.64 + 0.40 + 0.72) ÷ 3 = 0.5867; The calculation formula of Pearson correlation coefficient is: Correlation strength = [(average value of X1 - X)(average value of Y1 - Y) + (average value of X2 - X)(average value of Y2 - Y) + (average value of X3 - X)(average value of Y3 - Y)] / {[(average value of X1 - X)^2 + (average value of X2 - X)^2 + (average value of X3 - X)^2]^(1 / 2) × [(average value of Y1 - Y)^2 + (average value of Y2 - Y)^2 + (average value of Y3 - Y)^2]^(1 / 2)} Substitute the mean of X and the mean of Y to calculate: Numerator = (8.75-10.0)(0.64-0.5867) + (10.0-10.0)(0.40-0.5867) + (11.25-10.0)(0.72-0.5867) = 0.103; Denominator = [(8.75-10.0)^2 + (10.0-10.0)^2 + (11.25-10.0)^2]^(1 / 2) × [(0.64-0.5867)^2 + (0.40-0.5867)^2 + (0.72-0.5867)^2]^(1 / 2) = 0.1039; Therefore, the correlation strength = 0.103÷0.1039≈0.991, indicating that there is a positive correlation between the asymmetric load disturbance factor and the drift accumulation trend.

[0031] Based on the correlation strength between the symmetric load disturbance factor and the drift accumulation trend, a dynamic compensation strategy including load weight distribution parameters and phase drift correction parameters is generated; According to the correlation strength (0.991), it is determined that the asymmetric load disturbance factor has a significant impact on the drift accumulation trend, and a dynamic compensation strategy is generated, including load weight distribution parameters and phase drift correction parameters.

[0032] The load weight distribution parameter is determined by the ratio of the asymmetric load disturbance factor to the total disturbance factor. For example, the total disturbance factor of production channels A, B, and C is: 8.75 + 10.0 + 11.25 = 30.0. Then the load weight distribution parameter of production channel A is calculated as 8.75 ÷ 30 = 0.2917; production channel B is 10.0 ÷ 30 = 0.3333; and production channel C is 11.25 ÷ 30 = 0.3750.

[0033] The phase drift correction parameter is calculated by multiplying the accumulated drift trend by a fixed communication cycle. For example, based on a communication cycle of 100 milliseconds, the phase drift correction parameter for production channel A is calculated as 0.64 microseconds / cycle × 100 milliseconds = 64 microseconds; the phase drift correction parameter for production channel B is 0.40 microseconds / cycle × 100 milliseconds = 40 microseconds; and the phase drift correction parameter for production channel C is 0.72 microseconds / cycle × 100 milliseconds = 72 microseconds.

[0034] S5: Inject the dynamic compensation strategy into the production instruction scheduling queue and use the remapping mechanism to correct the sending timing of the production instructions on the target production channel, including: The production instruction scheduling queue calculates instruction sending time correction based on load weight distribution parameters; The production instruction scheduling queue represents a data storage structure managed by the master node industrial computer. It is used to store all production instructions to be sent to the production channel and arrange them in the order of sending. The sending order of production instructions is controlled by the load weight distribution parameters.

[0035] The load weight distribution parameter reflects the difference in load disturbance strength between production channels. A larger load weight distribution parameter indicates that the load disturbance of the production channel is more significant and the demand for sending timing correction is higher.

[0036] The instruction transmission time correction is calculated by the master control node industrial computer using the load weight distribution parameter to calculate the time offset that should be adjusted between the actual transmission time of each production instruction and the theoretical transmission time. Specifically, the master control node industrial computer sets a basic transmission delay correction benchmark, for example, 100 microseconds. The instruction transmission time correction for each production channel is calculated by multiplying the load weight distribution parameter by the transmission delay correction benchmark. For example, the instruction transmission time correction for production channel A is 0.2917 × 100 microseconds = 29.17 microseconds, the instruction transmission time correction for production channel B is 0.3333 × 100 microseconds = 33.33 microseconds, and the instruction transmission time correction for production channel C is 0.3750 × 100 microseconds = 37.50 microseconds, thereby obtaining the corresponding transmission time correction for each production channel.

[0037] Adjust the original timestamp tag carried by the production instruction according to the phase drift correction parameter; The original timestamp tag is the time information of the theoretical sending moment of each production instruction, such as "July 16, 2024 10:00:00.0001 seconds".

[0038] The master node's industrial computer adjusts the original timestamp based on the phase drift correction parameters to compensate for phase drift. For example, for production channel A, the original timestamp is 10:00:00.0001 seconds. After adjusting the phase drift correction parameters by 64 microseconds, the adjusted timestamp becomes 10:00:00.000164 seconds. Similarly, the adjusted timestamp for production channel B is 40 microseconds higher than the original, and for production channel C, 72 microseconds higher, offsetting the negative impact of long-term drift in the production channels.

[0039] Remap the adjusted timestamp label according to the instruction sending time correction amount to obtain a new timestamp label for the production instruction; The master node industrial computer uses the adjusted original timestamp tag as a basis and remaps the timestamp tag based on the instruction sending time correction amount. This means that the timestamp tag is refined and corrected to obtain the new timestamp tag used to send the production instruction. For example, if the adjusted timestamp tag of production channel A is 10:00:00.000164 seconds, and it is further adjusted based on the instruction sending time correction amount of 29.17 microseconds, the new timestamp tag is 10:00:00.000164 seconds + 29.17 microseconds = 10:00:00.00019317 seconds. Similarly, the new timestamp for production channel B is 10:00:00.000140 seconds + 33.33 microseconds = 10:00:00.00017333 seconds; and the new timestamp for production channel C is 10:00:00.000172 seconds + 37.50 microseconds = 10:00:00.00020950 seconds. The new timestamp reflects the actual transmission timing required under the combined effects of load disturbance and phase drift.

[0040] Rearrange the sending order of production instructions according to the new timestamp labels to form a production instruction sending queue; The master node industrial computer uses the new timestamp tags obtained by each production channel's production instructions as a standard to sort all pending production instructions and reorder their delivery. For example, if the new tag for production channel A is 10:00:00.00019317 seconds, for production channel B it is 10:00:00.00017333 seconds, and for production channel C it is 10:00:00.00020950 seconds, the production instruction delivery queue will be sorted from smallest to largest based on the new tag times, in the order of production channel B, production channel A, and production channel C. This creates a more accurate production instruction delivery queue after dynamic compensation, ensuring that production instructions are delivered to each channel of the production line in a stable and orderly manner.

[0041] According to the production instruction sending queue, the production instruction with the new timestamp tag is sent to the target production channel; Based on the established production instruction sending queue, the master control node's industrial computer sends the production instructions with the new timestamp tags to the corresponding target production channels in the order specified by the queue. For example, a production instruction with the new timestamp tag of 10:00:00.00017333 seconds is first sent to production channel B, followed by a production instruction with the new timestamp tag of 10:00:00.00019317 seconds to production channel A, and finally a production instruction with the new timestamp tag of 10:00:00.00020950 seconds to production channel C. The target production channel is the network communication link connecting the master control node's industrial computer to the execution node at the end of the speaker production line. After receiving the production instruction with the new timestamp tag, each production channel accurately determines the execution time of its own action based on the new tag, effectively ensuring the overall synchronization and collaborative accuracy of the production line.

[0042] S6: Collect the response time difference of the terminal execution node to the revised production instruction and generate the response consistency evaluation results, including: The terminal execution node records the actual receiving time corresponding to each received production instruction and extracts the marked theoretical sending time; The end execution node is the workstation equipment on the smart speaker production line, such as the speaker unit welding robot, speaker assembly robot or other automated production equipment, all of which are equipped with high-precision clock chips or dedicated timing modules to ensure that the actual receipt time of the production instructions can be recorded with high precision.

[0043] The actual receiving time indicates the time when the terminal execution node actually receives the production instruction sent by the industrial computer of the master control node.

[0044] Production instructions carry an adjusted theoretical sending time, called a theoretical sending time tag, such as 10:00:00.00019317 seconds on July 16, 2024. The end-execution node needs to read the theoretical sending time tag from the received production instruction data to obtain the theoretical reference time when the instruction is expected to arrive at the end-execution node.

[0045] For example, after receiving a production instruction, the terminal execution node in production channel A records the actual receipt time as 10:00:00.000215 seconds. It also extracts the theoretical sending time tag from the data packet carried by the production instruction, clearly indicating 10:00:00.00019317 seconds. Similarly, the terminal execution node in production channel B records the actual receipt time as 10:00:00.000198 seconds, with a theoretical sending time tag of 10:00:00.00017333 seconds. The terminal execution node in production channel C records the actual receipt time as 10:00:00.000232 seconds, with a theoretical sending time tag of 10:00:00.00020950 seconds. In this way, each terminal execution node accurately records the actual receipt time and theoretical sending time.

[0046] Calculate the production instruction response time difference of each terminal execution node based on the actual receiving time and the theoretical sending time; The production instruction response time difference refers to the difference between the actual reception time and the theoretical transmission time of each terminal execution node, which reflects the actual transmission delay of the production instruction in the link. Specifically: Production instruction response time difference = actual receiving time - theoretical sending time.

[0047] Taking the execution node at the end of production channel A as an example, the actual receiving time is recorded as 10:00:00.000215 seconds, and the theoretical sending time is 10:00:00.00019317 seconds. The production instruction response time difference is calculated as: The response time difference of production channel A = 0.000215 seconds - 0.00019317 seconds = 0.00002183 seconds, or 21.83 microseconds.

[0048] Similarly, the response time difference of production channel B is: The response time difference of production channel B = 0.000198 seconds - 0.00017333 seconds = 0.00002467 seconds, or 24.67 microseconds.

[0049] The response time difference of production channel C is: The response time difference of production channel C = 0.000232 seconds - 0.00020950 seconds = 0.0000225 seconds, or 22.50 microseconds.

[0050] The above calculation process is executed on all the terminal execution nodes in the production line, and the response time difference obtained by each terminal execution node is recorded separately.

[0051] Summarize the production instruction response time difference of each terminal execution node and calculate the difference between the maximum response time difference and the minimum response time difference among all terminal execution nodes; The master node industrial computer obtains the calculated response time difference from each terminal execution node. For example, taking the three terminal execution nodes of production channels A, B, and C as an example, the obtained response time differences are: The response time difference of production channel A is 21.83 microseconds; the response time difference of production channel B is 24.67 microseconds; the response time difference of production channel C is 22.50 microseconds.

[0052] The master node industrial computer determines the maximum and minimum values ​​of the response time difference: The maximum response time difference is clearly 24.67 microseconds for production channel B; the minimum response time difference is clearly 21.83 microseconds for production channel A.

[0053] The extreme value of the response time difference between all terminal execution nodes is calculated, that is, the difference between the maximum response time difference and the minimum response time difference. The extreme value of the response time difference = 24.67 microseconds - 21.83 microseconds = 2.84 microseconds, which reflects the degree of consistency of the production instruction reception timing between each terminal execution node.

[0054] Generate a response consistency evaluation result according to the difference between the maximum response time difference and the minimum response time difference; The response consistency evaluation results are used to judge the effectiveness of synchronous sending of production instructions based on the extreme value of the response time difference of the terminal execution node, reflecting the overall synchronization accuracy of the production line.

[0055] The industrial computer of the master control node pre-sets the threshold range allowed for response consistency, for example, 5 microseconds, which represents the maximum response time difference range allowed by the production line, and is used to evaluate whether the response consistency meets production requirements.

[0056] Specifically, the calculated response time difference range value of 2.84 microseconds is compared with the preset threshold of 5 microseconds to obtain the response consistency evaluation result, that is, 2.84 microseconds is less than the preset threshold of 5 microseconds, so it is determined that the current timing synchronization status of the production line meets the production requirements.

[0057] The response consistency assessment results can be expressed in numerical or hierarchical form, for example: Response consistency assessment results: extreme difference value = 2.84 microseconds, which is less than the allowable threshold of 5 microseconds. The assessment result is qualified, indicating that the multi-channel timing synchronization effect of the current production line is good.

[0058] If the extreme difference value exceeds the allowable threshold, the response consistency assessment result is unqualified, which in turn triggers the master node industrial computer to optimize and adjust the dynamic compensation strategy to achieve continuous optimization of the production line timing synchronization.

[0059] S7: Perform bidirectional convergence verification on the sending timing of the revised production instruction in the target production channel and the response consistency evaluation results, dynamically trigger the dynamic compensation strategy iteration or output a synchronization confirmation signal to the controller, including: Taking the production instruction carrying the new timestamp tag as the first input data; The new timestamp tags in the production instructions generated by the master node industrial computer for production channel A, production channel B, and production channel C are: The new timestamp tag for the production instructions in production channel A is 10:00:00.00019317 seconds on July 16, 2024; the new timestamp tag for the production instructions in production channel B is 10:00:00.00017333 seconds on July 16, 2024; and the new timestamp tag for the production instructions in production channel C is 10:00:00.00020950 seconds on July 16, 2024. All production instructions with new timestamp tags constitute the basic data source for bidirectional convergence verification and are defined as the first input data, reflecting the timing arrangement of production instruction transmission after dynamic compensation.

[0060] The response consistency assessment result is used as the second input data; The response time difference of the execution node at the end of production channel A is 21.83 microseconds, the response time difference of the execution node at the end of production channel B is 24.67 microseconds, and the response time difference of the execution node at the end of production channel C is 22.50 microseconds. The range of the response time difference calculated by the industrial computer of the master control node is: The maximum value of the response time difference is 24.67 microseconds minus 21.83 microseconds to get 2.84 microseconds.

[0061] The 2.84 microsecond range value constitutes the response consistency assessment result and is defined as the second input data for bidirectional convergence verification. It indicates the coordination of the actual response timing in the current production line and reflects the correspondence between synchronization performance and actual production requirements.

[0062] Performing bidirectional convergence verification on the sending timing of the first input data and the difference between the maximum value and the minimum value of the response time difference in the second input data; The specific steps for bidirectional convergence verification are as follows: Taking the theoretical sending timing as the expected benchmark, evaluate whether the actual response time difference range value meets the synchronization requirements expected by the theoretical sending timing; using the actual response time difference range value as the verification benchmark, evaluate whether the theoretical sending timing (new timestamp label) design can effectively reduce the response time difference range value.

[0063] For example, the theoretical timing differences between the new timestamp tags carried by the production instructions of production channels A, B, and C are 19.84 microseconds between production channels A and B, and 36.17 microseconds between production channels B and C, respectively. The response time difference range is 2.84 microseconds. Obviously, the response time difference range is significantly smaller than the time interval of the theoretical timing design, which preliminarily verifies that the actual execution meets the theoretical timing design requirements.

[0064] Conversely, based on the 2.84 microsecond range, it is verified that the minimum interval of the new timestamp tag arrangement, 19.84 microseconds, is much larger than the actual difference in response time difference, confirming that the design of the theoretical sending timing can effectively accommodate the uncertainty of the response time difference.

[0065] The above two-way convergence verification ensures full coordination between theoretical design and actual performance.

[0066] Pre-set the response time difference tolerance threshold for determining whether synchronization meets the requirements; Before operation, the master node industrial computer sets a quantitative standard, called the response time difference threshold, to determine whether the production line synchronization performance meets the requirements. For example, setting the response time difference threshold to 5 microseconds represents the maximum response time difference allowed at the end execution node of each production channel in the smart speaker production line. This requires that the response time difference of each production channel be controlled within 5 microseconds to meet the high-precision synchronization requirements of the production process.

[0067] When the result of the bidirectional convergence verification does not exceed the response time difference allowable threshold, a synchronization confirmation signal indicating successful production instruction timing synchronization is sent to the production line controller; For example, if the calculated response time difference range is 2.84 microseconds, which is less than the preset allowable threshold of 5 microseconds and meets the synchronization requirement, the master node industrial computer automatically generates a synchronization confirmation signal data packet and sends the synchronization confirmation signal to the production line controller via the network communication interface, indicating that the current production instruction transmission timing is synchronized successfully. The generated synchronization confirmation signal can include an identifier indicating the synchronization success and a timestamp tag.

[0068] When the result of the bidirectional convergence verification exceeds the response time difference allowable threshold, the dynamic compensation strategy iterative update is triggered; If, after two-way comparison verification, the extreme value of the response time difference exceeds the allowable threshold of 5 microseconds, it is determined that the current synchronization performance does not meet production requirements. At this time, the industrial computer of the master node automatically starts the dynamic compensation strategy iterative update process.

[0069] The iterative update is to recalculate and optimize the asymmetric load disturbance factor and phase drift correction parameters, and then adjust the load weight distribution parameters and phase drift correction parameters until a more optimized production instruction sending timing is regenerated to ensure that the actual response time difference of the end execution node is restored to the allowable threshold range again, and finally achieve continuous optimization and stable control of the production line synchronization performance.

[0070] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0071] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0072] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0073] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0075] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0076] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0077] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0078] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0079] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-channel timing synchronization communication method for a smart speaker production line, characterized in that: The steps include: S1: The master control node broadcasts production instructions with timestamp tags to each production channel and obtains the phase drift data of the link layer of each production channel; S2: Based on the phase drift data, a sliding window is used to calculate the asymmetric load disturbance factor between production channels; S3: Based on the timestamp tags carried by the production instructions, the drift accumulation trend of the production instruction transmission delay of each production channel is fitted by the least squares method; S4: Perform cross-layer correlation analysis on asymmetric load disturbance factors and drift accumulation trends to generate dynamic compensation strategies; S5: Inject the dynamic compensation strategy into the production instruction scheduling queue and correct the sending timing of the production instructions on the target production channel through the remapping mechanism; S6: Collect the response time difference of the terminal execution node to the revised production instruction and generate the response consistency evaluation result; S7: Perform bidirectional convergence verification on the sending timing and response consistency evaluation results of the corrected production instructions in the target production channel, dynamically trigger the dynamic compensation strategy iteration or output a synchronization confirmation signal to the controller.

2. The multi-channel timing synchronization communication method for smart speaker production line according to claim 1 is characterized in that: S1, specifically: The master control node broadcasts production instructions with timestamp tags to each production channel through the network communication interface in each communication cycle; The phase information field in the link layer data frame returned by each production channel is parsed to obtain the phase drift data of each production channel.

3. The multi-channel timing synchronization communication method for a smart speaker production line according to claim 2, characterized in that: S2, specifically: The phase drift data is continuously intercepted according to a preset fixed window length to obtain multiple sets of sliding window data sequences; Calculate the variance of each sliding window data sequence one by one and generate the variance eigenvalue corresponding to each sliding window; Calculate the phase difference between adjacent production channels based on the variance eigenvalues ​​to obtain the phase drift difference between each adjacent production channel; Calculating the deviation ratio of each production channel relative to a common reference value based on the phase drift difference between adjacent production channel pairs; The asymmetric load disturbance factor of each production channel is calculated based on the deviation ratio.

4. The multi-channel timing synchronization communication method for a smart speaker production line according to claim 3 is characterized in that: S3, specifically: Record the delay data between the production instruction sending and the receiving end timestamp of each production channel to generate a delay trend sequence; Based on the delay trend sequence, the delay trend data scatter distribution of each production channel is generated with the theoretical sending time marked by the timestamp label as the horizontal axis and the actual transmission delay value of the production instruction as the vertical axis; The least squares method is used to perform linear fitting on the scattered distribution of delay trend data to obtain a fitting straight line showing the change of production instruction transmission delay over time in each production channel. According to the fitted straight line, the transmission delay change slope of each production channel is calculated and used as the drift accumulation trend of the production instruction transmission delay of each production channel.

5. The multi-channel timing synchronization communication method for a smart speaker production line according to claim 4 is characterized in that: S4, specifically: Based on the asymmetric load disturbance factor and drift accumulation trend, a correlation matrix between production channel load disturbance and transmission delay is constructed; The correlation strength between the asymmetric load disturbance factor and the drift accumulation trend is calculated through the correlation matrix; Based on the correlation strength between the symmetric load disturbance factor and the drift accumulation trend, a dynamic compensation strategy including load weight distribution parameters and phase drift correction parameters is generated.

6. The multi-channel timing synchronization communication method for a smart speaker production line according to claim 5, characterized in that: S5, specifically: The production instruction scheduling queue calculates instruction sending time correction based on load weight distribution parameters; Adjust the original timestamp tag carried by the production instruction according to the phase drift correction parameter; Remap the adjusted timestamp label according to the instruction sending time correction amount to obtain a new timestamp label for the production instruction; Rearrange the sending order of production instructions according to the new timestamp labels to form a production instruction sending queue; According to the production instruction sending queue, a production instruction with a new timestamp tag is sent to the target production channel.

7. The multi-channel timing synchronization communication method for a smart speaker production line according to claim 6, characterized in that: S6, specifically: The terminal execution node records the actual receiving time corresponding to each received production instruction and extracts the marked theoretical sending time; Calculate the production instruction response time difference of each terminal execution node based on the actual receiving time and the theoretical sending time; Summarize the production instruction response time difference of each terminal execution node and calculate the difference between the maximum response time difference and the minimum response time difference among all terminal execution nodes; The response consistency evaluation result is generated according to the difference between the maximum response time difference and the minimum response time difference.

8. The multi-channel timing synchronization communication method for a smart speaker production line according to claim 7, characterized in that: S7, specifically: Taking the production instruction carrying the new timestamp tag as the first input data; The response consistency assessment result is used as the second input data; Performing bidirectional convergence verification on the sending timing of the first input data and the difference between the maximum value and the minimum value of the response time difference in the second input data; Pre-set the response time difference tolerance threshold for determining whether synchronization meets the requirements; When the result of the bidirectional convergence verification does not exceed the response time difference allowable threshold, a synchronization confirmation signal indicating successful production instruction timing synchronization is sent to the production line controller; When the result of the bidirectional convergence verification exceeds the response time difference allowable threshold, the dynamic compensation strategy iterative update is triggered.

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