Eye pattern intersection point estimation method and system

Abstract

The invention provides an eye pattern intersection point estimation method and system, relates to the technical field of communication link evaluation, and solves the problems of insufficient estimation precision and low efficiency caused by sparse data of an intersection point region in an existing eye pattern test. The method comprises the following steps: acquiring VNA frequency domain data, converting the VNA frequency domain data into time domain data, and performing convolution calculation by using a random sequence to generate a convolution sequence; according to a preset unit interval length, segmenting the convolution sequence into a plurality of eye pattern data units to form an eye pattern data set; screening out a rising edge data subset and a falling edge data subset based on the position relationship between the starting point and the ending point of each eye pattern data unit in the eye pattern data set and the middle position of the eye pattern; and respectively carrying out average processing to obtain rising edge data and falling edge data, and carrying out linear fitting to obtain an intersection point of the rising edge data and the falling edge data as an eye pattern intersection point. According to the method, calculation can be completed only through a small number of basic data points, the processing burden is greatly relieved, and the testing efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication link evaluation, in particular to an eye diagram crossing point estimation method and system. BACKGROUND

[0002] In the intelligent construction scene of the construction field, the fluency of the collaborative work of the construction robots and the real-time performance of the construction data transmission highly depend on the stable performance of the high-speed communication link deployed on site. The integrity of the communication signal in the transmission process will directly determine the accuracy of the robot action execution and the safety of the construction process. Therefore, it is crucial to accurately evaluate the actual quality of the communication link.

[0003] As a widely used core evaluation method in the industry, eye diagram analysis can intuitively reflect the quality condition of the signal in the transmission process. Among them, the accurate estimation of the eye diagram crossing point is an indispensable basic step for subsequent calculation of key parameters such as eye diagram width, signal peak-to-peak jitter, etc. The estimation accuracy of this step fundamentally determines the final judgment conclusion of the reliability of the entire communication link, which has an extremely important position in specific application scenarios such as transmission of control instructions between robots, return of on-site sensor data, and interaction of remote construction management data.

[0004] At present, the core principle of the mainstream eye diagram crossing point estimation technology in the industry generally relies on statistical analysis of a large amount of collected signal data. This method needs to accumulate extremely large sample data to barely reach the acceptable precision threshold, that is Figure 1 The multi-sampling point eye diagram scatter diagram is shown. The basic idea is to try to locate the crossing area of the signal level jump in the complex signal waveform by constructing a statistical model of massive data. However, when such a method is applied to the eye diagram test of the communication link based on the vector network analyzer (VNA), its inherent limitations become particularly prominent. This is because the process of generating the eye diagram by the VNA itself involves complex operation procedures, including wide frequency range scanning, multi-port calibration and other steps, and its data generation link is much more complex than that of conventional oscilloscopes and other instruments. The eye diagram data generated presents a significant non-uniform distribution characteristic, especially in the crossing point area where the signal level jumps, the effective data points are naturally sparse, and the VNA eye diagram scatter diagram can be referred to Figure 2 .

[0005] The massive data accumulation strategy relied on by the traditional method cannot effectively improve the accuracy of the crossing point positioning when facing the sparse data in this specific area, but on the contrary, due to its processing demand for a large amount of data, it greatly exacerbates the efficiency bottleneck of the test process. This high-cost, low-efficiency test mode has gradually been difficult to meet the growing actual needs in the modern intelligent construction environment that pursues high efficiency and precision. SUMMARY

[0006] The purpose of the present application is to solve the problem of insufficient estimation accuracy and low efficiency caused by sparse data in the cross-point area in the existing eye diagram test, and therefore an eye diagram cross-point estimation method and system are proposed. The present application only needs to use a small amount of basic data points collected by the vector network analyzer itself to complete the calculation process, without the need for additional interpolation operation to supplement the data amount, thereby greatly reducing the processing burden and improving the test efficiency.

[0007] The present application adopts the following technical solutions to achieve the purpose: An eye diagram cross-point estimation method, comprising the following steps: S1, acquiring the frequency domain data corresponding to the building robot collaborative operation communication link through the vector network analyzer, and converting it into time domain data; S2, using a random sequence to perform convolution calculation with the time domain data to generate a convolution sequence; S3, according to a preset unit interval length, the convolution sequence is divided into a plurality of eye diagram data units to form an eye diagram data set containing a plurality of eye diagram data units; S4, based on the position relationship between the starting point, ending point and middle position of each eye diagram data unit in the eye diagram data set, the rising edge data subset and the falling edge data subset are selected; S5, the rising edge data subset and the falling edge data subset are respectively subjected to average processing to obtain smoothed rising edge data and falling edge data; S6, linear fitting is performed on the smoothed rising edge data and falling edge data, and the intersection point of the two is determined as the eye diagram cross-point, and the estimation is completed.

[0008] Specifically, in step S1, the frequency domain data corresponding to the building robot collaborative operation communication link is converted into time domain data through inverse Fourier transform.

[0009] Preferably, in step S2, the random sequence is a pseudo-random binary sequence; the convolution calculation specifically includes: shifting and aligning the random sequence with the time domain data in the time dimension, and calculating the sum of the product of the two at each shift position.

[0010] Preferably, in step S3, the preset unit interval length is determined based on the data baud rate of the building robot collaborative operation communication link.

[0011] Specifically, after the preset unit interval length is determined, a data segment of a fixed length is continuously intercepted from the convolution sequence in a circular shift manner according to the unit interval length, and each data segment is an eye diagram data unit.

[0012] Specifically, in step S4, the middle position of the eye diagram is determined by calculating the average of the maximum and minimum level values ​​of all eye diagram data units in the eye diagram dataset.

[0013] Furthermore, the filtering of rising edge data subsets and falling edge data subsets specifically includes: If the starting point level value of a certain eye diagram data unit is less than the level value at the middle position of the eye diagram, and its ending point level value is greater than the level value at the middle position of the eye diagram, then the eye diagram data unit is classified into the rising edge data subset. If the starting point level value of an eye diagram data unit is greater than the level value at the middle position of the eye diagram, and its ending point level value is less than the level value at the middle position of the eye diagram, then the eye diagram data unit is classified into the falling edge data subset. If any of the above conditions are not met, the data will be excluded from the rising edge data subset and the falling edge data subset, and recorded as the non-jumping data subset.

[0014] Specifically, in step S5, the averaging process is performed as follows: the level values ​​of all eye diagram data units in the rising edge data subset at the same time point are arithmetically averaged to obtain smoothed rising edge data; the level values ​​of all eye diagram data units in the falling edge data subset at the same time point are arithmetically averaged to obtain smoothed falling edge data.

[0015] Specifically, in step S6, the linear fitting is performed using the least squares method. Two straight lines are fitted to the smoothed rising edge data and the smoothed falling edge data respectively, and the coordinates of the intersection point of these two straight lines are solved. The time value of the intersection point coordinates is the eye diagram intersection point.

[0016] This invention also provides an eye diagram intersection estimation system for implementing the aforementioned eye diagram intersection estimation method, the system comprising the following functional modules: The data acquisition and conversion module is used to collect frequency domain data corresponding to the collaborative operation communication link of the construction robot through a vector network analyzer, and convert the frequency domain data into time domain data. The sequence processing module is used to generate a random sequence and perform convolution calculation on the random sequence with the time domain data to generate a convolution sequence. Then, based on a preset unit interval length, the convolution sequence is divided into multiple eye diagram data units to form an eye diagram dataset. The data filtering module is used to filter out rising edge data subsets and falling edge data subsets from the eye diagram dataset based on the positional relationship between the start point level value, end point level value and the level value at the middle position of each eye diagram data unit in the eye diagram dataset. The intersection point calculation module is used to perform average processing on the rising edge data subset and the falling edge data subset respectively to generate smoothed rising edge data and falling edge data, and to determine the coordinates of the intersection point of the two by linear fitting, which is used as the eye diagram intersection point.

[0017] In summary, due to the adoption of this technical solution, the beneficial effects of this invention are as follows: This invention significantly reduces computational resource requirements while effectively improving the accuracy and reliability of eye diagram crossover point estimation. Compared to traditional methods that rely on massive sampling points, this invention only needs to utilize a small number of basic data points collected by the vector network analyzer itself to complete the calculation process, without the need for additional interpolation operations to supplement the data volume, thereby greatly reducing the processing burden and improving testing efficiency.

[0018] In non-uniform eye diagram data generated by vector network analyzers, the sparse data in the intersection region makes it difficult for traditional methods based on statistical models such as cluster analysis to accurately locate the true intersection position, and the estimation results often have large deviations. This invention effectively avoids direct dependence on the discrete points near the intersection, enabling it to maintain high estimation accuracy even under unfavorable data distribution conditions.

[0019] Furthermore, this invention exhibits excellent robustness, consistently obtaining accurate cross-point locations even for non-standard eye diagram shapes caused by channel distortion, noise interference, and other factors, as well as in practical application scenarios with significant noise impact, ensuring the consistency and reliability of the evaluation results. Attached Figure Description

[0020] The present invention is described in detail with reference to the following figures, which include three figures as follows: Figure 1 This is a schematic diagram of multi-sampling eye diagram scatter points obtained from conventional eye diagram crossover point estimation techniques. Figure 2 This is a schematic diagram of VNA eye diagram scatter points obtained from a standard VNA eye diagram test. Figure 3 This is a schematic diagram illustrating the overall process of the eye diagram intersection estimation method of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] An eye diagram intersection estimation method, Figure 3 The overall process of this method is briefly described below and can be viewed concurrently; the key steps of this method can be summarized as follows: S1. Using a vector network analyzer, acquire the frequency domain data corresponding to the collaborative operation communication link of the construction robot and convert it into time domain data; S2. Perform convolution calculation with the time-domain data using a random sequence to generate a convolution sequence; S3. Based on the preset unit interval length, the convolution sequence is divided into multiple eye diagram data units to form an eye diagram dataset containing multiple eye diagram data units. S4. Based on the positional relationship between the start point, end point and middle position of each eye diagram data unit in the eye diagram dataset, filter out the rising edge data subset and the falling edge data subset; S5. Average the rising edge data subset and the falling edge data subset respectively to obtain smoothed rising edge data and falling edge data; S6. Perform linear fitting on the smoothed rising edge data and falling edge data, determine the intersection point of the two as the eye diagram intersection point, and complete the estimation.

[0024] This embodiment will describe in detail the steps and preferred options in the order described above.

[0025] This implementation begins with the data acquisition and preprocessing stage. In practical applications of collaborative construction robot operations, the stability of the communication link directly affects the accuracy of command synchronization and the real-time performance of sensor data transmission between multiple robot systems. Therefore, this implementation also employs a Vector Network Analyzer (VNA) to test the target communication link. The VNA injects a sweeping signal within a specific frequency range into the link under test and measures its response to obtain frequency domain scattering parameter data characterizing the link's transmission properties. This frequency domain data is recorded in complex form, containing amplitude and phase information.

[0026] Since eye diagram analysis needs to be performed in the time domain, step S1 of this embodiment converts the frequency domain data to the time domain. This conversion process is typically achieved through inverse Fourier transform, using the inverse operation of the fast Fourier transform algorithm to reconstruct the discrete frequency domain data into a continuous time domain waveform. In this embodiment, attention must be paid to the relationship between the number of frequency domain sampling points and the time domain resolution to ensure that the converted time domain waveform has sufficient detail representation capability.

[0027] In step S2 of this embodiment, after obtaining the time-domain data, it is necessary to simulate the signal transmission pattern in the actual communication process. For this purpose, a random sequence conforming to the communication protocol standard needs to be generated. This embodiment uses a pseudo-random binary sequence, which consists of logic high and low levels, and its length is sufficient to cover multiple signal cycles.

[0028] The sequence is then convolved with the aforementioned time-domain data. The essence of convolution is to simulate the response of a signal through a linear system. Specifically, the random sequence is shifted relative to the time-domain data in the time dimension, and the sum of their products is calculated at each shift position. This process can be implemented using the discrete convolution formula, ultimately outputting a convolution sequence that contains the complete dynamic characteristics of the signal transmission. The convolutional sequence The length depends on the length of the input sequence and the length of the time-domain data, and its data points densely record the detailed features of the signal transition edges.

[0029] Step S3 in this embodiment is the eye diagram dataset construction stage. The eye diagram unit interval length is the core parameter of this step, which is defined as the duration of a single data symbol, and is usually expressed as... Symbol representation. In this embodiment, the unit interval length is determined based on the data baud rate of the communication link under test, specifically the following relationship: It is equal to the reciprocal of the baud rate. According to this... Length, for the aforementioned convolutional sequence Perform the cutting process.

[0030] In this embodiment, the cutting operation employs a cyclic shifting and interception mechanism: with The length is a window, from the convolution sequence The first eye diagram data unit is extracted from the starting position; then the window is moved forward by one sampling point to extract the second unit; this process is repeated until the end of the sequence. This method can generate a large number of overlapping eye diagram data units. , … To form an eye diagram dataset Each eye diagram data unit has a length strictly equal to [the specified value]. The corresponding number of sampling points, whose data points record the trajectory of signal level changes within that time window.

[0031] Eye diagram dataset Each unit in the data can be divided into three categories based on its waveform shape: non-jump data units. Rising data unit and falling data unit No transition data unit It exhibits a stable state characterized by a continuous high or low level; rising data unit This is manifested as a transition from a low level to a high level; falling data unit This manifests as a transition from a high level to a low level. In the eye diagram generated by the vector network analyzer, due to limitations in the testing mechanism, data points in the signal transition region are naturally sparse. Therefore, the vast majority of data units belong to the no-transition category, while the proportion of units that actually contain transition edges is very low. To accurately extract the crossover point information, it is necessary to first analyze the eye diagram dataset. Perform a screening process.

[0032] In step S4 of this embodiment, the core of the screening operation is to establish an objective waveform classification standard by calculating the eye diagram dataset. Characteristic reference level This is achieved by using the middle position of the eye diagram. The value is obtained by calculating the maximum level of all data units within the statistical data set. and minimum level value The arithmetic mean of the two values ​​is taken. This intermediate position represents the theoretical dividing point between high and low signal levels.

[0033] For each eye diagram data unit in the dataset, analyze its start and end point level values ​​relative to the feature reference level at that intermediate position. The relationship is as follows: If the starting point level value is less than the middle position level value At the same time, the level value at the end point is greater than the level value at the middle position. If so, the cell is determined to be an ascending data cell; If the starting point level value is greater than the middle position level value At the same time, the level value at the end point is lower than the level value at the middle position. If so, it is determined to be a falling data unit; All other cases are classified as non-jump data units.

[0034] This classification mechanism ensures that valid cells can be accurately captured even when the data is sparse along transition lines.

[0035] After classification, the set of rising data units is defined as a subset of rising edge data. The set of falling data cells is defined as a subset of the falling edge data. Then we can proceed to the averaging process in step S5 of the method.

[0036] Because individual data units may exhibit non-ideal transition patterns due to noise interference, statistical averaging is needed to improve the accuracy of signal feature representation. For the rising edge data subset... At the same time coordinate point, the arithmetic mean of the corresponding level values ​​of all units is calculated to generate smoothed rising edge data. Trajectory. Similarly, for the data subset on the falling edge. Perform the same operation to obtain smoothed falling edge data. Trajectory. This averaging process effectively suppresses the influence of random noise while preserving the common characteristics of transition edges. In construction sites where strong electromagnetic interference exists, this step in this implementation is particularly important for ensuring the robustness of the algorithm.

[0037] In step S6 of this embodiment, smooth rising edge data is obtained. and falling edge data Next, the intersection point of the two needs to be determined. Since the actual signal transitions usually exhibit approximately linear characteristics, this implementation uses a linear model for fitting, specifically employing the least squares method to fit straight lines to the rising and falling edge trajectories respectively. The least squares method solves for the optimal line parameters by minimizing the sum of squared perpendicular distances between the data points and the fitted line.

[0038] In this embodiment, the fitted line along the rising edge can be expressed as a linear function that changes with time, and the fitted line along the falling edge can also be expressed as another linear function. By solving the system of equations composed of these two linear equations, the precise time and level coordinates of the intersection point of the two lines can be calculated. This intersection point is the eye diagram crossover point, and its time coordinate value is directly used for subsequent calculations of key parameters such as eye diagram width and signal jitter, thereby completing the estimation of the eye diagram crossover point.

[0039] In the testing scenario of communication links for construction robots, traditional methods rely on the statistical analysis of massive amounts of data points. This implementation method, however, determines valid data through the aforementioned filtering process, requiring only a small number of original data points for calculation. In the non-uniform eye diagram generated by the vector network analyzer, the averaging technique of this implementation method overcomes the inherent problem of data sparsity in transition regions. For non-rectangular eye diagrams caused by channel distortion and waveform distortion caused by electromagnetic interference at the construction site, this implementation method still maintains stable intersection point localization capabilities. This provides good technical support for evaluating the reliability of communication links in robot collaborative control systems.

[0040] The above-described method process of this embodiment can be automated by a computer program, thereby obtaining an eye diagram crossover point estimation system that implements the eye diagram crossover point estimation method. This system may include the following functional modules: The data acquisition and conversion module is used to collect frequency domain data corresponding to the collaborative operation communication link of the construction robot through a vector network analyzer, and convert the frequency domain data into time domain data. The sequence processing module is used to generate a random sequence and perform convolution calculation on the random sequence with the time domain data to generate a convolution sequence. Then, based on a preset unit interval length, the convolution sequence is divided into multiple eye diagram data units to form an eye diagram dataset. The data filtering module is used to filter out rising edge data subsets and falling edge data subsets from the eye diagram dataset based on the positional relationship between the start point level value, end point level value and the level value at the middle position of each eye diagram data unit in the eye diagram dataset. The intersection point calculation module is used to perform average processing on the rising edge data subset and the falling edge data subset respectively to generate smoothed rising edge data and falling edge data, and to determine the coordinates of the intersection point of the two by linear fitting, which is used as the eye diagram intersection point.

[0041] After loading test data from a vector network analyzer, the computer program of the eye diagram intersection estimation system sequentially performs data transformation, convolution calculation, eye diagram segmentation, intelligent filtering, trajectory averaging, and linear fitting, ultimately outputting the coordinates of the eye diagram intersections. The entire processing involves significantly less computation than traditional methods, facilitating real-time operation in portable testing equipment. In intelligent construction field testing, this method provides an effective technical means to ensure communication security for collaborative operations of multi-robot systems.

Claims

1. A method for estimating eye diagram intersection points, characterized in that, The method includes the following steps: S1. Using a vector network analyzer, acquire the frequency domain data corresponding to the collaborative operation communication link of the construction robot and convert it into time domain data; S2. Perform convolution calculation with the time-domain data using a random sequence to generate a convolution sequence; S3. Based on the preset unit interval length, the convolution sequence is divided into multiple eye diagram data units to form an eye diagram dataset containing multiple eye diagram data units. S4. Based on the positional relationship between the start point, end point and middle position of each eye diagram data unit in the eye diagram dataset, filter out the rising edge data subset and the falling edge data subset; S5. Average the rising edge data subset and the falling edge data subset respectively to obtain smoothed rising edge data and falling edge data; S6. Perform linear fitting on the smoothed rising edge data and falling edge data, determine the intersection point of the two as the eye diagram intersection point, and complete the estimation.

2. The eye diagram intersection estimation method according to claim 1, characterized in that: In step S1, the frequency domain data corresponding to the collaborative operation communication link of the construction robot is converted into time domain data through inverse Fourier transform.

3. The eye diagram intersection estimation method according to claim 1, characterized in that: In step S2, the random sequence is a pseudo-random binary sequence; the convolution calculation specifically includes: shifting and aligning the random sequence and the time-domain data in the time dimension, and calculating the sum of their products at each shift position.

4. The eye diagram intersection estimation method according to claim 1, characterized in that: In step S3, the preset unit interval length is determined based on the data baud rate of the collaborative operation communication link of the construction robot.

5. The eye diagram intersection estimation method according to claim 4, characterized in that: Once the preset unit interval length is determined, data segments of a fixed length are continuously extracted from the convolution sequence in a cyclic shift manner according to the unit interval length, and each data segment is an eye diagram data unit.

6. The eye diagram intersection estimation method according to claim 1, characterized in that: In step S4, the middle position of the eye diagram is determined by calculating the average of the maximum and minimum level values ​​of all eye diagram data units in the eye diagram dataset.

7. The eye diagram intersection estimation method according to claim 6, characterized in that, The process of filtering the rising edge data subset and the falling edge data subset specifically includes: If the starting point level value of a certain eye diagram data unit is less than the level value at the middle position of the eye diagram, and its ending point level value is greater than the level value at the middle position of the eye diagram, then the eye diagram data unit is classified into the rising edge data subset. If the starting point level value of an eye diagram data unit is greater than the level value at the middle position of the eye diagram, and its ending point level value is less than the level value at the middle position of the eye diagram, then the eye diagram data unit is classified into the falling edge data subset. If any of the above conditions are not met, the data will be excluded from the rising edge data subset and the falling edge data subset, and recorded as the non-jumping data subset.

8. The eye diagram intersection estimation method according to claim 1, characterized in that, In step S5, the specific method for averaging is as follows: the level values ​​of all eye diagram data units in the rising edge data subset at the same time point are arithmetically averaged to obtain smoothed rising edge data; the level values ​​of all eye diagram data units in the falling edge data subset at the same time point are arithmetically averaged to obtain smoothed falling edge data.

9. The eye diagram intersection estimation method according to claim 1, characterized in that, In step S6, the linear fitting is performed using the least squares method. Two straight lines are fitted to the smoothed rising edge data and the smoothed falling edge data respectively, and the coordinates of the intersection point of these two straight lines are solved. The time value of the intersection point coordinates is the eye diagram intersection point.

10. An eye diagram intersection estimation system implementing the eye diagram intersection estimation method according to any one of claims 1-9, characterized in that, The system includes the following functional modules: The data acquisition and conversion module is used to collect frequency domain data corresponding to the collaborative operation communication link of the construction robot through a vector network analyzer, and convert the frequency domain data into time domain data. The sequence processing module is used to generate a random sequence and perform convolution calculation on the random sequence with the time domain data to generate a convolution sequence. Then, based on a preset unit interval length, the convolution sequence is divided into multiple eye diagram data units to form an eye diagram dataset. The data filtering module is used to filter out rising edge data subsets and falling edge data subsets from the eye diagram dataset based on the positional relationship between the start point level value, end point level value and the level value at the middle position of each eye diagram data unit in the eye diagram dataset. The intersection point calculation module is used to average the rising edge data subset and the falling edge data subset respectively to generate smoothed rising edge data and falling edge data, and to determine the coordinates of the intersection point of the two by linear fitting, which is used as the eye diagram intersection point.

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