Wireless communication coordination system of bridge construction tensioning equipment

Abstract

The application discloses a wireless communication coordination system of bridge construction tensioning equipment, and relates to the technical field of wireless communication.The system comprises an interference detection module, a selection module and a communication module, frequency hopping signals are divided into multiple sub-channels, the improved forward continuous mean removal algorithm is used for interference detection of each sub-channel, and interference judgment information is generated; the interference judgment information is received, signal selection is carried out through a double-depth Q network, and channel numbers are obtained; the channel numbers are sent through a control device, the channel numbers are processed, frequency hopping signals are obtained, the bridge construction tensioning equipment receives the frequency hopping signals and calculates an anti-interference detection time delay, and the anti-interference detection time delay and a propagation time delay are time-synchronized based on a master clock of the control device and a slave clock of the bridge construction tensioning equipment.The synchronization problem of the cooperative operation of multiple tensioning equipment in a bridge construction site is solved, the cooperative control precision of the bridge construction tensioning equipment is improved, and the tensioning operation efficiency is obviously improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more particularly to a wireless communication coordination system for bridge construction tensioning equipment. Background Technology

[0002] In recent years, with the development of bridge construction towards longer spans and heavier loads, the precision requirements for prestressed tensioning construction have become increasingly stringent. Traditional wired communication methods suffer from inherent drawbacks such as complex wiring and poor mobility, while existing wireless communication systems face severe challenges in complex construction environments. Strong electromagnetic interference, mechanical vibration, and the shielding effect of reinforcing bars at construction sites significantly affect communication quality. Especially when multiple tensioning devices operate collaboratively, traditional time synchronization methods are limited by insufficient compensation for equipment processing delays, making it difficult to meet the high-precision synchronization requirements. Simultaneously, existing interference detection algorithms exhibit significant performance degradation in non-stationary noise environments, resulting in limited system anti-interference capabilities. Therefore, there is an urgent need to develop a wireless communication system with strong anti-interference capabilities and high-precision synchronization performance to ensure the quality and efficiency of tensioning construction.

[0003] Currently, Chinese invention patent application number CN201510460690.3 discloses a wireless communication anti-interference method and a wireless communication module. The method includes the following steps: providing a wireless communication module, a GSM band network, and an anti-interference band network; interference detection; network switching; and network recovery. Compared with related technologies, the wireless communication anti-interference method provided by this invention can switch networks when signal interference exists, enabling normal data transmission. This invention also provides a wireless communication module. However, related technologies cannot achieve time synchronization through anti-interference detection delay and propagation delay, lacking accuracy, and have low accuracy and reliability in interference identification under complex environments. Summary of the Invention

[0004] The technical problem solved by this invention is that related technologies cannot achieve time synchronization by detecting anti-interference delay and propagation delay, lack accuracy, and have low accuracy and reliability in interference identification under complex environments.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A wireless communication coordination system for bridge construction tensioning equipment includes an interference detection module, a selection module, and a communication module;

[0007] The interference detection module is used to divide the frequency hopping signal into multiple sub-channels, and use an improved forward continuous mean removal algorithm to perform interference detection on each sub-channel to generate interference judgment information.

[0008] The selection module is used to receive the interference judgment information, perform signal selection through a dual deep Q network, and obtain the channel number;

[0009] The communication module is used to send a channel number through the control device, process the channel number to obtain a frequency hopping signal, receive the frequency hopping signal and calculate the anti-interference detection delay, and synchronize the anti-interference detection delay and propagation delay based on the master clock of the control device and the slave clock of the bridge construction tensioning device.

[0010] Preferably, the interference detection module includes a receiving unit and an improvement unit;

[0011] The receiving unit is used to receive frequency hopping signals and divide the frequency hopping signals into sub-channel signals by a filter bank, wherein the filter bank is composed of parallel bandpass filters.

[0012] The improved unit is used to detect sub-channel signals using the improved forward continuous mean removal algorithm to obtain interference judgment information.

[0013] Preferably, the filter bank construction methods specifically include:

[0014] By frequency shifting the low-pass filter, a band-pass filter is generated. The mathematical expression for the impulse response of the band-pass filter is:

[0015] ;

[0016] in, Let be the unit impulse response of the bandpass filter corresponding to the k-th sub-channel. Let be the discrete-time unit impulse response of the low-pass filter, and k be the sub-channel index number, ranging from 0 to D-1. For discrete time series indexing, The modulation function;

[0017] The mathematical expression for the impulse response frequency of the low-pass filter is:

[0018] ;

[0019] in, Digital angular frequency, This represents the total number of sub-channels. This is the normalized cutoff frequency of the low-pass filter. It is the imaginary unit.

[0020] Preferably, the improved forward continuous mean removal algorithm is used to detect sub-channel signals, and the interference judgment information obtained specifically includes:

[0021] The input signal is subjected to a Fast Fourier Transform to obtain spectral data. The spectral data is divided into spectral segments to obtain data points for each segment. An initial detection threshold is calculated through matrix eigenvalue noise estimation. Based on the initial detection threshold, iterative detection is performed through segment median estimation and the detection threshold is updated. The iteration terminates and the interference detection results of the spectral segments are output. The interference detection results of all spectral segments are combined to obtain interference judgment information.

[0022] Preferably, the matrix eigenvalue noise estimation operation specifically includes:

[0023] The data points of the segmented spectrum are reconstructed into a signal matrix. The sample covariance matrix of the signal matrix is ​​calculated. The sample covariance matrix is ​​decomposed into eigenvalues ​​to obtain an eigenvalue sequence. The eigenvalue sequence is sorted in descending order. An initial noise power estimate is calculated based on the eigenvalue sequence. The initial noise power estimate is converted into an initial noise estimate. The initial noise estimate and a preset threshold factor are calculated to obtain an initial detection threshold.

[0024] Preferably, the sample covariance matrix of the signal matrix is ​​calculated, and the calculation expression is:

[0025] ;

[0026] in, The sample covariance matrix, For the signal matrix, Let be the conjugate transpose of the signal matrix. is the number of columns in the signal matrix;

[0027] The calculation expression for the initial noise power estimate based on the aforementioned feature value sequence is as follows:

[0028] ;

[0029] in, This is the initial noise power estimate. It is the smallest eigenvalue.

[0030] Preferably, the piecewise median estimation operation specifically includes:

[0031] Using the data points of the current segmented spectrum as the set to be detected, and the initial detection threshold as the current detection threshold, an iteration is performed. The iteration process includes:

[0032] Traverse the current set to be detected, identify data points with amplitudes greater than the current detection threshold as interference points, remove the data points identified as interference points from the set to be detected and input them into the interference point set, calculate the median amplitude of the remaining set to be detected after removing interference points, and use it as a new noise estimate. Based on the new noise estimate and the preset threshold factor, calculate the detection threshold.

[0033] If the number of interference points detected remains unchanged during two consecutive iterations, or if the preset maximum number of iterations is reached, the iteration terminates, and the detection threshold updated in the last iteration is output, along with the interference detection results for the spectrum segments.

[0034] Preferably, the selection module is used to receive interference judgment information, and to perform channel selection on the interference judgment information through a dual deep Q network to obtain a channel number;

[0035] The interference judgment information is constructed into a channel state matrix. The Q value of each selectable channel action under the current channel state matrix is ​​calculated by forward propagation. The Q value vector is obtained by normalization. The channel is selected from the Q value vector using Markov decision to obtain the target communication channel. The channel number is sent, and the channel number includes the target communication channel and the frequency hopping signal.

[0036] The channel state matrix includes the real-time interference status of sub-channels, historical communication quality, and signal strength indicators.

[0037] Preferably, the communication module includes an execution unit and a synchronization unit;

[0038] The execution unit is used to receive the channel number, convert the channel number into frequency parameters by looking up a preset mapping table, obtain the target communication channel, and send a frequency hopping signal on the target communication channel.

[0039] The synchronization unit is used to calculate the anti-interference detection delay and perform time synchronization based on the anti-interference detection delay and the propagation delay.

[0040] Preferably, the timestamps for obtaining the transmission channel number, receiving the frequency hopping signal, transmitting interference judgment information, and receiving interference judgment information are obtained.

[0041] The timestamp of receiving the frequency hopping signal is used as the first timestamp, the timestamp of sending the interference judgment information is used as the second timestamp, the timestamp of receiving the interference judgment information is used as the third timestamp, and the timestamp of sending the channel number is used as the fourth timestamp.

[0042] The anti-interference detection delay is obtained by subtracting the second timestamp from the first timestamp. The calculation expression is as follows:

[0043] ;

[0044] in, For synchronization period, To mitigate interference and detect delay, This is the second timestamp. First timestamp;

[0045] Propagation delay is calculated by detecting interference delay:

[0046] ;

[0047] in, To delay the transmission time, To mitigate interference and detect delay, This is a third timestamp. This is the fourth timestamp;

[0048] Calculate the clock skew for the current cycle:

[0049] ;

[0050] in, This is due to clock skew;

[0051] The expression for calculating the master-slave clock frequency ratio is:

[0052] ;

[0053] in, For synchronization period, The ratio of master to slave clock frequencies. The clock offset calculated for the nth synchronization cycle. The clock offset calculated for the (n-1)th cycle. The synchronization period interval;

[0054] Based on the clock deviation and the master-slave clock frequency ratio, adjust the clock phase of the bridge construction tensioning equipment, and calculate the slave clock time for the next cycle. The calculation expression is as follows:

[0055] ;

[0056] in, The corrected time from the clock at the start of the (n+1)th synchronization cycle. The original time of the clock at the start of the nth synchronization cycle. This is the synchronization period interval.

[0057] The beneficial effects of this invention are as follows: By improving the forward continuous mean removal algorithm using matrix eigenvalue noise estimation and piecewise median iterative detection, the interference identification capability of bridge construction tensioning equipment in complex environments is enhanced, significantly improving detection accuracy and reliability. Furthermore, by reducing the synchronization mechanism of the interaction process and utilizing anti-interference detection delay and propagation delay for time synchronization, precise coordination among multiple tensioning devices is ensured, effectively solving the synchronization problem of collaborative operation of multiple tensioning devices on bridge construction sites. This significantly reduces the communication error rate, improves the collaborative control accuracy of bridge construction tensioning equipment, and substantially enhances the efficiency of tensioning operations. Attached Figure Description

[0058] Figure 1 This is a basic flowchart of a wireless communication collaborative system for bridge construction tensioning equipment, provided as an embodiment of the present invention. Detailed Implementation

[0059] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0060] Example, refer to Figure 1 As an embodiment of the present invention, a wireless communication coordination system for bridge construction tensioning equipment is provided, including an interference detection module, a selection module and a communication module;

[0061] The interference detection module is used to divide the frequency hopping signal into multiple sub-channels, and uses an improved forward continuous mean removal algorithm to perform interference detection on each sub-channel and generate interference judgment information.

[0062] The selection module is used to receive interference judgment information, select signals through a dual deep Q network, and obtain the channel number;

[0063] The communication module is used to send the channel number through the control device, process the channel number to obtain the frequency hopping signal, receive the frequency hopping signal and calculate the anti-interference detection delay based on the master clock of the control device and the slave clock of the bridge construction tensioning device, and synchronize the anti-interference detection delay and propagation delay based on the master clock of the control device and the slave clock of the bridge construction tensioning device.

[0064] This invention improves the forward continuous mean removal algorithm by utilizing matrix eigenvalue noise estimation and piecewise median iterative detection, enhancing the interference identification capability of bridge construction tensioning equipment in complex environments and significantly improving detection accuracy and reliability. Furthermore, by reducing the synchronization mechanism in the interaction process and using anti-interference detection delay and propagation delay for time synchronization, it ensures precise coordination among multiple tensioning devices, effectively solving the synchronization problem of collaborative operation of multiple tensioning devices on bridge construction sites, significantly reducing communication error rate, improving the collaborative control accuracy of bridge construction tensioning equipment, and significantly improving tensioning operation efficiency.

[0065] In practice, the interference detection module includes a receiving unit and an improvement unit;

[0066] The receiving unit is used to receive frequency hopping signals. It divides the frequency hopping signals into sub-channel signals by a filter bank, which is composed of parallel bandpass filters.

[0067] The improved unit is used to detect sub-channel signals using the improved forward continuous mean removal algorithm to obtain interference judgment information.

[0068] In practice, the methods for constructing filter banks include:

[0069] By frequency shifting the low-pass filter, a band-pass filter is generated. The mathematical expression for the impulse response of the band-pass filter is:

[0070] ;

[0071] in, Let be the unit impulse response of the bandpass filter corresponding to the k-th sub-channel. Let be the discrete-time unit impulse response of the low-pass filter, and k be the sub-channel index number, ranging from 0 to D-1. For discrete time series indexing, The modulation function;

[0072] The mathematical expression for the impulse response frequency of a low-pass filter is:

[0073] ;

[0074] in, Digital angular frequency, This represents the total number of sub-channels. This is the normalized cutoff frequency of the low-pass filter. It is the imaginary unit.

[0075] symbol Essentially, it refers to the frequency domain response of a low-pass filter. In actual time-domain filtering operations, according to the conventional principles of digital signal processing, this term in the formula is essentially the frequency domain response. Discrete-time unit impulse response after inverse discrete-time Fourier transform The frequency response characteristics of the low-pass filter are determined based on its frequency domain expression. Its corresponding time-domain unit impulse response is obtained through inverse transform. Then, based on the mathematical expression of the impulse response of the band-pass filter, its frequency is shifted using a cosine modulation function to generate the unit impulse response of the band-pass filter corresponding to the k-th sub-channel. .

[0076] It should be noted that the function of the modulation function is to shift the spectrum of the low-pass filter to a position centered on the digital angular frequency of the modulation function, thereby forming the bandpass filter of the k-th channel.

[0077] By generating the entire filter bank using a unified low-pass filter prototype combined with a cosine modulation function, the consistency of frequency response characteristics among the sub-channels is ensured. This effectively avoids inter-channel interference and signal distortion problems caused by individual differences in traditional multi-filter designs. The ideal rectangular frequency response characteristics of the low-pass filter ensure strict isolation between the sub-channels, and spectral leakage between adjacent channels is controlled to a minimum, providing clean sub-channel signals for subsequent interference detection. This frequency-shift-based filter bank structure significantly reduces hardware implementation complexity while ensuring performance. Multiple channel division can be achieved by using a single low-pass filter prototype in conjunction with digital modulation, laying a solid foundation for interference detection and channel selection in frequency-hopping communication systems.

[0078] In specific implementation, the improved forward continuous mean removal algorithm is used to detect sub-channel signals, and the interference judgment information obtained includes:

[0079] The input signal is subjected to a Fast Fourier Transform to obtain spectral data. The spectral data is divided into spectral segments to obtain data points for each segment. An initial detection threshold is calculated through matrix eigenvalue noise estimation. Based on the initial detection threshold, iterative detection is performed and the detection threshold is updated through segment median estimation. The iteration terminates and outputs the interference detection results of the spectral segments. The interference detection results of all spectral segments are combined to obtain interference judgment information.

[0080] It should be noted that the process of obtaining spectral data by performing a fast Fourier transform on the input signal specifically includes: performing a discrete Fourier transform on the time-domain signal after windowing to convert the signal from the time domain to the frequency domain, obtaining a spectral distribution containing amplitude and phase information, and then calculating the spectral amplitude to obtain spectral data that can be used for interference detection.

[0081] A more accurate initial detection threshold is obtained through matrix eigenvalue noise estimation, effectively overcoming the problem of large threshold estimation deviation in non-stationary noise environments encountered by traditional algorithms. The iterative detection mechanism employing piecewise median estimation dynamically adjusts the detection threshold, gradually eliminating interference points and updating the noise basis estimate, significantly improving detection accuracy in scenarios with both strong and weak interference. By segmenting the spectrum and combining iterative termination conditions, computational efficiency is optimized while maintaining detection accuracy, enabling the algorithm to meet the real-time communication needs of bridge construction sites. This ensures the reliability and stability of the wireless communication link for tensioning equipment in complex electromagnetic environments.

[0082] In practice, the matrix eigenvalue noise estimation operation specifically includes:

[0083] The data points of the segmented spectrum are reconstructed into a signal matrix. The sample covariance matrix of the signal matrix is ​​calculated. The sample covariance matrix is ​​decomposed into eigenvalues ​​to obtain an eigenvalue sequence. The eigenvalue sequence is sorted in descending order. The initial noise power estimate is calculated based on the eigenvalue sequence. The initial noise power estimate is converted into an initial noise estimate. The initial noise estimate and the preset threshold factor are calculated to obtain the initial detection threshold.

[0084] In practical implementation, the sample covariance matrix of the signal matrix is ​​calculated using the following expression:

[0085] ;

[0086] in, The sample covariance matrix, For the signal matrix, Let be the conjugate transpose of the signal matrix. is the number of columns in the signal matrix;

[0087] The calculation expression for the initial noise power estimate based on the eigenvalue sequence is as follows:

[0088] ;

[0089] in, This is the initial noise power estimate. It is the smallest eigenvalue.

[0090] It should be noted that the sample covariance matrix is ​​a P×P Hermitian matrix, where the main diagonal elements represent the power of each channel signal and the off-diagonal elements represent the correlation between different channel signals.

[0091] This application optimizes the forward continuous mean removal algorithm by using matrix eigenvalue noise estimation. Traditional methods use simple mean or median estimation as the noise benchmark, which is prone to estimation bias when strong interference exists. This application reconstructs the segmented spectrum data points into a signal matrix, calculates the sample covariance matrix and performs eigenvalue decomposition, and uses the minimum eigenvalue to estimate the noise power. Even in the case of strong interference in some channels, the minimum eigenvalue can still converge to the theoretical noise power, thus providing a more accurate noise basis estimation.

[0092] This noise estimation method based on eigenvalue decomposition can effectively distinguish between signal space and noise space, exhibiting stronger anti-interference capabilities in the complex electromagnetic environment of bridge construction sites. It does not raise the overall noise estimation due to individual strong interference frequencies, ensuring the stability of detection sensitivity. This enables the system to effectively identify real interference in construction environments with multiple interference sources such as hoisting machinery and welding equipment, avoiding false alarms or missed detections caused by inaccurate noise estimation. This ensures the reliability of command transmission between the tensioning equipment and the control center, guarantees precise control of tension force and the safety of multi-equipment collaborative operations, and optimizes spectrum utilization efficiency, allowing the system to support more tensioning equipment operating simultaneously with limited spectrum resources, comprehensively improving construction efficiency and safety levels.

[0093] In practice, the piecewise median estimation operation includes:

[0094] The data points of the current segmented spectrum are used as the set to be detected, and the initial detection threshold is used as the current detection threshold. The process is iterated, and the iteration process includes:

[0095] Traverse the current set to be detected, identify data points with amplitudes greater than the current detection threshold as interference points, remove the data points identified as interference points from the set to be detected and input them into the interference point set, calculate the median amplitude of the remaining set to be detected after removing interference points, and use it as a new noise estimate. Based on the new noise estimate and the preset threshold factor, calculate the detection threshold.

[0096] If the number of interference points detected remains unchanged during two consecutive iterations, or if the preset maximum number of iterations is reached, the iteration terminates, and the detection threshold updated in the last iteration is output, along with the interference detection results for the spectrum segments.

[0097] It should be noted that the preset threshold factor is a constant greater than 1, which sets the detection sensitivity by multiplying the calculated noise estimate by the threshold factor. The preset maximum number of iterations is set to 5, which limits the computation time in the worst case and provides sufficient processing margin for complex interference scenarios.

[0098] If the number of interference points detected remains unchanged during two consecutive iterations, or if the preset maximum number of iterations is reached, the iteration terminates. This means that the iteration terminates when either of these two termination conditions is met.

[0099] By utilizing dynamically updated detection thresholds and a median-based noise estimation method, this algorithm effectively adapts to non-stationary noise environments, reducing false alarms and missed detections caused by fixed thresholds in complex electromagnetic environments. By iteratively eliminating interference points and recalculating the noise floor, it achieves gradual separation of signal and interference, accurately identifying interference frequencies even in scenarios where strong interference and weak signals coexist. The introduced iteration termination condition ensures that the algorithm converges within a finite number of steps, guaranteeing both real-time detection and avoiding the waste of computational resources caused by excessive iteration, thus providing reliable interference distribution information for subsequent channel selection decisions.

[0100] In specific implementation, the selection module is used to receive interference judgment information, and performs channel selection on the interference judgment information through a dual deep Q network to obtain the channel number;

[0101] The interference judgment information is constructed into a channel state matrix. The Q value of each selectable channel action under the current channel state matrix is ​​calculated through forward propagation. The Q value vector is obtained by normalization. The channel is selected from the Q value vector using Markov decision to obtain the target communication channel. The channel number is sent, which includes the target communication channel and the frequency hopping signal.

[0102] The channel state matrix includes the real-time interference status of sub-channels, historical communication quality, and signal strength indicators.

[0103] It should be noted that the specific process of calculating the Q-value vector through the forward propagation of a deep Q-network is as follows: The current channel state matrix is ​​fed into the input layer of the deep Q-network. The input data passes through multiple hidden layers in the network for nonlinear transformation and feature extraction. Each hidden layer performs a linear transformation on the input using a weight matrix and a bias vector, and then applies the ReLU activation function to gradually abstract high-level features. Finally, at the output layer, a linear activation function is used to generate Q-values ​​equal to the number of actions for all selectable channels, forming a Q-value vector. Each Q-value represents the expected long-term cumulative reward that can be obtained by selecting the corresponding channel as the communication channel.

[0104] The process of selecting a channel from the Q-value vector using Markov decision employs an improved greedy strategy: the channel action corresponding to the largest Q-value in the current Q-value vector is selected with probability 1-ε, while a non-optimal channel action is randomly selected with probability ε. The ε value is dynamically adjusted based on the network's convergence state; a larger ε value is set initially to promote exploration, and the ε value is gradually decreased as training progresses to enhance utilization. The finally selected channel number is the target communication channel under the current channel state matrix.

[0105] In practice, the communication module includes an execution unit and a synchronization unit;

[0106] The execution unit is used to receive the channel number, convert the channel number into frequency parameters by looking up a preset mapping table, obtain the target communication channel, and send a frequency hopping signal on the target communication channel;

[0107] The synchronization unit is used to calculate the anti-interference detection delay and perform time synchronization based on the anti-interference detection delay and the propagation delay.

[0108] It should be noted that the channel number is converted into frequency parameters by looking up a preset mapping table to obtain the target communication channel. Specifically, this includes verifying the validity of the channel number and converting the numerical number into frequency parameters by looking up a preset mapping table, which includes the center frequency, bandwidth, and modulation method.

[0109] In specific implementation, the timestamps for obtaining the transmission channel number, receiving the frequency hopping signal, transmitting interference judgment information, and receiving interference judgment information are obtained.

[0110] The timestamp of receiving the frequency hopping signal is used as the first timestamp, the timestamp of sending the interference judgment information is used as the second timestamp, the timestamp of receiving the interference judgment information is used as the third timestamp, and the timestamp of sending the channel number is used as the fourth timestamp.

[0111] The anti-interference detection delay is obtained by subtracting the second timestamp from the first timestamp. The calculation expression is as follows:

[0112] ;

[0113] in, For synchronization period, To mitigate interference and detect delay, This is the second timestamp. First timestamp;

[0114] Propagation delay is calculated by detecting interference delay:

[0115] ;

[0116] in, To delay the transmission time, To mitigate interference and detect delay, This is a third timestamp. This is the fourth timestamp;

[0117] Calculate the clock skew for the current cycle:

[0118] ;

[0119] in, This is due to clock skew;

[0120] The expression for calculating the master-slave clock frequency ratio is:

[0121] ;

[0122] in, For synchronization period, The ratio of master to slave clock frequencies. The clock offset calculated for the nth synchronization cycle. The clock offset calculated for the (n-1)th cycle. The synchronization period interval;

[0123] Based on the clock deviation and the master-slave clock frequency ratio, adjust the clock phase of the bridge construction tensioning equipment, and calculate the slave clock time for the next cycle. The calculation expression is as follows:

[0124] ;

[0125] in, The corrected time from the clock at the start of the (n+1)th synchronization cycle. The original time of the clock at the start of the nth synchronization cycle. This is the synchronization period interval.

[0126] It should be noted that the dynamic nature of network states causes the calculated clock frequency to change rapidly. This change in clock frequency leads to inconsistent clock time changes, which in turn affects the calculation of the synchronization phase deviation value.

[0127] Compared to traditional time synchronization methods, such as the PTP protocol, which only consider propagation delay and ignore internal device processing delay, this invention fundamentally solves the problem of the impact of internal device processing time on synchronization accuracy by introducing an anti-interference detection delay parameter and establishing a propagation delay calculation method that includes this delay. Traditional methods typically assume a constant master-slave clock frequency; this application, by calculating the real-time master-slave clock frequency ratio and frequency offset, can dynamically track and compensate for clock drift caused by factors such as temperature changes and device aging. Traditional synchronization mechanisms often employ open-loop correction; this application establishes a closed-loop adjustment based on clock deviation feedback, achieving continuously optimized synchronization through periodic phase adjustments.

[0128] Precise time synchronization ensures the consistency of action of multiple tensioning devices during collaborative operation, avoiding tension control errors caused by clock asynchrony. A high-precision clock reference provides a reliable time reference for channel switching in the frequency-hopping communication system, ensuring the stability of the communication link in complex electromagnetic environments. By eliminating the impact of internal processing delays on synchronization accuracy, the system remains stable even when executing complex interference detection algorithms, providing reliable technical support for real-time control and safety assurance at the construction site.

[0129] This invention improves the forward continuous mean removal algorithm by utilizing matrix eigenvalue noise estimation and piecewise median iterative detection, enhancing the interference identification capability of bridge construction tensioning equipment in complex environments and significantly improving detection accuracy and reliability. Furthermore, by reducing the synchronization mechanism in the interaction process and using anti-interference detection delay and propagation delay for time synchronization, it ensures precise coordination among multiple tensioning devices, effectively solving the synchronization problem of collaborative operation of multiple tensioning devices on bridge construction sites, significantly reducing communication error rate, improving the collaborative control accuracy of bridge construction tensioning equipment, and significantly improving tensioning operation efficiency.

[0130] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A wireless communication coordination system for bridge construction tensioning equipment, characterized in that, It includes an interference detection module, a selection module, and a communication module; The interference detection module is used to divide the frequency hopping signal into multiple sub-channels, and use an improved forward continuous mean removal algorithm to perform interference detection on each sub-channel to generate interference judgment information. The selection module is used to receive the interference judgment information, perform signal selection through a dual deep Q network, and obtain the channel number. The communication module is used to send a channel number through the control device, process the channel number to obtain a frequency hopping signal, receive the frequency hopping signal and calculate the anti-interference detection delay, and synchronize the anti-interference detection delay and propagation delay based on the master clock of the control device and the slave clock of the bridge construction tensioning device.

2. The wireless communication coordination system for bridge construction tensioning equipment as described in claim 1, characterized in that, The interference detection module includes a receiving unit and an improvement unit; The receiving unit is used to receive frequency hopping signals and divide the frequency hopping signals into sub-channel signals by a filter bank, wherein the filter bank is composed of parallel bandpass filters. The improved unit is used to detect sub-channel signals using the improved forward continuous mean removal algorithm to obtain interference judgment information.

3. The wireless communication coordination system for bridge construction tensioning equipment as described in claim 2, characterized in that, The specific methods for constructing filter banks include: By frequency shifting the low-pass filter, a band-pass filter is generated. The mathematical expression for the impulse response of the band-pass filter is: ； in, Let be the unit impulse response of the bandpass filter corresponding to the k-th sub-channel. Let be the discrete-time unit impulse response of the low-pass filter, and k be the sub-channel index number, ranging from 0 to D-1. For discrete time series indexing, The modulation function; The mathematical expression for the impulse response frequency of the low-pass filter is: ； in, Digital angular frequency, This represents the total number of sub-channels. This is the normalized cutoff frequency of the low-pass filter. It is the imaginary unit.

4. The wireless communication coordination system for bridge construction tensioning equipment as described in claim 3, characterized in that, The improved forward continuous mean removal algorithm is used to detect sub-channel signals, and the interference judgment information obtained includes: The input signal is subjected to a Fast Fourier Transform to obtain spectral data. The spectral data is divided into spectral segments to obtain data points for each segment. An initial detection threshold is calculated through matrix eigenvalue noise estimation. Based on the initial detection threshold, iterative detection is performed through segment median estimation and the detection threshold is updated. The iteration terminates and the interference detection results of the spectral segments are output. The interference detection results of all spectral segments are combined to obtain interference judgment information.

5. The wireless communication coordination system for bridge construction tensioning equipment as described in claim 4, characterized in that, The matrix eigenvalue noise estimation operation specifically includes: The data points of the segmented spectrum are reconstructed into a signal matrix. The sample covariance matrix of the signal matrix is ​​calculated. The sample covariance matrix is ​​decomposed into eigenvalues ​​to obtain an eigenvalue sequence. The eigenvalue sequence is sorted in descending order. An initial noise power estimate is calculated based on the eigenvalue sequence. The initial noise power estimate is converted into an initial noise estimate. The initial noise estimate and a preset threshold factor are calculated to obtain an initial detection threshold.

6. The wireless communication coordination system for bridge construction tensioning equipment as described in claim 5, characterized in that, Calculate the sample covariance matrix of the signal matrix using the following expression: ； in, The sample covariance matrix, For the signal matrix, Let be the conjugate transpose of the signal matrix. is the number of columns in the signal matrix; The calculation expression for the initial noise power estimate based on the aforementioned feature value sequence is as follows: ； in, This is the initial noise power estimate. It is the smallest eigenvalue.

7. The wireless communication coordination system for bridge construction tensioning equipment as described in claim 6, characterized in that, The piecewise median estimation operation specifically includes: Using the data points of the current segmented spectrum as the set to be detected, and the initial detection threshold as the current detection threshold, an iterative process is performed. The iterative process includes: Traverse the current set to be detected, identify data points with amplitudes greater than the current detection threshold as interference points, remove the data points identified as interference points from the set to be detected and input them into the interference point set, calculate the median amplitude of the remaining set to be detected after removing interference points, and use it as a new noise estimate. Based on the new noise estimate and the preset threshold factor, calculate the detection threshold. If the number of interference points detected remains unchanged during two consecutive iterations, or if the preset maximum number of iterations is reached, the iteration terminates, and the detection threshold updated in the last iteration is output, along with the interference detection results for the spectrum segments.

8. The wireless communication coordination system for bridge construction tensioning equipment as described in claim 7, characterized in that, The selection module is used to receive interference judgment information, perform channel selection on the interference judgment information through a dual deep Q network, and obtain the channel number. The interference judgment information is constructed into a channel state matrix. The Q value of each selectable channel action under the current channel state matrix is ​​calculated by forward propagation. The Q value vector is obtained by normalization. The channel is selected from the Q value vector using Markov decision to obtain the target communication channel. The channel number is sent, and the channel number includes the target communication channel and the frequency hopping signal. The channel state matrix includes the real-time interference status of sub-channels, historical communication quality, and signal strength indicators.

9. The wireless communication coordination system for bridge construction tensioning equipment as described in claim 8, characterized in that, The communication module includes an execution unit and a synchronization unit; The execution unit is used to receive the channel number, convert the channel number into frequency parameters by looking up a preset mapping table, obtain the target communication channel, and send a frequency hopping signal on the target communication channel. The synchronization unit is used to calculate the anti-interference detection delay and perform time synchronization based on the anti-interference detection delay and the propagation delay.

10. The wireless communication coordination system for bridge construction tensioning equipment as described in claim 9, characterized in that, Acquire the timestamp of the transmitted channel number, the timestamp of the received frequency hopping signal, the timestamp of the transmitted interference judgment information, and the timestamp of the received interference judgment information; The timestamp of receiving the frequency hopping signal is used as the first timestamp, the timestamp of sending the interference judgment information is used as the second timestamp, the timestamp of receiving the interference judgment information is used as the third timestamp, and the timestamp of sending the channel number is used as the fourth timestamp. The anti-interference detection delay is obtained by subtracting the second timestamp from the first timestamp. The calculation expression is as follows: ； in, For synchronization period, To mitigate interference detection delay, This is the second timestamp. First timestamp; Propagation delay is calculated by detecting interference delay: ； in, To delay the transmission time, To mitigate interference detection delay, This is a third timestamp. This is the fourth timestamp; Calculate the clock skew for the current cycle: ； in, This is due to clock skew; The expression for calculating the master-slave clock frequency ratio is: ； in, For synchronization period, The ratio of master to slave clock frequencies. The clock offset calculated for the nth synchronization cycle. The clock offset calculated for the (n-1)th cycle. The synchronization period interval; Based on the clock deviation and the master-slave clock frequency ratio, adjust the clock phase of the bridge construction tensioning equipment, and calculate the slave clock time for the next cycle. The calculation expression is as follows: ； in, The corrected time from the clock at the start of the (n+1)th synchronization cycle. The original time of the clock at the start of the nth synchronization cycle. This is the synchronization period interval.

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