Wireless node cooperative communication low power control method based on distributed fiber sensing

Abstract

The application discloses a wireless node cooperative communication low-power control method based on distributed optical fiber sensing and belongs to the technical field of distributed optical fiber sensing and Internet of Things communication fusion. The method relies on a heterogeneous cooperative system composed of a distributed acoustic sensing (DAS) host, a center server and a wireless acoustic wave acquisition terminal, and realizes cooperative control through wireless terminal time synchronization, hardware silent cycle recording, adaptive wake-up instruction generation, historical data cross-border backtracking, narrowband feature extraction and heterogeneous data fusion decision. The application solves the problems of high energy consumption of wireless nodes, missing event starting data and bandwidth waste in the prior art, realizes low-power long-term deployment, complete event capture and high-reliability identification through on-demand wake-up and data processing guided by DAS, and is suitable for long-distance pipeline, power line and other safety monitoring scenes with strict requirements for real-time response and low power consumption.

Description

Technical Field

[0001] This invention relates to the field of distributed optical fiber sensing and Internet of Things (IoT) communication integration technology, specifically to a low-power control method for wireless node collaborative communication based on distributed optical fiber sensing. Background Technology

[0002] Distributed acoustic sensing (DAS) technology can utilize existing optical fiber cables to achieve long-distance, continuous vibration monitoring, and has become an important means of infrastructure safety monitoring. However, when faced with complex environmental noise such as wind, rain, and electromagnetic interference, a single DAS system is difficult to achieve reliable event identification, and there is a high risk of false alarms and missed alarms.

[0003] To address this, the industry has proposed a technical approach of "coordinated fiber optic and wireless heterogeneous sensing," which involves introducing wireless acoustic sensor nodes deployed at key locations. These nodes collect on-site audio information and use it to perform spatiotemporal correlation and joint identification with DAS signals to improve the overall system reliability. However, existing technologies have significant shortcomings at the system integration level for achieving this coordinated identification, hindering their large-scale, long-term application.

[0004] The contradiction between communication power consumption and node battery life: In order to perform joint identification, wireless nodes need to transmit the collected acoustic wave data back to the processing center. Continuous full transmission will quickly deplete the battery power, while intermittent sleep makes it difficult to capture transient events. Existing technologies cannot reduce the communication power consumption of wireless nodes to the extreme while ensuring the event capture rate.

[0005] The contradiction between trigger delay and data integrity: Existing solutions often put wireless nodes into deep sleep and wake them up by relying on a simple local sound pressure threshold. There is an inherent delay in the sensor from sleep to stable acquisition, which leads to the loss of characteristic waveforms in the initial stage of key transient events, resulting in "data loss" and affecting the accuracy and reliability of collaborative identification.

[0006] The rigid coordination mechanism fails to leverage the guiding advantages of fiber optic sensing: In existing coordination schemes, the DAS system and wireless nodes mostly rely on independent sensing and post-event comparison, lacking an efficient proactive guidance mechanism. The system fails to utilize the real-time, full-segment sensing results of DAS to dynamically direct the subsequent operations of wireless nodes, resulting in low coordination efficiency.

[0007] Current technologies lack intelligent collaborative communication methods that utilize a DAS system as the core scheduling unit to achieve precise on-demand wake-up of wireless nodes, zero-latency data acquisition, and high-efficiency data transmission. Although the industry has proposed a technical direction of "optical fiber and wireless heterogeneous sensing collaboration," existing technologies such as CN120508911B disclose a dual-branch acoustic-vibration fusion event recognition and localization method based on DAS and AI. This method achieves event recognition and localization through a sound recognition branch and a vibration localization branch. However, this method belongs to a pure optical fiber sensing architecture, does not introduce wireless sensing nodes, and does not address the issues of energy consumption control and data acquisition integrity assurance for wireless nodes. Another existing technology, CN120445386B, discloses an optical fiber sensing data processing module and monitoring and localization method, which achieves disturbance localization by fusing DAS and DVS signals. However, its system architecture is still limited to pure optical fiber sensing, does not introduce a wireless acoustic wave acquisition terminal, and cannot achieve low-power collaborative control of wireless nodes. In summary, current technologies lack intelligent collaborative communication methods that utilize a DAS system as the core scheduling unit to achieve precise on-demand wake-up of wireless nodes, zero-latency data acquisition, and high-efficiency data transmission. Summary of the Invention

[0008] The purpose of this invention is to provide a low-power control method for wireless node cooperative communication based on distributed optical fiber sensing, so as to solve the problems of low cooperative efficiency caused by the lack of an efficient active guidance mechanism, failure to utilize the full-time and full-segment sensing results of DAS to dynamically guide the subsequent operation of wireless nodes.

[0009] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0010] This invention provides a low-power control method for wireless node cooperative communication based on distributed optical fiber sensing. It relies on a heterogeneous cooperative system consisting of a distributed acoustic sensing (DAS) host, a central server, and several wireless acoustic acquisition terminals. Specifically, it includes the following steps: Figure 1 As shown:

[0011] S1: Configure the wireless acoustic wave acquisition terminal to perform network time synchronization and hardware silent loop recording. After power-on initialization and during periodic operation, the wireless acoustic wave acquisition terminal performs a time synchronization protocol with the central server or gateway through the wireless communication network to ensure that the local system clock is consistent with the reference time of the DAS host. At the same time, control the wireless acoustic wave acquisition terminal to enter a low-power listening mode, shut down the radio frequency transmission module and the high-power computing unit, and let the hardware data transmission controller take over the data transmission channel of the analog-to-digital converter (ADC). Allocate a ring buffer with consecutive physical addresses in the terminal memory, and automatically write the sampled data into the buffer to achieve seamless data overlay without CPU intervention.

[0012] Time synchronization establishes a unified time benchmark for subsequent "logical backtracking and historical data extraction," ensuring accurate correspondence between event moments. The hardware data transmission controller operates independently, overcoming the limitation of "CPU sleep stops data acquisition," and achieving continuous data caching under low power consumption. This avoids energy waste caused by CPU intervention in data acquisition, keeping the terminal in a low-power state for extended periods and extending battery life. The circular buffer seamlessly covers the storage, always retaining data from the most recent time period, providing a data foundation for solving the problem of "loss of event start data."

[0013] It should be noted that the hardware silent loop recording and logic backtracking mechanism described in this invention is fundamentally different from existing technologies that rely on continuous CPU acquisition or simple threshold wake-up. In existing technologies, sound data acquisition depends on continuously running AI models, making data caching in a low-power state impossible. In contrast, this invention, through hardware-level collaboration between DMA and a circular buffer, completes continuous data writing while the CPU is in sleep mode. Combined with time synchronization and logic backtracking, it achieves complete capture of event start data, significantly reducing the standby power consumption of wireless terminals and solving the problems of "data loss" and high energy consumption in existing technologies.

[0014] S2: The central server generates a wake-up command containing an adaptive time window. When the DAS host detects a vibration signal, the central server retrieves the coordinates of the nearest wireless acoustic wave acquisition terminal based on the pre-stored device topology information, calls the historical database to update the equivalent sound velocity of the environmental medium, and calculates the physical lag time of sound wave propagation. At the same time, it calculates the forward redundancy of the backtracking time window based on the DAS signal confidence level, encapsulates a wake-up command containing absolute start and end times and target frequency parameters, and sends it to the corresponding wireless acoustic wave acquisition terminal.

[0015] Retrieving the nearest terminal minimizes the impact of sound wave propagation delay on data acquisition; dynamically updating the equivalent sound velocity ensures that lag time calculations align with environmental changes (such as soil moisture differences); adjusting the backtracking window based on confidence level enables adaptive scheduling of "less data acquisition for energy saving in high-confidence scenarios and more data acquisition for high-fidelity scenarios in low-confidence scenarios." Replacing the rigid mode of traditional "fixed wake-up range," commands precisely match event characteristics and environmental conditions, ensuring complete event data acquisition while avoiding invalid data acquisition and transmission, thus improving collaborative efficiency.

[0016] S3: The wireless acoustic wave acquisition terminal performs cross-boundary historical data extraction. After the wireless acoustic wave acquisition terminal is woken up, it parses the adaptive time window in the wake-up command, obtains the current wake-up time and DMA write pointer offset, calculates the sample backtracking amount and the starting physical offset of the data to be extracted; compares the length of the data to be read with the remaining space at the end of the buffer, and restores the time-continuous historical signal sequence by single reading or segmented reading and logical splicing.

[0017] Based on a unified time base and pointer offset, the system accurately locates the historical data storage position corresponding to an event. Addressing the physical characteristic of the circular buffer being "end-to-end," it overcomes address limitations through segmented splicing, ensuring data temporal continuity. It offsets the effects of terminal wake-up latency and sound wave propagation delay, successfully capturing the characteristic waveform at the moment the event begins, completely resolving the "data head loss" problem. No large-scale data migration is required, adapting to the limited hardware resources of embedded terminals and guaranteeing real-time performance.

[0018] S4: The wireless acoustic wave acquisition terminal extracts and transmits narrowband features based on command guidance;

[0019] The wireless acoustic wave acquisition terminal configures the narrowband feature extraction algorithm coefficients based on the target frequency parameters in the wake-up command and its own sampling rate.

[0020] Perform feature calculations on the restored historical signal sequence to determine the energy amplitude at the target frequency;

[0021] Only this feature value is packaged and sent to the central server via the radio frequency module.

[0022] It then resumed low-power silent recording mode.

[0023] Guided by the target frequency initially identified by the DAS system, the system focuses on key characteristic frequencies of events, avoiding full-band data processing and transmission, and achieving "on-demand extraction and on-demand transmission." This significantly reduces terminal computing power consumption and communication data volume, and substantially reduces battery power consumption and wireless bandwidth usage. After transmission is completed, the system immediately returns to a low-power state, further extending terminal battery life and solving the dual problems of "high power consumption and bandwidth waste."

[0024] The narrowband feature extraction and transmission mechanism described in this invention differs significantly from existing technologies that use full data transmission or fixed feature extraction methods. Existing technologies extract full-band features using CNN and BiLSTM, resulting in high computational complexity and large data volumes; they also rely on phase and intensity matrix fusion, which still falls under the category of full signal processing. This invention, guided by the target frequency initially identified by DAS, transmits only the energy amplitude of the target frequency, greatly reducing communication energy consumption and bandwidth usage, achieving intelligent collaboration of "on-demand extraction and on-demand transmission."

[0025] S5: The central server performs spatiotemporal alignment and collaborative fusion decision-making for heterogeneous data. After receiving the feature data uploaded by the wireless acoustic wave acquisition terminal, the central server retrieves the DAS vibration data under the same spatiotemporal coordinates, first performs spatiotemporal alignment processing on the heterogeneous data, and then outputs the final event recognition result through waveform similarity calculation, joint energy verification and dual threshold hysteresis decision-making.

[0026] Spatiotemporal alignment addresses the heterogeneity between high-sampling-rate DAS data and low-rate wireless characteristic data, ensuring consistent comparison benchmarks. Waveform similarity verification reflects data trend correlation, while joint energy verification avoids false alarms caused by random high correlation. Dual-threshold hysteresis decision stabilizes alarm states. Integrating the advantages of fiber optic sensing's "long-distance coverage" and wireless sensing's "local precise identification," it effectively suppresses unilateral environmental noise interference, improving event recognition accuracy and false alarm resistance. A closed loop of "fiber optic initial screening + wireless verification" is formed, solving the shortcomings of false alarms and missed alarms in single-sensor systems.

[0027] The present invention has the following beneficial effects:

[0028] Low-power, long-term deployment: Through hardware silent loop recording and DAS-guided on-demand wake-up, the wireless terminal is in a low-power state most of the time, only waking up briefly when working. Compared with the continuous acquisition or simple threshold wake-up methods in the existing technology, theoretical analysis and simulation calculations show that power consumption is greatly reduced in typical monitoring scenarios, significantly extending the deployment life.

[0029] Complete event capture: By extracting historical data prior to wake-up from the circular buffer through a logical backtracking mechanism, the effects of sensor startup delay and sound wave propagation delay are eliminated, solving the "data loss" problem in existing technologies. Compared to existing technologies that rely on AI models to process real-time acquired data or on OTDR pulse acquisition, the hardware silent recording and logical backtracking mechanism of this invention can completely preserve the characteristic waveform at the moment of event initiation, ensuring the integrity of the event waveform and providing high-quality data support for subsequent collaborative decision-making.

[0030] High-efficiency bandwidth utilization: Based on DAS prior features, the wireless terminal is guided to transmit only narrowband feature values. Compared with the full-band AI model processing and full signal fusion transmission in the existing technology, it significantly reduces communication bandwidth occupation and network congestion risk.

[0031] High-reliability event identification: By employing a bidirectional complementary logic of fiber optic initial screening and wireless verification, combined with heterogeneous data spatiotemporal alignment and dual-threshold hysteresis decision-making, false triggering caused by environmental noise is effectively suppressed. Compared to existing technologies that rely on AI fusion decision-making based on single fiber optic data and coherent interferometric positioning based on coherent interferometry signals from the same fiber optic source, this invention significantly improves both identification accuracy and positioning reliability in complex environments through cross-medium heterogeneous data fusion.

[0032] Compared with existing technologies, the beneficial effects of this invention are as follows: Compared with the high-energy-consuming mode of existing technologies that rely on AI models to continuously process all data, this invention achieves a significant reduction in standby power consumption of wireless terminals through hardware silent recording and on-demand wake-up; Compared with existing technologies that are limited to single-medium monitoring of pure fiber optic sensing, this invention achieves cross-medium heterogeneous collaboration between fiber optic sensing and wireless sensing by introducing a wireless acoustic wave acquisition terminal, which has higher identification reliability and deployment flexibility in complex environments. Attached Figure Description

[0033] Figure 1 The diagram illustrates the specific steps of the method of the present invention. Detailed Implementation

[0034] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0035] Overall system deployment: A heterogeneous collaborative system consisting of a DAS host, a central server, and wireless acoustic acquisition terminals is built. The DAS host is connected to a communication optical cable to achieve long-distance continuous vibration monitoring. The wireless acoustic acquisition terminals are deployed at key locations in the monitoring area and communicate with the central server through the LoRaWAN network. The central server pre-stores device topology information, historical event database, and various algorithm parameters, and undertakes core tasks such as instruction generation and data fusion judgment.

[0036] The specific implementation of step S1: The wireless acoustic wave acquisition terminal uses a low-power microcontroller, equipped with an ADC module and SRAM memory. After power-on initialization, it performs time synchronization with the central server via the NTP protocol, with the synchronization period set to 1 hour to ensure that the time synchronization error is less than 10ms. After entering the low-power listening mode, the RF communication module and the high-power computing core of the microcontroller are shut down. The DMA controller is configured to take over the data transmission channel of the ADC. A circular buffer with contiguous physical addresses is allocated in the SRAM, and its physical base address is set to... Total capacity is The DMA controller responds to the ADC's sampling completion request by writing the sampled data x[n] to the physical address according to the following mapping relationship. :

[0037] With this configuration, the wireless acoustic wave acquisition terminal can continuously and cyclically record ambient acoustic wave data while the microcontroller is in sleep mode, and always retain historical data from the most recent period.

[0038] Specific implementation of step S2: The DAS host continuously monitors the vibration of the optical cable. When a vibration signal is detected, it synchronously records the absolute moment of the vibration. Fiber optic cable vibration location and signal confidence The above information is then uploaded to the central server in real time; upon receiving the information, the central server retrieves the distance based on the pre-stored device topology information. Coordinates of the nearest wireless acoustic acquisition terminal ; Retrieve the measured sound wave propagation velocity of the most recent confirmed event at this location from the historical database, and update the equivalent sound velocity of the environmental medium. If no historical data is available, a preset initial value is used, and the physical lag time of sound wave propagation is calculated according to the following formula. :

[0039] Meanwhile, the central server operates according to the preset minimum boundary. Maximum expansion coefficient and threshold parameters Calculate the forward redundancy of the backtracking time window using the following formula. :

[0040] Determine the absolute start time of the wake-up command. and absolute end time The wake-up command is encapsulated in the target frequency parameters identified by the DAS system and sent to the wireless sound wave acquisition terminal through the wireless gateway; wherein, the equivalent speed of sound of the environmental medium... Periodic adaptive updates are performed based on measured data from historically confirmed events to adapt to fluctuations in sound speed caused by environmental changes, and forward redundancy is considered in the backtracking time window. With DAS signal confidence Negative correlation The higher the The smaller, The lower The larger.

[0041] Specific implementation of step S3: After receiving the wake-up command, the radio frequency module of the wireless acoustic wave acquisition terminal triggers the microcontroller to wake up from the low-power state and parses the absolute start and end times in the command. Calculate the duration of data extraction and the corresponding number of samples. Get the current system wake-up time. and the offset of the current DMA write pointer The sample backtracking amount of the data to be extracted relative to the current pointer is calculated based on the time synchronization benchmark. ;

[0042] According to the wake-up time The start time of the data to be extracted in the instruction The time difference, combined with the terminal sampling rate Calculate the sample backtracking volume of the data to be extracted. ,satisfy: ;

[0043] The initial physical offset of the target historical data in the circular buffer is calculated using the following formula. :

[0044] ;

[0045] Calculate the remaining space at the tail of the buffer. ,like Then from The corresponding physical address is used to initiate a single memory copy operation, reading... Length of data; if Then start from Read the corresponding physical address The length of the data is then retrieved from the buffer base address. Read the remaining The data of a certain length is concatenated at the logic layer to form a sequential historical signal sequence.

[0046] Specific implementation of step S4: After the microcontroller of the wireless acoustic wave acquisition terminal is woken up, it configures the coefficients of the recursive digital frequency selection logic according to the target frequency parameters in the instruction and its own preset sampling rate. This recursive digital frequency selection logic adopts a second-order IIR recursive algorithm, and the state variable update rule is as follows: ;

[0047] Where C is the recursive coefficient determined by the target frequency, and y[n] is the restored historical signal sequence in step S3; the microcontroller processes the historical signal sequence using this algorithm, performs energy integration only for the target frequency, and calculates the energy amplitude at the target frequency after processing. The scalar feature value is packaged with the terminal identifier and timestamp into a feature data packet and sent to the central server through the radio frequency module. After the data is sent, the radio frequency module is immediately turned off, the microcontroller returns to a low power state, and the DMA controller continues to perform silent loop recording of ambient sound wave data.

[0048] After receiving the feature data packets uploaded by the wireless acoustic wave acquisition terminal, the central server parses them to obtain the feature vectors. and timestamp Simultaneously, the original vibration energy sequence of the DAS host in the same spatiotemporal coordinates is retrieved. ;by Generate a discrete-time series t[k] based on the baseline, and then... Anti-aliasing low-pass filtering is performed, and the vectors are mapped to a discrete time series t[k] using cubic spline interpolation to obtain the aligned DAS feature vectors. The zero-mean normalized cross-correlation algorithm is used to calculate... and The waveform similarity is calculated using the following formula: ;

[0049] Where M is the length of the feature vector. , They are respectively , The mean; and simultaneously calculate the joint energy index. : α is the system confidence weighting factor, ranging from 0.5 to 0.7, and α can be dynamically adjusted based on the environmental noise level or the statistical results of the historical event database; based on the above calculation results, a dual-threshold hysteresis decision is performed: when and If an intrusion event is confirmed, an alarm will be triggered and location information will be pushed; if an alarm is already in effect, as long as... or If the alarm status is maintained, then the alarm status will be maintained; when and When the alarm is deactivated and the incident log is archived, the alarm will be deactivated. For a high correlation threshold, For low correlation threshold, This is the energy threshold.

[0050] The core working principle of this invention is to construct a heterogeneous collaborative architecture with a distributed acoustic sensing (DAS) system as the core scheduling unit and wireless acoustic acquisition terminals as subordinate acquisition units. Through a closed-loop logic of "baseline establishment - precise scheduling - data restoration - efficient transmission - collaborative verification," it systematically solves the shortcomings of existing technologies: First, a unified time base is established for the entire system through network time synchronization. Simultaneously, the wireless terminals, in a low-power sleep state, achieve continuous silent caching of environmental sound wave data through independent hardware operation, laying the foundation for subsequent historical data backtracking. When the DAS system detects a vibration signal, the central server generates a precise time window and target frequency based on the device topology, environmental sound velocity, and signal confidence level. The adaptive wake-up command breaks through the rigid limitations of traditional wake-up modes. After the wireless terminal is woken up, it restores the complete historical signal containing the moment the event started from the cache through logical offset calculation and cross-boundary data splicing, thus offsetting the data loss caused by wake-up delay and propagation delay. Subsequently, the terminal extracts narrowband features based on the target frequency in the command and transmits only scalar feature values, which greatly reduces energy consumption and bandwidth usage. Finally, the central server solves the heterogeneity problem between DAS and wireless data through spatiotemporal alignment processing. Combining waveform similarity analysis and joint energy verification as dual criteria and hysteresis decision logic, it achieves bidirectional complementarity of "fiber optic screening + wireless verification", improving the reliability and anti-interference capability of event recognition.

[0051] Application Value: This solution can be directly deployed in scenarios with stringent requirements for both real-time response and low power consumption, such as power transmission lines, long-distance oil and gas pipelines, and perimeter security. Without significantly increasing hardware costs, it extends the battery life of wireless terminals, improves the accuracy of event recognition, and optimizes network bandwidth utilization, providing efficient, reliable, and long-term technical support for infrastructure security monitoring.

[0052] 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 within the protection scope of the present invention.

Claims

1. A low-power control method for wireless node cooperative communication based on distributed optical fiber sensing, characterized in that, Includes the following steps: S1: Configure the wireless acoustic wave acquisition terminal to perform network time synchronization and hardware silent loop recording, so that the local clock of the wireless acoustic wave acquisition terminal is consistent with the reference time of the DAS host, and in the hardware silent loop recording state where the radio frequency transmission module is turned off and the data acquisition channel is taken over by the direct memory access controller, the audio sampling data is continuously written to the ring buffer through the hardware data transmission controller. S2: When the DAS host detects a vibration signal, the central server generates a wake-up command containing an adaptive time window and target frequency parameters based on the device topology information, the equivalent sound velocity of the environmental medium, and the confidence level of the DAS signal, and sends it to the corresponding wireless sound wave acquisition terminal. S3: After the wireless acoustic wave acquisition terminal is woken up, it parses the adaptive time window in the wake-up command, locates the historical data position in the circular buffer through logical backtracking calculation, performs cross-boundary segmented reading and splicing, and restores the time-continuous historical signal sequence. S4: Based on the target frequency parameter in the wake-up command, the wireless acoustic wave acquisition terminal performs narrowband feature extraction and compression on the restored historical signal sequence, only packages the feature values ​​of the target frequency point and transmits them to the central server, and then resumes the low-power silent recording state. S5: After receiving the feature data, the central server retrieves the DAS vibration data under the same spatiotemporal coordinates, performs heterogeneous data spatiotemporal alignment, waveform similarity calculation, joint energy verification, and dual threshold hysteresis decision, and outputs the final event recognition result.

2. The low-power control method for wireless node cooperative communication based on distributed optical fiber sensing according to claim 1, characterized in that, The network time synchronization described in step S1 is achieved through the NTP protocol, PTP protocol, or a custom lightweight synchronization protocol, with a time synchronization error of less than 10ms.

3. The low-power control method for wireless node cooperative communication based on distributed optical fiber sensing according to claim 1, characterized in that, The hardware silent loop recording described in step S1 specifically involves: shutting down the radio frequency transmission module and high-power computing unit of the wireless acoustic wave acquisition terminal; having the DMA take over the data transmission channel of the analog-to-digital converter; allocating a circular buffer with contiguous physical addresses in the terminal's SRAM; and setting its physical base address to [value missing]. Total capacity is The DMA controller responds to the ADC's sampling completion request by automatically writing the sampled data x[n] to the physical address. Furthermore, seamless data overlay is achieved through modulo mapping operations without CPU intervention, and the mapping relationship satisfies: n is the sequence number of the sampled data, which starts from 0 and increments sequentially. mod is the modulo operation, which is used to realize the circular writing and seamless overwriting of sampled data in the circular buffer.

4. The low-power control method for wireless node cooperative communication based on distributed optical fiber sensing according to claim 1, characterized in that, The generation of the wake-up command in step S2 includes: S21: Retrieve the coordinates of the wireless acoustic acquisition terminal closest to the DAS-detected vibration location, and retrieve the measured acoustic propagation velocity of the most recent confirmed event at that location from the historical database to update the equivalent sound velocity of the environmental medium. If no historical data is available, a preset initial value is used to calculate the physical lag time of sound wave propagation. The calculation formula is: ； in, The location of optical cable vibration detected by DAS. The coordinates of the nearest wireless acoustic wave acquisition terminal; S22: Confidence based on DAS signal Calculate the forward redundancy of the backtracking time window The calculation formula is: ； in, To preset the minimum boundary, The maximum expansion coefficient, For threshold parameters; S23: Package includes absolute start and end times. A wake-up command with target frequency parameters, wherein the start time satisfies: ； In the formula, This represents the absolute moment when the DAS detected the vibration signal.

5. The low-power control method for wireless node cooperative communication based on distributed optical fiber sensing according to claim 4, characterized in that, The historical data location reading in step S3 specifically involves: S31: Obtain the wake-up time of the wireless acoustic wave acquisition terminal and current DMA write pointer offset According to the wake-up time The start time of the data to be extracted in the instruction The time difference, combined with the terminal sampling rate Calculate the sample backtracking volume of the data to be extracted. ,satisfy: ; S32: Calculate the initial physical offset using the formula. : In the formula Where is the total capacity of the circular buffer, and is based on the wake-up time. The calculated sample backtracking quantity; S33: Calculate the remaining space at the tail of the buffer. If the length of the data to be read is If so, then read data in one go; if Then first read from the starting physical address The length of the data is then retrieved from the buffer base address. Read the remaining data and concatenate it in the logic layer.

6. The low-power control method for wireless node cooperative communication based on distributed optical fiber sensing according to claim 5, characterized in that, The narrowband feature extraction in step S4 employs recursive digital frequency selection logic, performing energy integration only on the target frequency. This recursive digital frequency selection logic is a second-order IIR recursive algorithm, and the state variable update rule is as follows: Where C is the recursive coefficient determined by the target frequency, and y[n] is the restored historical signal sequence; after narrowband feature extraction is completed, the energy amplitude of the target frequency point is output. And package and transmit.

7. The low-power control method for wireless node cooperative communication based on distributed optical fiber sensing according to claim 4, characterized in that, The equivalent speed of sound in the environmental medium Periodic adaptive updates are performed based on measured data from historically confirmed events; the forward redundancy of the backtracking time window. With DAS signal confidence Negative correlation The higher the The smaller, The lower The larger.

8. The low-power control method for wireless node cooperative communication based on distributed optical fiber sensing according to claim 6, characterized in that, The spatiotemporal alignment of heterogeneous data mentioned in step S5 specifically refers to: using the timestamps of the feature data from the wireless acoustic wave acquisition terminal. Generate a discrete-time sequence t[k] based on the baseline, and then analyze the original vibrational energy sequence of the DAS. After performing anti-aliasing low-pass filtering, the DAS feature vector is mapped to the discrete time series t[k] using a cubic spline interpolation algorithm to obtain the aligned DAS feature vector. .

9. The low-power control method for wireless node cooperative communication based on distributed optical fiber sensing according to claim 8, characterized in that, The waveform similarity calculation in step S5 uses the zero-mean normalized cross-correlation algorithm, and the calculation formula is as follows: ; Where M is the length of the feature vector. For wireless feature vectors, , They are respectively , mean is the waveform similarity coefficient.

10. The low-power control method for wireless node cooperative communication based on distributed optical fiber sensing according to claim 9, characterized in that, The joint energy verification and dual-threshold hysteresis decision mentioned in step S5 includes: S51: Calculate the joint energy index : ； In the formula, α is the system confidence weighting factor; S52: Triggering condition: when and When this occurs, it is determined to be a confirmed intrusion event; S53: Hold Condition: If the alarm state is already in effect, as long as... or Maintain alarm status; S54: Reset condition: when and When the alarm is deactivated and the event log is archived, the alarm is deactivated. For a high correlation threshold, For low correlation threshold, This is the energy threshold.

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