LoRa-based underground pipe network wireless monitoring terminal

Abstract

The application belongs to the technical field of underground pipe network monitoring, and particularly relates to an underground pipe network wireless monitoring terminal based on LoRa, which comprises an environment adaptive wake-up module, a multi-source sensing trigger collection module, a dynamic spread spectrum transmission module and an energy-aware closed-loop calibration module. The environment adaptive wake-up module is used for determining effective events. The multi-source sensing trigger collection module is used for dynamically adjusting data sampling frequency and resolution according to the determined effective events. The dynamic spread spectrum transmission module is used for calculating the comprehensive link quality factor of the current channel, dynamically selecting a spread spectrum factor, a coding rate and a transmission power based on the determined data urgency label and the current battery residual energy, and combining the calculated comprehensive link quality factor. The energy-aware closed-loop calibration module is used for feeding back and adjusting the environment adaptive wake-up module, the multi-source sensing trigger collection module and the dynamic spread spectrum transmission module. The adaptive wake-up technical means based on multi-source environmental disturbance is adopted, and the effect that the terminal works only when a real underground event exists is realized.

Description

Technical Field

[0001] This invention belongs to the field of underground pipeline monitoring technology, specifically a LoRa-based wireless monitoring terminal for underground pipelines. Background Technology

[0002] As the scale of urban underground pipe networks (including water supply, drainage, gas, electricity and communication pipelines) continues to expand, real-time monitoring of the network's operating status has become an important technical means to ensure the safe operation of cities. Currently, underground pipe network monitoring systems mostly rely on wired communication or short-range wireless communication technologies to achieve data acquisition and transmission, while sensors are used to detect parameters such as pressure, flow rate, and gas concentration.

[0003] However, existing technologies are mainly limited by the strong attenuation characteristics of wireless signals in the underground environment and the limited power supply capacity of terminal equipment when realizing long-term stable remote monitoring and real-time data transmission of underground pipelines. This makes it difficult to balance long-distance stable communication capabilities and ultra-low power consumption long-term operation capabilities in practical applications, thus affecting overall performance. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a LoRa-based wireless monitoring terminal for underground pipeline networks, which solves the problems of strong attenuation of wireless signals in underground environments and limited power supply capabilities of terminal equipment in existing technologies.

[0005] A LoRa-based wireless monitoring terminal for underground pipeline networks includes: an environment-adaptive wake-up module for continuously listening to environmental disturbances in the underground pipeline network and determining valid events based on the listening results, specifically: Within a set time window, geomagnetic disturbance sequences and thermal radiation change sequences are collected and constructed into an event trigger vector set. Spatiotemporal consistency verification is performed based on the constructed event trigger vector set, and valid events are identified. The multi-source sensor-triggered acquisition module is used to dynamically adjust the data sampling frequency and resolution based on determined valid events, specifically: Pressure data, flow data, and hazardous gas data are collected sequentially and constructed into a three-dimensional state vector. At the same time, a historical background model is constructed, and the Mahalanobis distance between the current state vector and the historical background model is calculated. Based on the calculation results, the data sampling frequency and resolution are dynamically adjusted, and the data urgency label is determined. The dynamic spread spectrum transmission module is used to dynamically select the spreading factor, coding rate, and transmit power, specifically: The comprehensive link quality factor of the current channel is calculated. At the same time, based on the data urgency label determined by the multi-source sensor trigger acquisition module and the current battery remaining energy, and combined with the calculated comprehensive link quality factor, the spreading factor, coding rate and transmit power are dynamically selected. The energy sensing closed-loop calibration module is used to provide feedback and adjust other modules by calculating the battery's energy margin. It includes: an environment adaptive wake-up module, a multi-source sensor trigger acquisition module, and a dynamic spread spectrum transmission module.

[0006] Preferably, the spatiotemporal consistency verification based on the constructed event trigger vector set is performed as follows: Calculate the time delay offset of the event start point in the two sequences. And based on the event type, consistency verification is performed, then: Set the maximum time offset to and the feature orientation angle threshold of the two event sources Based on the time delay offset of the event starting point and the characteristic direction angle difference in the two sequences Perform event consistency verification, specifically as follows: like And the difference in characteristic direction angles corresponding to the two event sources If so, it means that the current disturbance event satisfies spatiotemporal consistency and is determined to be a valid underground activity event; Conversely, if or If the event is detected as a ground interference event, the system will continue to maintain a microampere-level sleep listening state and will not generate a wake-up output.

[0007] Preferably, the calculation of the Mahalanobis distance between the current state vector and the historical background model is as follows: The three parameters, pressure data P, flow rate data Q, and gas concentration data C, are normalized to form a three-dimensional state vector. And maintain a historical background model M with a sliding window length of K, the mean of model M is μ, and the covariance matrix is... ; Calculate the Mahalanobis distance between the current state vector and the background model M, then we have: ; in, Represents the current state vector. This represents the mean vector of the background model. This represents the covariance matrix of the background model. Denotes the inverse matrix of the covariance matrix. This represents the Mahalanobis distance between the current state vector and the background model.

[0008] Preferably, the step of dynamically adjusting the data sampling frequency and resolution based on the calculation results is as follows: Set three-level difference thresholds , , And satisfy and according to The sampling strategy decision is made based on the landing area, specifically as follows: like This indicates that the current state is not significantly different from the background model, and the system enters sparse sampling mode, reducing the sampling frequency to [value missing]. It does not generate reported data, but only updates the background model; like If the result is positive, it indicates a moderate difference, and the system will enter the normal data collection mode. Frequency acquisition, and data packaging into first-priority data packets; like This indicates a strong difference, triggering a high-precision acquisition mode. Frequency acquisition, enabling 24-bit ADC high-resolution conversion, generating second-priority data packets; like If a mutation event occurs, K complete cycles will be collected immediately and a third-priority data packet will be generated.

[0009] Preferably, the calculation of the overall link quality factor of the current channel is specifically as follows: Read the Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) of the current channel, calculate the overall link quality factor, and then we have: ; in, This represents the normalized value of the received signal strength indication. This represents the normalized value of the signal-to-noise ratio. , Represents the weighting coefficients, and satisfies the formula , This indicates the calculation of the overall link quality factor.

[0010] Preferably, the dynamic selection of the spreading factor is specifically as follows: Based on the data urgency level and remaining battery energy, the spreading factor is dynamically selected, specifically as follows: If the data packet has a priority of third priority, then the reliability of transmission is guaranteed first, the maximum spreading factor is fixed, and the highest redundancy coding rate is used. If the data urgency is not the third priority data packet, the spreading factor is dynamically selected based on the remaining battery energy, specifically: If the remaining battery energy is at a critical state, then power consumption should be reduced first, and the minimum spreading factor and the lowest transmit power should be selected. If the remaining battery energy is in a non-critical state, the spreading factor is dynamically selected by comprehensively considering the link quality factor.

[0011] Preferably, the dynamic selection of the spreading factor through the comprehensive link quality factor specifically includes: High, medium, and low thresholds for the integrated link quality factor are set respectively. The calculated integrated link quality factor is compared with the set thresholds, and the spreading factor is dynamically selected based on the comparison results. Specifically: When the calculated composite link quality factor is greater than the high threshold of the composite link quality factor, the minimum spreading factor is selected. 7. Transmission power is ; When the calculated composite link quality factor is greater than the threshold of the composite link quality factor but less than the high threshold of the composite link quality factor, the selected spreading factor is: 9, transmission power is ; When the calculated composite link quality factor is greater than the low threshold of the composite link quality factor but less than the middle threshold of the composite link quality factor, the selected spreading factor is: 11, transmission power is ; When the calculated composite link quality factor is less than the low threshold of the composite link quality factor, the selected spreading factor is: 12, transmission power is And mark the link as abnormal.

[0012] Preferably, the calculation of the battery's energy margin is as follows: Based on the remaining energy, predicted energy, and safe energy, the energy margin is calculated as follows: ; in, This indicates that energy is being safely conserved. This indicates the energy consumption within the predicted time window. Indicates the remaining energy. The calculated energy margin is used to provide feedback and adjust other modules.

[0013] Preferably, the feedback adjusts other modules as follows: And exceeding the upper limit , A value of 10% of full charge energy indicates ample energy, increasing the environmental wake-up sensitivity of the environmental adaptive wake-up module by one level, and simultaneously adjusting the difference threshold of the multi-source sensor trigger acquisition module. , , Overall reduction of 10%; like Between 0 and The interval between these values ​​indicates energy balance, keeping all current parameters unchanged. like If an energy deficit is indicated, a reverse calibration is performed.

[0014] Preferably, the reverse calibration is performed as follows: The angle threshold in the spatiotemporal consistency verification of the environment adaptive wake-up module. Increase by 15%, delay deviation Increase by 20%; Difference-driven threshold of multi-source sensor-triggered acquisition module , , Overall increase of 20%; The link quality threshold of the dynamic spread spectrum transmission module , , Each improvement ; After calibration is completed, the energy sensing closed-loop calibration module constructs a new set of configuration parameters based on all the adjusted parameters, and distributes them to the environment adaptive wake-up module, the multi-source sensor trigger acquisition module, and the dynamic spread spectrum transmission module, respectively.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention adopts an adaptive wake-up technology based on multi-source environmental disturbances, which enables the terminal to work only when there are real underground events, thereby reducing invalid data collection and communication behavior from the source. This effectively solves the technical problem of excessive power consumption of the terminal during long standby and its inability to support long-term operation in the prior art.

[0016] 2. This invention achieves the technical effect of a dynamic resource allocation mechanism that reduces sampling density when the state is stable and improves sampling accuracy when the state is abnormal by adopting a difference-driven adaptive sampling technique based on Mahalanobis distance. This solves the problem in the prior art that "fixed sampling strategy leads to an inability to balance power consumption and monitoring accuracy".

[0017] 3. This invention adopts LoRa dynamic spread spectrum communication technology based on three-dimensional collaborative decision-making of data urgency, link quality and remaining energy, and achieves the technical effect of adaptive matching of spread spectrum factor, transmit power and coding rate in complex underground strong attenuation environment, thereby effectively improving the reliability of long-distance communication, while avoiding unnecessary high power consumption transmission, and solving the technical bottleneck of difficulty in balancing communication distance and power consumption in the prior art.

[0018] 4. By adopting a closed-loop adaptive calibration technique based on energy prediction and energy margin assessment, this invention achieves the technical effect of an adaptive control mechanism that dynamically evolves the terminal operation strategy with the energy state, thereby solving the problem of premature equipment failure caused by uncontrollable battery energy consumption in the prior art. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall structure of a LoRa-based wireless monitoring terminal for underground pipelines according to the present invention. Detailed Implementation

[0020] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention. Example 1

[0021] Reference Figure 1 As an embodiment of the present invention, a LoRa-based wireless monitoring terminal for underground pipeline networks is provided, comprising: an environment adaptive wake-up module, a multi-source sensor trigger acquisition module, a dynamic spread spectrum transmission module, and an energy sensing closed-loop calibration module.

[0022] Specifically, the four modules are connected in sequence to form a closed-loop feedback control structure. The output of the environment adaptive wake-up module is connected to the wake-up end of the multi-source sensor trigger acquisition module. The data output of the multi-source sensor trigger acquisition module is connected to the input of the dynamic spread spectrum transmission module. The feedback output of the dynamic spread spectrum transmission module is connected to the input of the energy sensing closed-loop calibration module. The control output of the energy sensing closed-loop calibration module is connected to the configuration inputs of the environment adaptive wake-up module, the multi-source sensor trigger acquisition module, and the dynamic spread spectrum transmission module, respectively.

[0023] Furthermore, the environment-adaptive wake-up module is used to continuously monitor environmental disturbances in the underground pipe network at microampere-level power consumption, and wakes up subsequent modules only when a valid event is detected. The environment-adaptive wake-up module includes: a triaxial geomagnetic sensor unit, a passive infrared pyroelectric sensor unit, a time window counter, and a wake-up decision unit, which are implemented as follows: The triaxial geomagnetic sensor unit is used to detect disturbances in the Earth's magnetic field caused by the movement of ferromagnetic objects or deformation of pipelines. The passive infrared pyroelectric sensor unit is used to detect temperature gradient changes caused by pipeline leaks or external intrusion, as detailed below: After the terminal is installed in the inspection well of the underground pipeline network, the environment adaptive wake-up module enters a continuous listening state, specifically as follows: Let the unit time window length be The value is 200ms; Within the set time window, the triaxial geomagnetic sensor unit... The sampling rate captures geomagnetic disturbance sequences, and the passive infrared pyroelectric sensor unit uses... The sampling rate captures the sequence of thermal radiation changes.

[0024] The decision unit is awakened to perform spatiotemporal consistency verification, as follows: The terminal within a unit time window The captured triaxial geomagnetic disturbance sequence and the passive infrared thermal radiation change sequence are discretized and represented respectively. A set of event trigger vectors is then constructed, resulting in: ; in, Represents the first event in the set of event trigger vectors. A vector, including: perturbation intensity, duration, and characteristic orientation angle. This represents the set of event trigger vectors that have been constructed. This represents the total number of vectors in the event trigger vector set, which can be set by the implementer based on the actual application scenario.

[0025] It was determined that the characteristic orientation angle in the geomagnetic disturbance sequence continuously deflected beyond a preset angle threshold. The event point is a potential valid starting point. The value is set to 5 degrees; simultaneously, the rate of change of the temperature gradient in the infrared thermal radiation change sequence exceeds the threshold. The corresponding time point, Values ; Calculate the time delay offset of the event start point in the two sequences. And based on the event type, consistency verification is performed, then: Set the maximum time offset to and the feature orientation angle threshold of the two event sources Based on the time delay offset of the event starting point and the characteristic direction angle difference in the two sequences Perform event consistency verification, specifically as follows: like And the difference in characteristic direction angles corresponding to the two event sources This indicates that the current disturbance event satisfies spatiotemporal consistency and is determined to be a valid underground activity event; among which, Values , The value is 15 degrees; Conversely, if or If the event is detected as a ground interference event, the module will continue to maintain a microampere-level sleep listening state and will not generate a wake-up output. The valid events that pass verification are used as the output signal of the environment adaptive wake-up module. At the same time, according to the amplitude level L of the disturbance intensity, L is divided into three levels: L1, L2, and L3, which correspond to low, medium, and high disturbance intensities, respectively. Corresponding wake-up priority flags are generated and transmitted to the multi-source sensor trigger acquisition module to control the sampling density of subsequent sensors.

[0026] The multi-source sensor-triggered acquisition module is used to dynamically adjust the data sampling frequency and resolution based on determined valid events. The specific implementation is as follows: Using the range of A diffused silicon piezoresistive sensor is used to collect pressure data, an ultrasonic time-of-flight method is used to collect flow data, and an electrochemical sensor is used to detect methane and hydrogen sulfide concentrations. The collected pressure data P, flow rate data Q, and gas concentration data C are normalized to form a three-dimensional state vector. And maintain a historical background model M with a sliding window length of K, where K is 100, the mean of model M is μ, and the covariance matrix is... ; Calculate the Mahalanobis distance between the current state vector and the background model M, then we have: ; in, Represents the current state vector. This represents the mean vector of the background model. This represents the covariance matrix of the background model. Denotes the inverse matrix of the covariance matrix. This represents the Mahalanobis distance between the current state vector and the background model, used to dynamically adjust the sampling frequency and resolution of internal pressure, flow rate, and harmful gases. Set three-level difference thresholds , , And satisfy ,in The value is 1.5. The value is 3.0. The value is 5.0, and according to... The sampling strategy decision is made based on the landing area, specifically as follows: like This indicates that the current state is not significantly different from the background model, and the module enters sparse sampling mode, reducing the sampling frequency to [value missing]. , Values It does not generate reported data, but only updates the background model; like If the result is positive, it indicates a moderate difference, and the module enters the normal acquisition mode. Frequency acquisition, Values And package the data into a first-priority data packet; like If the difference is strong, the module enters high-precision acquisition mode. Frequency acquisition, Values It also enables 24-bit ADC high-resolution conversion and generates second-priority data packets; like If a sudden event occurs, the module immediately collects K complete cycles continuously, where K is 10, and generates an emergency data packet. At the same time, the data urgency label is set to the third priority data packet.

[0027] The packaged sensor data and corresponding urgency tags are transmitted together to the dynamic spread spectrum transmission module as the basis for selecting the spread spectrum factor.

[0028] The dynamic spread spectrum transmission module is used to receive data from the multi-source sensor-triggered acquisition module. Based on the characteristics of the LoRa physical layer, it dynamically selects the spreading factor, coding rate, and transmit power. The specific implementation is as follows: A three-dimensional decision tree matching strategy is implemented. The three-dimensional decision tree is a pre-defined rule tree, and its nodes are branched based on data urgency, power level, and link quality threshold, as detailed below: First, read the Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) of the current channel, and calculate the overall link quality factor. Then: ; in, This represents the normalized value of the received signal strength indication. This represents the normalized value of the signal-to-noise ratio. , Represents the weighting coefficients, and satisfies the formula , This indicates the calculation of the overall link quality factor; Simultaneously, it reads the urgency tags (including first-priority data packets, second-priority data packets, and third-priority data packets) from the multi-source sensor trigger acquisition module and the current remaining battery energy from the energy sensing closed-loop calibration module. , It is divided into three levels: abundant, normal, and critical, corresponding to a remaining battery percentage of more than 70%, 90%, 10 ... Less than 30%.

[0029] Based on the data urgency level and remaining battery energy, the spreading factor is dynamically selected as follows: If the data packet has a priority of third place, then transmission reliability is prioritized, and the maximum spreading factor is fixed. And using the highest redundancy coding rate ; If the data urgency is not the third priority data packet, the spreading factor is dynamically selected based on the remaining battery energy, specifically: If the battery's remaining energy is at a critical state, then power consumption should be reduced first, and the minimum spreading factor should be selected. 7 and minimum transmission power ; If the remaining battery energy is in a non-critical state, the spreading factor is dynamically selected based on the comprehensive link quality factor, specifically: High, medium, and low thresholds for the integrated link quality factor are set respectively. The calculated integrated link quality factor is compared with the set thresholds, and the spreading factor is dynamically selected based on the comparison results. Specifically: When the calculated comprehensive link quality factor is greater than the high threshold of the comprehensive link quality factor ( When the overall link quality factor threshold is set to 0.85, the minimum spreading factor is selected. 7. Transmission power is ; When the calculated comprehensive link quality factor is greater than the threshold of the comprehensive link quality factor, but less than the high threshold of the comprehensive link quality factor ( When the threshold value in the integrated link quality factor is 0.60, the selected spreading factor is... 9, transmission power is ; When the calculated comprehensive link quality factor is greater than the low threshold of the comprehensive link quality factor and less than the middle threshold of the comprehensive link quality factor ( When the overall link quality factor threshold is 0.40, the selected spreading factor is... 11, transmission power is ; When the calculated comprehensive link quality factor is less than the low threshold of the comprehensive link quality factor ( When ), the selected spreading factor is 12, transmission power is And mark the link as abnormal.

[0030] After the transmission is completed, the actual energy of this communication, as well as the selected spreading factor and transmit power value, are fed back to the energy sensing closed-loop calibration module for updating the energy consumption model.

[0031] The energy sensing closed-loop calibration module is used to monitor the battery's remaining charge, temperature, and cumulative depth of discharge, and dynamically adjusts the operating parameters of the environment adaptive wake-up module, the multi-source sensor trigger acquisition module, and the dynamic spread spectrum transmission module. The specific implementation of the energy sensing closed-loop calibration module is as follows: First, measure the battery open-circuit voltage and coulomb integral to calculate the remaining energy. It also calculates the average transmission power consumption of the dynamic spread spectrum transmission module over the past 24 hours and predicts future power consumption. The energy required within a period is: ; in, This indicates the average transmission power consumption of the dynamic spread spectrum transmission module over the past 24 hours. This indicates the number of transmissions within the predicted time window. This indicates the energy consumption within the predicted time window. This represents the prediction time window and satisfies the formula. ; Set the battery's full charge capacity to The energy to be safely retained is: ; in, This indicates that energy is reserved for safety, to prevent the system from entering an unrecoverable low-power state; Based on the remaining energy, predicted energy, and safe energy, the energy margin is calculated as follows: ; in, This indicates that energy is being safely conserved. This indicates the energy consumption within the predicted time window. Indicates the remaining energy. The calculated energy margin is used to dynamically adjust the operating parameters of the environment adaptive wake-up module, the multi-source sensor trigger acquisition module, and the dynamic spread spectrum transmission module, specifically: like And exceeding the upper limit , A value of 10% of the full charge energy indicates ample energy, which increases the environmental wake-up sensitivity of the environmental adaptive wake-up module by one level, i.e., lowers the disturbance trigger threshold. and , The temperature dropped from 5 degrees to 3 degrees. Depend on Down to Simultaneously, the difference threshold of the multi-source sensor trigger acquisition module is set. , , Overall reduction of 10%, that is Adjusted to 1.35. Adjusted to 2.70. The value was adjusted to 4.50 to make the system easier to trigger data collection and increase monitoring density. like Between 0 and The interval between these values ​​indicates energy balance, keeping all current parameters unchanged. like If an energy deficit is indicated, a reverse calibration will be performed, as follows: The angle threshold in the spatiotemporal consistency verification of the environment adaptive wake-up module. Increase by 15%, from 15 degrees to 17.25 degrees; time delay deviation Increase the time by 20%, from 50 ms to 60 ms, to reduce the probability of false wake-up; Difference-driven threshold of multi-source sensor-triggered acquisition module , , Overall increase of 20%, that is Adjusted to 1.80. Adjusted to 3.60. Adjusted to version 6.00 to reduce the number of high-power data acquisitions; The link quality threshold in the three-dimensional decision tree of the dynamic spread spectrum transmission module. , , Each improvement ,Right now The value was adjusted from 0.85 to 0.87. The value was adjusted from 0.60 to 0.62. The value was adjusted from 0.40 to 0.42, forcing terminals to use lower power consumption under the same link conditions. parameter.

[0032] After calibration is completed, the energy sensing closed-loop calibration module constructs a new set of configuration parameters based on all the adjusted parameters, and sends them to the environment adaptive wake-up module, the multi-source sensing trigger acquisition module and the dynamic spread spectrum transmission module respectively. Meanwhile, the energy sensing closed-loop calibration module continues to monitor. ,like Below the emergency threshold , If the value is set to 3% of the full charge energy, the terminal is forced into an emergency-only reporting mode, disabling the normal wake-up and data acquisition functions of the environment adaptive wake-up module and the multi-source sensor triggering acquisition module, and only responding to the multi-source sensor triggering acquisition module. The system is designed to detect sudden changes and ensure that major pipeline accidents can still be reported even under the worst conditions.

[0033] It should be noted that through the coordinated work of the four modules, the output of the environment adaptive wake-up module determines the working timing of the multi-source sensor trigger acquisition module, the output of the multi-source sensor trigger acquisition module determines the data priority of the dynamic spread spectrum transmission module, the energy consumption feedback of the dynamic spread spectrum transmission module is input to the energy sensing closed-loop calibration module, and the energy sensing closed-loop calibration module then reverse-calibrates the operating parameters of the first three modules to form a complete closed-loop control logic, realizing long-distance reliable communication and ultra-low power long-term operation of the underground pipeline wireless monitoring terminal in signal attenuation environment.

[0034] Furthermore, if the aforementioned function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0035] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0036] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0037] 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 protection claimed by the present invention.

Claims

1. A LoRa-based wireless monitoring terminal for underground pipeline networks, characterized in that: include: The environment-adaptive wake-up module is used to continuously monitor environmental disturbances in the underground pipe network and determine valid events based on the monitoring results. Specifically: Within a set time window, geomagnetic disturbance sequences and thermal radiation change sequences are collected and constructed into an event trigger vector set. Spatiotemporal consistency verification is performed based on the constructed event trigger vector set, and valid events are identified. The multi-source sensor-triggered acquisition module is used to dynamically adjust the data sampling frequency and resolution based on determined valid events, specifically: Pressure data, flow data, and hazardous gas data are collected sequentially and constructed into a three-dimensional state vector. At the same time, a historical background model is constructed, and the Mahalanobis distance between the current state vector and the historical background model is calculated. Based on the calculation results, the data sampling frequency and resolution are dynamically adjusted, and the data urgency label is determined. The dynamic spread spectrum transmission module is used to dynamically select the spreading factor, coding rate, and transmit power, specifically: The comprehensive link quality factor of the current channel is calculated. At the same time, based on the data urgency label determined by the multi-source sensor trigger acquisition module and the current battery remaining energy, and combined with the calculated comprehensive link quality factor, the spreading factor, coding rate and transmit power are dynamically selected. The energy sensing closed-loop calibration module is used to provide feedback and adjust other modules by calculating the battery's energy margin. It includes: an environment adaptive wake-up module, a multi-source sensor trigger acquisition module, and a dynamic spread spectrum transmission module.

2. The LoRa-based wireless monitoring terminal for underground pipelines as described in claim 1, characterized in that: The spatiotemporal consistency verification based on the constructed event trigger vector set is performed as follows: Calculate the time delay offset of the event start point in the two sequences. And based on the event type, consistency verification is performed, then: Set the maximum time offset to and the feature orientation angle threshold of the two event sources Based on the time delay offset of the event starting point and the characteristic direction angle difference in the two sequences Perform event consistency verification, specifically as follows: like And the difference in characteristic direction angles corresponding to the two event sources If so, it means that the current disturbance event satisfies spatiotemporal consistency and is determined to be a valid underground activity event; Conversely, if or If the event is detected as a ground interference event, the system will continue to maintain a microampere-level sleep listening state and will not generate a wake-up output.

3. The LoRa-based wireless monitoring terminal for underground pipelines as described in claim 2, characterized in that: The calculation of the Mahalanobis distance between the current state vector and the historical background model is as follows: The three parameters, pressure data P, flow rate data Q, and gas concentration data C, are normalized to form a three-dimensional state vector. And maintain a historical background model M with a sliding window length of K, the mean of model M is μ, and the covariance matrix is... ; Calculate the Mahalanobis distance between the current state vector and the background model M, then we have: ； in, Represents the current state vector. This represents the mean vector of the background model. This represents the covariance matrix of the background model. Denotes the inverse matrix of the covariance matrix. This represents the Mahalanobis distance between the current state vector and the background model.

4. The LoRa-based wireless monitoring terminal for underground pipelines as described in claim 3, characterized in that: The dynamic adjustment of data sampling frequency and resolution based on the calculation results is as follows: Set three-level difference thresholds , , And satisfy and according to The sampling strategy decision is made based on the landing area, specifically as follows: like This indicates that the current state is not significantly different from the background model, and the system enters sparse sampling mode, reducing the sampling frequency to [value missing]. It does not generate reported data, but only updates the background model; like If the result is positive, it indicates a moderate difference, and the system will enter the normal data collection mode. Frequency acquisition, and data packaging into first-priority data packets; like This indicates a strong difference, triggering a high-precision acquisition mode. Frequency acquisition, enabling 24-bit ADC high-resolution conversion, generating second-priority data packets; like If a mutation event occurs, K complete cycles will be collected immediately and a third-priority data packet will be generated.

5. The LoRa-based wireless monitoring terminal for underground pipelines as described in claim 4, characterized in that: The calculation of the overall link quality factor of the current channel is as follows: Read the Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) of the current channel, calculate the overall link quality factor, and then we have: ； in, This represents the normalized value of the received signal strength indication. This represents the normalized value of the signal-to-noise ratio. , Represents the weighting coefficients, and satisfies the formula , This indicates the calculation of the overall link quality factor.

6. The LoRa-based wireless monitoring terminal for underground pipelines as described in claim 5, characterized in that: The dynamic selection of the spreading factor is as follows: Based on the data urgency level and remaining battery energy, the spreading factor is dynamically selected, specifically as follows: If the data packet has a priority of third priority, then the reliability of transmission is guaranteed first, the maximum spreading factor is fixed, and the highest redundancy coding rate is used. If the data urgency is not the third priority data packet, the spreading factor is dynamically selected based on the remaining battery energy, specifically: If the remaining battery energy is at a critical state, then power consumption should be reduced first, and the minimum spreading factor and the lowest transmit power should be selected. If the remaining battery energy is in a non-critical state, the spreading factor is dynamically selected by comprehensively considering the link quality factor.

7. The LoRa-based wireless monitoring terminal for underground pipelines as described in claim 6, characterized in that: The dynamic selection of the spreading factor through the comprehensive link quality factor is specifically as follows: High, medium, and low thresholds for the integrated link quality factor are set respectively. The calculated integrated link quality factor is compared with the set thresholds, and the spreading factor is dynamically selected based on the comparison results. Specifically: When the calculated composite link quality factor is greater than the high threshold of the composite link quality factor, the minimum spreading factor is selected.

7. Transmission power is ; When the calculated composite link quality factor is greater than the threshold of the composite link quality factor but less than the high threshold of the composite link quality factor, the selected spreading factor is: 9, transmission power is ; When the calculated composite link quality factor is greater than the low threshold of the composite link quality factor but less than the middle threshold of the composite link quality factor, the selected spreading factor is: 11, transmission power is ; When the calculated composite link quality factor is less than the low threshold of the composite link quality factor, the selected spreading factor is: 12, transmission power is And mark the link as abnormal.

8. The LoRa-based wireless monitoring terminal for underground pipelines as described in claim 7, characterized in that: The calculation of the battery's energy margin is as follows: Based on the remaining energy, predicted energy, and safe energy, the energy margin is calculated as follows: ； in, This indicates that energy is being safely conserved. This indicates the energy consumption within the predicted time window. Indicates the remaining energy. The calculated energy margin is used to provide feedback and adjust other modules.

9. The LoRa-based wireless monitoring terminal for underground pipelines as described in claim 8, characterized in that: The feedback adjusts other modules as follows: And exceeding the upper limit , A value of 10% of full charge energy indicates ample energy, increasing the environmental wake-up sensitivity of the environmental adaptive wake-up module by one level, and simultaneously adjusting the difference threshold of the multi-source sensor trigger acquisition module. , , Overall reduction of 10%; like Between 0 and The interval between these values ​​indicates energy balance, keeping all current parameters unchanged. like If an energy deficit is indicated, a reverse calibration is performed.

10. The LoRa-based wireless monitoring terminal for underground pipelines as described in claim 9, characterized in that: The reverse calibration is performed as follows: The angle threshold in the spatiotemporal consistency verification of the environment adaptive wake-up module. Increase by 15%, delay deviation Increase by 20%; Difference-driven threshold of multi-source sensor-triggered acquisition module , , Overall increase of 20%; The link quality threshold of the dynamic spread spectrum transmission module , , Each improvement ; After calibration is completed, the energy sensing closed-loop calibration module constructs a new set of configuration parameters based on all the adjusted parameters, and distributes them to the environment adaptive wake-up module, the multi-source sensor trigger acquisition module, and the dynamic spread spectrum transmission module, respectively.

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