Data reliability transmission method and system for plateau monitoring network

Abstract

The application relates to the technical field of plateau environment monitoring, and discloses a data reliability transmission method and system for a plateau monitoring network, which comprises the following steps: determining a target monitoring site in the plateau monitoring network through monitoring data in the ice and snow melting period; acquiring soil conductivity data of the target monitoring site, and determining whether the corresponding monitoring site enters a high-interference risk state; injecting a test signal into the monitoring site entering the high-interference risk state and measuring the antenna voltage standing wave ratio thereof, to generate an interference event identifier; generating an interference spatial distribution map based on the interference event identifier, and dynamically scheduling a communication path according to the interference spatial distribution map. The application preliminarily divides the range through ice and snow melting monitoring, then analyzes the soil conductivity change and the antenna voltage standing wave ratio to determine the specific interference point coordinates, and finally dynamically adjusts the communication path according to the interference point coordinates, avoids the interference area, and guarantees the reliable transmission of communication data.

Description

Technical Field

[0001] This invention relates to the field of plateau environment monitoring technology, and more specifically, to a data reliability transmission method and system for plateau monitoring networks. Background Technology

[0002] To prevent natural geological disasters such as earthquakes, landslides, and debris flows, real-time monitoring of geologically active areas is required using environmental monitoring technologies. To better achieve continuous monitoring of large, unattended areas, extensive deployment of monitoring stations is employed to achieve regional coverage. These monitoring stations often integrate the high-precision positioning and timing capabilities of the BeiDou Navigation Satellite System and construct heterogeneous monitoring networks using low-power wide-area IoT communication technologies such as LoRa. This allows different monitoring stations to better aggregate sensor data via the LoRa network and then transmit it to the monitoring center via satellite or mobile communication backhaul links.

[0003] However, in high-altitude areas such as plateaus, some monitoring areas are covered by ice and snow for a long time. When the ice and snow melt, they melt in large areas under the sun. When the melted water seeps down, it will significantly increase the water content of the shallow soil of the monitoring station foundation. The increase in soil water content will directly lead to a continuous increase in its volumetric conductivity.

[0004] Based on this, the present invention proposes a data reliability transmission method and system for plateau monitoring networks. By screening monitoring stations that need further testing through snowmelt monitoring data and soil conductivity data, the antenna voltage standing wave ratio of the corresponding monitoring stations is measured before planned communication, and the communication path and resources of the plateau monitoring network are scheduled according to the subsequent testing results to improve the reliability of communication data transmission. Summary of the Invention

[0005] This invention provides the following technical solution:

[0006] In a first aspect, this invention discloses a method for reliable data transmission in a plateau monitoring network, the plateau monitoring network comprising multiple monitoring stations communicating via a LoRa wireless network and equipped with BeiDou positioning modules, the method comprising:

[0007] By using monitoring data during the snow and ice melt period, at least one target monitoring station in the plateau monitoring network located within the area affected by surface snow and ice melt can be identified. The monitoring data during the snow and ice melt period is used to identify the state and extent of surface snow and ice melt.

[0008] Obtain soil electrical conductivity data for the target monitoring stations, and determine whether the corresponding monitoring stations have entered a high-interference-risk state based on the soil electrical conductivity data;

[0009] For monitoring stations that have entered a high-interference-risk state, a test signal is injected before planned communication and their antenna voltage standing wave ratio is measured. When the antenna voltage standing wave ratio exceeds a preset threshold, an interference event identifier containing the BeiDou positioning coordinates of the station is generated.

[0010] Based on the BeiDou positioning coordinates of multiple interference events in the plateau monitoring network, a visualized spatial distribution map of the interference is generated.

[0011] The communication paths and resources of the plateau monitoring network are dynamically scheduled based on the spatial distribution map of interference to avoid interference.

[0012] Furthermore, methods for determining target monitoring stations using monitoring data from the snow and ice melt period include:

[0013] By accessing satellite remote sensing data on land temperature, snow cover, or snow and ice melting models, all monitoring stations in the plateau monitoring network located in areas where surface snow and ice are melting or have recently melted are identified and marked as the target monitoring stations.

[0014] Furthermore, methods for determining whether a corresponding monitoring station has entered a high-interference-risk state based on soil electrical conductivity data include:

[0015] The acquired soil electrical conductivity data is compared with the low electrical conductivity baseline value calibrated at the corresponding monitoring station during the period of complete snow and ice cover or drought and freezing. If the soil electrical conductivity data is higher than the baseline value, the station is determined to be in a state of high interference risk.

[0016] Furthermore, the method for injecting a test signal and measuring its antenna voltage standing wave ratio includes:

[0017] Within a preset time window before planned communication, the radio frequency circuit of the control monitoring station is switched to self-test mode, and a test signal is injected into the LoRa antenna feed port of the monitoring station. Simultaneously, the reflected signal is measured to calculate the real-time antenna voltage standing wave ratio.

[0018] Furthermore, the method also includes:

[0019] Based on the different preset ranges of the measured real-time antenna voltage standing wave ratio, different interference risk levels are assigned to the corresponding monitoring stations; among them, the interference event identifier contains the corresponding interference risk level information.

[0020] Furthermore, methods for generating visualized spatial distribution maps of interference include:

[0021] The BeiDou positioning coordinates in all received interference event identifiers are mapped to a digital geographic base map, and differentiated rendering or marking is performed based on the interference risk level information contained in each identifier, generating an interference spatial distribution map that integrates the spatial location and risk level of the interference.

[0022] Furthermore, methods for dynamically scheduling communication paths and resources to avoid interference include:

[0023] Based on the interference risk level of different geographical areas in the interference spatial distribution map, implement differentiated scheduling strategies;

[0024] For high-risk areas, at least one of the following strategies should be prioritized: channel switching, communication time slot delay, or designation of a dedicated relay path.

[0025] For areas with low to medium risk levels, implement at least one strategy, either reducing transmission power or adopting a more robust modulation and coding scheme.

[0026] Furthermore, the method also includes:

[0027] Based on the historical accumulation of interference event identifiers and corresponding communication scheduling results, a strategy for dynamically scheduling communication paths and resources is optimized through machine learning model training.

[0028] Secondly, this invention discloses a data reliability transmission system for a plateau monitoring network, used to implement the aforementioned data reliability transmission method for a plateau monitoring network. The data reliability transmission system for a plateau monitoring network includes:

[0029] Multiple monitoring stations, each monitoring station includes at least a sensor for collecting soil conductivity data, a BeiDou positioning module for providing positioning coordinates, and a LoRa communication unit containing controllable signal injection and voltage standing wave ratio measurement circuitry;

[0030] The coordination server, connected to all monitoring stations via a network, is used to execute the steps of determining target monitoring stations, generating interference spatial distribution maps, and dynamically scheduling communication paths and resources in the data reliability transmission method for the plateau monitoring network.

[0031] Furthermore, the LoRa communication unit includes a controllable signal injection and voltage standing wave ratio measurement circuit, comprising an RF switch, a directional coupler, and a detector; the coordination server is integrated into the LoRaWAN gateway or cloud server of the plateau monitoring network.

[0032] Compared with related technologies, the present invention has the following beneficial effects:

[0033] This invention utilizes monitoring data from the snowmelt season to screen monitoring stations across the vast plateau region for the first time, breaking through conventional communication approaches by identifying potential interference areas from meteorological and geological / hydrological perspectives. The solution then introduces soil electrical conductivity as a key mediating indicator. A sustained and significant increase in soil electrical conductivity provides direct and quantifiable evidence that snowmelt has penetrated the equipment's foundation and altered its electrical environment. By converting this geophysical change into electrical parameters, a logical leap from macro-environmental early warning to micro-site risk assessment is achieved. The communication equipment's own antenna system is then used as a diagnostic probe. By injecting test signals and simultaneously measuring the voltage standing wave ratio (VSWR), an interference event identifier is generated and embedded with the station's BeiDou positioning coordinates upon confirmation of an anomaly. Subsequently, the geographically tagged interference event identifiers scattered across various stations are aggregated, and a dynamic spatial distribution map of the interference is generated using spatial information fusion technology, making the invisible interference field visible and resolvable in digital space. Finally, based on the spatial distribution map of interference, the entire plateau monitoring network was directed to arrange communication paths and resource allocation between stations in an orderly manner, avoiding interference areas as much as possible to reduce interference to communication and ensure reliable transmission of communication data. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating the steps of the data reliability transmission method for a plateau monitoring network provided by the present invention.

[0035] Figure 2 This invention provides a data processing flowchart for a reliable data transmission system used in a plateau monitoring network. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1

[0038] Please see Figure 1 As shown, this embodiment provides a reliable data transmission method for a plateau monitoring network. The plateau monitoring network includes multiple monitoring stations that communicate via a LoRa wireless network and are equipped with BeiDou positioning modules. The transmission method includes steps one through five, which are described in detail below:

[0039] Step 1: Identify at least one target monitoring station within the affected area of ​​surface snow and ice melt in the plateau monitoring network using monitoring data during the snow and ice melt period. The monitoring data during the snow and ice melt period is used to identify the state and extent of surface snow and ice melt.

[0040] Please combine Figure 1 and Figure 2 In this embodiment, the monitoring data during the snow and ice melt period specifically refers to the time-series dataset obtained by satellite remote sensing that can directly or indirectly characterize the existence and melting state of surface snow and ice. Its core implementation method is to access the publicly available data products of satellites such as the Medium Resolution Imaging Spectroradiometer or Sentinel-2.

[0041] The target monitoring stations refer to those stations that, after being screened in this step, are identified as currently located within the area affected by snow and ice melting and require key monitoring for subsequent interference risks.

[0042] This step can be performed by a server in the plateau monitoring network, which has internet access to acquire satellite data and stores registration information for all monitoring stations in the network, including their unique BeiDou positioning coordinates.

[0043] The method for determining target monitoring stations using monitoring data during the snow and ice melt period includes: identifying all monitoring stations in the plateau monitoring network located in areas where surface snow and ice are melting or have recently melted by accessing satellite remote sensing surface temperature data, snow cover data, or snow and ice melt model data, and marking them as the target monitoring stations.

[0044] Specifically, because satellite data is not limited by terrestrial networks, it can uniformly and objectively cover the entire plateau monitoring area, including uninhabited areas. Therefore, remote sensing data covering the monitoring network area can be automatically retrieved periodically by a server from a designated satellite data distribution center (such as NASA's LAADS-DAAC or the European Space Agency's Copernicus Open Access Centre). In this embodiment, two key types of data are mainly acquired:

[0045] The first type is the Normalized Difference Snow Index (NDS), which can be used to identify the extent of snow and ice cover. It works by calculating the difference in reflectance characteristics of snow and ice in the visible light (green band) and shortwave infrared bands. Generally, the higher the value, the greater the likelihood of snow and ice cover. The second type is surface temperature data, which can be used to help determine the state of snow and ice melting. However, considering the difficulty of actual data collection, this data is only introduced as a further expansion, and it is not necessary to introduce this data.

[0046] When identifying areas affected by snow and ice melt, the server performs time-series analysis on the acquired normalized differential snow index. The specific criteria are as follows: the server performs differential comparison on two consecutive snow cover index images. When the index continuously decreases within a preset time window and the decrease exceeds a threshold, it is determined to be an area affected by snow and ice melt.

[0047] For example, comparing the latest normalized difference snow index image with an image of the same area from one week ago pixel by pixel, the criterion formula can be simply set as follows: Where SI represents the normalized difference snow index obtained today, and SI' represents the normalized difference snow index at the same location seven days ago. This indicates the change in SI.

[0048] Its judgment logic can be set as follows: For a given geographic pixel, if SI' is lower than an empirical threshold (this empirical threshold needs to be set in conjunction with the measurement value of the specific location, for example, set to 0.4), and If the value is less than a negative threshold (similarly, for example, set to -0.2), then the area where the pixel is located is determined to be an area where surface ice and snow are melting or have recently melted. If A negative value with a large absolute value likely indicates a significant decrease in snow and ice coverage in the short term, a clear signal of melting.

[0049] The server then performs spatial overlay analysis (also called GIS overlay analysis) on all the identified snow and ice melting pixels and the pre-stored monitoring station location database. This database records the unique number of each station (such as STN-001) and its precise BeiDou latitude and longitude coordinates.

[0050] The matching rule can be set as follows: if the coordinates of a monitoring station fall within the cell range of any area identified as a snow and ice melting zone, then the station is marked as the target monitoring station.

[0051] Detailed example: Suppose the coordinates of monitoring station STN-001 are (E90.123°, N30.456°). After querying, it is found that this coordinate point falls within the pixel marked as the melting area in the identification of today's snow and ice melting area. Then the coordination server will add STN-001 to the target monitoring station list for this round.

[0052] In other embodiments, the target monitoring station can also be determined in the following two ways:

[0053] Model prediction can be used, that is, by accessing the zero-degree Celsius layer height data and cumulative positive temperature data output by the regional numerical weather prediction model through the server. When the model predicts that the zero-degree Celsius layer height in the area where a certain station is located is higher than the station's altitude for several consecutive days and the cumulative positive temperature reaches the threshold, the station can be identified as a potential target monitoring station in advance.

[0054] Alternatively, reports can be submitted directly from the monitoring stations. This involves installing simple optical snow depth sensors or surface temperature probes at the stations. When the local sensors at a station continuously detect a decrease in snow depth or a sustained surface temperature above 0°C, they can proactively send a snow and ice melting status report to the coordination server. The server then collects these reports and can generate a list of target monitoring stations. This method can serve as a supplement to or verification of satellite remote sensing.

[0055] In summary, in the implementation of step one, the first step is to accurately select a subset of stations (i.e., target monitoring stations) that may be affected by this problem from hundreds or thousands of stations. This avoids performing indiscriminate and high-power subsequent diagnostics on all stations, and achieves precise resource allocation and optimization of system energy efficiency.

[0056] Step 2: Obtain soil electrical conductivity data for the target monitoring station, and determine whether the corresponding monitoring station has entered a high-interference-risk state based on the soil electrical conductivity data.

[0057] In this embodiment, soil electrical conductivity data represents a physical quantity characterizing the electrical conductivity of the soil near the grounding electrode of the monitoring station, directly measured by a soil electrical conductivity sensor (equipped at each monitoring station) using the time-domain reflectometry method. The unit is millisiemens per centimeter (mS / cm). This data directly reflects the liquid water content and ion concentration in the soil and is a key indicator for determining whether snowmelt has infiltrated into the shallow layers.

[0058] The method for determining whether a monitoring station has entered a high-interference-risk state based on soil electrical conductivity data includes: comparing the acquired soil electrical conductivity data with the low electrical conductivity baseline value calibrated by the corresponding monitoring station during the period of complete snow and ice cover or drought and freezing period; if the soil electrical conductivity data is higher than the baseline value, the station is determined to have entered a high-interference-risk state.

[0059] The baseline value for low electrical conductivity refers to the representative low soil electrical conductivity value determined by long-term measurements at each monitoring station during periods of complete snow and ice cover (e.g., the coldest month of winter) or drought and freezing (when soil moisture exists in solid form). It characterizes the background electrical conductivity state of the station under conditions without the influence of meltwater.

[0060] Step two can be completed collaboratively by the local controller and server at the monitoring site. For example, the site is responsible for collecting and reporting raw data, while the server is responsible for storing baseline data, executing judgment algorithms, and managing the status.

[0061] For each site in the list that has been marked as a target monitoring site, the server notifies the target monitoring site in the list to start soil conductivity monitoring via downlink commands. For example, a command is sent to site STN-001 to start soil conductivity measurement, with a total duration of 300 seconds and sampling every 60 seconds.

[0062] After receiving a command, the local controller at the monitoring station can drive its integrated TDR soil conductivity sensor to work. The sensor probe can be vertically buried at a depth of about 20 cm near the grounding body of the monitoring pole to sense the shallow soil that is first affected by meltwater. The sensor records the measured soil conductivity value as σ and packages it with the corresponding timestamp. Then, the monitoring station can upload the data packet containing the station ID, timestamp and σ value to the server via LoRa link.

[0063] Then, a risk status assessment is required. After receiving the data, the server automatically executes the following assessment process:

[0064] First, the server retrieves the pre-calibrated low conductivity baseline value, denoted as σ', from the site's historical database. This data collection requires continuous collection of soil conductivity sequences for at least one day during periods of complete snow and ice cover or freezing drought. The median of the quantile interval is taken as σ', and the temperature interval is recorded for temperature drift correction. For example, σ' for site STN-001 might be 0.8 mS / cm (this is determined by taking the median through continuous monitoring during the winter stable period).

[0065] The server then checks all σ data sequences uploaded by the monitoring site within the most recent time window (e.g., the past 2 hours).

[0066] For its judgment logic, a simple but reliable comparison condition can be used for judgment:

[0067] If the average of the last 5 σ readings is greater than σ' × risk coefficient K, and these 5 readings show a stable or upward trend (linear fitting slope greater than 0 or continuously increasing), then the site is determined to be in a high-interference-risk state. The risk coefficient K is an empirical coefficient greater than 1, mainly used to set the trigger threshold. This coefficient is set to avoid false triggering due to minor natural fluctuations, ensuring that the judgment is based on a significant increase in conductivity.

[0068] This condition also requires that the electrical conductivity level be significantly higher than the baseline (σ>K×σ'), and that this increase be sustained (not a momentary fluctuation), which together indicate that snowmelt has been continuously infiltrating and altering the electrical environment of the site's soil.

[0069] In other embodiments, more complex judgment models can be constructed by combining data from small weather stations on-site (such as temperature and sunshine intensity). For example, a high-risk condition can only be determined when soil electrical conductivity increases, temperature is continuously above 0°C, and sunshine is sufficient, further eliminating interference from other accidental factors (such as a small amount of rainfall).

[0070] In summary, once the determination is made, the server will mark the site as having a high interference risk in the site's status record and add the site ID to a list of high interference risk sites. This list is the direct basis for triggering subsequent core diagnostic steps (antenna voltage standing wave ratio measurement).

[0071] In the implementation of step two, the significant and sustained increase in soil electrical conductivity provides direct and measurable evidence that snowmelt has successfully infiltrated and reached the equipment's grounding area. Therefore, by monitoring this leading indicator in advance, the system can provide early warnings, gaining valuable preparation time for subsequent proactive diagnosis and network scheduling.

[0072] Step 3: For monitoring stations that have entered a high-interference-risk state, inject test signals and measure their antenna voltage standing wave ratio (VSWR) before planned communication. When the antenna voltage VSWR exceeds a preset threshold, generate an interference event identifier containing the BeiDou positioning coordinates of the station.

[0073] In this embodiment, the antenna voltage standing wave ratio (VSWR) is a dimensionless value. Ideally, it is 1:1 (i.e., VSWR=1), which means that the antenna and feed line are perfectly matched and all energy is radiated. The larger the VSWR (e.g., >2.0), the more severe the impedance mismatch, and the more energy is reflected back to the transmitter.

[0074] Specifically, the method for injecting test signals and measuring the antenna voltage standing wave ratio includes: within a preset time window before planned communication, controlling the radio frequency circuit of the monitoring station to switch to self-test mode, then injecting test signals into the LoRa antenna feed port of the monitoring station, and simultaneously measuring the reflected signals to calculate the real-time antenna voltage standing wave ratio value.

[0075] The self-test mode refers to a special working state that the LoRa communication unit of the monitoring station temporarily switches to in order to perform this diagnosis. In this mode, its internal radio frequency path is reconstructed, which allows a miniature test signal source to be connected to the antenna port via a directional coupler and the reflected signal to be measured at the same time.

[0076] Step three is executed autonomously and locally by the monitoring station, which is in a high-interference-risk state. Its local controller can control the switching and measurement of radio frequency circuits according to the preset program, and then provide positioning coordinates through the Beidou positioning module.

[0077] Specifically, for each site in the list of high-interference-risk sites, the plateau monitoring network initiates the following diagnostic process before its next scheduled LoRa data reporting slot:

[0078] The local controller at the monitoring site can initiate a self-test process within a preset time window (which can be set to 500 milliseconds) before its planned communication time slot, based on its internal clock. Then, the controller will send a command to switch the single-pole double-throw RF switch in the LoRa communication unit from the default "communication mode" to "self-test mode".

[0079] Once the switch is complete, the antenna's feed port will be connected to the main input of the directional coupler; a low-power continuous wave test signal source (frequency precisely tuned to the center frequency of the LoRa operating channel, such as 470.3MHz) will be connected to the forward coupling port of the directional coupler, indicating that the test signal has been successfully injected; a detector will also be connected to the reverse coupling port of the directional coupler, mainly for sampling the reflected signal.

[0080] When the test signal source is activated, it generates a pure continuous wave signal with a very short duration (possibly only 10 milliseconds) and extremely low power (far below the communication power, for example, 10 dBm). This signal is injected into the antenna feed port through a directional coupler.

[0081] During this time period, synchronous sampling is also required. That is, while the detector is injecting the signal, it needs to sample the signal power reflected back from the antenna port. The directional coupler also samples a portion of the forward incident signal power. For reference, these two analog power values ​​will then be converted into digital values ​​by a high-speed ADC.

[0082] After the detector outputs the forward power sample value Pf and the reverse power sample value Pr respectively, the local controller performs ratio processing on Pf and Pr to obtain the reflection ratio parameter, and converts the reflection ratio parameter into the real-time antenna voltage standing wave ratio according to the pre-stored mapping table. The mapping table is generated by the standard load and standard antenna prototype during the equipment factory calibration stage. It is used to map different reflectivity ranges to the corresponding VSWR ranges, thereby avoiding misjudgments caused by on-site calculation errors.

[0083] The controller can then calculate the real-time... The value is compared with a preset normal range threshold Q, which is set based on the typical VSWR value (e.g., 1.5) of this antenna model under interference-free, standard conditions, with a certain safety margin. For example, multiple self-tests can be performed on the same site during interference-free periods to obtain the result. The baseline distribution is used, and the mean plus a safety margin or the high quantile is taken as Q; Q is reset after antenna replacement or feeder maintenance.

[0084] if If the value is greater than Q, then an interference event is confirmed. The local controller will immediately generate an interference event identifier, which includes: a unique identifier for the monitoring station, a timestamp of the event occurrence, BeiDou positioning coordinates, and the measured VSWR value. ).

[0085] In other embodiments, VSWR can also be measured in the following ways: For example, in scenarios with extremely high reliability requirements, a dedicated test signal source can be omitted, and the preamble portion of the LoRa data packet itself can be used as a probe signal. The VSWR can be estimated by measuring the reflection during the transmission of the preamble. This method saves more hardware, but places higher demands on the speed of the measurement circuitry and the algorithm.

[0086] After the above operations are completed, regardless of the measurement results, communication should be restored immediately. Before the end of the time window, the RF switch must be switched back to "communication mode" so that the monitoring station can send sensor data on time as originally planned, ensuring that the communication task is not affected by the diagnosis.

[0087] In the implementation of step three, since the change in soil conductivity is an indirect condition and not sufficient to directly affect communication, further confirmation is required.

[0088] Step four includes steps a and b. Step a involves classifying the corresponding monitoring stations into different interference risk levels based on the different preset ranges in which the measured real-time antenna voltage standing wave ratio (VSWR) values ​​fall. The interference event identifier contains the corresponding interference risk level information.

[0089] In this embodiment, the server is mainly responsible for aggregating the interference event identifiers uploaded by all monitoring sites, running the map generation algorithm, and making network-wide communication scheduling decisions based on the map.

[0090] Specifically, the server continuously receives and analyzes data packets identifying interference events from various monitoring stations, and then determines the appropriate response based on the measured VSWR value in the identifier. Each event is assigned a disturbance risk level. For example, the following mapping rule can be defined:

[0091] Low risk (Lv1): 2.0 < ≤3.0;

[0092] Medium risk (Lv2): 3.0 < ≤4.5;

[0093] High risk (Lv3): >4.5;

[0094] It should be noted that the threshold Q can be adjusted based on actual measured data; the above is just a specific example in this embodiment.

[0095] Each event identifier can then be converted into a data point with geographic coordinates, risk level, and timestamp, and stored in a temporary spatial database to facilitate long-term recording and traversal.

[0096] Step b: Based on the BeiDou positioning coordinates of multiple interference event identifiers in the plateau monitoring network, generate a visualized spatial distribution map of the interference.

[0097] In this embodiment, the interference spatial distribution map refers to a data visualization and decision-making model in the form of a dynamic heatmap. It is not a simple point labeling, but rather a system that uses spatial interpolation algorithms to fuse discrete interference event identifiers into an electronic map that can continuously and intuitively represent the distribution of interference intensity and risk level throughout the entire monitoring area.

[0098] Specifically, the method for generating an interference spatial distribution map includes: mapping the BeiDou positioning coordinates in all received interference event identifiers to a digital geographic base map, and performing differentiated rendering or marking based on the interference risk level information contained in each identifier to generate an interference spatial distribution map that integrates the spatial location and risk level of the interference.

[0099] Specifically, the server periodically (which can be set to every 5 minutes) processes all valid data points (from step a) within the current time period to generate an interference spatial distribution map.

[0100] The geographical area of ​​the entire monitoring area can be divided into regular grids (such as 500m × 500m). For each grid cell, the inverse distance weighted interpolation algorithm can be used to calculate the comprehensive risk value of its center point. The core idea is that the closer the interference event point is to the center of the grid, the greater its impact; the higher the risk level of the event point, the greater its impact.

[0101] For example, a computational logic is briefly described as follows: the overall risk value RC of a grid can be determined by the risk level Li and distance di of all event points i within a certain radius around it.

[0102] Specifically, when performing spatial interpolation on discrete event points, the server employs a distance decay weighting mechanism: the overall risk value of the same grid cell decreases as the distance to the event point increases, and increases as the risk level of the event point increases. Optionally, the server uses inverse distance weighting interpolation, assigning a weight wi=1 / (di) to the distance di between the grid center and the event point. p +ε), and perform weighted fusion on the risk levels Li of surrounding event points to obtain the comprehensive risk value RC, where p is the attenuation coefficient, which can be set according to the average spacing between stations and the density of event points. When event points are sparse, the grid size is increased to avoid overfitting, and when event points are dense, the grid size is decreased to improve positioning resolution; ε is a minimal constant to prevent zero-distance divergence.

[0103] Then, based on the RC value of each grid, a color is assigned to it; for example, green (safe) for RC 0-3, yellow (warning) for RC 3-7, and red (high risk) for RC 7-10. This ultimately generates a dynamic enhanced heatmap (interference spatial distribution map) with continuously transitioning colors, visually displaying the interference "hotspot" areas.

[0104] In the implementation of step four, by integrating scattered event points (interference event identifiers) into a continuous situation map (interference spatial distribution map), network administrators can more intuitively grasp the overall interference situation, which facilitates subsequent strategy formulation and resource allocation.

[0105] Step 5: Dynamically schedule the communication paths and resources of the plateau monitoring network according to the spatial distribution map of interference to avoid interference.

[0106] Specifically, methods for dynamically scheduling communication paths and resources to avoid interference include: implementing differentiated scheduling strategies based on the interference risk level of different geographical areas in the interference spatial distribution map; for high-risk areas, prioritizing at least one of the following strategies: channel switching, communication time slot delay, or designating a dedicated relay path; and for medium- and low-risk areas, implementing at least one of the following strategies: reducing transmission power or adopting a more robust modulation and coding scheme.

[0107] Specifically, the server can issue scheduling instructions in real time based on the newly generated enhanced heat map, and all scheduling will take effect at the beginning of the next communication cycle.

[0108] For high-risk areas (red areas in the figure), a strong avoidance strategy is implemented, which first involves channel switching: all monitoring stations in the area are instructed to switch from the default working channel CH0 to the pre-assigned, clean backup channel CH1 for communication.

[0109] Then, the reporting time slots of the stations in this area are uniformly delayed by a random time window (which can be set to 1-5 seconds).

[0110] If data requires multiple relays, the network routing protocol will bypass the nodes in this area and choose to bypass them from the nodes in the outer "green" area.

[0111] For low- to medium-risk areas (yellow / green areas in the figure), an optimization strategy is implemented, which first involves parameter adaptation: the monitoring stations in this area are instructed to use communication parameters with stronger anti-interference capabilities, such as increasing the spreading factor from SF7 to SF9, or increasing the overhead of forward error correction coding. This may sacrifice some data rate, but it can greatly improve anti-interference capabilities.

[0112] It is worth mentioning that for sites located on the edge of risk areas, the transmission power can be slightly reduced to reduce the excitation effect of their own signals on possible weak interference, while saving energy.

[0113] In the implementation of step five, by binding the scheduling strategy with the real-time generated map, the network resource configuration can be dynamically adjusted in response to the interference environment that changes over time and space. By utilizing network collaboration capabilities, interference can be actively avoided, ensuring that critical data can always be transmitted through the optimal path and resources at that time. Even if some areas experience anomalies, overall communication remains uninterrupted.

[0114] It is worth mentioning that the transmission method also includes: optimizing the strategy of dynamically scheduling communication paths and resources by training a machine learning model based on the interference event identifiers accumulated in historical cycles and the corresponding communication scheduling results.

[0115] Since this part pertains to model optimization and does not involve key core functions, only a brief introduction to this optimization section will be provided:

[0116] A lightweight machine learning model training module can be introduced into the server. This module does not participate in real-time scheduling decisions, but runs offline during system idle periods (such as at night) to learn from historical data and optimize the quality of future scheduling decisions.

[0117] Model training relies on two types of historical data logs that accumulate naturally during system operation:

[0118] One is feature data: a set of historical periodic interference event identifiers, which includes the spatiotemporal information (coordinates, time) and intensity information (VSWR value, risk level) of each event.

[0119] Secondly, there is tag data: the communication scheduling results corresponding to each scheduling decision, specifically the performance indicators such as the data packet delivery rate, average latency, and retransmission count of relevant sites or areas in a subsequent period under a specific scheduling strategy.

[0120] During training, historical data from the past period is extracted from the database, and the characteristics of interference events are aligned and correlated with the network performance indicators after scheduling to form a training sample set, which is then used to train the model.

[0121] After training and validation, the new model is used to update the scheduling policy decision engine in the coordination server. When a similar interference distribution map is generated next time, the decision engine will refer to the model's predictions and select a policy from the candidate policies that is expected to have better network performance to execute, thereby gradually replacing the initially set fixed rules.

[0122] Through continuous historical learning, the system can predict the consequences of different scheduling actions with increasing accuracy, thereby making better decisions and enabling the reliability of data transmission in the entire plateau monitoring network to be continuously and autonomously improved when dealing with recurring and patterned interference scenarios.

[0123] In summary, this approach, which initially screens monitoring stations across the vast plateau using data from the snowmelt season, breaks through conventional communication methods by identifying potential interference areas from meteorological and geological / hydrological perspectives. The solution then introduces soil electrical conductivity as a key mediating indicator. A sustained and significant increase in soil electrical conductivity provides direct and quantifiable evidence that snowmelt has penetrated the equipment foundation and altered its electrical environment. By converting this geophysical change into electrical parameters, a logical leap from macro-environmental early warning to micro-site risk assessment is achieved. The communication equipment's own antenna system is then used as a diagnostic probe. Once an anomaly is confirmed, an interference event identifier is generated and embedded with the station's BeiDou positioning coordinates. Next, the geographically tagged interference event identifiers scattered across various stations are aggregated, and spatial information fusion technology is used to generate a dynamic spatial distribution map of the interference, making the invisible interference field visible and resolvable in digital space. Finally, based on the spatial distribution map, the entire plateau monitoring network is directed to systematically arrange communication paths and resource allocation between stations, minimizing interference in affected areas and ensuring reliable data transmission.

[0124] Example 2

[0125] This embodiment provides a data reliability transmission system for a plateau monitoring network, used to implement the data reliability transmission method for a plateau monitoring network disclosed in Embodiment 1. This system can be loaded into the plateau monitoring network. The data reliability transmission system for the plateau monitoring network includes:

[0126] Multiple monitoring stations, each including at least a sensor for collecting soil conductivity data, a BeiDou positioning module for providing positioning coordinates, and a LoRa communication unit containing controllable signal injection and voltage standing wave ratio measurement circuitry.

[0127] The coordination server, connected to all monitoring stations via a network, is used to execute the steps of determining target monitoring stations, generating interference spatial distribution maps, and dynamically scheduling communication paths and resources in the data reliability transmission method for the plateau monitoring network.

[0128] In this embodiment, the controllable signal injection and voltage standing wave ratio measurement circuit within the LoRa communication unit includes an RF switch, a directional coupler, and a detector; the coordination server is integrated into the LoRaWAN gateway or cloud server of the plateau monitoring network.

[0129] In this embodiment, the radio frequency switch is used to switch the antenna feed port between the "normal communication path" and the "self-test path" during diagnostics.

[0130] A directional coupler is used to provide a conduction signal after connecting an RF switch. The forward coupling port is used to extract a portion of the incident test signal power (for calculation). The reverse coupling port is used to extract the reflected signal power (for calculation). ).

[0131] A detector is a device that converts radio frequency power into DC voltage (such as a diode detector). It is used to convert the forward and reverse radio frequency power values ​​acquired by the directional coupler into analog voltage signals that can be read by the local controller's ADC, thereby completing the process. and Measurement.

[0132] If the coordination server is integrated into the LoRaWAN gateway, in this architecture, the coordination server's software module runs directly on the LoRaWAN gateway in the network. This gateway is responsible for receiving uplink data (including interference event identifiers) from all terminals, running map generation and scheduling algorithms, and sending scheduling commands directly to the terminals via the downlink. This deployment has low latency and fast response, making it suitable for regional networks with high real-time requirements.

[0133] If the coordination server is integrated into the cloud server, in this architecture, the LoRaWAN gateway acts only as a transparent relay, sending all data to the remote cloud server via the backhaul network (such as 4G / satellite). The coordination server operates as a cloud service, completing all calculations and then issuing scheduling instructions to the gateway, which in turn forwards them to the terminals. This deployment offers powerful computing resources, is easy to centrally manage and maintain, and is suitable for large-scale, cross-regional networks.

[0134] Both of these deployment methods are reasonable and conventional system architecture choices for implementing the method of the present invention, and their specific implementation does not involve any undisclosed difficulties.

[0135] Since this system uses the data reliability transmission method for plateau monitoring networks in Example 1, it has the same effect, and will not be described again here.

[0136] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0137] In conclusion, 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 method for reliable data transmission in a plateau monitoring network, the plateau monitoring network comprising multiple monitoring stations communicating via a LoRa wireless network and equipped with BeiDou positioning modules, characterized in that... The method includes: By using monitoring data during the snow and ice melt period, at least one target monitoring station in the plateau monitoring network located within the area affected by surface snow and ice melt can be identified. The monitoring data during the snow and ice melt period is used to identify the state and extent of surface snow and ice melt. Soil conductivity data of target monitoring stations are acquired, and the soil conductivity data is used to determine whether the corresponding monitoring station has entered a high interference risk state. The soil conductivity data characterizes the electrical conductivity of the soil near the grounding electrode of the corresponding monitoring station. For each target monitoring station, a low conductivity baseline value is determined based on a soil conductivity sequence collected continuously for at least one day during the period of complete snow and ice cover or freezing drought. The most recent five consecutive soil conductivity readings of the corresponding monitoring station are also acquired. When the average value of the most recent five consecutive soil conductivity readings is greater than the product of the low conductivity baseline value and a risk coefficient greater than 1, and the most recent five consecutive soil conductivity readings show a stable or upward trend, the corresponding monitoring station is determined to have entered the high interference risk state. For monitoring stations entering a high-interference-risk state, a test signal is injected and the antenna voltage standing wave ratio (VSWR) is measured before the next predetermined LoRa data reporting time slot. After the measurement is completed, communication is restored, allowing the monitoring station to perform data reporting as planned. When the antenna VSWR exceeds a preset threshold, an interference event identifier containing the BeiDou positioning coordinates of the monitoring station is generated. Within a preset time window before the next predetermined LoRa data reporting time slot, the radio frequency circuit of the corresponding monitoring station is controlled to switch to self-test mode, injecting a test signal into the LoRa antenna feed port and simultaneously acquiring the forward incident signal power and the reverse reflected signal power. The reflection ratio parameter is determined based on the forward incident signal power and the reverse reflected signal power, and the reflection ratio parameter is converted into a real-time antenna VSWR according to a pre-stored mapping relationship. The mapping relationship is established based on a standard load and a standard antenna prototype during the equipment calibration phase. The preset threshold is determined based on the antenna VSWR baseline distribution obtained by the corresponding monitoring station performing multiple self-tests during an interference-free period and is re-determined after antenna replacement or feeder maintenance. Based on the BeiDou positioning coordinates of multiple interference events in the plateau monitoring network, a visualized spatial distribution map of the interference is generated. The communication paths and resources of the plateau monitoring network are dynamically scheduled based on the spatial distribution map of interference to avoid interference.

2. The data reliability transmission method for a plateau monitoring network according to claim 1, characterized in that, Methods for determining target monitoring stations using monitoring data from the snow and ice melt period include: By accessing satellite remote sensing data on land temperature, snow cover, or snow and ice melting models, all monitoring stations in the plateau monitoring network located in areas where surface snow and ice are melting or have recently melted are identified and marked as target monitoring stations.

3. The data reliability transmission method for a plateau monitoring network according to claim 1, characterized in that, The method further includes: Based on the different preset ranges of the measured real-time antenna voltage standing wave ratio, different interference risk levels are assigned to the corresponding monitoring stations; among them, the interference event identifier contains the corresponding interference risk level information.

4. The data reliability transmission method for a plateau monitoring network according to claim 3, characterized in that, Methods for generating spatial distribution maps of interference include: The BeiDou positioning coordinates in all received interference event identifiers are mapped to a digital geographic base map, and differentiated rendering or marking is performed based on the interference risk level information contained in each identifier, generating an interference spatial distribution map that integrates the spatial location and risk level of the interference.

5. The data reliability transmission method for a plateau monitoring network according to claim 4, characterized in that, Methods for dynamically scheduling communication paths and resources include: Based on the interference risk level of different geographical areas in the interference spatial distribution map, implement differentiated scheduling strategies; For high-risk areas, implement at least one of the following strategies: channel switching, communication time slot delay, or designation of a dedicated relay path. For areas with low to medium risk levels, implement at least one strategy, either reducing transmission power or adopting a more robust modulation and coding scheme.

6. The data reliability transmission method for a plateau monitoring network according to claim 1, characterized in that, The method further includes: Based on the historical accumulation of interference event identifiers and corresponding communication scheduling results, a strategy for dynamically scheduling communication paths and resources is optimized through machine learning model training.

7. A reliable data transmission system for a plateau monitoring network, characterized in that, The system is used to perform the data reliability transmission method for a plateau monitoring network according to any one of claims 1 to 6, the system comprising: Multiple monitoring stations, each monitoring station includes at least a sensor for collecting soil conductivity data, a BeiDou positioning module for providing positioning coordinates, and a LoRa communication unit containing controllable signal injection and voltage standing wave ratio measurement circuitry; The coordination server, connected to all monitoring stations via a network, is used to perform steps such as identifying target monitoring stations, generating spatial distribution maps of interference, and dynamically scheduling communication paths and resources.

8. The data reliability transmission system for a plateau monitoring network according to claim 7, characterized in that, The LoRa communication unit includes a controllable signal injection and voltage standing wave ratio measurement circuit, comprising an RF switch, a directional coupler, and a detector; the coordination server is integrated into the LoRaWAN gateway or cloud server of the plateau monitoring network.

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