Water conservancy water regimen monitoring management system based on Beidou short message communication

Abstract

The invention discloses a water conservancy water regimen monitoring management system based on Beidou short message communication, which relates to the technical field of water regimen monitoring and comprises a scheduling module, a data compression module, a data preprocessing module and an early warning module. The scheduling module determines a transmission queue by marking a water level state, calculating various scores and correction parameters, and implements a preemption mechanism and setting optimal transmission in a silent period; the data compression module classifies and processes data, adjusts the compression rate according to channel signals, and adopts forward error correction coding and the like to guarantee the data accuracy; the data preprocessing module filters the original data, detects abnormity by using space-time correlation analysis, and fills missing values; the system effectively realizes water conservancy water regimen monitoring management, has significant advantages in the aspects of data transmission, processing, early warning and the like, improves the accuracy and timeliness of water regimen monitoring, and provides powerful support for water conservancy management.

Description

Technical Field

[0001] The present invention relates to the technical field of water regime monitoring, and in particular to a water conservancy and water regime monitoring and management system based on Beidou short message communication. Background Art

[0002] Water conservancy and water regime monitoring is of great significance for flood control and drought relief, water resources management, and water conservancy project construction and management. Traditional water conservancy and water regime monitoring systems mainly rely on wired communication or short-range wireless communication technologies, such as fiber optic communication, GPRS, etc. However, these communication methods have many limitations in remote areas or complex terrain environments. Wired communication requires laying a large number of cables, which is costly and easily damaged by natural disasters; wireless communications such as GPRS cannot be used in areas where signals are not covered, and when extreme weather or natural disasters occur, the base station may be damaged, resulting in communication interruption. In addition, traditional monitoring systems also have shortcomings in the real-time, reliability, and bandwidth utilization of data transmission, making it difficult to meet the needs of quickly and accurately obtaining water information.

[0003] The existing water conservancy and water regime monitoring system also has some problems in data processing and early warning. On the one hand, the monitoring data is often interfered by noise, the data quality is uneven, and it is easy to be lost or erroneous during the data transmission process. Traditional data processing methods are difficult to effectively remove noise and fill in missing data, resulting in inaccurate monitoring results. On the other hand, the early warning mechanism of the existing system is mostly based on fixed thresholds, which cannot be dynamically adjusted according to real-time environmental changes and historical data, and the accuracy and timeliness of the early warning are insufficient. In addition, when facing sudden water conditions, the transmission and push methods of early warning information are relatively single, and it is impossible to ensure that the information is conveyed to relevant personnel in a timely and accurate manner, affecting the efficiency of emergency response.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] The purpose of the present invention is to solve the problems that the current water conservancy and water regime monitoring system based on Beidou short message communication still needs to be further improved in terms of data transmission optimization, compression and error correction, anomaly detection and early warning, and proposes a water conservancy and water regime monitoring and management system based on Beidou short message communication.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A water conservancy and water regime monitoring and management system based on Beidou short message communication, comprising: The scheduling module is used to dynamically calculate the comprehensive score of nodes, optimize the transmission queue and support preemption and continuation of transmission to ensure the real-time transmission of high-priority data; Data compression module, used for differential compression according to data type, dynamically adjusting compression ratio and fault tolerance redundancy based on channel quality, improving bandwidth utilization and reliability; Data preprocessing module, used for filtering and denoising, spatiotemporal correlation anomaly detection and missing value interpolation, to ensure data quality and consistency; The early warning module is used to integrate multi-source data to predict trends, dynamically adjust thresholds and trigger early warnings in a graded manner, and accurately push Beidou short messages.

[0007] Furthermore, the execution process of the scheduling module is as follows: When the monitoring node generates a data packet, it marks the current water level status, including normal, warning, and over-limit, and calculates the data urgency score. The over-limit status score is 10, the warning score is 5, and the normal score is 1; The node calculates its own historical transmission frequency score. If the number of transmissions in the past hour is > 3 times, the score is 0.5; otherwise, it is 1. Introduce correction parameters to optimize the rationality of transmission queue; The silent period is set from 0:00 to 6:00 every day, and only over-limit nodes are allowed to transmit, reducing channel occupancy.

[0008] Furthermore, the specific operation steps of introducing the modified parameters to optimize the transmission queue in the scheduling module are as follows: The correction parameters include: Node remaining power E: The monitoring node reports the remaining power percentage in real time; Geographical risk level coefficient G: Predefined node geographical risk level: In high-risk areas, G=1.5; in medium-risk areas, G=1.2; in low-risk areas, G=1.0; Data timeliness attenuation factor T: Calculate the difference between the data generation time and the current time, and define the attenuation factor formula: , is the difference between the data generation time and the current time. If >50 minutes, T=0.5; otherwise, linear decay; Comprehensive score = urgency score × frequency score × E × G × T. The comprehensive scores are sorted from high to low to generate a transmission queue; The Beidou dispatch center allocates transmission time slots to the first N nodes in the queue, where N = channel capacity / single packet length, and the remaining nodes enter the waiting pool; If the water level of a node is upgraded, the preemption mechanism is triggered immediately to interrupt the transmission of low-priority nodes; specifically: When preemption is triggered, it detects whether the data packet currently transmitted by the interrupted node has been sent more than 50%. If it has been completed, it is allowed to complete the current packet transmission. If it is less than 50%, the transmission is immediately terminated and the breakpoint position is recorded; Add a breakpoint identifier for the interrupted node and store it at the head of the waiting pool queue. In the next scheduling cycle, give priority to allocating time slots to the interrupted node and continue to transmit the remaining data from the breakpoint position.

[0009] Furthermore, the specific operation steps for adjusting the node water level status according to the dynamic threshold are as follows: When the node status is upgraded, an additional check is performed to see whether the water level change rate exceeds the threshold for 3 minutes. If so, it is considered as a valid preemption. If not, the preemption is not triggered and the node is marked as a pending observation state. Real-time monitoring of the current channel occupancy rate. When the load is high, only the upgrade from over-limit to critical state is allowed to trigger preemption; when the load is low, the upgrade from normal to alert state will trigger preemption; When the status of a node is upgraded, the dispatch center sends a verification request to the three adjacent nodes. If at least two adjacent nodes report similar water level change trends, the preemption is confirmed to be effective. Before triggering the preemption, the node needs to upload the original water level data of the last 10 minutes, and the dispatch center verifies the authenticity of the data through anomaly detection algorithms.

[0010] Furthermore, the specific operation steps of the data compression module are as follows: Data classification: classify sensor data into water level, flow rate, and rainfall; Select a compression algorithm: Water level data: Differential encoding is used to store only the difference between the current value and the previous moment; Flow velocity data: Apply wavelet transform compression to retain frequency components; Rainfall data: Huffman coding is used to perform entropy compression on the accumulated values; Dynamically adjust the compression rate: monitor the Beidou channel signal strength RSSI. If RSSI <-100dBm, start the aggressive compression mode, that is, the compression rate ≥ 70%; if RSSI ≥-90dBm, enable the lossless compression mode, that is, the compression rate ≤ 30%; Encapsulate the compressed data packet, add the data type identifier and decompression key; and add a fault tolerance mechanism before encapsulating the compressed packet.

[0011] Furthermore, the specific operation steps of the data compression module to add a fault tolerance mechanism before encapsulating the compressed package are as follows: Forward error correction coding (FEC) integration: Reed-Solomon code is used, and the error correction capability is set to recover 5% of lost or erroneous bytes in the data packet; the original data is divided into blocks of fixed length before encapsulating and compressing the data packet; RS error correction code is added to each block of data, with a redundancy ratio of 10%; the Beidou channel bit error rate (BER) is monitored. If BER>1e-3, the redundancy ratio is increased to 15%; if BER≤1e-4, the redundancy ratio is reduced to 5%; Block checksum and marking: Divide the compressed data packet into 512-byte blocks, calculate the CRC-32 checksum value for each block of data, and append it to the end of the block; add a unique identifier for each block in the format of packet number + block number; data block structure: [Block ID (2B) | Data (512B) | CRC-32 (4B) | RS redundancy (in proportion)]; Block ID: data block unique identifier in the format of packet number + block number; data is the compressed data block; CRC-32 is a 32-bit cyclic redundancy check code used to verify data integrity; RS redundant Reed-Solomon error correction code redundant data, the proportion of which is dynamically adjusted according to the bit error rate;

[0012] Retransmission strategy: The receiving end decapsulates each block one by one and verifies the CRC-32 check code first. If the check passes, the data is extracted and a confirmation signal is fed back. If the check fails, the failed block ID is recorded and a negative signal is fed back. After receiving the negative signal, the dispatch center only retransmits the specific failed block instead of the entire data packet. The retransmitted data is marked with a priority tag to ensure that it is sent first in the next transmission cycle. The maximum number of retransmissions for a single block is 2. If it still fails, the error correction code recovery mechanism is started and the RS code is used to repair the data.

[0013] Furthermore, the specific operation steps of the data preprocessing module are as follows: Perform sliding window mean filtering on the raw sensor data; Detect abnormal values: If the water level change rate at a certain moment is >10cm / min, it is marked as suspicious data and redundant sensor verification is started; Fill in missing data: Use the ARIMA model to predict the missing period values ​​and cross-validate with adjacent node data;

[0014] Introduce spatiotemporal correlation analysis in anomaly detection and missing value filling.

[0015] Furthermore, the specific operation steps of the data preprocessing module for introducing spatiotemporal correlation analysis in anomaly detection and missing value filling are as follows: Modeling of spatiotemporal correlation features: Generate an adjacency matrix and define distance thresholds based on the geographical locations of monitoring nodes; maintain a dynamic neighbor list for each node and update it in real time; Calculate the water level change rate of the current node and its change trend in the past hour, synchronously obtain the water level data of adjacent nodes, and calculate the spatial gradient; Multi-condition fusion judgment of anomaly detection: If the water level change rate of a single node is >10cm / min, but the spatial gradient of the adjacent nodes is <2cm, it is judged as a local anomaly and triggers redundancy check; if the water level change rate of the current node and more than 50% of the adjacent nodes is >8cm / min, it is judged as a regional abnormal event, directly marked as a valid anomaly and an early warning is initiated; Define the scoring calculation logic: ,in is the spatiotemporal confidence score, which is used to determine whether it is a real anomaly. The time change rate is the water level change rate of the current node. The threshold is the preset anomaly threshold. The spatial gradient is the average water level difference between the current node and the adjacent nodes. The baseline value is the reference value of the spatial gradient under normal conditions. , When a signal is judged as abnormal, the threshold is dynamically relaxed or tightened according to the score; Missing data filling of spatiotemporal coordination: When data of a node is missing, the optimal interpolation weight is calculated based on the spatiotemporal covariance model, combining historical data with real-time data of adjacent nodes; interpolation calculation logic: ,in is the spatial weight, is the time decay factor, is the interpolated water level of the current node at time t, is the water level value of the i-th adjacent node at time t; Training the GNN model: using node location, historical water level, and adjacent node data as input, predict the missing value at the current moment; online reasoning: if the missing duration is >30 minutes, call GNN to generate the predicted value, and weightedly fuse it with the ARIMA result, with the weight dynamically adjusted according to the prediction error.

[0016] Furthermore, the specific operation steps of the early warning module are as follows: Receive the decompressed data and input it into the LSTM neural network to predict the water level trend in the next 2 hours; If the predicted water level exceeds the threshold, generate warning information and calculate the impact range; The thresholds are dynamically adjusted based on real-time environmental data and historical patterns; When the warning is triggered, it is triggered through progressive measurement, and the process is as follows: Warning classification and triggering conditions: Observation level, i.e. blue: triggered when the predicted water level reaches 80% of the dynamic threshold; village inspectors are notified to strengthen monitoring. Beidou short message content: BDS-GEO: longitude, latitude, BLUE; monitoring suggestions; Preparation level, i.e. yellow: triggered when the predicted water level reaches 95% of the dynamic threshold or the water level at the upstream node exceeds the threshold; township emergency material preparation is initiated, message content: BDS-GEO: longitude, latitude, YELLOW; material inspection; Action level, i.e. red: triggered when the predicted water level exceeds the dynamic threshold or the river water storage capacity is <10%; the city-level command center is linked to evacuate personnel. Message content: BDS-GEO: longitude, latitude, RED; evacuation instructions; village list; Automatic upgrade and downgrade mechanism: If the water level is predicted to rise continuously for three consecutive times, the warning level will be automatically upgraded; if the water level falls below the threshold and remains stable for 30 minutes, the warning will be automatically downgraded or lifted; Beidou short message intelligent compression and multi-channel distribution: using key-value abbreviations and predefined codes: longitude and latitude: LAT=31.23,LON=121.47→L=3123,12147; village list: pre-assigned numbers, message content is VL=01,02; command type: evacuation command→CMD=EVAC; Multi-channel redundant transmission: When a red warning is issued, the warning is sent simultaneously through Beidou short message, backup radio frequency band, and satellite phone; Receiving end confirmation mechanism: The responsible person must reply with a confirmation code within 5 minutes, otherwise it will trigger automatic retransmission.

[0017] Furthermore, the process of dynamically adjusting the threshold in the early warning module according to the real-time environmental data and the historical pattern is as follows: Dynamic threshold generation model: multi-factor weight calculation, including: real-time rainfall intensity: receiving the current regional rainfall through Beidou short message, with a weight of 30%; upstream water level change rate: obtaining upstream node water level data and calculating the average change rate, with a weight of 25%; historical water level peak: retrieving the maximum water level in the same period of the past five years, with a weight of 20%; river water storage capacity: calculating the remaining water storage space in the current river based on DEM data, with a weight of 25%; Dynamic threshold calculation logic: ,in is the basic threshold, , For real-time rainfall, The highest rainfall in history. is the water level change rate of the upstream node, is the critical rate of change.

[0018] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention, through the optimized design of the scheduling module, realizes the dynamic comprehensive scoring of the monitoring nodes and the reasonable optimization of the transmission queue, and supports the preemptive retransmission mechanism. This mechanism can ensure the real-time transmission of high-priority data, especially in emergency situations, such as when the water level exceeds the limit, it can give priority to the allocation of transmission resources to ensure the rapid transmission of critical data. At the same time, the data compression module adopts differentiated compression algorithms according to different data types, and dynamically adjusts the compression rate and fault-tolerant redundancy in combination with the channel quality, which effectively improves the bandwidth utilization and data transmission reliability. Through forward error correction coding and retransmission strategies, the anti-interference ability and integrity of data in complex communication environments are further enhanced, reducing the risk of data loss and errors; (2) In the present invention, the data preprocessing module significantly improves the quality and consistency of monitoring data through technical means such as filtering denoising, spatiotemporal correlation anomaly detection, and missing value interpolation. Sliding window mean filtering can effectively remove noise interference in sensor data, while spatiotemporal correlation analysis can combine data from multiple nodes for anomaly detection to avoid misjudgment due to single point failures or local anomalies. In addition, the use of the ARIMA model and graph neural network (GNN) to collaboratively fill in missing data can more accurately predict and fill in missing values, further ensuring the integrity and accuracy of the data. The application of these technologies enables the monitoring system to provide higher quality data support, providing a reliable basis for subsequent water situation analysis and decision-making; (3) In the present invention, the early warning module integrates multi-source data, predicts water level trends through LSTM neural networks, and dynamically adjusts the early warning threshold according to real-time environmental data and historical patterns, thereby achieving accurate graded early warning. This dynamic adjustment mechanism can timely adjust the early warning level according to actual water conditions, avoiding false alarms or missed alarms caused by fixed thresholds. At the same time, the early warning information is accurately pushed through Beidou short messages, and intelligent compression and multi-channel distribution technology are used to ensure that the early warning information can be accurately delivered to relevant personnel at the first time. In addition, the automatic upgrade and downgrade mechanism and the receiving end confirmation mechanism further enhance the intelligence level and emergency response capabilities of the early warning system, effectively ensuring the timeliness and accuracy of water conservancy and water conditions monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0023] like Figure 1 As shown, a water conservancy and water regime monitoring and management system based on Beidou short message communication includes a scheduling module, a data compression module, a data preprocessing module and an early warning module; The scheduling module dynamically calculates the comprehensive score of nodes (urgency, power consumption, geographical risk, etc.), optimizes the transmission queue and supports preemption and resumption of transmission to ensure real-time transmission of high-priority data; When a monitoring node generates a data packet, it marks the current water level status (normal / warning / overlimit) and calculates the data urgency score (overlimit status score = 10, warning = 5, normal = 1); the node calculates its own historical transmission frequency score (if the number of transmissions in the past hour is > 3 times, the score = 0.5; otherwise = 1); Introduce correction parameters to optimize the rationality of the transmission queue. The correction parameters include: Node remaining power E: The remaining power percentage reported by the monitoring node in real time (e.g. 80% is recorded as 0.8); Geographical risk level coefficient G: Predefined node geographical risk level: high-risk area (G=1.5) medium-risk area (G=1.2) low-risk area (G=1.0); Data timeliness attenuation factor T: Calculate the difference between the data generation time and the current time, and define the attenuation factor formula: , is the difference between the data generation time and the current time. If >50 minutes, T=0.5; otherwise, linear decay; Comprehensive score = urgency score × frequency score × E × G × T. The comprehensive scores are sorted from high to low to generate a transmission queue. The Beidou dispatch center allocates transmission time slots to the first N nodes in the queue (N = channel capacity / single packet length), and the remaining nodes enter the waiting pool. If the water level status of a node is upgraded, the preemption mechanism is immediately triggered to interrupt the transmission of low-priority nodes. Specifically: When preemption is triggered, check whether the data packet currently transmitted by the interrupted node has been sent more than 50%. If more than 50% has been completed, it is allowed to complete the current packet transmission (the remaining part continues after preemption). If less than 50%, the transmission is terminated immediately and the breakpoint position is recorded; Add a breakpoint identifier (such as "Offset=256B") to the interrupted node and store it in the head of the waiting pool queue. In the next scheduling cycle, give priority to allocating time slots to the interrupted node and continue to transmit the remaining data from the breakpoint position. The node water level status is adjusted according to the dynamic threshold. The specific process is as follows: When the node status is upgraded, it will additionally detect whether the water level change rate exceeds the threshold (such as ≥5cm / min) for 3 minutes. If it meets the threshold, it will be determined as effective preemption; if it does not meet the threshold, preemption will not be triggered for the time being and it will be marked as a pending observation state; the current channel occupancy rate will be monitored in real time (such as more than 80% for high load). When the load is high, only the upgrade from over-limit to critical state will trigger preemption; when the load is low, normal → warning can trigger preemption;

[0024] When the status of a node is upgraded, the dispatch center sends a verification request to the three adjacent nodes. If at least two adjacent nodes report similar water level change trends, the preemption is confirmed to be effective. Before triggering the preemption, the node needs to upload the original water level data (unfiltered) of the last 10 minutes, and the dispatch center verifies the authenticity of the data through anomaly detection algorithms (such as isolation forests).

[0025] The “silent period” is set from 0:00 to 6:00 every day, and only over-limit nodes are allowed to transmit, reducing channel occupancy.

[0026] The data compression module performs differential compression (differential / wavelet / Huffman coding) according to data type, dynamically adjusts the compression rate and fault-tolerant redundancy based on channel quality, and improves bandwidth utilization and reliability; Data classification: divide sensor data into water level (low-frequency slow change), flow velocity (high-frequency fluctuation), and rainfall (sudden increment) by type; select compression algorithm: water level data: use differential coding (only store the difference between the current value and the previous moment); flow velocity data: apply wavelet transform compression to retain the main frequency components; rainfall data: use Huffman coding to perform entropy compression on the accumulated value; Dynamically adjust the compression rate: monitor the Beidou channel signal strength (RSSI). If RSSI <-100dBm, start the aggressive compression mode (compression rate ≥ 70%); if RSSI ≥-90dBm, enable the lossless compression mode (compression rate ≤ 30%). Encapsulate the compressed data packet, add the data type identifier and decompression key; add a fault tolerance mechanism before encapsulating the compressed packet. The specific process is as follows: Forward error correction coding (FEC) integration: Reed-Solomon (RS) code is used, and the error correction capability is set to recover 5% of lost or erroneous bytes in the data packet; before encapsulating and compressing the data packet, the original data is divided into blocks of fixed length (such as 240 bytes per block); RS error correction code is added to each block of data, with a redundancy ratio of 10% (that is, 24 bytes of error correction code are added to every 240 bytes of data, and the total block length is 264 bytes); the Beidou channel bit error rate (BER) is monitored. If BER>1e-3 (high error environment), the redundancy ratio is increased to 15%; if BER≤1e-4 (low error environment), the redundancy ratio is reduced to 5%; Block checksum and marking: Divide the compressed data packet into blocks of 512 bytes (for example, if the compressed data is 1024 bytes, it is divided into 2 blocks), calculate the CRC-32 checksum value (4 bytes) for each block of data, and append it to the end of the block; add a unique identifier (Block ID, 2 bytes) to each block, the format is "packet number + block number" (such as 0x0001 represents the first block of the first packet); data block structure: [Block ID (2B) | Data (512B) | CRC-32 (4B) | RS redundancy (proportional)]; Block ID: data block unique identifier (2 bytes), the format is "packet number + block number"; data: compressed data block (512 bytes); CRC-32: 32-bit cyclic redundancy check code (4 bytes), used to verify data integrity; RS redundancy: redundant data of Reed-Solomon error correction code, the proportion is dynamically adjusted according to the bit error rate; Retransmission strategy: The receiving end decapsulates each block and verifies the CRC-32 checksum first. If the check passes, the data is extracted and an acknowledgment signal (ACK) is fed back. If the check fails, the failed block ID is recorded and a negative acknowledgement signal (NACK) is fed back. After receiving the NACK, the dispatch center only retransmits the specific failed block (such as Block ID = 0x0003) instead of the entire data packet. The retransmitted data is marked with a priority tag to ensure that it is sent first in the next transmission cycle. The maximum number of retransmissions for a single block is 2. If it still fails, the error correction code recovery mechanism is started and the RS code is used to repair the data.

[0027] The data preprocessing module is used for filtering and denoising, spatiotemporal correlation anomaly detection, and missing value interpolation (ARIMA / GNN collaboration) to ensure data quality and consistency; Perform sliding window mean filtering on the raw sensor data (window size = 5 minutes); detect outliers: if the water level change rate at a certain moment is >10cm / min, mark it as suspicious data and start redundant sensor verification; fill missing data: use the ARIMA model to predict the value of the missing period and cross-validate with the adjacent node data. Introduce spatiotemporal correlation analysis in anomaly detection and missing value filling. The specific process is as follows: Modeling of spatiotemporal correlation features: Generate an adjacency matrix based on the geographical location (latitude and longitude) of the monitoring node, define the distance threshold (such as nodes within 5 kilometers are adjacent nodes); maintain a dynamic neighbor list for each node and update it in real time (such as removing nodes when they fail due to floods); calculate the water level change rate of the current node (such as ΔH / Δt) and its change trend in the past hour (first-order derivative), synchronously obtain the water level data of adjacent nodes, and calculate the spatial gradient (such as the average water level difference between the current node and the neighboring nodes); Multi-condition fusion judgment of anomaly detection: If the water level change rate of a single node is >10cm / min, but the spatial gradient of the adjacent nodes is <2cm (i.e., high spatial consistency), it is judged as a local anomaly (possibly a sensor failure) and triggers redundancy check; if the water level change rate of the current node and more than 50% of the adjacent nodes is >8cm / min, it is judged as a regional abnormal event (such as a flood peak caused by heavy rain), directly marked as a valid anomaly and an early warning is initiated; Define the scoring calculation logic: ,in is the spatiotemporal confidence score, which is used to determine whether it is a real anomaly. The time change rate is the water level change rate of the current node (unit: cm / minute), the threshold is the preset anomaly threshold, the spatial gradient is the average water level difference between the current node and the adjacent nodes, and the baseline value is the reference value of the spatial gradient under normal conditions. , When it is judged as abnormal, the threshold is dynamically relaxed or tightened according to the score (such as When the change rate threshold is adjusted down to 8cm / min).

[0028] Missing data filling of spatiotemporal coordination: When data of a node is missing, the optimal interpolation weight is calculated based on the spatiotemporal covariance model, combining historical data with real-time data of adjacent nodes; interpolation calculation logic: ,in is the spatial weight, is the time decay factor, is the interpolated water level of the current node at time t, is the water level value of the i-th adjacent node at time t; train the GNN model: use the node position, historical water level, and adjacent node data as input to predict the missing value at the current moment; online reasoning: if the missing time is >30 minutes, call GNN to generate the predicted value and weightedly fuse it with the ARIMA result (the weight is dynamically adjusted according to the prediction error).

[0029] The early warning module integrates multi-source data (water level / rainfall / topography) to predict trends, dynamically adjusts thresholds and triggers early warnings in different levels (blue / yellow / red), and accurately pushes Beidou short messages; Receive the decompressed data and input it into the LSTM neural network to predict the water level trend in the next 2 hours. If the predicted water level exceeds the threshold, generate warning information and calculate the impact range (villages within 10 kilometers downstream). The threshold is dynamically adjusted according to real-time environmental data and historical patterns. The process is as follows: Dynamic threshold generation model: multi-factor weight calculation, including: real-time rainfall intensity: receiving the current regional rainfall (unit: mm / h) through Beidou short message, with a weight of 30%; upstream water level change rate: obtaining upstream node water level data and calculating the average change rate (ΔH / Δt), with a weight of 25%; historical water level peak: retrieving the maximum water level in the same period of the past five years, with a weight of 20%; river water storage capacity: calculating the remaining water storage space in the current river based on DEM data, with a weight of 25%; Dynamic threshold calculation logic: ,in is the basic threshold (set by the water conservancy department), , For real-time rainfall, The highest rainfall in history. is the water level change rate of the upstream node, is the critical rate of change.

[0030] When the warning is triggered, it is triggered through progressive measurement, and the process is as follows: Warning levels and triggering conditions: Observation level (blue): triggered when the predicted water level reaches 80% of the dynamic threshold; notify village inspectors to strengthen monitoring, Beidou short message content: BDS-GEO: longitude, latitude, BLUE; monitoring recommendations; Preparation level (yellow): triggered when the predicted water level reaches 95% of the dynamic threshold or the upstream node water level exceeds the threshold; start township emergency material preparation, message content: BDS-GEO: longitude, latitude, YELLOW; material inspection; Action level (red): triggered when the predicted water level exceeds the dynamic threshold or the river water storage capacity is <10%; link the municipal command center to evacuate personnel, message content: BDS-GEO: longitude, latitude, RED; evacuation instructions; village list.

[0031] Automatic upgrade and downgrade mechanism: If the water level is predicted to rise continuously for three consecutive times, the warning level will be automatically upgraded (such as yellow → red); if the water level drops below the threshold and stabilizes for 30 minutes, the warning will be automatically downgraded or lifted; Beidou short message intelligent compression and multi-channel distribution: key-value abbreviations and predefined codes are used: longitude and latitude: LAT=31.23,LON=121.47 → L=3123,12147; village list: pre-assigned number (such as village A=01, village B=02), message content is VL=01,02; command type: evacuation command → CMD=EVAC; final message example: BDS-GEO:L=3123,12147;CLS=RED;CMD=EVAC;VL=01,02 (length compressed by 40%); Multi-channel redundant transmission: When a red warning is issued, the warning is sent simultaneously through Beidou short message, backup radio frequency band, and satellite phone; Receiving end confirmation mechanism: The responsible person must reply with a confirmation code (such as ACK=RED123) within 5 minutes, otherwise it will trigger automatic retransmission.

[0032] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A water conservancy and water regime monitoring and management system based on Beidou short message communication, characterized in that: include: The scheduling module is used to dynamically calculate the comprehensive score of nodes, optimize the transmission queue and support preemption and continuation of transmission to ensure the real-time transmission of high-priority data; Data compression module, used for differential compression according to data type, dynamically adjusting compression ratio and fault tolerance redundancy based on channel quality, improving bandwidth utilization and reliability; Data preprocessing module, used for filtering and denoising, spatiotemporal correlation anomaly detection and missing value interpolation, to ensure data quality and consistency; The early warning module is used to integrate multi-source data to predict trends, dynamically adjust thresholds and trigger early warnings in a graded manner, and accurately push Beidou short messages.

2. According to claim 1, a water conservancy and water regime monitoring and management system based on Beidou short message communication is characterized in that: The execution process of the scheduling module is as follows: When the monitoring node generates a data packet, it marks the current water level status, including normal, warning, and over-limit, and calculates the data urgency score. The over-limit status score is 10, the warning score is 5, and the normal score is 1; The node calculates its own historical transmission frequency score. If the number of transmissions in the past hour is > 3 times, the score is 0.5; otherwise, it is 1. Introduce correction parameters to optimize the rationality of transmission queue; The silent period is set from 0:00 to 6:00 every day, and only over-limit nodes are allowed to transmit, reducing channel occupancy.

3. A water conservancy and water regime monitoring and management system based on Beidou short message communication according to claim 2, characterized in that: The specific operation steps of introducing the modified parameters to optimize the transmission queue in the scheduling module are as follows: The correction parameters include: Node remaining power E: The monitoring node reports the remaining power percentage in real time; Geographical risk level coefficient G: Predefined node geographical risk level: In high-risk areas, G=1.5; in medium-risk areas, G=1.2; in low-risk areas, G=1.0; Data timeliness attenuation factor T: Calculate the difference between the data generation time and the current time, and define the attenuation factor formula: , is the difference between the data generation time and the current time. If >50 minutes, T=0.5; otherwise, linear decay; Comprehensive score = urgency score × frequency score × E × G × T. The comprehensive scores are sorted from high to low to generate a transmission queue; The Beidou dispatch center allocates transmission time slots to the first N nodes in the queue, where N = channel capacity / single packet length, and the remaining nodes enter the waiting pool; If the water level of a node is upgraded, the preemption mechanism is triggered immediately to interrupt the transmission of low-priority nodes; specifically: When preemption is triggered, it detects whether the data packet currently transmitted by the interrupted node has been sent more than 50%. If it has been completed, it is allowed to complete the current packet transmission. If it is less than 50%, the transmission is immediately terminated and the breakpoint position is recorded; Add a breakpoint identifier for the interrupted node and store it at the head of the waiting pool queue. In the next scheduling cycle, give priority to allocating time slots to the interrupted node and continue to transmit the remaining data from the breakpoint position.

4. A water conservancy and water regime monitoring and management system based on Beidou short message communication according to claim 3, characterized in that: The specific steps for adjusting the node water level status according to the dynamic threshold are as follows: When the node status is upgraded, an additional check is performed to see whether the water level change rate exceeds the threshold for 3 minutes. If so, it is considered as a valid preemption. If not, the preemption is not triggered and the node is marked as a pending observation state. Real-time monitoring of the current channel occupancy rate. When the load is high, only the upgrade from over-limit to critical state is allowed to trigger preemption; when the load is low, the upgrade from normal to alert state will trigger preemption; When the status of a node is upgraded, the dispatch center sends a verification request to the three adjacent nodes. If at least two adjacent nodes report similar water level change trends, the preemption is confirmed to be effective. Before triggering the preemption, the node needs to upload the original water level data of the last 10 minutes, and the dispatch center verifies the authenticity of the data through anomaly detection algorithms.

5. The water conservancy and water regime monitoring and management system based on Beidou short message communication according to claim 1 is characterized in that: The specific operation steps of the data compression module are as follows: Data classification: classify sensor data into water level, flow rate, and rainfall; Select a compression algorithm: Water level data: Differential encoding is used to store only the difference between the current value and the previous moment; Flow velocity data: Apply wavelet transform compression to retain frequency components; Rainfall data: Huffman coding is used to perform entropy compression on the accumulated values; Dynamically adjust the compression rate: monitor the Beidou channel signal strength RSSI. If RSSI <-100dBm, start the aggressive compression mode, that is, the compression rate ≥ 70%; if RSSI ≥-90dBm, enable the lossless compression mode, that is, the compression rate ≤ 30%; Encapsulate the compressed data packet, add the data type identifier and decompression key; and add a fault tolerance mechanism before encapsulating the compressed packet.

6. A water conservancy and water regime monitoring and management system based on Beidou short message communication according to claim 5, characterized in that: The specific operation steps of the data compression module to add a fault tolerance mechanism before encapsulating the compressed package are as follows: Forward error correction coding (FEC) integration: Reed-Solomon code is used, and the error correction capability is set to recover 5% of lost or erroneous bytes in the data packet; the original data is divided into blocks of fixed length before encapsulating and compressing the data packet; RS error correction code is added to each block of data, with a redundancy ratio of 10%; the Beidou channel bit error rate (BER) is monitored. If BER>1e-3, the redundancy ratio is increased to 15%; if BER≤1e-4, the redundancy ratio is reduced to 5%; Block checksum and marking: Divide the compressed data packet into 512-byte blocks, calculate the CRC-32 checksum for each block of data, and append it to the end of the block; add a unique identifier for each block in the format of packet number + block number; data block structure: [BlockID (2B) | Data (512B) | CRC-32 (4B) | RS redundancy (proportional)]; Block ID: unique identifier of data block, the format is packet number + block number; data is the compressed data block; CRC-32 is a 32-bit cyclic redundancy check code used to verify data integrity; RS is the redundant data of the Reed-Solomon error correction code, and its proportion is dynamically adjusted according to the bit error rate; Retransmission strategy: The receiving end decapsulates each block one by one and verifies the CRC-32 check code first. If the check passes, the data is extracted and a confirmation signal is fed back. If the check fails, the failed block ID is recorded and a negative signal is fed back. After receiving the negative signal, the dispatch center only retransmits the specific failed block instead of the entire data packet. The retransmitted data is marked with a priority tag to ensure that it is sent first in the next transmission cycle. The maximum number of retransmissions for a single block is 2. If it still fails, the error correction code recovery mechanism is started and the RS code is used to repair the data.

7. A water conservancy and water regime monitoring and management system based on Beidou short message communication according to claim 1, characterized in that: The specific operation steps of the data preprocessing module are as follows: Perform sliding window mean filtering on the raw sensor data; Detect abnormal values: If the water level change rate at a certain moment is >10cm / min, it is marked as suspicious data and redundant sensor verification is started; Fill in missing data: Use the ARIMA model to predict the missing period values ​​and cross-validate with adjacent node data; Introduce spatiotemporal correlation analysis in anomaly detection and missing value filling.

8. A water conservancy and water regime monitoring and management system based on Beidou short message communication according to claim 7, characterized in that: The specific operation steps of the data preprocessing module for introducing spatiotemporal correlation analysis in anomaly detection and missing value filling are as follows: Modeling of spatiotemporal correlation features: Generate an adjacency matrix and define distance thresholds based on the geographical locations of monitoring nodes; maintain a dynamic neighbor list for each node and update it in real time; Calculate the water level change rate of the current node and its change trend in the past hour, synchronously obtain the water level data of adjacent nodes, and calculate the spatial gradient; Multi-condition fusion judgment of anomaly detection: If the water level change rate of a single node is >10cm / min, but the spatial gradient of the adjacent nodes is <2cm, it is judged as a local anomaly and triggers redundancy check; if the water level change rate of the current node and more than 50% of the adjacent nodes is >8cm / min, it is judged as a regional abnormal event, directly marked as a valid anomaly and an early warning is initiated; Define the scoring calculation logic: ,in is the spatiotemporal confidence score, which is used to determine whether it is a real anomaly. The time change rate is the water level change rate of the current node. The threshold is the preset anomaly threshold. The spatial gradient is the average water level difference between the current node and the adjacent nodes. The baseline value is the reference value of the spatial gradient under normal conditions. , When a signal is judged as abnormal, the threshold is dynamically relaxed or tightened according to the score; Missing data filling of spatiotemporal coordination: When data of a node is missing, the optimal interpolation weight is calculated based on the spatiotemporal covariance model, combining historical data with real-time data of adjacent nodes; interpolation calculation logic: ,in is the spatial weight, is the time decay factor, is the interpolated water level of the current node at time t, is the water level value of the i-th adjacent node at time t; Training the GNN model: using node location, historical water level, and adjacent node data as input, predict the missing value at the current moment; online reasoning: if the missing duration is >30 minutes, call GNN to generate the predicted value, and weightedly fuse it with the ARIMA result, with the weight dynamically adjusted according to the prediction error.

9. A water conservancy and water regime monitoring and management system based on Beidou short message communication according to claim 1, characterized in that: The specific operation steps of the early warning module are as follows: Receive the decompressed data and input it into the LSTM neural network to predict the water level trend in the next 2 hours; If the predicted water level exceeds the threshold, generate warning information and calculate the impact range; The thresholds are dynamically adjusted based on real-time environmental data and historical patterns; When the warning is triggered, it is triggered through progressive measurement, and the process is as follows: Warning classification and triggering conditions: Observation level, i.e. blue: triggered when the predicted water level reaches 80% of the dynamic threshold; village inspectors are notified to strengthen monitoring. Beidou short message content: BDS-GEO: longitude, latitude, BLUE; monitoring suggestions; Preparation level, i.e. yellow: triggered when the predicted water level reaches 95% of the dynamic threshold or the water level at the upstream node exceeds the threshold; township emergency material preparation is initiated, message content: BDS-GEO: longitude, latitude, YELLOW; material inspection; Action level, i.e. red: triggered when the predicted water level exceeds the dynamic threshold or the river water storage capacity is <10%; the city-level command center is linked to evacuate personnel. Message content: BDS-GEO: longitude, latitude, RED; evacuation instructions; village list; Automatic upgrade and downgrade mechanism: If the water level is predicted to rise continuously for three consecutive times, the warning level will be automatically upgraded; if the water level falls below the threshold and remains stable for 30 minutes, the warning will be automatically downgraded or lifted; Beidou short message intelligent compression and multi-channel distribution: using key-value abbreviations and predefined codes: longitude and latitude: LAT=31.23,LON=121.47→ L=3123,12147; village list: pre-assigned numbers, message content is VL=01,02; command type: evacuation command→CMD=EVAC; Multi-channel redundant transmission: When a red warning is issued, the warning is sent simultaneously through Beidou short message, backup radio frequency band, and satellite phone; Receiving end confirmation mechanism: The responsible person must reply with a confirmation code within 5 minutes, otherwise it will trigger automatic retransmission.

10. A water conservancy and water regime monitoring and management system based on Beidou short message communication according to claim 9, characterized in that: The process of dynamically adjusting the threshold in the early warning module according to real-time environmental data and historical patterns is as follows: Dynamic threshold generation model: multi-factor weight calculation, including: real-time rainfall intensity: receiving the current regional rainfall through Beidou short message, with a weight of 30%; upstream water level change rate: obtaining upstream node water level data and calculating the average change rate, with a weight of 25%; historical water level peak: retrieving the maximum water level in the same period of the past five years, with a weight of 20%; river water storage capacity: calculating the remaining water storage space in the current river based on DEM data, with a weight of 25%; Dynamic threshold calculation logic: ,in is the basic threshold, , For real-time rainfall, The highest rainfall in history. is the water level change rate of the upstream node, is the critical rate of change.

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