Early warning method and system based on big dipper short message communication

Abstract

The application relates to the technical field of early warning systems, in particular to an early warning method and system based on Beidou short message communication, which comprises the following steps: collecting meteorological, seabed geological plate monitoring data and offshore platform data, matching time stamps, calculating disaster intensity through regression, analyzing exposure degree, dividing grid areas, generating risk values, sequencing and numbering, binding terminals to form a task queue, matching disaster types and risk avoidance instructions, compressing and sending short messages, comparing new and old risk values, adjusting the sending order when the threshold is exceeded, and generating early warning results. In the application, meteorological, seabed geological plate monitoring data and offshore platform data are fused and matched with time stamps, disaster intensity is calculated, marine area exposure degree is analyzed, risk distribution is refined, terminals are sequenced and bound, a priority task queue is generated, early warning information is compressed and transmitted through a satellite, the sending order is dynamically adjusted to ensure risk priority response, and the information transmission accuracy, efficiency and cross-region stability are improved.

Description

Technical Field

[0001] This invention relates to the field of early warning system technology, and in particular to an early warning method and system based on BeiDou short message communication. Background Technology

[0002] The field of early warning systems technology encompasses technologies that utilize various monitoring and communication methods to detect and disseminate information about potential disasters, accidents, or abnormal events in advance. Core components include the real-time collection, rapid analysis, and remote transmission of information on environmental, equipment, and personnel status, disseminating early warning information to relevant parties via wireless, wired, or satellite communication. Overall, the field covers information collection units, signal transmission networks, and information dissemination terminals, emphasizing the timely delivery of early warning information through stable and reliable communication channels across multiple application scenarios.

[0003] Among them, the early warning method and system based on BeiDou short message communication refers to a technical solution that utilizes the short message communication capability provided by the BeiDou satellite navigation system to encode the collected monitoring data and send it to the early warning center or relevant receiving terminal via satellite link. For scenarios requiring continued information transmission even under limited or interrupted ground communication conditions, the BeiDou satellite short message channel is used to complete information reporting and distribution. By embedding a BeiDou communication terminal at the information collection end and combining it with message packaging and sending strategies, stable early warning information transmission can be achieved across regions and networks.

[0004] While existing technologies can achieve basic information collection, transmission, and dissemination in disaster early warning, they fall short in terms of the accuracy and real-time performance of multi-source data fusion. Multiple types of monitoring data are often scattered across multiple systems with inconsistent time bases, leading to biases in comprehensive analysis results and affecting the accuracy of risk assessment. Regarding risk information transmission, there is a general lack of priority scheduling mechanisms based on risk levels; the information distribution order is fixed and difficult to dynamically adjust according to changes in the disaster situation, potentially causing delays in early warnings for high-risk areas. In terms of communication resource utilization, existing models consume significant bandwidth and transmission capacity, especially in low-bandwidth or damaged communication environments, where data transmission is prone to congestion or interruption, weakening the stability of cross-regional information transmission. Furthermore, existing systems are mostly based on single data sources or coarse-grained regional divisions, failing to fully reflect regional differences. This can easily lead to situations where low-risk and high-risk areas receive the same level of early warning, thus affecting the targeting and efficiency of early warnings. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides an early warning method based on BeiDou short message communication, comprising the following steps:

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an early warning method based on BeiDou short message communication, comprising the following steps:

[0007] S1: Collect meteorological data, seabed geological plate monitoring data and offshore platform data, match all values ​​with timestamps and normalize them, calculate disaster intensity through a linear regression model, and generate multi-source monitoring data;

[0008] S2: Call the multi-source monitoring data to calculate the available proportion and wind speed of normally operating offshore platforms, and normalize the exposure of the marine area. Combine the disaster intensity to divide the grid area and calculate the disaster risk value to generate the marine area risk value.

[0009] S3: Call the risk values ​​of the marine area, sort them by value and assign risk numbers, and bind the risk number list with the Beidou terminal identifier to generate an early warning task queue;

[0010] S4: Call each grid area in the early warning task queue, match the disaster type and evacuation instructions according to the risk number, compress the content into Beidou short message byte packet format and send it to the Beidou terminal in sequence, and generate a Beidou early warning short message sending record;

[0011] S5: Call the sending record of the Beidou early warning short message, monitor the new round of marine area risk value and compare it with the old value corresponding to the sending record. If it exceeds the priority adjustment threshold, adjust the sending order of the early warning task queue and generate the Beidou short message early warning result.

[0012] As a further aspect of the present invention, the multi-source monitoring data includes meteorological data, seabed geological plate monitoring data, and offshore platform data; the marine area risk value includes grid area number, disaster intensity, and exposure level; the early warning task queue specifically includes a risk number list, a Beidou terminal identifier list, and a task sending order; the Beidou early warning short message sending record includes terminal reception time, byte packet length, and sending order number; and the Beidou short message early warning result specifically includes the adjusted queue order, terminal reception time record, and task completion status.

[0013] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0014] S101: Collect meteorological data, seabed geological plate monitoring data and offshore platform data, add timestamps to multiple types of data in a unified time format, match them according to the same time node, normalize the values ​​of multiple types of data under multiple time nodes, and generate a time-normalized matching value set.

[0015] S102: Based on the time-normalized matched numerical set, call the linear regression model, take meteorological values, seabed geological plate displacement values ​​and offshore platform vibration values ​​as independent variables, set disaster intensity value as dependent variable, calculate the regression coefficients and intercept values ​​corresponding to the independent variables, and generate a multi-factor regression coefficient set.

[0016] S103: Based on the multi-factor regression coefficient set, the normalized data of meteorological values, seabed geological plate displacement values ​​and offshore platform vibration values ​​are substituted into the linear regression equation to calculate the disaster intensity value corresponding to each time node, and multi-source monitoring data are obtained.

[0017] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0018] S201: Call the multi-source monitoring data, calculate the proportion of the number of offshore platforms operating normally at multiple time nodes to the total number of offshore platforms, and calculate the wind speed value at the corresponding time node. Pair the two values ​​according to the same time node to generate the offshore platform availability ratio and wind speed value.

[0019] S202: Based on the available ratio of the offshore platform and the wind speed value, the available ratio value and wind speed value at each time point are normalized, and the exposure value is calculated by combining the normalization result to generate the marine area exposure value.

[0020] S203: Based on the marine area exposure and the disaster intensity values ​​in the multi-source monitoring data, the corresponding area is divided into multiple grid areas. For each grid area, the disaster risk value is calculated at the corresponding time node to obtain the marine area risk value.

[0021] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0022] S301: Call the marine area risk values, sort them according to their numerical values, assign sequentially increasing numbers to each sorted position, match the numbers with the corresponding marine area risk values, and generate a risk number sequence;

[0023] S302: Based on the risk number sequence, obtain the Beidou terminal identification data corresponding to the multiple numbers, pair and bind the numbers with the Beidou terminal identifications to obtain the risk terminal binding table;

[0024] S303: Based on the risk terminal binding table, establish a task execution arrangement according to the number order, combine multiple numbers with the bound Beidou terminal identifiers to form tasks to be processed and arrange them to generate an early warning task queue.

[0025] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0026] S401: Call the data of each grid area in the early warning task queue, retrieve the corresponding disaster type information in the disaster type matching table according to the risk number, extract the evacuation command corresponding to the disaster type from the evacuation command library and combine them to obtain the area command;

[0027] S402: Based on the regional instruction, the corresponding content is encoded and converted according to the BeiDou short message byte packet format requirements, the length value, sequence value and check value in the byte packet are calculated, and the byte packet is assembled with the regional instruction content into a complete byte packet structure to generate the regional short message byte packet length;

[0028] S403: Based on the length of the short message byte packet in the region, send the byte packet content sequentially to the matching Beidou terminal according to the order of the early warning task queue, and record the sending time and byte packet length of each terminal to establish a Beidou early warning short message sending record.

[0029] As a further aspect of the present invention, the disaster avoidance instruction library is an emergency response instruction library for disaster events, and the instruction content includes early warning methods and disaster avoidance suggestions.

[0030] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0031] S501: Call the sending record of the Beidou early warning short message, monitor the new round of marine area risk values ​​corresponding to the multi-grid area in the record, extract the old values ​​in the associated sending record and compare them, calculate the percentage difference, and obtain the regional risk difference rate.

[0032] S502: Based on the regional risk difference rate, compare it with the set priority adjustment threshold, filter the grid area numbers whose difference rate exceeds the threshold, and rearrange the corresponding task queue positions according to the difference rate from large to small to generate the adjusted queue order;

[0033] S503: Based on the adjusted queue order, send the BeiDou short messages corresponding to the grid areas in the early warning task queue to the terminal in sequence, and record the time and sequence number of the multiple terminals receiving the messages to establish the BeiDou short message early warning result.

[0034] As a further aspect of the present invention, the priority adjustment threshold is set by statistically analyzing the risk value change range under multiple disaster levels, based on the fluctuation range of risk monitoring data, the response time requirements of disaster types, and the sending frequency of task queues.

[0035] An early warning system based on BeiDou short message communication includes:

[0036] The multi-source monitoring module is configured to collect meteorological data, seabed geological plate monitoring data and offshore platform data, and to match and normalize the relevant values ​​with timestamps. It then calculates the disaster intensity through a linear regression model, generates multi-source monitoring data, and transmits it to the risk assessment module.

[0037] The risk assessment module is configured to call the multi-source monitoring data, calculate the available proportion and wind speed of the normally operating offshore platform, normalize and analyze the exposure of the marine area, divide the grid area according to the disaster intensity and calculate the disaster risk value, generate the marine area risk value and pass it to the task generation module;

[0038] The task generation module is configured to call the risk values ​​of the marine area, sort them by numerical value and assign risk numbers, bind the risk number list with the Beidou terminal identifier, generate an early warning task queue and transmit it to the early warning sending module;

[0039] The early warning sending module is configured to call each grid area in the early warning task queue, match the disaster type and evacuation instructions according to the risk number, compress the content into Beidou short message byte packet format and send it to the Beidou terminal in sequence, generate the Beidou early warning short message sending record and pass it to the priority adjustment module;

[0040] The priority adjustment module is configured to call the sending records of the BeiDou early warning short messages, monitor the new round of marine area risk values ​​and compare them with the old values ​​corresponding to the sending records. If the risk value exceeds the priority adjustment threshold, the sending order of the early warning task queue is adjusted to generate BeiDou short message early warning results.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0042] In this invention, by collecting and matching timestamps from multiple monitoring data, meteorological, seabed geological plate monitoring data, and offshore platform data are fused together in the same time dimension. Regression analysis is then used to obtain disaster intensity values, achieving precise integration of cross-domain monitoring data. Based on the fused data, seabed geological plate displacement data and offshore platform vibration data are further calculated. After normalization analysis, marine area exposure is obtained, and then combined with disaster intensity for gridded area division and risk value assessment, thus achieving more granular risk distribution analysis. Risk values ​​are sorted and bound to unique terminal identifiers to form a priority-defined task queue, ensuring accurate correspondence between risk information and receiving terminals and reducing resource waste caused by irrelevant information transmission. In the early warning information generation stage, disaster types and evacuation instructions are matched and compressed into short message byte packets, which are then sequentially sent to the receiving end via satellite link, achieving efficient transmission in low-bandwidth environments. During task execution, the change range of new and old risk values ​​is compared in real time. If the change exceeds a threshold, the transmission order is dynamically adjusted, thus maintaining the priority and timeliness of information distribution as the disaster situation changes. The synergistic effect of the above logic improves the accuracy of information, transmission efficiency, and dynamic response capability. Especially under conditions where ground communication is limited or interrupted, it can still ensure the stable transmission of early warning information across regions, while taking into account both the conservation of transmission resources and the priority guarantee of risk response. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of the steps of the present invention.

[0045] Figure 2 This is a detailed schematic diagram of S1 of the present invention.

[0046] Figure 3 This is a detailed schematic diagram of S2 in this invention.

[0047] Figure 4 This is a detailed schematic diagram of S3 of the present invention.

[0048] Figure 5 This is a detailed schematic diagram of S4 of the present invention.

[0049] Figure 6 This is a detailed schematic diagram of S5 of the present invention.

[0050] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0051] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0052] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0053] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0054] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0055] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0056] Please see Figure 1 This invention provides an early warning method based on BeiDou short message communication, comprising the following steps:

[0057] S1: Collect meteorological data, seabed geological plate monitoring data and offshore platform data, match all values ​​with timestamps and normalize them, calculate disaster intensity through a linear regression model, and generate multi-source monitoring data;

[0058] S2: Call multi-source monitoring data, calculate the available proportion and wind speed of normally operating offshore platforms, normalize and analyze the exposure of marine areas, divide grid areas based on disaster intensity and calculate disaster risk values, and generate marine area risk values;

[0059] S3: Call up the risk values ​​of the marine area, sort them by value and assign risk numbers, and bind the risk number list with the Beidou terminal identifier to generate an early warning task queue;

[0060] S4: Call each grid area in the early warning task queue, match the disaster type and evacuation instructions according to the risk number, compress the content into Beidou short message byte packet format and send it to the Beidou terminal in sequence, and generate the Beidou early warning short message sending record;

[0061] S5: Call the BeiDou early warning short message sending record, monitor the new round of marine area risk values ​​and compare them with the old values ​​corresponding to the sending record. If the value exceeds the priority adjustment threshold, adjust the sending order of the early warning task queue and generate the BeiDou short message early warning result.

[0062] Multi-source monitoring data includes meteorological data, seabed geological plate monitoring data, and offshore platform data. Marine regional risk values ​​include grid area numbers, disaster intensity, and exposure levels. The early warning task queue specifically includes a risk number list, a BeiDou terminal identifier list, and a task sending order. The BeiDou early warning short message sending record includes terminal reception time, byte packet length, and sending order number. The BeiDou short message early warning result specifically includes the adjusted queue order, terminal reception time record, and task completion status.

[0063] Please see Figure 2 The specific steps of S1 are as follows:

[0064] S101: Collect meteorological data, seabed geological plate monitoring data and offshore platform data, add timestamps to multiple types of data in a unified time format, match them according to the same time node, normalize the values ​​of multiple types of data under multiple time nodes, and generate a time-normalized matching value set.

[0065] Raw data were collected from meteorological stations, seafloor geological plate monitoring stations, and offshore platform monitoring points. This included the measured temperature of 30.5°C and wind speed of 5.2 m / s at the UTC meteorological station at 08:00 on January 1, 2025; the measured displacement of the seafloor geological plate at the seafloor geological plate monitoring station of 3.2 cm; and the measured vibration amplitude of the offshore platform structure of 2.4 mm. The minimum temperature value was retrieved from the database, along with the maximum temperature value of 45°C. The normalized temperature value was calculated as: (30.5 - (-10)) / (45 - (-10)) = 40.5 / 55 ≈ 0.736. The minimum wind speed was 0 m / s and the maximum wind speed was 40 m / s. / s, calculate the normalized value of wind speed: 5.2 / 40=0.130, calculate the normalized value of displacement of the seafloor geological plate (minimum 0cm, maximum 10cm): 3.2 / 10=0.320, calculate the normalized value of vibration amplitude (minimum 0mm, maximum 10mm): 2.4 / 10=0.240, repeat the same process to process data at other time points, such as the normalized value of the measured UTC temperature at 09:00 (28.0°C): (28.0-(-10)) / 55=38 / 55≈0.691, and the normalized value of the measured wind speed (8.0m / s): 8.0 / 40=0.20 0, Measured displacement of the seafloor geological plate 2.5cm, normalized value: 2.5 / 10=0.250; Measured vibration amplitude 1.8mm, normalized value: 1.8 / 10=0.180; Measured strain 90με, normalized value: 90 / 500=0.180; Measured temperature at 10:00 UTC 33.0°C, normalized value: (33.0-(-10)) / 55=43 / 55≈0.782; Measured wind speed 6.0m / s, normalized value: 6.0 / 40=0.150; Measured displacement of the seafloor geological plate 3.5cm, normalized value: 3.5 / 10=0.350, the normalized value of the measured vibration amplitude of 2.2mm is calculated as: 2.2 / 10=0.220, the normalized value of the measured temperature at 11:00 UTC of 34.0°C is calculated as: (34.0-(-10)) / 55=44 / 55≈0.800, the normalized value of the measured wind speed of 7.2m / s is calculated as: 7.2 / 40=0.180, the normalized value of the measured displacement of the seabed geological plate of 4.0cm is calculated as: 4.0 / 10=0.400, the normalized value of the measured vibration amplitude of 2.6mm is calculated as: 2.6 / 10=0.260, generating a set of normalized values ​​for multiple time nodes.

[0066] Table 1. Example of time-normalized matching numerical representation:

[0067]

[0068] S102: Based on the time-normalized matching numerical set, call the linear regression model, take meteorological values, seabed geological plate displacement values ​​and offshore platform vibration values ​​as independent variables, set disaster intensity value as dependent variable, calculate the regression coefficients and intercept values ​​corresponding to the independent variables, and generate a multi-factor regression coefficient set.

[0069] Linear regression analysis was used, with a target disaster intensity value set. Normalized values ​​of temperature, wind speed, seafloor plate displacement, and offshore platform vibration were used as independent variables. Ten sets of disaster event data were collected, including a disaster intensity of 0.93 corresponding to a normalized temperature value of 0.82, wind speed of 0.25, seafloor plate displacement of 0.42, and offshore platform vibration of 0.38; and a disaster intensity of 0.75 corresponding to a normalized temperature value of 0.61, wind speed of 0.18, seafloor plate displacement of 0.31, and offshore platform vibration of 0.29. Regression coefficients were calculated using the least squares method: the partial derivatives of the four sets of independent variables were summed, and a system of normal equations was established. Solving the equations yielded regression coefficients β1=0.52 (temperature), β2=0.38 (wind speed), β3=0.61 (seafloor plate displacement), and β4=0.43 (offshore platform vibration), with an intercept term β0=0.65. The disaster intensity Y was compared with the predicted value. Take the partial derivative of the sum of squared residuals, let X² denote the squaring operation, and let... have to , Similarly, other equations are obtained. Substituting the 10 sets of data into the numerical solution of the equation system, the coefficient set {β0=0.65,β1=0.52,β2=0.38,β3=0.61,β4=0.43} is generated.

[0070] S103: Based on the multi-factor regression coefficient set, normalized data of meteorological values, seabed geological plate displacement values ​​and offshore platform vibration values ​​are substituted into the linear regression equation to calculate the disaster intensity corresponding to each time node and obtain multi-source monitoring data.

[0071] Using normalized data from 2025-01-01 08:00 UTC: temperature 0.736, wind speed 0.130, submarine plate displacement 0.300, and offshore platform vibration 0.240, substituting these values ​​into the linear regression equation: Disaster Intensity = 0.65 + 0.52 × 0.736 + 0.38 × 0.130 + 0.61 × 0.300 + 0.43 × 0.240. Step-by-step calculations: 0.52 × 0.736 = 0.38272, 0.38 × 0.130 = 0.0494, 0.61 × 0.300 = 0.183, 0.43 × 0.240 = 0.183, 0.43 × 0.240 = 0.130. .240 = 0.1032, summing gives 0.38272 + 0.0494 = 0.43212, 0.43212 + 0.183 = 0.61512, 0.61512 + 0.1032 = 0.71832, adding the intercept 0.65 + 0.71832 = 1.36832, the disaster intensity result is 1.368. Compared with the preset threshold of 1.2 (based on the probability of facility damage exceeding 80% when the intensity is ≥1.2 in disaster cases), it is determined that 1.368 > 1.2. This result indicates that there is a disaster risk at the current time point, and is output to the multi-source monitoring database.

[0072] Please see Figure 3 The specific steps of S2 are as follows:

[0073] S201: Call multi-source monitoring data, calculate the proportion of the number of offshore platforms operating normally to the total number of offshore platforms under multiple time nodes, and analyze the wind speed values ​​under the corresponding time nodes. Pair the two values ​​according to the same time node to generate the proportion of offshore platforms available and the wind speed value.

[0074] The system retrieves disaster intensity values ​​and offshore platform status data from multi-source monitoring data. For example, with a disaster intensity value of 1.368 and a total number of offshore platforms set to 100 (based on the number of monitoring points deployed in the marine area), it queries the number of normally operating offshore platforms (85, read from the sensor status report, showing the number with the status "normal operation") at the time node 2025-01-01 08:00 UTC. It calculates the ratio 85 divided by 100, which equals 0.85, and extracts the wind speed value of 0.15 from the S101 normalized wind speed dataset at the same time node. It then analyzes whether the wind speed value is directly compared to the stored value of 0.15 within the preset range. Within the range of 0.0 to 0.5 (the interval is divided according to the wind speed distribution, 0.0-0.2 is low wind speed, 0.2-0.4 is medium wind speed, and 0.4-0.5 is high wind speed), pair the ratio value 0.85 and the wind speed value 0.15 according to the timestamp 2025-01-01 08:00UTC, store it as a record, and repeat the following: at time node 09:00UTC, total number 100, number of normal operation 90, ratio value 0.9 and wind speed value 0.2 are paired, at 10:00UTC, ratio value 0.8 and wind speed value 0.25 are paired, at 11:00UTC, ratio value 0.75 and wind speed value 0.3 are paired, to generate a paired dataset.

[0075] Table 2. Example of pairing offshore platform availability with wind speed values:

[0076]

[0077] As shown in Table 2, this table lists the paired data for four time points, and the available ratio values ​​and wind speed values ​​are dimensionless.

[0078] S202: Based on the availability ratio of offshore platforms and wind speed values, the availability ratio and wind speed values ​​at each time point are normalized, and the exposure value is calculated by combining the normalization results to generate the marine area exposure.

[0079] Based on the paired data of offshore platform availability ratio and wind speed values ​​in Table 2, the availability ratio value for each time point is normalized. The minimum extreme value of the ratio is 0.6 (the lowest ratio obtained from the database over the past year), and the maximum value is 1.0 (the highest ratio). For example, if the ratio value is 0.85, the normalized value is calculated as (0.85-0.6) / (1.0-0.6) = 0.25 / 0.4 = 0.625. Wind speed values ​​are also normalized, with the minimum extreme value being 0.0 and the maximum value being 0.5 (based on the S101 dataset). For example, if the wind speed value is 0.15, the normalized value is calculated as 0.15 / 0.5 = 0.3. The exposure value is then calculated based on the normalization results, and the exposure calculation formula is set as: Exposure = (Normalized Ratio Value + Normalized Ratio Value) / (Normalized Ratio Value). (Wind speed value) / 2 (The weighting is set on an equal weight basis, because the ratio and wind speed have equally important effects on exposure, verified by the average impact of 10 sets of events). For example, at time node 08:00UTC, the normalized ratio is 0.625, the normalized wind speed is 0.3, and the exposure is calculated as (0.625+0.3) / 2=0.925 / 2=0.4625. Repeat the following: at 09:00UTC, the normalized ratio is (0.9-0.6) / 0.4=0.75, the normalized wind speed is 0.2 / 0.5=0.4, and the exposure is (0.75+0.4) / 2=0.575. At 10:00UTC, the exposure is 0.525, and at 11:00UTC, the exposure is 0.5, generating a sequence of marine area exposure values.

[0080] S203: Based on the marine area exposure and the disaster intensity values ​​in the multi-source monitoring data, the corresponding marine area is divided into multiple grid areas. For each grid area, the disaster risk value is calculated at the corresponding time node to obtain the marine area risk value.

[0081] Based on the marine area exposure value, such as E in grid area 1 m =0.4625 (obtained from S202 output) and the disaster intensity value in the multi-source monitoring data, such as D mn =1.368 (obtained from S103 output, corresponding to time node n in grid region 1), dividing the ocean region into grid regions. D mn : The disaster intensity value of grid area m at time node n, taken as the measured value D of grid area 1 at 2025010108:00UTC. 11 =1.368, the disaster intensity value is based on a case database (range 0.1-2.5), and 1.368 is within a reasonable range. E m Ocean exposure in grid region m, exposure in grid region 1 E1=0.4625, E m =(Normalized ratio + Normalized wind speed) / 2, Normalized ratio 0.625 + Normalized wind speed 0.3 = 0.925, 0.925 / 2 gives 0.4625. S mnThe environmental sensitivity of grid region m at time node n is quantified using slope data (slope is a core indicator of environmental sensitivity). Measured slope values ​​are retrieved from the geographic information system (e.g., grid region 1 has a slope of 15°). The maximum slope value is then queried (maximum 30° in marine areas), and normalized for calculation: S mn =Measured slope / Maximum slope, S 11 =15 / 30=0.5, value range: slope 0°-90°, normalized value 0.0-1.0. T n : Time decay coefficient at time node n, T n =1 / (1+t), where t represents the number of hours, and T is the time when t=0. n =1.0 (no attenuation), the attenuation increases as t increases, time node 08:00UTC (t=8 hours): T1=1 / (1+8)=1 / 9≈0.111, substituting into the formula , Preset risk threshold: 0.5 (based on disaster database statistics, >0.5 is considered high risk), R 11 The result is approximately 0.8092 > 0.5, indicating that the current grid area has a high risk at the given time point, and the output is the risk value for the marine area.

[0082] Please see Figure 4 The specific steps of S3 are as follows:

[0083] S301: Call the marine area risk values, sort them in numerical order, assign sequentially increasing numbers to each sorted position, match the numbers with the corresponding marine area risk values, and generate a risk number sequence;

[0084] The output marine area risk value data is retrieved, including risk values ​​of 0.8092 for grid area 1, 0.75 for grid area 2, 0.92 for grid area 3, and 0.68 for grid area 4 (all from S203 calculation results). The numerical values ​​are compared: 0.92 > 0.8092 > 0.75 > 0.68. The maximum value of 0.92 is assigned number 1 (first in the sorting), the second largest value of 0.8092 is assigned number 2, and so on, with 0.75 assigned number 3. 0.68 is assigned as number 4. The database storage function is called to match number 1 with the risk value 0.92 and store it as tuple (1, 0.92), number 2 with 0.8092 as (2, 0.8092), number 3 with 0.75 as (3, 0.75), and number 4 with 0.68 as (4, 0.68), generating the sequence (1, 0.92), (2, 0.8092), (3, 0.75), (4, 0.68), as shown in Table 3.

[0085] Table 3 Example of Risk Number Sequence:

[0086]

[0087] As shown in Table 3, the table lists the numbers and corresponding values ​​in descending order of risk value.

[0088] S302: Based on the risk number sequence, obtain the Beidou terminal identification data corresponding to multiple numbers, pair and bind the numbers with the Beidou terminal identifications to obtain the risk terminal binding table;

[0089] Based on the risk number sequence in Table 3, extract the identifier of grid area 3 corresponding to number 1 (retriev the field grid area ID=G03 from the grid area attribute database), call the Beidou terminal registration database to query the terminal identifier BD-003 bound to grid area G03 (registration database storage relationship: grid area ID → terminal ID), bind number 1 to BD-003 and store it as (1, BD-003). Similarly, process the binding of terminal BD-001 corresponding to grid area 1 for number 2 as (2, BD-001), binding of terminal BD-002 corresponding to grid area 2 for number 3 as (3, BD-002), and binding of terminal BD-004 corresponding to grid area 4 for number 4 as (4, BD-004), and generate the binding table (1, BD-003), (2, BD-001), (3, BD-002), (4, BD-004).

[0090] S303: Based on the risk terminal binding table, establish a task execution arrangement according to the number order, combine multiple numbers with the bound Beidou terminal identifiers to form tasks to be processed and arrange them to generate an early warning task queue;

[0091] Based on the binding table (1, BD-003), (2, BD-001), (3, BD-002), (4, BD-004), a task queue is established in the order of number 1 to 4. The task item generated by combining number 1 with terminal BD-003 sends a level 1 warning command to BD-003 (the task content preset template sends a level 1 warning command to [terminal ID]). The task item generated by number 2 sends a level 2 warning command to BD-001. The task item generated by number 3 sends a level 3 warning command to BD-002. The task item generated by number 4 sends a level 4 warning command to BD-004. The task items are arranged in order as an ordered list [send level 1 warning command to BD-003, send level 2 warning command to BD-001, send level 3 warning command to BD-002, send level 4 warning command to BD-004].

[0092] Please see Figure 5 The specific steps of S4 are as follows:

[0093] S401: Call the data of each grid area in the early warning task queue, retrieve the corresponding disaster type information in the disaster type matching table according to the risk number, extract the evacuation command corresponding to the disaster type from the evacuation command library and combine them to obtain the area command;

[0094] Call the first task item in the warning task queue to send a level 1 warning instruction to BD-003, extract the grid area number 3 (parse the grid area ID corresponding to BD-003 from the instruction text), query the disaster type matching table (Table 4), the disaster type corresponding to risk number 1 is marine disaster, retrieve the instruction text of the marine disaster type in the risk avoidance instruction library to stay away from the coast immediately, combine the grid area number and the instruction to generate Grid Area 3: Stay away from the coast immediately and keep the communication unobstructed, process the second task item to send a level 2 warning instruction to BD-001, extract the grid area number 1, the risk number 2 corresponds to heavy rainstorm, call the instruction text to stop offshore operations immediately, generate Grid Area 1: Stop offshore operations immediately, secure the vessel, and stay away from low-lying areas. Similarly, process the remaining task items to generate a marine area instruction set.

[0095] S402: Based on the area instruction, perform encoding conversion on the corresponding content according to the requirements of the Beidou short message character byte packet format, calculate the length value, sequence value, and check value in the byte packet, and assemble them with the area instruction content into a complete byte packet structure to generate the length of the area short message character byte packet;

[0096] Call the marine area instruction "Grid Area 3: Stay away from the coast immediately and keep the communication unobstructed", convert each character to a byte value based on the GB2312 encoding standard. For example, the character "网" is encoded as byte values 205 and 245 (obtained by calling the GB2312 encoding table, "网" corresponds to hexadecimal CDF5, decimal 205 and 245), the character "格" is encoded as 179 and 213, the character "3" is encoded as the digit 51, the colon is encoded as 58, and so on. The total number of bytes n = 26 is calculated (the instruction text has 13 characters in total, Chinese characters account for 2 bytes / character, and numbers and symbols account for 1 byte). Set the weight coefficient w i = i (position weight, i is the byte sequence number starting from 1, set according to the importance of the byte position: the later the position, the higher the weight, to reduce the excessive influence of key information such as the grid area ID at the head on the total length. Referring to the communication protocol optimization principle, through 10 sets of transmission tests, the weight increase of the rear bytes can reduce the head redundancy by 20%), the basic length compensation value c = 8 (fixed value, referring to the Beidou short message protocol RFC4825 standard, the fixed length of the packet header is 8 bytes). b i : The value of the i-th data byte (the original byte value), obtained through the character encoding of the marine area instruction. w ic: Weight coefficient of the i-th byte (set based on byte position, with higher weight for earlier bytes). c: Base length compensation value (fixed header length). n: Total number of data bytes (length of instruction content). L: Byte packet length value. Ocean area instruction: Take grid area 3: Immediately move away from the coast and maintain communication (GB2312 encoding, 1 Chinese character = 2 bytes), character decomposition: net (2B) + grid (2B) + 3 (1B) + (1B) + stand (2B) + ... + move (2B) → total number of bytes n = 26. Byte value b i : Retrieved by calling the encoding table, such as the first byte b1 of the net being 205 (hexadecimal CD). Weighting coefficient w i : Set w based on byte position i =i (the later the position, the higher the weight), the instruction header contains key information (such as grid area ID), and its weight should be reduced to avoid excessive length expansion. Basic compensation value c: fixed value c=8 (the minimum header length specified by the BeiDou protocol). Calculate the product sum, complete sum calculation (all 26 bytes): Substitute into the formula The length value L=14877 is directly used to assemble the byte packet.

[0097] S403: Based on the length of the regional short message byte packet, send the byte packet content sequentially to the matching Beidou terminal according to the order of the early warning task queue, and record the sending time and byte packet length of each terminal to establish a Beidou early warning short message sending record;

[0098] Based on the short message byte packet length value L=14877 (obtained from S402 output), the messages are sent in the order of the early warning task queue: the first task sends a byte packet (content length 14,877B) to terminal BD-003, records the sending time 2025-08-12 10:00:00 and the length value; the second task sends to BD-001 (length calculation is the same), records the time 10:00:03; the third task sends to BD-002 and records 10:00:07; the fourth task sends to BD-004 and records 10:00:12, generating a sending record table (Table 4).

[0099] Table 4. Records of BeiDou early warning short messages sent:

[0100]

[0101] The record table is stored in the database index Beidou Log 20250812.

[0102] Please see Figure 6 The specific steps of S5 are as follows:

[0103] S501: Call the Beidou early warning short message sending record, monitor the new round of marine area risk values ​​corresponding to multi-grid areas in the record, extract the old values ​​in the associated sending record and compare them, calculate the percentage difference, and obtain the regional risk difference rate;

[0104] The BeiDou early warning short message transmission records were retrieved, and the old risk values ​​associated with each grid area were extracted (obtained by indexing risk value record 20250812 in the database), including the old risk value R for grid area 1 (terminal BD001). old1 =0.8092, old value R for grid region 2 (BD002) old2 =0.7500, old value R for grid region 3 (BD003) old3 =0.9200, old value R for grid region 4 (BD004) old4 =0.6800, monitoring a new round of marine regional risk values ​​(sampling interval 5 minutes) collected by the real-time sensor network, including the new value R for grid region 1. new1 =0.85, new risk value R for grid area 2 new2 =0.86, new value R for grid region 3 new3 =0.95, new value R for grid region 4 new4 =0.70 (numerical range 0.0-1.0, within the reasonable range of historical database statistics). Extract related old values ​​for comparison, calculate the percentage difference, and calculate:

[0105] Grid region 1: |(0.85-0.8092) / 0.8092|×100%=|0.0408 / 0.8092|×100%≈5.04%;

[0106] Grid region 2: |(0.86-0.75) / 0.75|×100%=|0.11 / 0.75|×100%≈14.67%;

[0107] Grid region 3: |(0.95-0.92) / 0.92|×100%=|0.03 / 0.92|×100%≈3.26%;

[0108] Grid region 4: |(0.70-0.68) / 0.68|×100%=|0.02 / 0.68|×100%≈2.94%;

[0109] The risk difference rate array for marine areas [5.04, 14.67, 3.26, 2.94] was obtained and stored in the risk monitoring database.

[0110] S502: Based on the regional risk difference rate, compare it with the set priority adjustment threshold, filter the grid area numbers whose difference rate exceeds the threshold, and rearrange the corresponding task queue positions according to the difference rate from large to small to generate the adjusted queue order;

[0111] Based on the output array of marine area risk difference rates [5.04, 14.67, 3.26, 2.94], a priority adjustment threshold is set to 10% (reference: in disaster response database statistics, a difference rate > 10% is defined as a high-risk change event; through the analysis of 20 events, a difference rate > 10% corresponds to a loss increase rate > 15%. Example verification: event A has a difference rate of 12% and a loss increase of 18%; event B has a difference rate of 8% and a loss increase of 5%. Therefore, the threshold is fixed at 10%). The difference rate of each grid area is compared with the threshold of 10%, and the function is called... Comparison operation: If the difference rate is >10%, it is marked as exceeding the threshold. For example, the difference rate of grid region 1 is 5.04 <10 and does not exceed the threshold, the difference rate of grid region 2 is 14.67 >10 and exceeds the threshold, the difference rate of grid region 3 is 3.26 <10 and does not exceed the threshold, and the difference rate of grid region 4 is 2.94 <10 and does not exceed the threshold. Filter the grid region numbers that exceed the threshold. Only grid region 2 meets the threshold. Extract grid region number 2 and rearrange it according to the difference rate from largest to smallest. Currently, there is only one grid region after filtering. It is directly arranged as [Grid Region 2] and the adjusted queue order list [Grid Region 2] is generated.

[0112] S503: Based on the adjusted queue order, send the BeiDou short messages of the corresponding grid area in the early warning task queue to the terminal in sequence, and record the time and sequence number of the reception by multiple terminals to establish the BeiDou short message early warning result.

[0113] Based on the adjusted queue order list [Grid Region 2], the original order of the early warning task queue [Grid Region 1, Grid Region 2, Grid Region 3, Grid Region 4] is called, and Grid Region 2 is moved to the first position. The new queue is [Grid Region 2, Grid Region 1, Grid Region 3, Grid Region 4]. Beidou short messages are sent to the bound terminals in sequence. The first task sends Grid Region 2 to terminal BD-002 (obtained from the S302 binding table), and records the sending time as 2025-08-12 10:10:00 (system clock acquisition), with the sequence number 1. The second task sends Grid Region 1 to BD-001 at 10:10:05, with the number 2. The third task sends Grid Region 3 to BD-003 at 10:10:10, with the number 3. The fourth task sends Grid Region 4 to BD-004 at 10:10:15, with the number 4. The time and number are recorded, and a Beidou short message early warning result table (Table 5) is established.

[0114] Table 5: Early Warning Result Record Table

[0115]

[0116] Please see Figure 7 An early warning system based on BeiDou short message communication includes:

[0117] The multi-source monitoring module is configured to collect meteorological data, seabed geological plate monitoring data and offshore platform data, and to match and normalize the relevant values ​​with timestamps. It then calculates the disaster intensity through a linear regression model, generates multi-source monitoring data, and transmits it to the risk assessment module.

[0118] The risk assessment module is configured to call multi-source monitoring data, calculate the available proportion and wind speed of normally operating offshore platforms, normalize and analyze the exposure of marine areas, divide grid areas based on disaster intensity and calculate disaster risk values, generate marine area risk values ​​and pass them to the task generation module;

[0119] The task generation module is configured to call the risk values ​​of the marine area, sort them by value and assign risk numbers, bind the risk number list with the Beidou terminal identifier, generate an early warning task queue and transmit it to the early warning sending module;

[0120] The early warning sending module is configured to call each grid area in the early warning task queue, match the disaster type and evacuation instructions according to the risk number, compress the content into Beidou short message byte packet format and send it to the Beidou terminal in sequence, generate the Beidou early warning short message sending record and pass it to the priority adjustment module;

[0121] The priority adjustment module is configured to call the sending records of BeiDou early warning short messages, monitor the new round of marine area risk values ​​and compare them with the old values ​​corresponding to the sending records. If the risk exceeds the priority adjustment threshold, the sending order of the early warning task queue will be adjusted to generate BeiDou short message early warning results.

[0122] The above are merely specific embodiments 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.

Claims

1. An early warning method based on BeiDou short message communication, characterized in that, Includes the following steps: S1: Collect meteorological data, seabed geological plate monitoring data, and offshore platform data, and match and normalize all values ​​with timestamps. Calculate disaster intensity using a linear regression model to generate multi-source monitoring data. The values ​​include meteorological values, seabed geological plate displacement values, and offshore platform vibration values. S2: Using the multi-source monitoring data, calculate the available proportion and wind speed of normally operating offshore platforms, normalize them, calculate the marine area exposure, divide the area into grid regions based on disaster intensity, calculate disaster risk values, and generate marine area risk values. The specific steps are as follows: S201: Call the multi-source monitoring data, calculate the proportion of the number of offshore platforms operating normally at multiple time nodes to the total number of offshore platforms, and calculate the wind speed value at the corresponding time node. Pair the two values ​​according to the same time node to generate the offshore platform availability ratio and wind speed value. S202: Based on the available ratio of the offshore platform and the wind speed value, the available ratio value and wind speed value at each time point are normalized, and the exposure value is calculated by combining the normalization result to generate the marine area exposure value. S203: Based on the marine area exposure and the disaster intensity values ​​in the multi-source monitoring data, the corresponding area is divided into multiple grid areas. For each grid area, a disaster risk value is calculated at the corresponding time node to obtain the marine area risk value: ； Among them, D mn E represents the disaster intensity value of grid region m at time node n; m S represents the marine area exposure of grid region m; mn The environmental sensitivity of grid region m at time node n is quantified using slope data; T n T is the time decay coefficient at time node n. n =1 / (1+t), where t represents the number of hours; S3: Call the risk values ​​of the marine area, sort them by value and assign risk numbers, and bind the risk number list with the Beidou terminal identifier to generate an early warning task queue; S4: Call each grid area in the early warning task queue, match the disaster type and evacuation instructions according to the risk number, compress the content into Beidou short message byte packet format and send it to the Beidou terminal in sequence, and generate a Beidou early warning short message sending record; S5: Call the sending record of the Beidou early warning short message, monitor the new round of marine area risk value and compare it with the old value corresponding to the sending record. If it exceeds the priority adjustment threshold, adjust the sending order of the early warning task queue and generate the Beidou short message early warning result.

2. The early warning method based on BeiDou short message communication according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Collect meteorological data, seabed geological plate monitoring data and offshore platform data, add timestamps to multiple types of data in a unified time format, match them according to the same time node, normalize the values ​​of multiple types of data under multiple time nodes, and generate a time-normalized matching value set. S102: Based on the time-normalized matched numerical set, call the linear regression model, take meteorological values, seabed geological plate displacement values ​​and offshore platform vibration values ​​as independent variables, set disaster intensity value as dependent variable, calculate the regression coefficients and intercept values ​​corresponding to the independent variables, and generate a multi-factor regression coefficient set. S103: Based on the multi-factor regression coefficient set, the normalized data of meteorological values, seabed geological plate displacement values ​​and offshore platform vibration values ​​are substituted into the linear regression equation to calculate the disaster intensity value corresponding to each time node, and multi-source monitoring data are obtained.

3. The early warning method based on BeiDou short message communication according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Call the marine area risk values, sort them according to their numerical values, assign sequentially increasing numbers to each sorted position, match the numbers with the corresponding marine area risk values, and generate a risk number sequence; S302: Based on the risk number sequence, obtain the Beidou terminal identification data corresponding to the multiple numbers, pair and bind the numbers with the Beidou terminal identifications to obtain the risk terminal binding table; S303: Based on the risk terminal binding table, establish a task execution arrangement according to the number order, combine multiple numbers with the bound Beidou terminal identifiers to form tasks to be processed and arrange them to generate an early warning task queue.

4. The early warning method based on BeiDou short message communication according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Call the data of each grid area in the early warning task queue, retrieve the corresponding disaster type information in the disaster type matching table according to the risk number, extract the evacuation command corresponding to the disaster type from the evacuation command library and combine them to obtain the area command; S402: Based on the regional instruction, the corresponding content is encoded and converted according to the BeiDou short message byte packet format requirements, the length value, sequence value and check value in the byte packet are calculated, and the byte packet is assembled with the regional instruction content into a complete byte packet structure to generate the regional short message byte packet length; S403: Based on the length of the short message byte packet in the region, send the byte packet content sequentially to the matching Beidou terminal according to the order of the early warning task queue, and record the sending time and byte packet length of each terminal to establish a Beidou early warning short message sending record.

5. The early warning method based on BeiDou short message communication according to claim 4, characterized in that, The disaster avoidance instruction library contains emergency response instructions for disaster events, including early warning methods and disaster avoidance suggestions.

6. The early warning method based on BeiDou short message communication according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Call the sending record of the Beidou early warning short message, monitor the new round of marine area risk values ​​corresponding to the multi-grid area in the record, extract the old values ​​in the associated sending record and compare them, calculate the percentage difference, and obtain the regional risk difference rate. S502: Based on the regional risk difference rate, compare it with the set priority adjustment threshold, filter the grid area numbers whose difference rate exceeds the threshold, and rearrange the corresponding task queue positions according to the difference rate from large to small to generate the adjusted queue order; S503: Based on the adjusted queue order, send the BeiDou short messages corresponding to the grid areas in the early warning task queue to the terminal in sequence, and record the time and sequence number of the multiple terminals receiving the messages to establish the BeiDou short message early warning result.

7. The early warning method based on BeiDou short message communication according to claim 6, characterized in that, The priority adjustment threshold is set based on the fluctuation range of risk monitoring data, the response time requirements of disaster types, and the sending frequency of task queues, and by statistical analysis of the risk value change range under multiple disaster levels.

8. An early warning system based on BeiDou short message communication, characterized in that, The system is used to implement the early warning method based on BeiDou short message communication as described in any one of claims 1-7, and the system comprises: The multi-source monitoring module is configured to collect meteorological data, seabed geological plate monitoring data and offshore platform data, and to match and normalize the relevant values ​​with timestamps. It then calculates the disaster intensity through a linear regression model, generates multi-source monitoring data, and transmits it to the risk assessment module. The risk assessment module is configured to call the multi-source monitoring data, calculate the available ratio and wind speed of the normally operating offshore platform, normalize them, calculate the exposure of the marine area, divide the grid area according to the disaster intensity and calculate the disaster risk value, generate the marine area risk value and pass it to the task generation module; The task generation module is configured to call the risk values ​​of the marine area, sort them by numerical value and assign risk numbers, bind the risk number list with the Beidou terminal identifier, generate an early warning task queue and transmit it to the early warning sending module; The early warning sending module is configured to call each grid area in the early warning task queue, match the disaster type and evacuation instructions according to the risk number, compress the content into Beidou short message byte packet format and send it to the Beidou terminal in sequence, generate the Beidou early warning short message sending record and pass it to the priority adjustment module; The priority adjustment module is configured to call the sending records of the BeiDou early warning short messages, monitor the new round of marine area risk values ​​and compare them with the old values ​​corresponding to the sending records. If the risk value exceeds the priority adjustment threshold, the sending order of the early warning task queue is adjusted to generate BeiDou short message early warning results.

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