High-precision flood forecasting and early warning device

Abstract

The invention discloses a high-precision flood forecasting and early warning device, which belongs to the technical field of hydrological monitoring and early warning and comprises a monitoring layout density analysis module, an equipment performance analysis module, a data quality analysis module, an operation and maintenance quality analysis module, a basic performance analysis module and an interruption frequency optimization module. A multi-dimensional evaluation model is constructed, a monitoring layout density coefficient, an equipment performance coefficient, a data quality coefficient, a system operation and maintenance coefficient and a basic performance support degree are respectively calculated, and the interruption frequency of a target communication network is dynamically determined by using an interruption frequency optimization model based on the coefficients and the interruption frequency of the current communication network. According to the method, quantitative evaluation and collaborative optimization of full-chain factors of the flood forecasting and early warning system are realized, and the early warning precision, reliability and adaptive capacity are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of hydrological monitoring and early warning technology, and in particular relates to a high-precision flood forecasting and early warning device. Background Technology

[0002] In communication network management, outage frequency is a key parameter for measuring network reliability. Traditional network management methods typically set outage frequency thresholds based on the state of network devices themselves (such as link load and device failure rate), ignoring the dynamic performance requirements of the services they carry. For example, in emergency communication scenarios such as flood warnings, the network can tolerate a certain degree of outage when monitoring data quality is high and equipment performance is good; conversely, an extremely low outage frequency is required to ensure the delivery of critical data. Existing technologies lack a mechanism to link service layer requirements (such as data quality and monitoring density) with network layer parameters, leading to a mismatch between network resource allocation and service needs. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a high-precision flood forecasting and early warning device, which solves the aforementioned problems.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a high-precision flood forecasting and early warning device, comprising:

[0005] The monitoring deployment density analysis module, based on the density of rainfall monitoring stations, the density of hydrological stations, and the coverage of the target watershed by meteorological radar, obtains the monitoring deployment density coefficient through the monitoring deployment density model.

[0006] The equipment performance analysis module obtains equipment performance coefficients based on the mean time between failures of monitoring equipment, the backup equipment rate of satellite communication terminals, and the maximum accuracy deviation of sensors at monitoring sites through the equipment performance model.

[0007] The data quality analysis module obtains data quality coefficients based on the grid resolution of the digital elevation model, the timeliness of land use data updates on the underlying surface, and the completeness of historical hydrological data through the data quality model.

[0008] The operation and maintenance quality analysis module obtains the system operation and maintenance coefficient based on the average inspection and maintenance cycle of field monitoring stations and the availability of the internal network of the system through the system operation and maintenance model.

[0009] The basic performance analysis module obtains the basic performance support level through the basic performance support level model, based on the data reporting rate and the average latency of early warning information being released to the target user terminal under the deployment density coefficient and equipment performance coefficient.

[0010] The interruption frequency optimization module obtains the target communication network interruption frequency through an interruption frequency optimization model, based on the basic performance support level, monitoring deployment density coefficient, data quality coefficient, and the current average interruption frequency of the communication network.

[0011] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0012] Further technical solution: In the interruption frequency optimization model, the target communication network interruption frequency is positively correlated with the current communication network interruption frequency, and is adaptively adjusted as the difference between the monitoring deployment density coefficient, data quality coefficient, and basic performance support degree and a preset target value increases.

[0013] Further technical solution: The basic performance analysis module performs the following steps:

[0014] Acquire data reporting rate and average latency for delivering early warning information to target users;

[0015] The average delay of the early warning information being sent to the target user terminal is processed by maximum-min normalization and its complement is taken to obtain the delay index;

[0016] Based on the equipment performance coefficient and the system operation and maintenance coefficient, a basic capability driving coefficient that characterizes the comprehensive contribution of the two is determined.

[0017] The basic capability driving coefficient, deployment density coefficient, equipment performance coefficient, data reporting rate, and latency index are imported into the basic performance support model to obtain the basic performance support.

[0018] In the aforementioned basic performance support model, the basic performance support is positively correlated with the data arrival rate, negatively correlated with the delay index, and positively driven by the basic capability driving coefficient.

[0019] Further technical solution: The operation and maintenance quality analysis module performs the following steps:

[0020] Obtain the average inspection and maintenance cycle of field monitoring stations and the availability of the system's internal network;

[0021] The inspection cycle index is obtained by comparing the shortest allowable inspection cycle with the average inspection cycle of field monitoring stations.

[0022] Import the system's internal network availability and inspection cycle index into the system operation and maintenance model to obtain the system operation and maintenance coefficient;

[0023] In the system operation and maintenance model, the system operation and maintenance coefficient is positively correlated with the internal network stability index and the field site maintenance timeliness index.

[0024] Further technical solution: The data quality analysis module performs the following steps:

[0025] To obtain the grid resolution of the digital elevation model, the timeliness of the update of the underlying land use data, and the completeness of historical hydrological data;

[0026] The hydrological data integrity index is obtained by taking the complement after performing maximum-min normalization on the historical hydrological data integrity rate.

[0027] The resolution index and update timeliness index of the digital elevation model grid resolution and the land use data update timeliness of the underlying surface are obtained by comparing them with the corresponding reference values.

[0028] The resolution index, update timeliness index, and hydrological data completeness index are imported into the data quality model to obtain the data quality coefficients.

[0029] In the data quality model, the data quality coefficient is negatively correlated with the resolution index, update timeliness index, and hydrological data completeness index.

[0030] Further technical solution: The equipment performance analysis module performs the following steps:

[0031] Acquire the mean time between failures of monitoring equipment, the backup availability rate of satellite communication terminals, and the maximum accuracy deviation of sensors at monitoring sites;

[0032] The mean time between failures (MTBF) of the monitoring equipment and the backup equipment availability rate of the satellite communication terminal are subjected to maximum-min normalization to obtain the MTBF index and the availability rate index.

[0033] The accuracy deviation index is obtained by performing maximum-min normalization on the maximum accuracy deviation of the sensors at the monitoring station and then taking its complement.

[0034] The fault-free uptime index, equipment availability index, and accuracy deviation index are imported into the equipment performance model to obtain the equipment performance coefficients.

[0035] In the equipment performance model, the equipment performance coefficient is positively correlated with the fault-free operating time index, the equipment availability index, and the accuracy deviation index.

[0036] Further technical solution: The monitoring deployment density analysis module performs the following steps:

[0037] Obtain the density of rainfall monitoring stations, the density of hydrological stations, and the coverage of the target watershed by meteorological radar;

[0038] The density of rainfall monitoring stations, the density of hydrological stations, and the coverage of the target watershed by meteorological radar are subjected to maximum-min normalization to obtain the density index of rainfall monitoring stations, the density index of hydrological stations, and the radar coverage index.

[0039] The rainfall monitoring station network density index, hydrological station network density index, and radar coverage index are imported into the monitoring deployment density model to obtain the monitoring deployment density coefficient.

[0040] In the monitoring deployment density model, the monitoring deployment density coefficient is positively correlated with the rainfall monitoring station network density index, the hydrological station network density index, and the radar coverage index.

[0041] This invention provides a high-precision flood forecasting and early warning device, which has the following advantages compared with the prior art:

[0042] 1. This invention constructs a service-aware communication network management mechanism to achieve dynamic matching of network resources and service requirements. This invention overcomes the limitations of traditional network management, which only sets interruption thresholds based on device status (such as link load and device failure rate). By introducing multi-dimensional evaluation indicators at the service layer (including monitoring deployment density coefficient, device performance coefficient, data quality coefficient, and system operation and maintenance coefficient), the communication network can perceive the quality requirements of the services it carries in real time. When service data quality is high and monitoring capabilities are strong (i.e., the monitoring deployment density coefficient and data quality coefficient values ​​are large), the system appropriately relaxes the interruption frequency tolerance to avoid excessive network resource consumption due to an excessive pursuit of low interruptions. Conversely, when the criticality of service data increases or front-end monitoring capabilities are insufficient, the system automatically tightens the interruption frequency to ensure reliable transmission of critical service data. This service-driven adaptive adjustment mechanism for network parameters achieves precise matching between network resource configuration and actual service requirements, significantly improving overall operating efficiency under limited network resource conditions.

[0043] 2. This invention establishes a multi-dimensional and quantifiable network performance evaluation system, enhancing the scientific rigor and accuracy of network management. For the first time, this invention introduces a comprehensive multi-dimensional evaluation system in communication network management, encompassing monitoring deployment density, equipment performance, data quality, operation and maintenance quality, and operational performance. By using quantitative indicators such as monitoring deployment density coefficient, equipment performance coefficient, data quality coefficient, system operation and maintenance coefficient, and basic performance support level, abstract business requirements are transformed into calculable and comparable network management input parameters. Each coefficient employs normalization processing and explicit mathematical models (e.g., weighted summation for the monitoring deployment density model, multiplicative form for the equipment performance model, exponential function for the data quality model, and hyperbolic tangent function for the system operation and maintenance model), ensuring the comparability and stability of the evaluation results. This quantitative evaluation-based network management method overcomes the limitations of traditional network management that relies on single thresholds or manual experience, making network parameter adjustments more scientific and precise, and providing strong support for the refined operation and maintenance of communication networks.

[0044] 3. This invention introduces a nonlinear optimization model to enhance the adaptability of communication networks to complex service environments. The proposed interruption frequency optimization model employs an exponential nonlinear function, enabling sensitive responses to comprehensive changes in service layer indicators. This model organically integrates monitoring deployment density coefficients, data quality coefficients, and the difference between basic performance support and target values, forming a dynamic adjustment factor. When the basic performance support approaches or exceeds the target value, the model allows the target interruption frequency to increase moderately, avoiding excessive redundancy of network resources; when the basic performance support is significantly lower than the target value, the model rapidly compresses the target interruption frequency, strengthening network reliability. This nonlinear adaptive adjustment mechanism enables communication networks to quickly and smoothly adjust their parameters when facing complex situations such as service quality fluctuations and environmental changes, always maintaining the best match with service requirements, significantly enhancing network robustness and environmental adaptability.

[0045] 4. The modular architecture of this invention facilitates integration and expansion, and is suitable for network management needs in various business scenarios. This invention adopts a modular design, with relatively independent and clearly defined interfaces for modules such as deployment density analysis, equipment performance analysis, data quality analysis, operation and maintenance quality analysis, basic performance analysis, and interruption frequency optimization. Each module is coupled through coefficients, maintaining functional independence while forming a complete evaluation-optimization closed loop. This architecture is not only suitable for network management in emergency communication scenarios such as flood warnings, but can also be easily extended to other business areas with dynamic network reliability requirements, such as geological disaster monitoring, environmental IoT, smart grids, and industrial automation. By replacing or adding / removing business indicator acquisition modules, it can quickly adapt to the network management needs of different industries, possessing good versatility and scalability, and providing a feasible technical path for building a business-aware next-generation communication network management system. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0049] Please see Figure 1 A high-precision flood forecasting and early warning device, provided in one embodiment of the present invention, includes:

[0050] The monitoring deployment density analysis module, based on the density of rainfall monitoring stations, the density of hydrological (water level / flow) stations, and the coverage of the target watershed by meteorological radar, obtains the monitoring deployment density coefficient through the monitoring deployment density model.

[0051] The equipment performance analysis module obtains equipment performance coefficients based on the mean time between failures of monitoring equipment, the backup equipment rate of satellite communication terminals, and the maximum accuracy deviation of sensors at monitoring sites through the equipment performance model.

[0052] The data quality analysis module obtains data quality coefficients based on the grid resolution of the digital elevation model, the timeliness of land use data updates on the underlying surface, and the completeness of historical hydrological data through the data quality model.

[0053] The operation and maintenance quality analysis module, based on the average inspection and maintenance cycle of field monitoring sites and the availability of the internal network of the system (the percentage of availability of the internal network of the forecast and early warning business platform within the statistical period, directly obtained from the IT operation and maintenance monitoring platform (such as Zabbix)), obtains the system operation and maintenance coefficient through the system operation and maintenance model;

[0054] The basic performance analysis module obtains the basic performance support level through the basic performance support level model based on the data reporting rate (the percentage of successfully received data packets out of the total number of data packets to be reported within a specified data reporting time window (e.g., 5 minutes) and the average delay of the early warning information being released to the target user terminal, based on the deployment density coefficient and equipment performance coefficient.

[0055] The interruption frequency optimization module, based on the basic performance support level, monitoring deployment density coefficient, data quality coefficient, and the current average interruption frequency of the communication network, obtains the target communication network interruption frequency (tolerable interruption level) through the interruption frequency optimization model.

[0056] Traditional communication network management systems, when deployed in complex business environments like Location A, often set interruption frequency thresholds solely based on the network devices' own state parameters (such as link load and device failure rate), neglecting the dynamic requirements of front-end business needs on network performance. For example, when monitoring data quality is high and device performance is good, the network can tolerate a certain degree of interruption; conversely, an extremely low interruption frequency is required to ensure the delivery of critical data. The system of this invention, by introducing a monitoring deployment density analysis module, a device performance analysis module, a data quality analysis module, and an operation and maintenance quality analysis module, achieves multi-dimensional and quantitative evaluation of business layer requirements, and uses this as the basis for adjusting network parameters.

[0057] Preferably, the monitoring deployment density analysis module performs the following steps:

[0058] Obtain the density of rainfall monitoring stations, the density of hydrological (water level / flow) stations, and the coverage of the target watershed by meteorological radar;

[0059] The density of rainfall monitoring stations, the density of hydrological (water level / flow) stations, and the coverage of the target watershed by meteorological radar are subjected to maximum-min normalization to obtain the density index of rainfall monitoring stations, the density index of hydrological stations, and the radar coverage index.

[0060] The rainfall monitoring station density index, hydrological station density index, and radar coverage index are imported into the monitoring deployment density model to obtain the monitoring deployment density coefficient. In the monitoring deployment density model, the monitoring deployment density coefficient is positively correlated with the rainfall monitoring station density index, hydrological station density index, and radar coverage index. The monitoring deployment density model is expressed as follows:

[0061] ;

[0062] in, This represents the monitoring deployment density coefficient. This represents the density index of the rainfall monitoring station network. Indicates the density index of hydrological station network. This indicates the radar coverage index. Represents the weight coefficient and The Furthermore, the larger the value, the more complete the spatial deployment of the monitoring network.

[0063] The steps of obtaining the density of rainfall monitoring stations, the density of hydrological (water level / flow) stations, and the coverage of meteorological radar over the target watershed are designed to provide raw, fundamental data input for subsequent assessment of monitoring deployment density. These data directly reflect the spatial distribution and coverage capacity of the monitoring network. This step can be achieved using Geographic Information System (GIS) tools, combined with the geographic coordinates of deployed rainfall and hydrological (water level / flow) stations, the scanning range of meteorological radar, and the boundary data of the target watershed, to calculate the density and coverage of various types of monitoring facilities within the target watershed. Alternatively, remote sensing imagery and ground survey data, combined with statistical methods, can be used to conduct sampling surveys and estimations of the distribution of monitoring stations within the target watershed, thereby obtaining the corresponding station density and coverage data. The monitoring deployment density model clarifies the calculation method of the monitoring deployment density coefficient. Through weighted summation, it allows for adjustments to the contribution of different types of monitoring station density and radar coverage in the overall assessment based on actual needs and the importance of each monitoring element. This mathematical formula can be directly implemented in the software code, where the weighting coefficients... , , These can be pre-set constants, such as those determined based on expert experience or historical data analysis. Additionally, weighting coefficients... It can also be dynamically adjusted, for example, by optimizing based on the actual effects of flood forecasts using machine learning algorithms, or by customizing configurations according to the characteristics and monitoring needs of different watersheds. Meanwhile, the model defines the meaning of each variable, the constraints on the weighting coefficients, and the range and physical meaning of the monitoring deployment density coefficients, ensuring the standardization and interpretability of the evaluation results.

[0064] This solution, through the steps described above, transforms scattered and multidimensional business front-end data into a unified and quantifiable monitoring deployment density coefficient. This provides a comprehensive and objective basis for evaluating business needs in communication network management. As one of the inputs to the interruption frequency optimization model, this coefficient can work in conjunction with other business indicators (such as data quality coefficient and basic performance support) to jointly influence the calculation of the target communication network interruption frequency. This enables the communication network to dynamically adjust its network management strategy based on the deployment of business front-ends.

[0065] Through the above technical solution, this application provides a systematic and standardized method for evaluating monitoring deployment density. First, by acquiring multi-source monitoring data, the comprehensiveness of the evaluation is ensured. Second, the use of maximum-minimum normalization effectively eliminates the dimensional differences between different monitoring indicators, making the evaluation results comparable and accurate. Finally, through a weighted summation monitoring deployment density model, the contribution of each monitoring element can be flexibly adjusted according to actual needs, thereby obtaining an intuitive and comprehensive monitoring deployment density coefficient. This coefficient accurately reflects the spatial integrity of the monitoring network, providing a reliable monitoring basis for the evaluation of high-precision flood forecasting and early warning devices. Furthermore, this monitoring deployment density coefficient, as an important input to the basic performance support model, can synergistically work with other performance indicators (such as equipment performance coefficients and data quality coefficients) to jointly influence the calculation of the target communication network interruption frequency by the interruption frequency optimization module. This enables flood forecasting and early warning devices to dynamically adjust system operation strategies based on the actual deployment of the monitoring network. For example, in areas with low monitoring density, the system may conservatively set the communication interruption frequency to ensure the transmission of critical data, thereby improving the overall accuracy and reliability of the early warning and effectively solving the problems of inaccurate assessment and inability to fully reflect the spatial integrity of the monitoring network in traditional methods.

[0066] Preferably, the equipment performance analysis module performs the following steps:

[0067] Acquire the mean time between failures of monitoring equipment, the backup availability rate of satellite communication terminals, and the maximum accuracy deviation of sensors at monitoring sites;

[0068] The mean time between failures (MTBF) of the monitoring equipment and the backup equipment availability rate of the satellite communication terminal are subjected to maximum-min normalization to obtain the MTBF index and the availability rate index.

[0069] The accuracy deviation index is obtained by performing maximum-min normalization on the maximum accuracy deviation of the sensors at the monitoring station and then taking its complement.

[0070] The fault-free operating time index, equipment availability index, and accuracy deviation index are imported into the equipment performance model to obtain the equipment performance coefficient. In the equipment performance model, the equipment performance coefficient is positively correlated with the fault-free operating time index, equipment availability index, and accuracy deviation index. The equipment performance model is expressed as follows:

[0071] ;

[0072] in, Indicates the equipment performance coefficient. Indicates the time to failure index. This indicates the equipment rate index. The precision deviation index is represented by the following. The higher the value, the better the equipment performance.

[0073] The mean time between failures (MTBF) of monitoring equipment can be obtained from the technical specifications provided by the equipment manufacturer, historical operation logs, or through statistical analysis of equipment failure records. The backup availability rate of satellite communication terminals can be determined by the ratio of the total number of currently deployed satellite communication terminals to the number of available backup terminals, or calculated based on the equipment procurement and deployment plan. The maximum accuracy deviation of sensors at monitoring sites can be obtained from the sensor's factory calibration report, periodic calibration records, or by comparing actual test values ​​with standard values. The equipment performance model uses a multiplicative approach, meaning that the overall performance of the equipment is a comprehensive reflection of its reliability, redundancy, and accuracy. A significant deficiency in any one of these dimensions will have a substantial impact on the overall performance coefficient. This model can be implemented in a software module through simple multiplication operations, using the three previously calculated indices as input and outputting the final equipment performance coefficient. .

[0074] Through the above technical solution, this application provides a method for accurately quantifying the comprehensive performance of service terminal equipment. This accurately quantified equipment performance coefficient provides high-quality input for subsequent basic performance analysis modules, enabling the system to more accurately assess the service's demand on the communication network, thereby achieving dynamic optimization of the communication network interruption frequency.

[0075] Preferably, the data quality analysis module performs the following steps:

[0076] Obtain the grid resolution of the digital elevation model, the update timeliness of the underlying land use data, and the completeness rate of historical hydrological data (for a selected historical period (e.g., 30 years) and core stations, count the actual number of valid records of key data (e.g., daily flow), divide the number of records by the theoretically required total number of records, and the resulting percentage is the "completeness rate of historical hydrological data" for that station).

[0077] The hydrological data integrity index is obtained by taking the complement after performing maximum-min normalization on the historical hydrological data integrity rate.

[0078] The resolution index and update timeliness index of the digital elevation model grid resolution and the land use data update timeliness of the underlying surface are obtained by comparing them with the corresponding reference values.

[0079] The resolution index, update timeliness index, and hydrological data completeness index are imported into the data quality model to obtain data quality coefficients. In the data quality model, the data quality coefficients are negatively correlated with the resolution index, update timeliness index, and hydrological data completeness index. The data quality model is expressed as follows:

[0080] ;

[0081] in, Indicates the data quality coefficient. Indicates the resolution index. This indicates the update timeliness index. The index represents the completeness of hydrological data. Furthermore, the larger the value, the better the data quality.

[0082] The resolution of the digital elevation model (DEM) grid can be obtained by reading the metadata or attribute information of the DEM file using Geographic Information System (GIS) software. For example, this can be done by parsing the header information of GeoTIFF or ESRI ASCII Grid files, or by directly querying the spatial resolution parameters of the DEM data through the API interface of a professional remote sensing data processing library (such as the GDAL library). The update timeliness of the underlying land use data can be calculated by querying the metadata of the land use database to obtain the most recent update date and comparing it with the current date. Alternatively, it can be calculated by establishing a data interface with the data provider, receiving data update notifications periodically, and recording the timestamp of each update. The completeness rate of historical hydrological data can be calculated by statistically analyzing the actual number of valid records of key data (such as water level and flow) from core hydrological stations within a specific time period and comparing it with the theoretically expected total number of records. Alternatively, the data management system can automatically run data quality check scripts to periodically scan the historical hydrological database, identify missing or invalid data points, and generate a completeness report. The data quality model aims to integrate multiple indices and calculate the final data quality coefficient through a mathematical model, providing a unified quantitative indicator.

[0083] Through the above technical solution, this application can comprehensively and systematically quantify the quality of the basic data upon which services depend. This refined data quality assessment provides a more accurate and reliable input for the interruption frequency optimization module, enabling the communication network to dynamically adjust network reliability requirements based on the quality level of service data.

[0084] Preferably, the operation and maintenance quality analysis module performs the following steps:

[0085] Obtain the average inspection and maintenance cycle of field monitoring stations and the availability of the internal network of the system (the percentage of availability of the internal network of the forecasting and early warning business platform within the statistical period, obtained directly from the IT operation and maintenance monitoring platform (such as Zabbix)).

[0086] The inspection cycle index is obtained by comparing the shortest allowable inspection cycle with the average inspection cycle of field monitoring stations.

[0087] The system's internal network availability and inspection cycle index are imported into the system operation and maintenance model to obtain the system operation and maintenance coefficient. In the system operation and maintenance model, the system operation and maintenance coefficient is positively correlated with the system's internal network stability index and also positively correlated with the field site maintenance timeliness index. The system operation and maintenance model is expressed as follows:

[0088] ;

[0089] in, Indicates the system operation and maintenance coefficient. This indicates the inspection cycle index. Indicates the availability of the internal network of the system. Represents the weight coefficient and The Furthermore, the higher the value, the better the system's ability to maintain stable operation.

[0090] The average inspection and maintenance cycle of field monitoring stations can be obtained through statistical analysis of historical inspection records. For example, the date and station of each inspection can be recorded, the time interval between two adjacent inspections can be calculated, and then the average value of all stations can be obtained. The availability of the internal network can be directly obtained from the IT operation and maintenance monitoring platform of the forecasting and early warning business platform. This platform can monitor the operating status of network devices, link bandwidth, data transmission success rate, and other indicators in real time, and calculate the availability percentage within the statistical period. Another approach is to deploy an automated inspection system. This system can automatically record inspection time and calculate the average cycle; simultaneously, it uses network performance management tools (NPM) to continuously monitor network status and generate detailed availability reports. The system operation and maintenance model uses the hyperbolic tangent function tanh, which can map input values ​​to the range [0,1), giving the system operation and maintenance coefficients good interpretability and stability. and These are weighting coefficients used to balance the impact of the inspection cycle index and the availability of the internal network on the system's operation and maintenance coefficient. Their sum is 1, ensuring a reasonable allocation of the contributions of each factor. For example, they can be set through expert experience. and The values ​​of these values ​​can be determined through historical data analysis and optimization algorithms (such as gradient descent) to identify the optimal weight coefficients, ensuring the model outputs the correct values. It best reflects the actual operation and maintenance quality.

[0091] Through the above technical solution, this application can accurately and quantitatively assess the operation and maintenance support capabilities of business systems based on actual operation and maintenance data. Since the system operation and maintenance coefficient is one of the key inputs for calculating the basic performance support level, its accuracy directly improves the accuracy of business requirement assessment, thereby helping the interruption frequency optimization module to more accurately calculate the interruption frequency of the target communication network.

[0092] Preferably, the basic performance analysis module performs the following steps:

[0093] Acquire the data reporting rate (the percentage of successfully received data packets out of the total number of data packets to be reported within a specified data reporting time window (e.g., 5 minutes) and the average delay in the delivery of warning information to the target user terminal;

[0094] The average delay of the early warning information being sent to the target user terminal is processed by maximum-min normalization and its complement is taken to obtain the delay index;

[0095] Based on the equipment performance coefficient and the system operation and maintenance coefficient, a basic capability driving coefficient representing the combined contribution of the two is determined. Specifically, the deployment density coefficient and the equipment performance coefficient are imported into a preset basic capability driving model to obtain the basic capability driving coefficient. The basic capability driving model is expressed as follows:

[0096] ;

[0097] in, Indicates the basic capability driving coefficient. Indicates the equipment performance coefficient. Indicates the system operation and maintenance coefficient. Represents the weight coefficient and ;

[0098] The basic capability driving coefficient, deployment density coefficient, equipment performance coefficient, data arrival rate, and latency index are imported into the basic performance support level model. In this model, the basic performance support level is positively correlated with the data arrival rate, negatively correlated with the latency index, and positively driven by the basic capability driving coefficient. The basic performance support level is then obtained, and the model is expressed as follows:

[0099] ;

[0100] in, Indicates the degree of basic performance support. Indicates the basic capability driving coefficient. Indicates the equipment performance coefficient. Indicates the system operation and maintenance coefficient. Indicates the data reporting rate. Indicates the delay index, the Furthermore, the higher the value, the better the basic capabilities match the current performance requirements.

[0101] The data reporting rate refers to the percentage of successfully received data packets out of the total number of packets to be reported within a preset data reporting time window. This indicator reflects the reliability and timeliness of monitoring data transmission. It can be obtained through the log recording and statistical functions of the system communication module, or through real-time counting and comparison of received data packets by the data receiving server. The average latency from the release of warning information to the target user refers to the average time elapsed from the generation of the warning information to the final user receiving it. This indicator directly measures the efficiency and timeliness of warning information transmission. It can be obtained by setting timestamps at the information publishing end and the user receiving end, recording the time of information transmission and reception, and calculating the average time difference, or by using network monitoring tools to monitor and statistically analyze the latency along the information transmission path in real time. The basic capability-driven model is used to quantify the comprehensive capabilities of the system at the equipment and operation and maintenance levels by using equipment performance coefficients. and system maintenance coefficient By performing a weighted summation, a comprehensive fundamental capability driving coefficient can be obtained. Weighting coefficients and The basic capability driving coefficient can be set based on actual business needs or expert experience, or determined using the analytic hierarchy process. The basic performance support level model comprehensively reflects the performance level of monitoring equipment and the system's operation and maintenance capabilities, and is an important indicator for evaluating the system's basic support capabilities. This model is a non-linear model used to comprehensively evaluate the degree of matching between the system's basic capabilities and current performance requirements. It incorporates the basic capability driving coefficient... Equipment performance coefficient System maintenance coefficient Data reporting rate and latency index By organically combining multiple key indicators and using an exponential function, when each indicator performs well, The value approaches 1, and vice versa; this nonlinear relationship better reflects the complex influence of various factors on the overall performance of a real system. (Basic performance support) The overall performance of the flood forecasting and early warning system in terms of monitoring, data transmission, equipment operation, and information dissemination was quantified.

[0102] Through the above technical solution, this application can more accurately assess the service layer's requirements for the communication network. This multi-dimensional and refined assessment method ensures that the obtained basic performance support level truly reflects the degree of matching between service requirements and network transmission capabilities, providing reliable input parameters for the subsequent interruption frequency optimization module.

[0103] Preferably, in the interruption frequency optimization model, the target communication network interruption frequency is positively correlated with the current communication network interruption frequency, and it adaptively adjusts as the difference between the monitoring deployment density coefficient, data quality coefficient, and basic performance support level and a preset target value increases. The interruption frequency optimization model is expressed as follows:

[0104] ;

[0105] in, Indicates the frequency of interruptions in the target communication network. Indicates the current frequency of communication network outages. This represents the monitoring deployment density coefficient. Indicates the data quality coefficient. Indicates the degree of basic performance support. This indicates the degree of support for the target's basic performance.

[0106] The interruption frequency optimization model aims to comprehensively consider various performance indicators of a flood forecasting and early warning system to dynamically determine the tolerable interruption level of the communication network, thereby optimizing resource allocation while ensuring system reliability. The target communication network interruption frequency is... This is the output of the model, representing the highest interruption frequency that the communication network should maintain under current system performance and environmental conditions. This frequency value will serve as the basis for adjusting system communication strategies or resource allocation, for example, guiding communication modules to adjust retransmission mechanisms, switch to backup links, or optimize data transmission paths. Current communication network interruption frequency. This is an input parameter of the model, reflecting the actual outage status of the communication network at a certain moment or within a certain statistical period. It can be historical statistical data, real-time monitoring data, or predicted data, providing a benchmark for the dynamic adjustment of the model. Monitoring deployment density coefficient This is an indicator that measures the spatial coverage integrity of a monitoring network. A higher monitoring deployment density coefficient indicates that the monitoring network covers the target area more comprehensively and can provide richer and more representative monitoring data. Data quality coefficient This is an indicator reflecting the accuracy, reliability, and timeliness of the data acquired by the system. A higher data quality coefficient means that the system can rely on more reliable data for forecasting and early warning, thereby improving the accuracy of forecasts. Basic performance support level. This is an indicator that comprehensively evaluates the degree to which the basic capabilities of a flood forecasting and early warning system match current performance requirements. A high level of basic performance support indicates that the system has strong basic capabilities in data acquisition, transmission, processing, and information dissemination, and can effectively support forecasting and early warning operations. Target Basic Performance Support It is a preset reference standard that represents the ideal or minimum acceptable basic performance level that the system is expected to achieve. This value can be set according to actual business needs, risk tolerance, or industry standards, and is used to assess the current basic performance support level. The benchmark.

[0107] The interruption frequency optimization model proposed in this application optimizes the interruption frequency of the current communication network. As a benchmark, and by introducing a dynamic adjustment factor, the interruption frequency of the target communication network was adjusted. The precise calculation is based on the monitoring deployment density coefficient. Data quality coefficient and basic performance support Support for target basic performance The differences between them jointly determine this. Specifically, when the monitoring deployment density is high or the data quality is good, that is... or A larger value indicates that the system has stronger monitoring capabilities and more reliable data sources. In this case, the system's tolerance for communication interruptions may be relatively high, or in other words, the system can maintain good performance even when faced with certain interruptions. Simultaneously, the basic performance support level... Support for target basic performance Differences It directly reflects the gap between the system's current operating state and its expected state. When Close to or higher When the system's basic capabilities are good and it can effectively support forecasting and early warning services, the model allows for a certain frequency of target communication network outages. It can be relatively lenient; conversely, when far below If this occurs, it means that the system's basic capabilities are insufficient. To ensure the reliability of forecasts and early warnings, the model will calculate a lower target communication network outage frequency. This model aims to prompt the system to take measures to reduce the risk of communication outages. It integrates multiple dimensions, including monitoring deployment, data quality, and system fundamental performance, into a unified mathematical framework, avoiding the limitations of traditional methods that optimize only a single aspect. In this way, the model can dynamically respond to changes in system performance, ensuring the calculated target communication network outage frequency is accurately predicted. It accurately reflects the system's tolerable level of interruption under current conditions. This is organically combined with the various coefficients and support levels provided by the monitoring deployment density analysis module, data quality analysis module, and basic performance analysis module in the aforementioned high-precision flood forecasting and early warning device. This enables the entire device to systematically evaluate and optimize early warning capabilities from multiple dimensions, thereby significantly improving the accuracy and reliability of early warnings.

[0108] Through the above technical solution, this application provides a specific and operable mathematical model for accurately calculating and dynamically optimizing the target communication network outage frequency in flood forecasting and early warning systems. This model overcomes the problem in existing technologies where the lack of specific mathematical definitions makes it difficult to accurately assess and adjust the outage frequency. By organically integrating multiple key performance indicators such as monitoring deployment density coefficient, data quality coefficient, and basic performance support level into an exponential function, refined management of the communication network outage frequency is achieved. This enables the system to adaptively adjust its tolerance level for communication outages based on real-time operating status and performance, thereby ensuring the reliability of forecasts and early warnings while avoiding unnecessary resource waste.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0110] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-precision flood forecasting and early warning device, characterized in that, include: The monitoring deployment density analysis module, based on the density of rainfall monitoring stations, the density of hydrological stations, and the coverage of the target watershed by meteorological radar, obtains the monitoring deployment density coefficient through the monitoring deployment density model. The equipment performance analysis module obtains equipment performance coefficients based on the mean time between failures of monitoring equipment, the backup equipment rate of satellite communication terminals, and the maximum accuracy deviation of sensors at monitoring sites through the equipment performance model. The data quality analysis module obtains data quality coefficients based on the grid resolution of the digital elevation model, the timeliness of land use data updates on the underlying surface, and the completeness of historical hydrological data through the data quality model. The operation and maintenance quality analysis module obtains the system operation and maintenance coefficient based on the average inspection and maintenance cycle of field monitoring stations and the availability of the internal network of the system through the system operation and maintenance model. The basic performance analysis module obtains the basic performance support level through the basic performance support level model, based on the data reporting rate and the average latency of early warning information being released to the target user terminal under the deployment density coefficient and equipment performance coefficient. The interruption frequency optimization module obtains the target communication network interruption frequency through an interruption frequency optimization model, based on the basic performance support level, monitoring deployment density coefficient, data quality coefficient, and the current average interruption frequency of the communication network.

2. The high-precision flood forecasting and early warning device according to claim 1, characterized in that, The interruption frequency optimization model is positively correlated with the current communication network interruption frequency, and it is adaptively adjusted as the difference between the monitoring deployment density coefficient, data quality coefficient, and basic performance support degree and a preset target value increases.

3. The high-precision flood forecasting and early warning device according to claim 2, characterized in that, The basic performance analysis module performs the following steps: Acquire data reporting rate and average latency for delivering early warning information to target users; The average delay of the early warning information being sent to the target user terminal is processed by maximum-min normalization and its complement is taken to obtain the delay index; Based on the equipment performance coefficient and the system operation and maintenance coefficient, a basic capability driving coefficient that characterizes the comprehensive contribution of the two is determined. The basic capability driving coefficient, deployment density coefficient, equipment performance coefficient, data reporting rate, and latency index are imported into the basic performance support model to obtain the basic performance support. In the aforementioned basic performance support model, the basic performance support is positively correlated with the data arrival rate, negatively correlated with the delay index, and positively driven by the basic capability driving coefficient.

4. The high-precision flood forecasting and early warning device according to claim 3, characterized in that, The operation and maintenance quality analysis module performs the following steps: Obtain the average inspection and maintenance cycle of field monitoring stations and the availability of the system's internal network; The inspection cycle index is obtained by comparing the shortest allowable inspection cycle with the average inspection cycle of field monitoring stations. Import the system's internal network availability and inspection cycle index into the system operation and maintenance model to obtain the system operation and maintenance coefficient; In the system operation and maintenance model, the system operation and maintenance coefficient is positively correlated with the internal network stability index and the field site maintenance timeliness index.

5. The high-precision flood forecasting and early warning device according to claim 2, characterized in that, The data quality analysis module performs the following steps: To obtain the grid resolution of the digital elevation model, the timeliness of the update of the underlying land use data, and the completeness of historical hydrological data; The hydrological data integrity index is obtained by taking the complement after performing maximum-min normalization on the historical hydrological data integrity rate. The resolution index and update timeliness index of the digital elevation model grid resolution and the land use data update timeliness of the underlying surface are obtained by comparing them with the corresponding reference values. The resolution index, update timeliness index, and hydrological data completeness index are imported into the data quality model to obtain the data quality coefficients. In the data quality model, the data quality coefficient is negatively correlated with the resolution index, update timeliness index, and hydrological data completeness index.

6. The high-precision flood forecasting and early warning device according to claim 3, characterized in that, The equipment performance analysis module performs the following steps: Acquire the mean time between failures of monitoring equipment, the backup availability rate of satellite communication terminals, and the maximum accuracy deviation of sensors at monitoring sites; The mean time between failures (MTBF) of the monitoring equipment and the backup equipment availability rate of the satellite communication terminal are subjected to maximum-min normalization to obtain the MTBF index and the availability rate index. The accuracy deviation index is obtained by performing maximum-min normalization on the maximum accuracy deviation of the sensors at the monitoring station and then taking its complement. The fault-free uptime index, equipment availability index, and accuracy deviation index are imported into the equipment performance model to obtain the equipment performance coefficients. In the equipment performance model, the equipment performance coefficient is positively correlated with the fault-free operating time index, the equipment availability index, and the accuracy deviation index.

7. The high-precision flood forecasting and early warning device according to claim 2, characterized in that, The monitoring deployment density analysis module performs the following steps: Obtain the density of rainfall monitoring stations, the density of hydrological stations, and the coverage of the target watershed by meteorological radar; The density of rainfall monitoring stations, the density of hydrological stations, and the coverage of the target watershed by meteorological radar are subjected to maximum-min normalization to obtain the density index of rainfall monitoring stations, the density index of hydrological stations, and the radar coverage index. The rainfall monitoring station network density index, hydrological station network density index, and radar coverage index are imported into the monitoring deployment density model to obtain the monitoring deployment density coefficient. In the monitoring deployment density model, the monitoring deployment density coefficient is positively correlated with the rainfall monitoring station network density index, the hydrological station network density index, and the radar coverage index.

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