A flood warning system and method based on multi-mode broadcasting

Abstract

The disclosure provides a flood discharge early warning system and method based on multi-mode broadcast, and relates to the technical field of early warning systems. The system comprises: a multi-mode broadcast module for issuing early warning information through wireless broadcast signals, sound and light signals and visual screens; a range adjustment module for adjusting the broadcast range of the multi-mode broadcast module according to the personnel distribution information in the target flood discharge area; a priority adjustment module for adjusting the broadcast priority of the multi-mode broadcast module; a positioning module for obtaining the real-time position information of the target object through a communication base station and an intelligent terminal; a directional early warning module for determining the transmission path of the early warning information issued by the multi-mode broadcast module; a data acquisition module for acquiring a plurality of real-time data from the multi-mode broadcast module, the positioning module and the environmental sensor; and a data fusion module for fusion processing of the plurality of real-time data. The technical scheme in the disclosure can improve the response efficiency and coverage accuracy of the flood discharge early warning.

Description

Technical Field

[0001] This disclosure relates to the field of early warning system technology, and more specifically, to a flood discharge early warning system and method based on multi-mode broadcasting. Background Technology

[0002] Currently, flood warning systems primarily rely on traditional broadcasting methods for information transmission. While this method can provide some early warning, it often fails to achieve comprehensive coverage under varying environmental conditions. Especially in complex terrain and variable weather conditions, information transmission is prone to delays or omissions. Furthermore, traditional systems typically depend on fixed broadcast ranges and preset rules, lacking the ability to provide precise warnings to different target groups, leading to inaccurate warning information delivery and even failing to meet the needs of emergency response.

[0003] With the increasing demands for emergency management of flood discharge disasters, the shortcomings of existing systems in terms of early warning range, transmission accuracy, and flexibility have become increasingly apparent. Traditional systems typically issue warnings based solely on static regional scope and time rules, but they cannot make timely and flexible adjustments when faced with sudden weather changes, water level fluctuations, or changes in population distribution. The lack of intelligent decision support makes the system difficult to adapt to complex and ever-changing flood discharge scenarios, resulting in slow or inaccurate early warning responses and increasing the risk of potential accidents. Therefore, there is still room for improvement in the response efficiency and coverage accuracy of flood discharge early warning systems in related technologies.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a flood discharge early warning system based on multi-mode broadcasting, a flood discharge early warning method based on multi-mode broadcasting, an electronic device, and a computer-readable storage medium, thereby improving the response efficiency and coverage accuracy of the flood discharge early warning system to at least a certain extent.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to a first aspect of the present disclosure, a flood discharge early warning system based on multi-mode broadcasting is provided. The system includes: a multi-mode broadcasting module for disseminating early warning information via wireless broadcasting signals, audible and visual signals, and a visual screen; a range adjustment module for adjusting the broadcast range of the multi-mode broadcasting module based on personnel distribution information within a target flood discharge area; a priority adjustment module for adjusting the broadcast priority of the multi-mode broadcasting module according to preset rules, wherein the priority is determined based on personnel distribution information provided by the range adjustment module; a positioning module for acquiring real-time location information of a target object via a communication base station and a smart terminal, and sending the location information to the multi-mode broadcasting module; a directional early warning module for determining the transmission path of the early warning information disseminated by the multi-mode broadcasting module based on the location information of the target object acquired by the positioning module; a data acquisition module for acquiring various real-time data from the multi-mode broadcasting module, the positioning module, and environmental sensors; and a data fusion module for fusing the various real-time data to generate the early warning information and transmitting the early warning information to the multi-mode broadcasting module.

[0008] According to a second aspect of the present disclosure, a flood discharge early warning method based on multi-mode broadcasting is provided. The method includes: obtaining real-time location information of a target object through a communication base station and a smart terminal, and determining the transmission path of the early warning information; adjusting the broadcast range of the multi-mode broadcasting based on the personnel distribution information within the target flood discharge area; dynamically adjusting the priority of the early warning information according to the personnel distribution information based on preset rules; collecting real-time data from multiple data sources, and performing fusion processing on the real-time data to generate the early warning information; and publishing the early warning information through wireless broadcasting signals, audio-visual signals, and a visual screen.

[0009] The technical solutions provided in this disclosure may have the following beneficial effects:

[0010] The flood warning system in this embodiment can achieve comprehensive coverage in diverse environments. First, through the synergistic effect of wireless broadcasting, audio-visual signals, and visual screens, it ensures that warning information can be transmitted efficiently and accurately in different situations, whether in complex terrain, severe weather, or densely populated areas, overcoming the problems of insufficient coverage and delayed information transmission caused by traditional single broadcasting methods.

[0011] Furthermore, through the collaboration of the range adjustment module and the priority adjustment module, the broadcast range and priority can be dynamically adjusted based on the population distribution information within the target flood discharge area. Specifically, the range adjustment module, based on real-time population distribution data, can accurately determine the areas that need to be broadcast, thereby reducing unnecessary coverage and improving the targeting of broadcasts. Meanwhile, the priority adjustment module intelligently adjusts the urgency of the broadcast content based on factors such as population density and disaster risk, ensuring that early warning information is delivered to all key areas in a timely and accurate manner, avoiding the missed reports common in traditional early warning systems.

[0012] Furthermore, regarding the precise location of target objects, the positioning module and the directional early warning module work together to ensure that early warning information can be accurately delivered to the specific target objects requiring alerts. Through the collaboration of communication base stations and smart terminals, the system can obtain the location of target objects in real time and dynamically adjust the broadcast path based on their location. This allows early warning information to not only cover a wide area but also be delivered to target objects in a targeted manner, avoiding the risk of information transmission being too large or too small, and improving the accuracy and response efficiency of the early warning system.

[0013] Meanwhile, the data acquisition module gathers data from multiple sources in real time, forming a comprehensive environmental awareness capability. The data fusion module further integrates these data in real time to generate unified early warning information. This fusion process not only eliminates information biases that may arise from a single data source, but also provides more comprehensive and accurate disaster predictions by comprehensively analyzing multiple data dimensions, ensuring that the early warning system can respond more intelligently and flexibly in the face of complex and ever-changing flood discharge scenarios.

[0014] In summary, the flood discharge early warning system disclosed herein effectively solves the problems of single broadcasting method, fixed rules and lack of flexible response mechanism in the prior art through multi-dimensional and comprehensive information collection, dynamic adjustment and precise transmission mechanism. It significantly improves the system's adaptability and early warning efficiency in complex environments, and ensures the response efficiency, coverage accuracy and coverage of early warning information.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] Figure 1The illustration shows a schematic diagram of the composition of a flood discharge early warning system based on multi-mode broadcasting according to some embodiments of the present disclosure.

[0018] Figure 2 The illustration shows a schematic diagram of the composition of another flood discharge early warning system based on multi-mode broadcasting according to some embodiments of the present disclosure.

[0019] Figure 3 The illustration shows a schematic flowchart of a flood discharge early warning method based on multi-mode broadcasting according to some embodiments of the present disclosure.

[0020] Figure 4 The schematic diagram illustrates the structural schematic of a computer system of an electronic device according to some embodiments of the present disclosure.

[0021] Figure 5 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is shown.

[0022] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this specification as detailed in the appended claims.

[0024] The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of this specification. The singular forms “a,” “the,” and “the” as used in this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0025] It should be understood that although the terms first, second, third, etc., may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0026] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0027] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0028] Furthermore, the accompanying drawings are for illustrative purposes only and are not necessarily drawn to scale. The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0029] In the example embodiments disclosed herein, a flood discharge early warning system based on multi-mode broadcasting is first provided. This flood discharge early warning system based on multi-mode broadcasting can be applied to terminal devices, such as mobile phones, computers and other electronic devices. Figure 1 A schematic diagram illustrating the composition of a flood discharge early warning system based on multi-mode broadcasting according to some embodiments of the present disclosure is shown. (Reference) Figure 1 As shown, the flood discharge early warning system based on multi-mode broadcasting may include the following modules:

[0030] The multi-mode broadcast module 1 can be used to publish early warning information through wireless broadcast signals, sound and light signals, and visual screens;

[0031] The range adjustment module 2 can be used to adjust the broadcast range of the multi-mode broadcast module according to the personnel distribution information within the target flood discharge area;

[0032] Priority adjustment module 3 can be used to adjust the broadcast priority of the multi-mode broadcast module according to preset rules. The priority is determined based on the personnel distribution information provided by the range adjustment module.

[0033] Positioning module 4 can be used to obtain the real-time location information of the target object through communication base stations and smart terminals, and send the location information to the multi-mode broadcast module;

[0034] The directional early warning module 5 can be used to determine the transmission path of the early warning information issued by the multi-mode broadcast module based on the location information of the target object obtained by the positioning module.

[0035] Data acquisition module 6 can be used to acquire various real-time data from the multi-mode broadcast module, positioning module and environmental sensors;

[0036] The data fusion module 7 can be used to fuse and process multiple real-time data, generate early warning information, and transmit the early warning information to the multi-mode broadcast module.

[0037] The flood discharge early warning system based on multi-mode broadcasting in this example embodiment will be further described below.

[0038] The multi-mode broadcast module 1 disseminates warning information through three methods: wireless broadcast signals, audio-visual signals, and visual screens, ensuring broad coverage under various environmental conditions. Specifically, the wireless broadcast signal, by adjusting frequency and power, can flexibly adapt to different geographical and climatic conditions, such as complex mountainous areas or densely populated urban areas, avoiding signal blockage. The strength and propagation range of the broadcast signal are dynamically adjusted based on real-time data, ensuring that warning information can still be received even in areas with severe signal attenuation. In different environments, the audio signal can adjust volume and frequency to suit the needs of busy or quiet areas; for example, high-frequency alarms penetrate noise in busy traffic areas, while low-frequency alarms ensure wider propagation at night. The visual signal section provides intuitive visual warnings in low-visibility conditions using high-brightness LED screens or flashing lights. The screen display can include real-time alarms, emergency evacuation information, and water level information, enabling different target groups to quickly understand the current situation.

[0039] The range adjustment module 2 automatically adjusts the broadcast range of the multi-mode broadcast module 1 by monitoring the real-time distribution of people within the target area. This module relies on multiple data sources, such as sensors, surveillance cameras, and personnel flow data, to obtain real-time personnel density information within the flood discharge area. Using this data, the range adjustment module 2 can flexibly adjust the broadcast coverage. When personnel density is high, the system expands the broadcast range to ensure coverage of critical areas; while in sparsely populated or low-risk areas, the system can reduce the broadcast range to minimize unnecessary interference. By combining environmental information, such as meteorological and geographical data, the range adjustment module 2 can also consider the characteristics of different areas, such as urban centers, rural areas, or waterways, ensuring that the broadcast range matches the environment and personnel distribution, avoiding over-broadcasting or omissions. Furthermore, the system can continuously track changes in personnel distribution during operation, achieving real-time and accurate range adjustment to ensure maximum coverage and timely delivery of early warning information.

[0040] Priority adjustment module 3 dynamically adjusts broadcast priority based on the level of flood discharge risk, population density, and other environmental factors. This module continuously monitors environmental data, such as water level, rainfall, and wind speed, and combines this with population distribution information to determine broadcast priorities for different areas and target groups. In high-risk areas, such as near dams or low-lying areas, when water levels rise sharply, priority adjustment module 3 automatically increases the broadcast priority for that area, ensuring timely delivery of emergency warnings. Furthermore, priority adjustment module 3 can adjust the urgency and method of information delivery for different target groups, such as vehicles in motion or residents in buildings, according to preset rules. For example, when a vehicle enters a high-risk area, the system automatically increases the broadcast priority for that area and, in conjunction with the vehicle navigation system, directly sends warning information to the vehicle, ensuring the driver receives the relevant alerts promptly. The system flexibly adjusts the priority of broadcast information based on environmental changes and the real-time location of target groups to avoid information delays or omissions.

[0041] Positioning module 4 can acquire the real-time location information of target objects through cooperation with communication base stations and smart terminals. Utilizing multiple positioning technologies such as GPS, Wi-Fi, Bluetooth, and communication base stations, the system can accurately track the current location of target objects and transmit it to the multi-mode broadcast module 1. Based on the target object's movement trajectory and real-time location, positioning module 4 can dynamically update information to ensure the accuracy of the broadcast information transmission path. For stationary targets, such as residents inside buildings, the system obtains precise location information via Wi-Fi or Bluetooth signals; while for moving targets, such as vehicles in motion or pedestrians, a comprehensive judgment is made by combining GPS positioning and communication base station signals. This positioning module 4 not only has high-precision positioning capabilities but can also update the location of target objects in real time, ensuring that the system can provide the latest location information under any circumstances and providing accurate data support for the directional warning module 5.

[0042] The directional early warning module 5 automatically determines the transmission path of early warning information based on the real-time location information provided by the positioning module 4. This module combines the real-time location of the target object, surrounding environmental information, and geographic data to optimize the route and method of information transmission. For example, when the target object is located in an area with a high risk of flooding, the directional early warning module 5 will choose the most direct and effective way to transmit information, such as through short-range wireless broadcast signals or directional sound and light signals. For moving targets (such as vehicles or pedestrians), the directional early warning module 5 can adjust the path and content of the alarm transmission in real time according to the target's trajectory and direction, ensuring the timeliness and accuracy of information transmission. Furthermore, the directional early warning module 5 also has an adaptive adjustment function. When the target object's location changes or environmental factors change, the module can automatically update the early warning transmission path to ensure that the early warning information always matches the target object's current location. This dynamic adjustment capability greatly improves the system's flexibility and accuracy, helping to reduce the lag and mistransmission of broadcast information.

[0043] The data acquisition module 6 can acquire real-time information from multiple data sources, including environmental sensors, positioning modules, and smart devices, ensuring that the system can comprehensively and accurately reflect the current state of the target area. This module collects environmental data in real time, such as temperature, humidity, wind speed, water level, rainfall, and traffic flow, by deploying meteorological sensors, water level monitoring equipment, and personnel distribution sensors. In addition, location information provided by smart terminal devices such as mobile phones and wearable devices also provides the data acquisition module with detailed data about the target object. All this data is transmitted to the data fusion module 7 to generate timely and accurate early warning information. To ensure the accuracy of data acquisition, the data acquisition module 6 can employ high-precision sensors and various information fusion technologies. For example, meteorological data acquisition can be cross-validated using data from multiple meteorological stations to eliminate errors that may occur from a single data source. In water level monitoring, it can be combined with equipment such as buoys and radar to monitor water level changes in real time, ensuring that flood discharge risks are reflected in a timely manner.

[0044] The data fusion module 7 processes and analyzes multi-source data to generate accurate early warning information, which is then transmitted to the multi-mode broadcast module 1. This module integrates data from various sensors, positioning modules, and smart terminals, combining it with data analysis algorithms for real-time fusion. Through comprehensive analysis of multi-dimensional data such as meteorological data, water level data, and population distribution data, the data fusion module 7 can assess the risk level of the flood discharge area and dynamically adjust the early warning level. The data fusion process may include steps such as data cleaning, noise reduction, and pattern recognition to ensure the accuracy of the final output early warning information. By comparing and analyzing historical data, potential flood discharge risks can be identified, and decisions can be dynamically optimized based on real-time conditions. For example, when water levels rise rapidly and are accompanied by heavy rainfall, the early warning intensity is increased, and high-risk areas are prioritized. All processed data is aggregated to generate accurate early warning information, which is then disseminated through the multi-mode broadcast module 1 to ensure that all target objects receive timely warnings.

[0045] The flood discharge early warning system based on multi-mode broadcasting described above will be described in detail in other embodiments of this disclosure.

[0046] In some embodiments, reference Figure 2 As shown, the multi-mode broadcast module 1 may include a wireless broadcast signal unit 11, an audio-visual signal unit 12, and a visualization screen unit 13.

[0047] The wireless broadcast signal unit 11 can be used to transmit early warning information to receiving devices within the target flood discharge area via broadcast signals within a preset frequency band. Specifically, the main function of the wireless broadcast signal unit is to transmit early warning information to receiving devices within the target flood discharge area via broadcast signals within a radio frequency range. This unit may include a frequency modulation component, a signal modulation component, a transmitting component, and a receiving device interface component. The frequency modulation component selects the frequency according to the preset frequency band, enabling the signal to penetrate various obstacles and cover a large area, especially suitable for complex terrain (such as mountainous areas, low-lying areas, etc.), and adaptable to different weather conditions (such as heavy rain, fog, etc.). The signal modulation component is used to modulate the early warning information and convert it into a signal format suitable for wireless transmission, ensuring the efficiency and stability of information transmission. The transmitting component sends the modulated signal into the air, ensuring that the signal can cover the designated area. In actual operation, the wireless broadcast signal unit can dynamically adjust the signal transmission power to adapt to different transmission needs and environmental conditions. For example, in a large-scale flood discharge area, the signal transmission power will be enhanced to ensure that the signal can cover the farthest distance; while in smaller or less populated areas, the power will be reduced to reduce unnecessary signal interference. The signal receiving device interface component is used to receive feedback from devices within the target area, confirm the effective reception of the signal, and make real-time adjustments according to environmental changes (such as weather conditions, terrain, etc.).

[0048] The audible and visual signal unit 12 can be used to issue early warning information via an alarm and a flashing light. The alarm emits an audible siren, and the flashing light blinks at a preset frequency. The frequencies of the audible siren and the flashing light are automatically adjusted by a priority adjustment module based on the population density and real-time conditions within the target area. Specifically, the audible and visual signal unit is used to issue early warning information via an alarm and a flashing light. This unit can consist of an audible alarm component and a visual alarm component, and can adjust the audio and light frequencies according to the population density and real-time conditions of the target area. The audible alarm component includes an audio device and a frequency modulation circuit. The audio device is used to emit an audible alarm, and the frequency, volume, and duration can be adjusted according to preset rules. For example, when the population density is high, the volume of the audio device will be increased to ensure that the sound penetrates the crowd or environmental noise; in special circumstances, such as at night or in noisy traffic environments, a high-frequency audible alarm can be used to increase its penetration power and ensure the efficiency of alarm transmission. The visual alarm component can consist of a flashing light and a frequency control component. When a flood warning occurs, the flashing light blinks at a preset frequency, and the flashing frequency or brightness is adjusted so that it can still be clearly seen under low visibility conditions, such as at night, in foggy or rainy weather. The light alarm component can automatically adjust the flashing frequency and brightness based on personnel density and real-time conditions through a priority adjustment mechanism, thereby ensuring the visibility of alarm information in different environments. In high-density areas or areas with limited visibility, the frequency will be appropriately increased to improve alarm recognition.

[0049] The visualization screen unit 13 can be used to display early warning information and evacuation routes at designated locations within the target flood discharge area. The evacuation routes are updated in real time based on the location information of the target objects obtained by the positioning component. Specifically, the visualization screen unit is used to display early warning information and evacuation routes at designated locations within the target flood discharge area. This unit includes multiple displays and their control components, which work collaboratively with the positioning component and data fusion component to dynamically display real-time early warning information, evacuation routes, and emergency guidance. The display of evacuation routes depends on the real-time location information of the target objects. After obtaining the location of the target objects through the positioning component, the system calculates the safest and fastest evacuation route by combining dynamically analyzed data. The evacuation routes on the display screen not only indicate the exit direction but also dynamically adjust according to different environments and real-time data, such as road congestion and flood spread, to ensure that the evacuation channels are always effective. For different target groups, the display of evacuation routes will be adapted to different methods. For example, for pedestrians, walking routes are provided through the display screen; for vehicles, appropriate traffic signs are provided to guide vehicles to quickly leave the danger zone. In addition, the display unit can also show information from the data acquisition components, such as real-time water levels, weather changes, and other emergency response measures, ensuring that personnel in the target area have access to comprehensive emergency information. By working in conjunction with other units of the multi-mode broadcasting component, the display can update information promptly to respond to the ever-changing disaster situation in real time.

[0050] In some embodiments, the evacuation path displayed on the visualization screen unit is updated in real time based on the location information of the target object obtained by the positioning module, specifically including the following technical steps:

[0051] The first step is to obtain the real-time location information P of the target object based on the positioning module. target (t)=(x target (t),y target (t) and the location of the obstacle, where t represents time, x target (t) and y target (t) represents the x-coordinate and y-coordinate of the target object at time t, respectively.

[0052] Specifically, based on the real-time location information of the target object obtained by the positioning module, the current location of the target object is first obtained through precise positioning technology. This location information includes the coordinates of the target object at a specific moment. Through collaborative work with communication base stations and smart terminals, the location data of the target object can be updated at any time, ensuring accuracy and real-time performance in complex environments. By continuously tracking the dynamic location of the target object, data support is provided for subsequent path calculation, ensuring that the real-time location of the target object is always under monitoring.

[0053] The second step is based on real-time location information Ptarget (t) and the target location, through the path cost function:

[0054]

[0055] For evacuation route P path (t)={(x1, y1), (x2, y2),..., (x m y m )} is updated in real time, where d i G represents the Euclidean distance between path nodes. i h represents the actual cost of reaching the path node. i The heuristic estimated cost of the target node is represented by α and β, which are adjustment factors, and n represents the number of path nodes.

[0056] Specifically, based on the real-time location information of the target object, the evacuation route is further intelligently optimized through a path cost function. This path cost function considers multiple factors, including the physical distance between path nodes, the actual cost of reaching the node, and heuristically estimated costs. The route is dynamically adjusted based on these factors to select the shortest and lowest-cost evacuation route. By introducing adjustment factors, the path optimization strategy can be flexibly adjusted according to specific circumstances. For example, in areas with high population density, the requirements for path safety can be increased to ensure that the evacuation process is both efficient and safe. This process not only considers the static environment but also repeatedly calculates and corrects the route based on real-time data, maximizing the practicality and emergency response capability of the evacuation route.

[0057] The third step, according to:

[0058]

[0059] Determine the degree of influence of obstacles And based on the impact and the distance between each path node and the obstacle location, the position of the path node is updated, where (x i y i ) indicates the location of a path node. Indicates the location of the obstacle. Indicates the radius of the obstacle.

[0060] Specifically, when updating evacuation routes in real time, the impact of obstacles on route planning must also be considered. By detecting and analyzing obstacles in the environment, the impact of obstacles on path nodes is calculated, and the route planning is dynamically adjusted accordingly. The calculation of impact is based not only on the distance between the obstacle and the path node, but also on the type and size of the obstacle, as well as the complexity of the surrounding environment. By continuously updating the positions of path nodes, the route is prevented from being blocked by obstacles or its safety reduced, ensuring that the evacuation route remains unobstructed at all times.

[0061] In some embodiments, reference Figure 2 As shown, the priority adjustment module 3 may include a personnel density calculation unit 31, a priority weight allocation unit 32, a primary adjustment unit 33, and a secondary adjustment unit 34.

[0062] in:

[0063] Personnel density calculation unit 31 can be used to calculate based on personnel distribution information, according to:

[0064]

[0065] Determine the population density data ρ for each area i , where N i Let A represent the number of people in the i-th region. i Let represent the area of ​​region i. Specifically, the personnel density calculation unit determines the number of people and their area in each region by performing a detailed analysis of the personnel distribution information within the target region. Based on this, the personnel density of each region can be calculated, thus providing basic data for subsequent priority allocation.

[0066] Priority weight allocation unit 32 can be used based on personnel density data, according to:

[0067] p i =α′×ρ i +β′×h i

[0068] Assign initial priority p to each region i Where α′ and β′ are preset weighting coefficients, h i This represents the altitude of region i. Specifically, the priority weight allocation unit assigns a preliminary priority to each region based on the data provided by the personnel density calculation unit. This priority is related not only to personnel density but also to the region's altitude. Through preset weight coefficients, the priority weight allocation unit can assign reasonable priority values ​​to different regions based on their personnel density and altitude. High-density regions and high-altitude regions will be given higher priority to ensure that regions that are more affected and at higher risk receive early warning information first during flood discharge warnings.

[0069] The adjustment unit 33 can be used according to:

[0070] P b =p i ×(1+γ′×Δρ i )

[0071] The initial priority is adjusted using changes in regional population density, where γ′ represents the dynamic adjustment coefficient, and Δρi P represents the change in population density in a region. b This indicates the adjusted priority weight. Specifically, the priority adjustment unit further adjusts the initial priority based on changes in regional population density. This adjustment is based on fluctuations in regional population density; by calculating the changes, the adjustment coefficient dynamically reflects the impact of increases or decreases in population density on priority. As population density changes, the priority weight will be increased or decreased in a timely manner, ensuring that the system can reflect the impact of population density on the demand for early warning information in real time. The introduction of the dynamic adjustment coefficient allows priority adjustment to consider not only static population density but also changes brought about by population movement.

[0072] The secondary adjustment unit 34 can be used to utilize the real-time location information provided by the positioning module, according to:

[0073] P u =P b ×exp(-λ′×d i )

[0074] For the adjusted priority weight P b A second adjustment is made, where λ′ represents the attenuation coefficient, and d i This indicates the distance between the target object and the designated broadcast center. Specifically, the secondary adjustment unit uses the real-time location information of the target object provided by the positioning module to further optimize the initially adjusted priority. By calculating the actual distance between the target object and the designated broadcast center, the system can adjust the priority based on distance factors. For areas with greater distance, the priority weight will be gradually reduced through an attenuation coefficient to reflect the increased actual transmission difficulty. This ensures that the priority of the broadcast signal matches the actual location and propagation conditions of the target object, avoiding excessive interference with broadcast content in areas with greater distance.

[0075] In some embodiments, reference Figure 2 As shown, the directional early warning module 5 may include a path identification unit 51, an information transmission optimization unit 52, a path tracking unit 53, and a path feedback unit 54. Wherein:

[0076] The path identification unit 51 can be used to calculate the transmission path of the warning information based on the target object location information obtained by the positioning module. Specifically, this unit determines the optimal path for the warning information from the broadcast source to the target object by analyzing the real-time position and movement trajectory of the target object. In this embodiment, the position of the target object at time t can be represented as a three-dimensional vector p(t), where p(t) = [p x (t), p y (t), p z (t)] T, representing the coordinates of the target object in space. The velocity of the target object v(t) = [v x (t), v y (t), v z (t)] T This velocity vector, obtained through the positioning module, describes the target object's movement in space. In calculating the path for transmitting early warning information, the path cost is assumed to be C(t), which considers both the target object's motion and obstacles in the surrounding environment. The path cost function can be expressed as follows:

[0077]

[0078] Here, f(p(t′)) represents the cost of the path based on the target object's position p(t′) and dynamic environmental factors, such as obstacles and terrain changes. By optimizing this cost function, the path recognition unit determines the optimal path and adjusts the path in real time to ensure that the warning information can be transmitted to the target object in the shortest possible time.

[0079] In addition, in some embodiments, the warning information transmission path is calculated based on the target object location information obtained by the positioning module, specifically including the following steps: based on the target object location information, the regional location of the target object within a preset area is obtained; based on the regional location and the preset broadcast area boundary, the shortest communication path between the target object and the multi-mode broadcast module is determined, and the communication path includes at least one relay node or forwarding point.

[0080] Specifically, through the boundary A of the preset area broadcast Compare and determine whether the target object is within the broadcast area. If p target (t)∈A broadcast If the target object is located within the broadcast area, proceed to the subsequent path calculation step. Calculate the shortest communication path between the target object and the broadcast module, assuming the multi-mode broadcast module is located at... The shortest distance between the target object and the multi-mode broadcast module is calculated using the Euclidean distance formula:

[0081] d min (t)=‖p target (t)-p broadcast ||

[0082] If there are obstacles or communication signals that cannot directly reach the target object and the broadcast module, the shortest path needs to be optimized through relay nodes. Let the set of relay nodes be R. relay ={r1, r2, ..., r n The path passes through relay node r. iAt this time, the transmission quality of the path is optimized based on signal attenuation. The optimized shortest path L min (t) can be expressed by the following formula:

[0083]

[0084] This optimization process effectively improves communication efficiency by taking into account signal propagation loss and path delay.

[0085] The information transmission optimization unit 52 can be used to optimize the propagation channel and signal strength in the transmission path based on the early warning information transmission path. Specifically, this unit is mainly used to adjust the signal propagation strength and propagation path to ensure that the information can maintain the best transmission quality under different environmental conditions. The signal propagation strength S(t) attenuates as the distance d(t) increases, and its mathematical expression is:

[0086]

[0087] Where S0 is the initial signal strength of the signal source, and d(t) represents the distance between the target object and the signal source. The propagation attenuation index is represented by A(t), which can be determined by environmental factors such as air density, obstacles, and climate conditions. To account for the influence of the environment on the signal, signal propagation attenuation also needs to be considered in conjunction with environmental factors, where A(t) is the environmental factor, representing the degree of signal attenuation caused by factors such as weather, terrain, and obstacles. The influence of environmental factors can be expressed by the following integral formula:

[0088]

[0089] Where g(p(t′)) represents the influence function of environmental factors on signal propagation at time t′. By combining environmental factors with propagation attenuation, the final signal strength S f (t) can be represented as:

[0090] S f (t)=S(t)×A(t)

[0091] By optimizing signal strength and propagation path, the information transmission optimization unit can ensure that early warning information can still be transmitted efficiently and reliably in complex environments.

[0092] The path tracking unit 53 can be used to update the transmission path of the warning information in real time based on the movement of the target object. Specifically, the path tracking unit is mainly used to track the movement of the target object in real time and dynamically update the transmission path of the warning information. The motion state of the target object at time t is described by the velocity vector v(t), which is used to calculate the position change of the target object at each time. The position p(t) of the target object can be obtained by integrating the target velocity vector:

[0093]

[0094] Where p(t0) is the position of the target object at initial time t0, and v(t′) is the velocity of the target object at time t′. Based on the real-time acquired velocity and position information, the path tracking unit continuously updates the position of the target object and adjusts the transmission path of the warning information in real time. The path tracking update process can be described by the following formula:

[0095] p new (t)=p old (t)+Δp(t)

[0096] Where, p new (t) represents the updated target position, p old Δp(t) represents the target position at the previous moment, and Δp(t) represents the displacement caused by the motion of the target object, which can be determined by the target's velocity vector and time step. The path tracking unit updates the target object's position in real time and dynamically adjusts the transmission path of the warning information according to its position to ensure that the information can be accurately transmitted to the target.

[0097] The path feedback unit 54 can be used to dynamically adjust the transmission path of the warning information based on the target object's response to the warning information. Specifically, the target object's response can be represented by the response value R(t), where R(t) = 1 indicates that the target object has responded to the warning, and R(t) = 0 indicates that the target object has not responded. When the target object does not respond to the warning, the path feedback unit needs to increase the magnitude of the path adjustment, and vice versa. The path adjustment amount Δp(t) can be weighted according to the target object's response, and the specific adjustment coefficient ω(t) changes dynamically according to the target object's response, expressed as:

[0098]

[0099] Where ω(t) represents the path adjustment coefficient, and τ represents the adjustment parameter used to control the sensitivity of path adjustment. The path feedback unit dynamically adjusts the path based on the target's response, ensuring the accuracy and timeliness of early warning information.

[0100] In some embodiments, reference Figure 2 As shown, the data fusion module 7 may include a signal fusion unit 71, a warning information generation unit 72, and a data transmission unit 73. Wherein:

[0101] The signal fusion unit 71 can receive various real-time data from the multi-mode broadcast module, the positioning module, and the environmental sensor, and perform fusion processing on the real-time data according to the weighted average method to generate fused signal data S. f The fusion process is represented as:

[0102]

[0103] Among them, X i Let α represent the i-th data source. i Let α represent the weight coefficient of the i-th data source, and let α represent the weight coefficient of the ith data source. i according to:

[0104]

[0105] To determine, where Var(X) j ) represents the variance of the j-th data source.

[0106] The signal fusion unit is primarily used to receive real-time information from different data sources and perform weighted fusion processing. Specifically, this unit integrates real-time data from the multi-mode broadcast module, positioning module, and environmental sensors using a weighted average method to generate an optimized fused signal. By dynamically adjusting the weight coefficients of each data source and optimizing the fusion result based on the volatility and reliability of the data sources, the accuracy and reliability of the final signal are improved, ensuring that the system can cope with environmental changes and provide more accurate early warning information.

[0107] The early warning information generation unit 72 can be used to generate early warning information based on fused signal data S f Generate early warning information W based on:

[0108]

[0109] Generate early warning information W, where F(S) f T e ) represents the function for generating early warning information, S f T represents the signal fusion result. e Indicates environmental parameters.

[0110] The early warning information generation unit generates early warning information suitable for the current environmental conditions by applying a predetermined generation function to comprehensively calculate the fused signal based on the relationship between the fused signal data and environmental parameters. This ensures that the generated early warning information not only considers information from different data sources but also adjusts according to real-time environmental changes, guaranteeing high accuracy and timeliness of the output early warning information.

[0111] The data transmission unit 73 can be used to transmit early warning information to the multi-mode broadcast module through multiple communication channels. Specifically, the data transmission unit can select and transmit early warning information to the multi-mode broadcast module through the optimal communication channel. For example, this unit can automatically switch the transmission path according to real-time changes in the communication environment. Whether through wireless signals, satellite communication, or the Internet, the data transmission unit can maintain a highly stable and low-latency transmission effect to improve the reliability and speed of information transmission.

[0112] In some embodiments, reference Figure 2 As shown, the flood discharge early warning system based on multi-mode broadcasting also includes a self-organizing network module 8, which can be used to automatically build and manage a temporary wireless communication network in environments lacking communication infrastructure, ensuring the real-time transmission of early warning information.

[0113] Specifically, the self-organizing network module 8 can automatically discover available nodes in the vicinity and self-organize to establish a temporary wireless communication network, ensuring the transmission of early warning information in environments lacking traditional communication infrastructure. The module first scans and identifies surrounding nodes, dynamically selecting the optimal transmission path using distributed routing protocols (such as AODV and OLSR). Each node decides whether to forward data based on the surrounding network conditions. Network topology management ensures dynamic adjustment of node connections during operation to avoid communication interruptions caused by node failures. Simultaneously, the module ensures successful delivery of early warning information in the event of signal interference or network congestion through reliable transmission and retry mechanisms.

[0114] In some embodiments, reference Figure 2 As shown, the self-organizing network module 8 may include a node creation unit 81, a data interaction unit 82, a node monitoring and management unit 83, and a network topology adjustment unit 84. Wherein:

[0115] The node creation unit 81 can be used to automatically establish a temporary wireless ad hoc network in environments lacking communication infrastructure by connecting device nodes via wireless signals. An environment lacking communication infrastructure can refer to a specific area where traditional communication infrastructure, such as base stations, internet access points, communication towers, switches, and routers, is unavailable or not deployed. In such environments, existing communication networks cannot achieve effective signal transmission or information exchange, rendering conventional communication methods unusable. Specifically, the node creation unit communicates through wireless signal connections between device nodes and uses an adaptive signal reception algorithm to evaluate the signal strength and quality of each device. During network initialization, each node first enters scanning mode to identify surrounding device nodes and determine their signal strength and communication capabilities. In the initial stage, these nodes identify each other with nearby devices through broadcast signals and establish preliminary adjacency relationships based on information such as signal strength and communication quality between devices. In this way, the node creation unit can quickly form a preliminary network topology. Connections are established between device nodes with good signal quality and strong communication capabilities, establishing the first-level communication links. Once a node successfully establishes connections with multiple nodes, the node creation unit selects the optimal node as a relay node based on the current network connectivity and signal quality of each device to extend network coverage. At this time, the wireless communication frequency band and power of each device node are dynamically adjusted according to the real-time requirements of the ad hoc network environment to optimize signal coverage and data transmission quality across the entire network. The node creation unit adjusts the node connection method in real-time during communication through a low-latency feedback mechanism to ensure stable network operation even in dynamically changing environments.

[0116] The data interaction unit 82 can be used for data interaction between device nodes via a wireless ad hoc network. Specifically, the data interaction unit can utilize distributed routing protocols in the ad hoc network, such as AODV (Ad-hoc On-demand Distance Vector) or OLSR (Optimized Link State Routing), to achieve dynamic data routing. Each device node evaluates the optimal data forwarding path based on its own location information, received signal quality, and information about connected nodes. When a device node receives a data request from another node, the data interaction unit determines the data transmission path based on the network topology and current signal conditions. This path is a result of considering multiple factors, including transmission delay, inter-node bandwidth, and link quality. The data interaction unit can also have data caching and transmission optimization functions. In the event of network interruption or packet loss during transmission, caching technology is used to temporarily store the data to be transmitted, and retransmission is performed when the network recovers or the link quality improves, ensuring reliable data transmission. To adapt to network requirements in different environments, the data interaction unit can also dynamically adjust the size of data packets, transmission frequency, and routing selection according to changes in data traffic to improve data transmission efficiency and reduce unnecessary latency.

[0117] The node monitoring and management unit 83 can be used to monitor the communication status of each device node in a wireless ad hoc network, and to obtain the signal strength and connection quality of the nodes in real time. This unit comprehensively understands the real-time operating status of each node in the network by collecting information such as signal strength, communication quality, network load, and connection status of each device node. Specifically, the node monitoring and management unit periodically sends signal quality probe packets to each device node and calculates key performance indicators such as signal strength, signal-to-noise ratio (SNR), and throughput for each node based on the received feedback information. For nodes with poor performance or weak signals, the node monitoring and management unit can issue warning signals and automatically adjust the communication strategies of these nodes, such as increasing transmission power, switching to a more suitable communication channel, or reassigning them to a more suitable network location. The node monitoring and management unit can also have a self-healing function; when a node fails or the network is interrupted, it can automatically detect and find a replacement node to ensure the connectivity of the entire network. Furthermore, this unit can continuously monitor the performance changes of each node through adaptive algorithms, and use machine learning models for prediction and adjustment to optimize network stability and traffic balance. To cope with network fluctuations caused by environmental changes, the node monitoring and management unit also dynamically adjusts network parameters, such as scheduling frequency and signal enhancement level, to ensure that the entire network can still operate stably in complex environments.

[0118] The network topology adjustment unit 84 can be used to dynamically adjust the network topology of a wireless ad hoc network based on communication status. This unit acquires performance data of each node in the network in real time and uses optimization algorithms to precisely adjust the network topology. After network startup, the network topology adjustment unit can adjust the network topology based on communication quality between nodes, node signal strength, node load, and other factors. Specifically, by analyzing bottleneck locations or communication obstacles in the network, the network topology adjustment unit automatically identifies unstable or weak signal parts of the network topology and readjusts the connection methods of these parts. For example, it can select better relay nodes, improve the connection methods between nodes, or reconfigure frequency bands and transmission power to avoid data bottlenecks or transmission interruptions. The network topology adjustment unit can also predict the network load in the future based on deep learning algorithms and optimize network paths in advance based on the prediction results. When some nodes in the network are overloaded or their signal quality deteriorates, the topology adjustment unit will automatically adjust the routes to avoid excessive traffic concentration on certain nodes, thereby improving the network's load balancing capability and transmission efficiency. This unit can also dynamically adjust the network topology based on information such as the remaining power of nodes, device type, and priority, to ensure the efficient operation of the entire ad hoc network.

[0119] In some embodiments, the flood discharge early warning system based on multi-mode broadcasting further includes a risk value prediction module, which can be used to predict the flood discharge risk value based on real-time environmental data, historical flood discharge events and prediction models, and send the flood discharge risk value to the data fusion module to provide risk information for the generation of early warning information.

[0120] Real-time environmental data refers to various environmental data acquired in real time during system operation, including but not limited to meteorological information (such as rainfall, temperature, and wind speed), hydrological information (such as water level and flow rate), and geological information (such as topography and soil moisture). Historical flood discharge events refer to data records of past flood discharge events collected and stored by the system. These records include information such as the time, location, environmental conditions, impact range, and damage of the flood discharge events, used to analyze and predict potential future flood discharge risks. The prediction model refers to an algorithmic structure built based on machine learning, deep learning, or statistical regression techniques, used to analyze and learn the correlation between real-time environmental data and historical flood discharge event data. The output value generated by this model is the flood discharge risk value. The flood discharge risk value can be represented by a numerical value calculated by the risk value prediction module, indicating the probability and severity of flood disasters occurring in a certain area or time period, used to guide the generation of early warning information and the adjustment of the broadcast range.

[0121] For example, the risk value prediction module can use an architecture combining deep neural networks and recurrent neural networks to predict flood discharge risk values. The prediction process of the model can be represented as follows:

[0122]

[0123] Among them, R flood X(t) represents the flood discharge risk value at time t; X(t) = [X1(t), X2(t), ..., X... n (t)] T H represents the feature vector of real-time environmental data at time t, containing multiple environmental variables such as water level, precipitation, and flow rate; H = [h1, h2, ..., ht]. k ] T The data matrix h represents historical flood discharge events. i Z is the data vector of the i-th historical flood discharge event; env (t) represents the nonlinear transformation of environmental variables at time t, which is extracted and mapped using a deep neural network layer; Z historical (t) represents the temporal transformation of historical flood discharge event data, and a recurrent neural network is used for temporal learning and data fitting.

[0124] Furthermore, the environmental data X(t) is processed through a deep neural network layer. and activation function f env The data is processed and subjected to nonlinear mapping to obtain a high-dimensional representation Z of the environmental data. env (t), that is:

[0125]

[0126] Among them, f env (·) represents a non-linear activation function (such as ReLU or Sigmoid). This indicates a network layer that has undergone processing such as convolution and fully connected layers.

[0127] Historical flood discharge event data H and the risk prediction value R at the previous moment flood (t-1) is input into a recurrent neural network for time-series learning. Through time recursion, historical data and the risk prediction value from the previous moment are merged into time-series transformed data Z. historical (t), this process can be represented as follows:

[0128]

[0129] Among them, f historical (·) is the activation function of the RNN layer. This describes the time-series computation process for recursive networks.

[0130] The environmental data Z after feature extraction env (t) and historical data Z historical (t) After merging, a fully connected layer is used. And a final nonlinear activation function f final Then, the final risk value prediction calculation is performed. This calculation process can be expressed as:

[0131]

[0132] in, f represents a fully connected layer final (·) represents the activation function of the output layer, used to map the predicted values ​​to the final risk value R. flood (t). Upon receiving the flood discharge risk value, the data fusion module automatically adjusts the warning intensity, broadcast content, and broadcast range of the early warning information based on that risk value. Specifically, when the flood discharge risk value is high, the warning intensity is increased, the broadcast range is expanded, and detailed warning content (such as the flood discharge area, water level changes, evacuation routes, etc.) is provided. Conversely, if the risk value is low, the warning intensity is reduced, the broadcast range is narrowed, and the warning content is simplified.

[0133] The flood discharge early warning system based on multi-mode broadcasting in the above embodiments can accurately determine the location of the target object based on real-time data acquisition and fusion processing from multiple data sources, and dynamically adjust the release method and priority of early warning information according to environmental changes, thereby ensuring efficient emergency response under various complex environmental conditions.

[0134] First, utilizing a multi-mode broadcast module to disseminate early warning information via wireless broadcast signals, audio-visual signals, and visual screens effectively covers a wide range of disaster-stricken areas, especially densely populated areas within the flood discharge zone. The introduction of multi-mode broadcasting ensures that even when broadcast signals are limited, alarm information can still be transmitted through multiple channels, thus avoiding omissions or miscommunications. Adjusting the broadcast range and priority based on population distribution information ensures that high-priority early warning information is delivered first in densely populated and high-risk areas, guaranteeing timely evacuation.

[0135] Secondly, the coordinated operation of the positioning module and the directional early warning module ensures that flood discharge early warning information can be accurately transmitted to the target object. Furthermore, the early warning path is dynamically adjusted based on the real-time location of the target object and its surrounding environment, further improving the accuracy and timeliness of information transmission. Especially during the movement of the target object, the path tracking and feedback mechanism can update the transmission path of the early warning information in real time, ensuring that the alarm information reaches the area where personnel are located in a timely manner.

[0136] In addition, the priority adjustment module dynamically adjusts the broadcast priority of each area based on real-time population density data and other environmental factors. This adjustment not only improves the accuracy of early warning information but also enables rapid response to changes in emergencies, ensuring the real-time nature and effectiveness of information transmission.

[0137] In environments lacking communication infrastructure, the self-organizing network module provides temporary wireless self-organizing network support, ensuring the flood warning system can still operate normally without existing communication infrastructure. Through node creation, data interaction, node monitoring, and network topology adjustment, the self-organizing network module can flexibly adjust the network structure according to actual on-site needs and network conditions, ensuring the wireless communication network remains stable in complex environments and effectively transmits early warning information.

[0138] Finally, the data fusion module integrates real-time data from multiple sensors, positioning modules, and broadcasting modules, and applies weighted averaging and other fusion algorithms to generate unified early warning information. This process ensures that information from different data sources can be accurately combined, avoiding errors that may arise from a single data source, and further enhancing the system's reliability and accuracy.

[0139] In summary, the flood discharge early warning system based on multi-mode broadcasting disclosed herein can effectively cope with complex geographical environments, weather changes, and dense population situations, ensuring accurate dissemination and real-time response of flood discharge early warning information, and greatly improving the efficiency and coverage accuracy of emergency response in flood discharge events.

[0140] It should be noted that although several modules or units of the flood discharge early warning system based on multi-mode broadcasting have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0141] Secondly, in an exemplary embodiment of this disclosure, a flood discharge early warning method based on multi-mode broadcasting is also provided. (See reference...) Figure 3 As shown, the flood discharge early warning method based on multi-mode broadcasting may include the following steps:

[0142] Step S310: Obtain the real-time location information of the target object through the communication base station and the smart terminal, and determine the transmission path of the warning information;

[0143] Step S320: Adjust the broadcast range of the multi-mode broadcast based on the personnel distribution information within the target flood discharge area;

[0144] Step S330: Based on preset rules, dynamically adjust the priority of the early warning information according to the personnel distribution information;

[0145] Step S340: Collect real-time data from multiple data sources and perform fusion processing on the real-time data to generate early warning information, and publish the early warning information through wireless broadcast signals, sound and light signals and visual screens.

[0146] The above-described flood discharge early warning method based on multi-mode broadcasting will be further explained below in an example embodiment.

[0147] First, real-time location information of the target objects is obtained through communication base stations and smart terminals to determine the transmission path of the early warning information. This path is determined based on the location of the target objects, the boundaries of the flood discharge area, and the coverage of the broadcast system, ensuring that the early warning information is accurately delivered to the target objects. Next, based on the distribution information of people within the target flood discharge area, the broadcast range of the multi-mode broadcast is dynamically adjusted to ensure that the broadcast signal covers all high-risk areas and densely populated locations during a flood discharge event, achieving comprehensive coverage. Subsequently, based on preset rules and combined with personnel distribution information, the broadcast priority of the early warning information is dynamically adjusted. Priority adjustment is based not only on personnel density but also on the level of flood discharge risk and the danger of different areas, ensuring that the most urgent early warning information is delivered to the most needy target objects first. Finally, real-time data is collected from multiple data sources, including environmental sensor data, meteorological information, and water level monitoring data, and processed using data fusion technology to generate accurate flood discharge early warning information. This information is disseminated through multiple methods, including wireless broadcast signals, audio-visual signals, and visual screens, to ensure that all target objects receive accurate early warning information in a timely manner under various environments, enabling them to make appropriate emergency preparations.

[0148] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0149] Furthermore, in an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described flood discharge early warning method based on multi-mode broadcasting is also provided.

[0150] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be embodied in the following forms: a completely hardware embodiment, a completely software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0151] The following reference Figure 4 To describe an electronic device 400 according to such an embodiment of the present disclosure. Figure 4 The electronic device 400 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0152] like Figure 4 As shown, the electronic device 400 is manifested in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, a bus 430 connecting different system components (including storage unit 420 and processing unit 410), and a display unit 440.

[0153] The storage unit stores program code that can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) unit 421 and / or a cache memory unit 422, and may further include a read-only memory unit (ROM) unit 423.

[0154] Storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0155] Bus 430 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0156] Electronic device 400 can also communicate with one or more external devices 470 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. As shown, network adapter 460 communicates with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0157] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware.

[0158] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of this disclosure may also be implemented as a program product including program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0159] refer to Figure 5 As shown, a program product 500 for implementing the above-described multi-mode broadcast-based flood warning method according to embodiments of the present disclosure is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0160] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0161] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0162] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0163] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A flood discharge early warning system based on multi-mode broadcasting, characterized in that, include: A multi-mode broadcast module is used to disseminate early warning information via wireless broadcast signals, audio-visual signals, and visual screens; The range adjustment module is used to adjust the broadcast range of the multi-mode broadcast module according to the personnel distribution information within the target flood discharge area; The priority adjustment module is used to adjust the broadcast priority of the multi-mode broadcast module according to preset rules, wherein the priority is determined based on the personnel distribution information provided by the range adjustment module. The positioning module is used to obtain the real-time location information of the target object through the communication base station and the smart terminal, and send the location information to the multi-mode broadcast module; The directional early warning module is used to determine the transmission path of the early warning information issued by the multi-mode broadcasting module based on the location information of the target object obtained by the positioning module. The data acquisition module is used to acquire various real-time data from the multi-mode broadcast module, the positioning module, and the environmental sensor. The data fusion module is used to fuse and process the various real-time data, generate the early warning information, and transmit the early warning information to the multi-mode broadcast module; The risk value prediction module is used to predict the flood discharge risk value based on real-time environmental data, historical flood discharge events and prediction models, and send the flood discharge risk value to the data fusion module to provide risk information for the generation of early warning information; The self-organizing network module is used to automatically build and manage temporary wireless communication networks in environments lacking communication infrastructure, ensuring the real-time transmission of the early warning information; The multi-mode broadcast module includes: A wireless broadcast signal unit is used to transmit early warning information to receiving devices within the target flood discharge area via broadcast signals within a preset frequency band; The sound and light signal unit is used to issue the warning information through an alarm and a flashing light. The alarm emits an audible alarm, and the flashing light flashes at a preset frequency. The frequency of the audible alarm and the flashing light is automatically adjusted by the priority adjustment module according to the population density and real-time situation in the target area. A visualization screen unit is used to display the warning information and evacuation path at a designated location within the target flood discharge area via a display screen. The evacuation path is updated in real time based on the location information of the target object obtained by the positioning module. The evacuation path is updated in real time based on the location information of the target object obtained by the positioning module, including: The positioning module obtains the real-time location information of the target object. and the location of obstacles, among which, Indicates time, and These represent the target object at time. The x and y coordinates of time; Based on the real-time location information And the target location, through the path cost function: Evacuation routes Real-time updates are performed, among which, Represents the Euclidean distance between path nodes. This represents the actual cost of reaching the path node. This represents the heuristic cost estimate for the target node. and As a regulating factor, Indicates the number of path nodes; according to: Determine the degree of influence of obstacles The positions of the path nodes are updated based on the influence and the distance between each path node and the obstacle location. Indicates the location of the path node. Indicates the location of the obstacle. Indicates the radius of the obstacle; The risk value prediction module uses a combination of deep neural networks and recurrent neural networks to predict flood discharge risk values. The prediction process of the model is represented as follows: in, Indicates time The flood discharge risk value is below; Indicates time Feature vectors of real-time environmental data; A data matrix representing historical flood discharge events. It is the first A data vector of historical flood discharge events; Indicates time Nonlinear transformation of environmental variables; This represents the time-series transformation of historical flood discharge event data; Environmental data After deep neural network layers and activation function The data is processed and then subjected to nonlinear mapping to obtain a high-dimensional representation of the environmental data. ,Right now: in, Represents a non-linear activation function. This represents a network layer that has undergone convolution and fully connected processing. Historical flood discharge data Risk prediction value at the previous moment The data is fed into a recurrent neural network for time-series learning; through time recursion, historical data and the risk prediction value from the previous moment are merged into time-series transformed data. : in, The activation function for the RNN layer. This describes the temporal computation process of a recursive network. Environmental data with feature extraction and historical data After merging, a fully connected layer is used. And a final nonlinear activation function Perform risk value prediction calculation: in, Indicates a fully connected layer. This represents the activation function of the output layer, used to map the predicted values ​​to the final risk values. After receiving the flood discharge risk value, the data fusion module automatically adjusts the warning intensity, broadcast content, and broadcast range of the early warning information based on the flood discharge risk value. The priority adjustment module includes: The personnel density calculation unit is used to calculate the personnel density based on the personnel distribution information, according to: Determine population density data for each area ,in, Indicates the first The number of people in the area Indicates the first The area of ​​the region; The priority weight allocation unit is used to assign weights based on the personnel density data, according to: Assign initial priorities to each region ,in, and For preset weighting coefficients, Indicates the first The altitude of the region; A primary adjustment unit is used to adjust based on: The initial priority is adjusted using changes in regional population density, wherein... Indicates the dynamic adjustment coefficient. This indicates the change in population density in a region. This indicates the adjusted priority weight; The secondary adjustment unit is used to utilize the real-time location information provided by the positioning module, according to: The adjusted priority weights A second adjustment was made, in which... Indicates the attenuation coefficient. Indicates the distance between the target object and the designated broadcasting center; The targeted early warning module includes: The path identification unit is used to calculate the transmission path of the warning information based on the target object location information obtained by the positioning module; The information transmission optimization unit is used to optimize the propagation channel and signal strength in the transmission path based on the early warning information transmission path; The path tracking unit is used to update the transmission path of the early warning information in real time based on the movement of the target object; The path feedback unit is used to dynamically adjust the transmission path of the warning information based on the response of the target object to the warning information; The step of calculating the warning information transmission path based on the target object location information obtained by the positioning module includes: obtaining the regional location of the target object within a preset area based on the target object location information; determining the shortest communication path between the target object and the multi-mode broadcast module based on the regional location and the preset broadcast area boundary, wherein the communication path includes at least one relay node or forwarding point; The self-organizing network module includes: The node creation unit is used to automatically establish a temporary wireless ad hoc network in environments lacking communication infrastructure by connecting device nodes via wireless signals. A data interaction unit is used to perform data interaction between device nodes through the wireless ad hoc network; The node monitoring and management unit is used to monitor the communication status of each device node in the wireless ad hoc network and obtain the signal strength and connection quality of the node in real time. The network topology adjustment unit is used to dynamically adjust the network topology of the wireless ad hoc network according to the communication status.

2. The flood discharge early warning system based on multi-mode broadcasting according to claim 1, characterized in that, The data fusion module includes: The signal fusion unit receives various real-time data from the multi-mode broadcast module, the positioning module, and the environmental sensor, and performs fusion processing on the real-time data according to the weighted average method to generate fused signal data. The fusion process is represented as: in, Indicates the first Various data sources Indicates the first The weighting coefficients of the various data sources, and the weighting coefficients according to: To determine, among which, Indicates the first The variance of the data sources; Early warning information generation unit, used for generating early warning information based on fused signal data Generate early warning information ,according to: Generate early warning information ,in, This represents the function for generating early warning information. Indicates the signal fusion result. Indicates environmental parameters; The data transmission unit is used to transmit the early warning information to the multi-mode broadcast module through multiple communication channels.

3. A flood discharge early warning method based on multi-mode broadcasting, applied to the flood discharge early warning system based on multi-mode broadcasting as described in any one of claims 1 to 2, characterized in that, The method includes: Real-time location information of the target object is obtained through communication base stations and smart terminals to determine the transmission path of the early warning information; Based on the distribution information of people within the target flood discharge area, adjust the broadcast range of the multi-mode broadcast; Based on preset rules, the priority of the early warning information is dynamically adjusted according to the personnel distribution information; Real-time data is collected from multiple data sources and fused to generate the warning information, which is then disseminated via wireless broadcast signals, audio-visual signals, and a visual screen.

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