Intelligent emergency early warning broadcasting system

Abstract

This invention relates to the field of emergency warning broadcasting technology, specifically an intelligent emergency warning broadcasting system, comprising: a data fusion module for receiving and parsing raw warning data streams to generate composite warning event objects; an impact range analysis module for spatially overlaying the object with a static geographic feature layer to dynamically identify target broadcasting units; a content generation module for matching templates based on event types and generating formatted warning text; an instruction scheduling module for allocating resources and generating broadcast instruction sets based on the real-time online status and channel load of the target broadcasting units; and an instruction execution module for issuing the instruction set to trigger emergency broadcasting. This system achieves accurate determination of the warning range based on real-time geospatial calculations and reliable instruction issuance adaptively based on network status, improving the accuracy and timeliness of warning delivery.

Description

Technical Field

[0001] This invention relates to the field of emergency warning broadcasting technology, and in particular to an intelligent emergency warning broadcasting system. Background Technology

[0002] Traditional emergency warning broadcasting systems typically employ a fixed-zone management model for determining warning targets. The system pre-binds geographical areas to broadcasting equipment, and warning information is released by associating it with corresponding administrative region codes or equipment group lists based on event type. This method relies on manually pre-defined static correspondences; the warning scope is fixed at system deployment and cannot be dynamically adjusted according to the actual geographical contours of the disaster's impact.

[0003] Existing technologies have shortcomings in determining the scope of early warning. The system can only trigger broadcasts based on preset fixed areas, failing to perform refined spatial analysis considering the specific geographical location, spread direction, and intensity level of the disaster event. This leads to a misalignment between the warning scope and the actual risk area, resulting in insufficient coverage of risk areas or unnecessary warning interference to safe areas, affecting the accuracy of warnings and public trust. At the command transmission level, the system generally uses simple list polling or multicast address broadcasting to issue commands, lacking a mechanism for sensing and judging the real-time operating status of broadcast terminals and the current load of communication channels.

[0004] This invention addresses the challenges of automatically and accurately delineating the geographical scope of early warning systems based on dynamic disaster parameters, and ensuring the reliable and timely delivery of early warning commands to target terminals in complex network environments. This requires the system to overcome the limitations of static area management, achieve dynamic matching of early warning ranges based on real-time spatial computation, and establish an adaptive command delivery channel with state awareness and resource scheduling capabilities. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent emergency early warning broadcast system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent emergency early warning broadcast system, comprising: The data fusion module receives raw early warning data streams from at least one external monitoring source, performs fusion and parsing on the raw early warning data streams, and generates composite early warning event objects containing event type, event location, and impact level. The impact range analysis module performs spatial overlay analysis between the composite early warning event object and the static geographic feature layer stored in the geographic information database to identify the set of dynamic target broadcast units located within the impact range; The content generation module, based on the event type of the composite early warning event object, matches a standard early warning text template from the contingency plan knowledge base, and generates formatted early warning broadcast text content by combining the event location and impact level; The instruction scheduling module allocates broadcast channel resources for the formatted warning broadcast text content based on the current online status and channel load of the dynamic target broadcast unit set, and generates a preliminary broadcast instruction set. The instruction execution module sends the initial broadcast instruction set to each broadcast unit in the corresponding dynamic target broadcast unit set to trigger emergency broadcasting.

[0007] As a further aspect of the present invention, a raw early warning data stream is received from at least one external monitoring source, and the raw early warning data stream is fused and parsed to generate a composite early warning event object containing event type, event location, and impact level, including: The system receives the original early warning data stream from the geological disaster monitoring source via a dedicated data interface. The original early warning data stream contains at least the original early warning code and the original geographic coordinates. The original warning code is standardized and decoded, and mapped to a preset event type code; The original geographic coordinates are transformed into a standard coordinate system to obtain the standard event location coordinates in the standard coordinate system. The impact level is calculated based on the original intensity parameters carried in the original early warning data stream and in combination with the preset impact level classification rules. The event type code, the standard event location coordinates, and the impact level are encapsulated into a structured composite early warning event object.

[0008] As a further aspect of the present invention, the composite early warning event object is spatially overlaid with a static geographic feature layer stored in a geographic information database to identify a set of dynamic target broadcast units located within the influence range, including: Based on the event type code in the composite early warning event object, load the corresponding spatial impact model parameters from the rule configuration library; Using the spatial influence model parameters and the standard event location coordinates, a dynamic influence range geometry is constructed in the spatial computing engine; Read the static geographic feature layer from the geographic information database, wherein the static geographic feature layer includes a broadcast unit location point layer and a population distribution heat map layer; The dynamic influence range geometry is spatially intersected with the broadcast unit location point layer to filter out all broadcast unit points whose geometric positions are located within the dynamic influence range geometry, thus forming a candidate broadcast unit set. The dynamic influence range geometry is spatially overlaid with the population distribution heat map layer, and the population density weight within the overlaid area is calculated. Based on the population density weight, the broadcast units in the candidate broadcast unit set are prioritized and the broadcast units that are offline are removed to form the dynamic target broadcast unit set.

[0009] As a further aspect of the present invention, based on the event type of the composite early warning event object, a standard early warning text template is matched from the contingency plan knowledge base, and combined with the event location and impact level, formatted early warning broadcast text content is generated, including: Based on the event type code in the composite early warning event object, an index is performed in the contingency plan knowledge base to retrieve the corresponding standard early warning text template, which contains a text structure and several variable placeholders. Query the standard geographic location name database to find the standard geographic location name that matches the standard event location coordinates; Based on the impact level, the corresponding level description phrase library is retrieved from the contingency plan knowledge base, and a level description phrase is randomly or selected according to rules; Fill the standard geographic location name and the level description phrase into the corresponding variable placeholders in the standard early warning text template; The padded text is then subjected to syntax validation and length optimization to generate the final formatted warning broadcast text content.

[0010] As a further aspect of the present invention, based on the current online status and channel load of the dynamic target broadcast unit set, broadcast channel resources are allocated to the formatted warning broadcast text content to generate a preliminary broadcast instruction set, including: The communication status of each broadcast unit in the dynamic target broadcast unit set is polled in real time to obtain the current online status and current channel occupancy rate of each broadcast unit; The urgency and data volume level of the broadcast task are determined based on the byte length of the formatted warning broadcast text content and the impact level of the composite warning event object. Taking into account the urgency of the broadcast task, the data volume level, and the current channel occupancy rate of each broadcast unit, a channel allocation algorithm is used to calculate the optimal broadcast channel or broadcast frequency band for each online broadcast unit; The formatted warning broadcast text content, the allocated broadcast channel or broadcast frequency band information, and the broadcast start timestamp are encapsulated into an independent unicast instruction package; A corresponding unicast instruction packet is generated for each online broadcast unit in the set of dynamic target broadcast units, and all unicast instruction packets together constitute the preliminary broadcast instruction set.

[0011] As a further aspect of the present invention, the preliminary broadcast instruction set is sent to each broadcast unit in the corresponding dynamic target broadcast unit set to trigger an emergency broadcast, including: Establish real-time communication links with each online broadcast unit in the set of dynamic target broadcast units; Through the real-time communication link, each unicast instruction packet in the initial broadcast instruction set is sent to the corresponding target broadcast unit. Receive instructions and confirmation feedback from each target broadcast unit; For broadcast units that do not receive acknowledgment, the command packet is retransmitted according to the preset retransmission strategy until the maximum number of retransmissions is reached or an acknowledgment is received. After all instruction packets have been successfully sent or the maximum number of retransmissions has been reached, a unified broadcast execution trigger signal is sent to each target broadcast unit.

[0012] As a further aspect of the present invention, using the spatial influence model parameters and the standard event location coordinates, a dynamic influence range geometry is constructed in the spatial computing engine, including: The spatial influence model parameters are input into the spatial computing engine. The spatial influence model parameters include at least the influence radius parameter and the influence direction parameter. Using the standard event location coordinates as the center point, and based on the influence radius parameter, an initial circular influence area is generated in the spatial computing engine; Based on the influence direction parameter, the initial circular influence area is geometrically transformed. If the influence direction parameter indicates a unidirectional or fan-shaped influence, the circular area is clipped into a fan-shaped area at a specified angle using the geometric clipping function of the spatial computing engine. The geometrically transformed regional graphic is overlaid with topographic elevation data from a geographic information database. A digital elevation model is then used to correct the terrain of the regional graphic, eliminating blind spots caused by terrain occlusion and generating the final dynamic influence range geometry.

[0013] As a further aspect of the present invention, a channel allocation algorithm is employed to calculate the optimal broadcast channel or broadcast frequency band for each online broadcast unit, including: Obtain the status information of all currently available broadcast channels, including the noise interference intensity and existing service load of each channel; Based on the byte length of the formatted warning broadcast text content and the impact level of the composite warning event object, calculate the minimum channel bandwidth and maximum tolerable delay required for this broadcast task; A multi-objective optimization function for channel allocation is established with the optimization objectives of minimizing channel interference and balancing the load of broadcast units. A heuristic search algorithm is used to solve the multi-objective optimization function, and an optimal broadcast channel or broadcast frequency band is assigned to each online broadcast unit, so that the overall communication quality of all broadcast units is optimal.

[0014] As a further aspect of the present invention, after triggering the emergency broadcast, it further includes: A broadcast effect monitoring cycle is initiated. During the broadcast effect monitoring cycle, broadcast status telemetry data returned by broadcast units in the dynamic target broadcast unit set is continuously received. The broadcast status telemetry data includes at least signal strength and estimated number of people reporting in the coverage area. The received broadcast status telemetry data is compared with the expected threshold preset in the effect evaluation model; When the signal strength is lower than the expected signal strength threshold, or the estimated number of people responding in the coverage area is lower than the expected number of people responding, it is determined that the broadcast effect of the area to which the broadcast unit belongs is not up to standard; For areas where the broadcast effect is not up to standard, a broadcast enhancement strategy is generated.

[0015] As a further aspect of the present invention, the system includes: For areas where the broadcast effect is not up to standard, obtain detailed location information of the broadcast units corresponding to the area; Using the detailed location information as the center, search for other backup broadcast units within a preset radius in the broadcast unit location point layer of the geographic information database to form a backup broadcast unit list; Assess the overlap between the current available load and theoretical coverage of each backup broadcast unit in the backup broadcast unit list and the non-compliant area; Select one or more backup broadcast units with the best evaluation results as supplementary broadcast units; Based on the original formatted warning broadcast text content, generate a supplementary broadcast instruction; The reinforcement broadcast command is sent to the reinforcement broadcast unit, and the reinforcement broadcast is triggered.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: By performing real-time spatial overlay calculations on composite early warning event objects, including event location and impact level, with a database storing detailed geographic feature information, the system can dynamically generate the geographic boundaries of the early warning range based on the physical parameters of the disaster itself. This process transforms the selection of early warning targets from relying on a preset list of administrative regions to real-time analysis based on geospatial relationships, enabling precise matching between the scope of early warning information dissemination and the actual impact area of ​​the disaster. This solves the problem of inaccurate early warning range delineation and improves the accuracy of spatial orientation.

[0017] By acquiring and analyzing the online operating status of each unit in the target broadcast unit set and the real-time load data of its communication channel before executing broadcast commands, the system can allocate resources and select paths based on these dynamic network parameters. This mechanism transforms the command issuance strategy from simple broadcasting or polling to optimized scheduling based on real-time network conditions. The system can automatically avoid faulty nodes and select transmission paths with lighter loads, thereby ensuring the reliability and timeliness of critical early warning command transmission in complex real-world network environments.

[0018] By accurately associating and binding the formatted warning broadcast text with a dynamic set of target broadcast units determined through spatial analysis, and a set of broadcast instructions generated through real-time scheduling, the system achieves fully automated and intelligent processing from warning information generation to terminal broadcasting. This closed-loop process eliminates the delays and errors that may be caused by manual operation in multiple stages in traditional methods, ensuring that the precisely calculated and optimized warning content can be accurately and quickly transmitted to broadcast terminals in the target area and ultimately reach the public. Attached Figure Description

[0019] Figure 1 This is a timing diagram of the intelligent emergency early warning broadcast system described in this invention; Figure 2 A flowchart for impact range analysis and target broadcast unit identification; Figure 3 A flowchart for generating and formatting warning content; Figure 4 A comparative chart showing the correlation between broadcast unit status and early warning task attributes; Figure 5 A bar chart showing the results of data fusion and analysis for broadcast early warning. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] See Figure 1 The data fusion module receives raw early warning data streams from at least one external monitoring source and performs fusion analysis on these streams to generate composite early warning event objects containing event type, event location, and impact level. The impact range analysis module performs spatial overlay analysis on this composite early warning event object and static geographic feature layers stored in the geographic information database to identify a set of dynamic target broadcast units located within the impact range. The content generation module matches standard early warning text templates from the contingency plan knowledge base based on the event type of the composite early warning event object and combines this with the event location and impact level to generate formatted early warning broadcast text content. The instruction scheduling module allocates broadcast channel resources to this formatted early warning broadcast text content based on the current online status and channel load of the dynamic target broadcast unit set, generating a preliminary broadcast instruction set. The instruction execution module sends the preliminary broadcast instruction set to each broadcast unit in the corresponding dynamic target broadcast unit set, triggering an emergency broadcast.

[0023] In one embodiment of the present invention, the data fusion module receives raw early warning data streams from meteorological monitoring sources and geological disaster monitoring sources through a dedicated data interface. The raw early warning data streams contain at least raw early warning codes and raw geographic coordinates. The data fusion module performs standardized decoding on the raw early warning codes, mapping them to preset event type codes. These preset event type codes are used to uniquely identify different types of early warning events, such as typhoons, earthquakes, or floods. In a specific implementation, the data fusion module performs a coordinate system transformation on the raw geographic coordinates to obtain standard event location coordinates in a standard coordinate system. The coordinate system transformation uses the WGS84 coordinate system as the target standard coordinate system. The raw geographic coordinates are calculated using a seven-parameter transformation model based on the definition of their source coordinate system. The seven-parameter transformation model includes translation parameters, rotation parameters, and scale parameters. In some embodiments, the coordinate system transformation process also integrates elevation datum transformation to ensure the accuracy of the standard event location coordinates in three-dimensional space. The data fusion module calculates the impact level based on the raw intensity parameters carried in the original early warning data stream and a preset impact level classification rule. This preset rule defines a mapping relationship between the raw intensity parameters and discrete impact levels for different event types. For example, for earthquake events, the raw intensity parameter is the Richter magnitude value, and the preset rule maps a specific magnitude range to a Level 1, Level 2, or Level 3 impact level. It can be understood that the calculation of the impact level must select the corresponding mapping rule from the preset rule based on the event type. In practice, the impact level is calculated using the following normalized quantification formula:

[0024] Where: L represents the calculated integer impact level, and P represents the specific value of the original intensity parameter extracted from the original warning data stream. This indicates the preset minimum intensity threshold for the current event type. This represents the maximum intensity threshold preset for the current event type, where N represents the total number of influence levels defined by the system. This represents the floor function. This formula normalizes the continuous values ​​of the original intensity parameter and maps them to an integer impact level from 1 to N. The data fusion module encapsulates the event type code, standard event location coordinates, and impact level into a structured composite early warning event object. This composite early warning event object is organized in JSON data format and includes an event type code field, a standard event location coordinate field, an impact level field, and metadata fields such as timestamps and data source identifiers.

[0025] In some embodiments, the encapsulation process further includes generating a globally unique identifier for the composite early warning event object. This globally unique identifier is used to track the composite early warning event object throughout the system. Optionally, after completing the mapping and transformation, the data fusion module performs a logical consistency check. The logical consistency check verifies whether the event type code exists in the system's predefined code list and whether the standard event location coordinates are within a preset geographical boundary. It is understood that the logical consistency check is a necessary step to ensure the validity of the composite early warning event object. Optionally, the data fusion module deploys an asynchronous message queue mechanism to buffer and process multiple concurrently arriving raw early warning data streams. This asynchronous message queue mechanism improves the throughput and reliability of the data fusion module.

[0026] In one embodiment of the present invention, see [reference] Figure 2The impact range analysis module loads the corresponding spatial impact model parameters from the rule configuration library based on the event type code in the composite early warning event object. These parameters are then fully loaded into memory as input variables for impact range calculation. The module uses the spatial impact model parameters and standard event location coordinates to construct a dynamic impact range geometry in the spatial calculation engine. The construction process begins by inputting the spatial impact model parameters into the engine. These parameters include at least an impact radius parameter and an impact direction parameter. The impact radius parameter defines the initial radiation distance centered on the event location, while the impact direction parameter defines the dominant azimuth angle or sector of the impact. Using the standard event location coordinates as the center point, an initial circular impact area is generated in the spatial calculation engine based on the impact radius parameter. This circular impact area consists of a set of continuous boundary coordinate points. Based on the impact direction parameter, a geometric transformation is performed on the initial circular impact area. If the impact direction parameter indicates a unidirectional or sector-shaped impact, the circular area is clipped into a sector-shaped area with a specified angle using the geometric clipping function of the spatial calculation engine. The clipping operation is performed based on the starting and ending azimuth angles provided by the impact direction parameter. The geometrically transformed regional graphic is overlaid with topographic elevation data from a geographic information database. A digital elevation model (DEM) is used to correct the terrain of the regional graphic. This correction process calculates the actual obstruction effect of terrain undulations on the propagation of sound waves or radio signals, eliminating blind spots caused by terrain obstruction and generating the final dynamic influence range geometry. The dynamic influence range geometry is a complex set of polygons that may contain multiple unconnected sub-regions. In some embodiments, the spatial influence model parameters also include a diffusion attenuation coefficient, which is used to calculate the attenuation gradient of the influence based on distance and medium characteristics. The influence range analysis module reads static geographic feature layers from the geographic information database. These layers include a broadcast unit location point layer and a population distribution heatmap layer. The broadcast unit location point layer stores the precise latitude and longitude coordinates of all broadcast units, while the population distribution heatmap layer stores historical or real-time estimated population density data in a grid format. The influence range analysis module performs a spatial intersection operation between the dynamic influence range geometry and the broadcast unit location point layer. This spatial intersection operation uses a point-polygon inclusion detection algorithm to filter out all broadcast unit points whose geometric locations are within the dynamic influence range geometry, forming a candidate broadcast unit set. The impact range analysis module spatially overlays the dynamic impact range geometry with a population distribution heatmap layer, calculates the population density weight within the overlaid area, and iterates through each grid cell covered by the dynamic impact range geometry, accumulating the population density values ​​of each grid cell and normalizing them. The population density weight value is then calculated. The calculation formula is expressed as follows:

[0027] in: This represents the calculated population density weight, where k represents the total number of grid cells covered by the dynamic influence geometry. This represents the population density value of the i-th covered grid cell. This represents the proportion of the area where the i-th covered mesh cell intersects with the geometry of the dynamic influence range, and m represents the total number of all mesh cells in the entire analysis region. This represents the population density value of the j-th grid cell. This is understandable. This is a factor between 0 and 1, used to accurately calculate the contribution of grids located within the geometry of the dynamic influence range. The influence range analysis module prioritizes broadcast units in the candidate broadcast unit set based on population density weights, with broadcast units corresponding to areas with higher population density weights receiving higher priority. After sorting, the influence range analysis module removes broadcast units in an offline state, which is determined through a real-time status heartbeat mechanism, ultimately forming the dynamic target broadcast unit set.

[0028] In some embodiments, the result of the spatial intersection operation undergoes buffer expansion analysis, which considers the area within a preset distance around the broadcast unit location to address edge effects on signal coverage. Optionally, the data sources for the population distribution heatmap layer include mobile communication signaling data, household registration statistics, or land use type data. It is understood that the update frequency of the population distribution heatmap layer directly affects the timeliness of the population density weight calculation. When constructing the candidate broadcast unit set, the system also assigns a basic coverage capability weight to each broadcast unit based on its device model and power level. This basic coverage capability weight, along with the population density weight, is used together in the final priority ranking calculation.

[0029] In one embodiment of the present invention, see [reference] Figure 3The content generation module indexes the contingency plan knowledge base based on the event type code in the composite warning event object. The contingency plan knowledge base is a structured database storing standard warning text templates associated with various event type codes and other predefined content. The content generation module retrieves the corresponding standard warning text template, which contains a predefined text structure and several variable placeholders. These placeholders are marked with specific symbols to indicate the positions of content to be filled. The content generation module queries the standard geographic name database for standard geographic location names that match the standard event location coordinates. This database establishes a mapping between precise geographic coordinates and official administrative names and important feature names. The query operation is performed using a spatial latitude and longitude matching algorithm or a pre-established coordinate-place name index table. In some embodiments, when the standard event location coordinates cannot precisely match a single geographic name, the query operation returns a list containing multiple candidate names, selecting the most specific or commonly used standard geographic location name based on preset priority rules. The content generation module calls the corresponding level description phrase library from the contingency plan knowledge base based on the impact level in the composite warning event object. This level description phrase library is a collection of predefined descriptive phrases for each impact level. The content generation module randomly or according to rules selects a level description phrase from the level description phrase library. Random selection is achieved through a pseudo-random number generator, while rule-based selection may be based on hash values ​​of timestamps, event numbers, or other deterministic algorithms. The module then fills the standard geographic location name and level description phrase into the corresponding variable placeholders in the standard warning text template. The filling process involves string replacement to ensure that placeholders are correctly replaced and the text structure remains intact. Finally, the module performs syntax validation and length optimization on the filled text. Syntax validation checks the text's syntactic correctness and terminology consistency, while length optimization ensures that the generated text meets the length limits of the target broadcast channel or display device, generating the final formatted warning broadcast text content.

[0030] In some embodiments, length optimization employs a text compression algorithm or a key information priority pruning strategy. The text compression algorithm removes redundant words while preserving semantics, while the key information priority pruning strategy retains the most important information segments according to predefined rules. After performing grammar verification and length optimization on the padded text, the content generation module calculates a text optimization score. To quantitatively evaluate the optimization effect, text optimization scoring is used. The calculation formula is expressed as follows:

[0031] in: This indicates a text optimization score. This represents the syntax correctness score output by the syntax checking module, with a value range of 0 to 1. L represents the character length of the optimized text. Indicates the maximum text length allowed by the channel or device. These are preset weighting coefficients used to balance the importance of grammatical correctness and text length in the scoring. Optionally, the standard warning text templates in the contingency plan knowledge base support multiple languages. The content generation module selects the appropriate language version of the template based on the preset language preferences of the region where the dynamic target broadcast unit set is located. It is understandable that maintaining multilingual versions requires language alignment with the standard geographic name database and the level description phrase database. Before filling in the variable placeholders, the content generation module performs localization adaptation on the standard geographic location names and level description phrases. Localization adaptation includes using local dialect terms or descriptions that conform to local cultural understanding.

[0032] In one embodiment of the present invention, the instruction scheduling module polls the communication status of each broadcast unit in the dynamic target broadcast unit set in real time. The polling operation is achieved by sending a status query request to the address of each broadcast unit in the set and waiting for a response. The instruction scheduling module obtains the current online status and current channel occupancy rate of each broadcast unit. The current channel occupancy rate is expressed as a percentage, representing the ratio of the channel bandwidth currently used by a specific broadcast unit to its total available bandwidth. The instruction scheduling module determines the urgency and data volume level of the broadcast task based on the byte length of the formatted warning broadcast text content and the impact level of the composite warning event object. The determination process converts the discrete values ​​of the text byte length and impact level into corresponding urgency and data volume level codes according to a preset mapping rule. Combining the urgency of the broadcast task, the data volume level, and the current channel occupancy rate of each broadcast unit, the instruction scheduling module uses a channel allocation algorithm to calculate the optimal broadcast channel or broadcast frequency band for each online broadcast unit. The channel allocation algorithm obtains the status information of all currently available broadcast channels, including the noise interference intensity and existing service load of each channel. The noise interference intensity is measured in decibels and milliwatts, and the existing service load is represented by the proportion of the data throughput currently allocated to the channel to the total capacity. The channel allocation algorithm calculates the minimum channel bandwidth and maximum tolerable delay required for this broadcast task based on the byte length of the formatted warning broadcast text and the impact level of the composite warning event. The minimum channel bandwidth is calculated based on the text byte length, target transmission time, and coding efficiency, while the maximum tolerable delay is obtained from a preset delay level table based on the impact level. The channel allocation algorithm establishes a multi-objective optimization function for channel allocation with the optimization objectives of minimizing channel interference and balancing the load of broadcast units. The multi-objective optimization function uses interference intensity and load imbalance as penalty terms. The channel allocation algorithm uses a heuristic search algorithm to solve the multi-objective optimization function, allocating an optimal broadcast channel or broadcast frequency band to each online broadcast unit, thereby optimizing the overall communication quality of all broadcast units. The overall communication quality is evaluated using a scalarized quality index. The calculation formula is expressed as follows:

[0033] in: This represents the overall communication quality index that needs to be maximized, where U represents the total number of online broadcast units. This represents the channel allocated to the u-th broadcast unit. Indicates channel The noise interference intensity value, This represents the estimated total load rate after the u-th broadcast unit is assigned a task. This represents the average estimated total load factor across all online broadcast units. This is a positive coefficient used to balance the weights of interference and load balancing items. The instruction scheduling module encapsulates the formatted warning broadcast text content, the allocated broadcast channel or frequency band information, and the broadcast start timestamp into an independent unicast instruction packet. The unicast instruction packet adopts a binary protocol format and includes a packet header, payload data, and checksum. The instruction scheduling module generates a corresponding unicast instruction packet for each online broadcast unit in the dynamic target broadcast unit set. All unicast instruction packets together constitute the initial broadcast instruction set. See Table 1.

[0034] Table 1: Mapping Rules between Broadcast Task Urgency and Data Volume Level

[0035] In some embodiments, the broadcast start timestamp is uniformly calculated by the instruction scheduling module based on the estimated network transmission delay and system time synchronization calibration value, ensuring that all target broadcast units can start broadcasting at the synchronized time point or time window. The instruction execution module establishes a real-time communication link with each online broadcast unit in the dynamic target broadcast unit set, and the real-time communication link is established based on the TCP or UDP protocol. The instruction execution module sends each unicast instruction packet in the initial broadcast instruction set to the corresponding target broadcast unit through the real-time communication link. The sending process adopts an asynchronous non-blocking method to improve efficiency. The instruction execution module receives instruction reception confirmation feedback from each target broadcast unit. The instruction reception confirmation feedback is a short confirmation data packet. For broadcast units that do not receive confirmation feedback, the instruction execution module retransmits the instruction packet according to a preset retransmission strategy. The retransmission strategy specifies the retransmission time interval and the maximum number of retransmissions. After all instruction packets are successfully sent or the maximum number of retransmissions is reached, the instruction execution module sends a unified broadcast execution trigger signal to each target broadcast unit. The broadcast execution trigger signal is a short instruction that commands all broadcast units to start executing the content in the unicast instruction packet they received. It is understandable that retransmission strategies are necessary to ensure reliable transmission of instructions in unreliable network environments. In some embodiments, the instruction execution module digitally signs the instruction packet before sending it, and the target broadcast unit verifies the signature upon receipt to ensure the integrity and authenticity of the instruction's origin. Optionally, the establishment process of the real-time communication link includes two-way authentication and link encryption negotiation procedures to ensure communication security. It is understood that the authentication process is based on a pre-shared key or digital certificate mechanism. Optionally, the instruction execution module maintains an instruction issuance status table to track the issuance status, acknowledgment status, and retransmission count of each unicast instruction packet in real time, providing data for system monitoring.

[0036] See Figure 4This is a comparative chart analyzing the correlation between broadcast unit status and early warning task attributes, clearly showing the relationship between channel occupancy and early warning text byte length for different broadcast units. For nodes like broadcast unit 5, which have "high load + large text," the system should prioritize allocating channels with lower interference and lighter load, or employ multi-channel aggregation technology to ensure reliable transmission of early warning information. When both channel occupancy and text byte length of a broadcast unit are high, an emergency scheduling mechanism should be triggered to dynamically adjust the broadcast task priority of that unit, preventing critical early warning information from being delayed due to insufficient resources. The peaks and abrupt changes in the chart can serve as abnormal trigger points for system monitoring, used for real-time early warning of potential transmission bottlenecks or task anomalies.

[0037] In one embodiment of the present invention, after triggering an emergency broadcast, the system initiates a broadcast effect monitoring period. This monitoring period is a window of preset length used to collect and analyze feedback data following the broadcast execution. During the monitoring period, the system continuously receives broadcast status telemetry data returned by broadcast units in the dynamic target broadcast unit set. This telemetry data includes at least signal strength and an estimated number of people responding in the coverage area. Signal strength data is collected and reported by the field strength monitoring module onboard the broadcast unit or by nearby sensor nodes. The estimated number of people responding in the coverage area is estimated by analyzing changes in the signaling response of mobile terminals within the broadcast unit's coverage area or by a preset feedback mechanism. The system compares the received broadcast status telemetry data with preset thresholds in the effect evaluation model. The model stores preset signal strength thresholds and estimated number of people responding in the coverage area thresholds for different event types, impact levels, and regional characteristics. When the signal strength is lower than the expected signal strength threshold, or the estimated number of people responding in the coverage area is lower than the expected number of people responding, the system determines that the broadcast effect in the area to which the broadcast unit belongs is substandard, and generates a record containing a problem area identifier and the type of substandard indicator. The system generates broadcast enhancement strategies for areas where broadcast coverage is substandard. These strategies are a set of instructions designed to improve broadcast coverage in these areas. When generating these strategies, the system obtains detailed location information for the broadcast units corresponding to the substandard areas. This detailed information includes the broadcast unit's precise geographic coordinates, physical address code, and administrative region code. Using this detailed location information as the center, the system searches for other backup broadcast units within a preset radius in the broadcast unit location point layer of the geographic information database. The search operation utilizes the point buffer query function of the spatial database, forming a list of backup broadcast units. The system evaluates the overlap between the current available load and theoretical coverage of each backup broadcast unit in the list and the substandard areas. The current available load is calculated based on the backup broadcast unit's processor utilization and network bandwidth usage, while the theoretical coverage is calculated based on the backup broadcast unit's transmit power, antenna gain, and terrain data model. The evaluation process calculates a coverage optimization index to quantify the coverage. Evaluation of effectiveness:

[0038] in: This indicates the coverage optimization index. This represents the signal quality coefficient of the backup broadcast unit (based on its historical operating status and hardware performance). This represents the geographical overlap between the theoretical coverage area of ​​the backup broadcast unit and the non-compliant area. This represents the total area of ​​the non-compliant areas. This indicates the current available load value of the standby broadcast unit. This indicates the maximum load capacity of the backup broadcast units. The system selects one or more backup broadcast units with the best evaluation results as supplementary broadcast units, based on the coverage optimization index. The value is the highest and exceeds the preset activation threshold. Based on the original formatted warning broadcast text content, the system generates a supplementary broadcast instruction. The supplementary broadcast instruction is identical to the original instruction in content but differs in scheduling priority and target address. The system sends the supplementary broadcast instruction to the supplementary broadcast unit and triggers the supplementary broadcast. The sending process reuses the communication link and protocol of the instruction execution module.

[0039] In some embodiments, the length of the preset broadcast effect monitoring period is dynamically adjusted according to the impact level of the composite early warning event, with higher impact levels corresponding to longer monitoring periods. When comparing broadcast status telemetry data with the expected threshold, the system uses a sliding window averaging algorithm to smooth out misjudgments caused by instantaneous fluctuations. The sliding window averaging algorithm averages data points from multiple consecutive reporting periods. It is understood that the accuracy of the estimated number of people reported in the coverage area depends on the mobile terminal penetration rate and the coverage rate of the signaling acquisition system within the area. In some embodiments, the expected threshold in the effect evaluation model is not a fixed value, but a curve or surface dynamically adjusted based on time, weather conditions, and historical data from the same period. Optionally, when the area where the broadcast effect fails to meet the standard is large, the broadcast reinforcement strategy generated by the system may include a scheme to activate multiple backup broadcast units to form relays or coordinated broadcasts. It is understood that the issuance of reinforcement broadcast instructions needs to ensure that it does not cause unacceptable interference to other non-emergency broadcast services that the reinforcement broadcast unit may be currently conducting.

[0040] See Figure 5 This is a bar chart showing the results of broadcast early warning data fusion and analysis, illustrating the performance of different monitoring data sources in three dimensions: original data volume, processed data volume, and number of valid events. The processed data volume of all data sources is significantly smaller than the original data volume, with compression rates between 13% and 19%, indicating that the system's data cleaning and fusion modules can effectively filter redundant information. Data sources with larger original data volumes (such as meteorological monitoring) ultimately produce more valid early warning events, demonstrating that data volume is the fundamental guarantee for the production of valid events. Based on the positive correlation between the number of valid events and the data volume, a dynamic threshold can be established to provide early warnings of potential valid event outbreak risks when the original data volume of a certain type of data source shows abnormal fluctuations. Data fusion algorithms can be optimized to address the differences in compression rates among different data sources. For example, for high-compression data sources like meteorological monitoring, the ability to identify and filter redundant information can be further improved.

[0041] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An intelligent emergency early warning broadcast system, characterized in that, The system includes: The data fusion module receives raw early warning data streams from at least one external monitoring source, performs fusion and parsing on the raw early warning data streams, and generates composite early warning event objects containing event type, event location, and impact level. The impact range analysis module performs spatial overlay analysis between the composite early warning event object and the static geographic feature layer stored in the geographic information database to identify the set of dynamic target broadcast units located within the impact range; The content generation module, based on the event type of the composite early warning event object, matches a standard early warning text template from the contingency plan knowledge base, and generates formatted early warning broadcast text content by combining the event location and impact level; The instruction scheduling module allocates broadcast channel resources for the formatted warning broadcast text content based on the current online status and channel load of the dynamic target broadcast unit set, and generates a preliminary broadcast instruction set. The instruction execution module sends the initial broadcast instruction set to each broadcast unit in the corresponding dynamic target broadcast unit set to trigger emergency broadcasting.

2. The intelligent emergency early warning broadcast system according to claim 1, characterized in that, Receive raw early warning data streams from at least one external monitoring source, perform fusion and parsing on the raw early warning data streams, and generate composite early warning event objects containing event type, event location, and impact level, including: The system receives the original early warning data stream from the geological disaster monitoring source via a dedicated data interface. The original early warning data stream contains at least the original early warning code and the original geographic coordinates. The original warning code is standardized and decoded, and mapped to a preset event type code; The original geographic coordinates are transformed into a standard coordinate system to obtain the standard event location coordinates in the standard coordinate system. The impact level is calculated based on the original intensity parameters carried in the original early warning data stream and in combination with the preset impact level classification rules. The event type code, the standard event location coordinates, and the impact level are encapsulated into a structured composite early warning event object.

3. The intelligent emergency early warning broadcast system according to claim 2, characterized in that, Spatial overlay analysis is performed between the composite early warning event object and the static geographic feature layer stored in the geographic information database to identify a set of dynamic target broadcast units located within the influence area, including: Based on the event type code in the composite early warning event object, load the corresponding spatial impact model parameters from the rule configuration library; Using the spatial influence model parameters and the standard event location coordinates, a dynamic influence range geometry is constructed in the spatial computing engine; Read the static geographic feature layer from the geographic information database, wherein the static geographic feature layer includes a broadcast unit location point layer and a population distribution heat map layer; The dynamic influence range geometry is spatially intersected with the broadcast unit location point layer to filter out all broadcast unit points whose geometric positions are located within the dynamic influence range geometry, thus forming a candidate broadcast unit set. The dynamic influence range geometry is spatially overlaid with the population distribution heat map layer, and the population density weight within the overlaid area is calculated. Based on the population density weight, the broadcast units in the candidate broadcast unit set are prioritized and the broadcast units that are offline are removed to form the dynamic target broadcast unit set.

4. The intelligent emergency early warning broadcast system according to claim 3, characterized in that, Based on the event type of the composite early warning event object, a standard early warning text template is matched from the contingency plan knowledge base, and combined with the event location and impact level, formatted early warning broadcast text content is generated, including: Based on the event type code in the composite early warning event object, an index is performed in the contingency plan knowledge base to retrieve the corresponding standard early warning text template, which contains a text structure and several variable placeholders. Query the standard geographic location name database to find the standard geographic location name that matches the standard event location coordinates; Based on the impact level, the corresponding level description phrase library is retrieved from the contingency plan knowledge base, and a level description phrase is randomly or selected according to rules; Fill the standard geographic location name and the level description phrase into the corresponding variable placeholders in the standard early warning text template; The padded text is then subjected to syntax validation and length optimization to generate the final formatted warning broadcast text content.

5. The intelligent emergency early warning broadcast system according to claim 4, characterized in that, Based on the current online status and channel load of the dynamic target broadcast unit set, broadcast channel resources are allocated for the formatted warning broadcast text content, and a preliminary broadcast instruction set is generated, including: The communication status of each broadcast unit in the dynamic target broadcast unit set is polled in real time to obtain the current online status and current channel occupancy rate of each broadcast unit; The urgency and data volume level of the broadcast task are determined based on the byte length of the formatted warning broadcast text content and the impact level of the composite warning event object. Taking into account the urgency of the broadcast task, the data volume level, and the current channel occupancy rate of each broadcast unit, a channel allocation algorithm is used to calculate the optimal broadcast channel or broadcast frequency band for each online broadcast unit; The formatted warning broadcast text content, the allocated broadcast channel or broadcast frequency band information, and the broadcast start timestamp are encapsulated into an independent unicast instruction package; A corresponding unicast instruction packet is generated for each online broadcast unit in the set of dynamic target broadcast units, and all unicast instruction packets together constitute the preliminary broadcast instruction set.

6. The intelligent emergency early warning broadcast system according to claim 5, characterized in that, The initial broadcast instruction set is sent to each broadcast unit in the corresponding dynamic target broadcast unit set to trigger an emergency broadcast, including: Establish real-time communication links with each online broadcast unit in the set of dynamic target broadcast units; Through the real-time communication link, each unicast instruction packet in the initial broadcast instruction set is sent to the corresponding target broadcast unit. Receive instructions and confirmation feedback from each target broadcast unit; For broadcast units that do not receive acknowledgment, the command packet is retransmitted according to the preset retransmission strategy until the maximum number of retransmissions is reached or an acknowledgment is received. After all instruction packets have been successfully sent or the maximum number of retransmissions has been reached, a unified broadcast execution trigger signal is sent to each target broadcast unit.

7. The intelligent emergency early warning broadcast system according to claim 3, characterized in that, Using the spatial influence model parameters and the standard event location coordinates, a dynamic influence range geometry is constructed in the spatial computing engine, including: The spatial influence model parameters are input into the spatial computing engine. The spatial influence model parameters include at least the influence radius parameter and the influence direction parameter. Using the standard event location coordinates as the center point, and based on the influence radius parameter, an initial circular influence area is generated in the spatial computing engine; Based on the influence direction parameter, the initial circular influence area is geometrically transformed. If the influence direction parameter indicates a unidirectional or fan-shaped influence, the circular area is clipped into a fan-shaped area at a specified angle using the geometric clipping function of the spatial computing engine. The geometrically transformed regional graphic is overlaid with topographic elevation data from a geographic information database. A digital elevation model is then used to correct the terrain of the regional graphic, eliminating blind spots caused by terrain occlusion and generating the final dynamic influence range geometry.

8. The intelligent emergency early warning broadcast system according to claim 5, characterized in that, A channel allocation algorithm is used to calculate the optimal broadcast channel or broadcast frequency band for each online broadcast unit, including: Obtain the status information of all currently available broadcast channels, including the noise interference intensity and existing service load of each channel; Based on the byte length of the formatted warning broadcast text content and the impact level of the composite warning event object, calculate the minimum channel bandwidth and maximum tolerable delay required for this broadcast task; A multi-objective optimization function for channel allocation is established with the optimization objectives of minimizing channel interference and balancing the load of broadcast units. A heuristic search algorithm is used to solve the multi-objective optimization function, and an optimal broadcast channel or broadcast frequency band is assigned to each online broadcast unit, so that the overall communication quality of all broadcast units is optimal.

9. The intelligent emergency early warning broadcast system according to claim 6, characterized in that, After the emergency broadcast is triggered, it also includes: A broadcast effect monitoring cycle is initiated. During the broadcast effect monitoring cycle, broadcast status telemetry data returned by broadcast units in the dynamic target broadcast unit set is continuously received. The broadcast status telemetry data includes at least signal strength and estimated number of people reporting in the coverage area. The received broadcast status telemetry data is compared with the expected threshold preset in the effect evaluation model; When the signal strength is lower than the expected signal strength threshold, or the estimated number of people responding in the coverage area is lower than the expected number of people responding, it is determined that the broadcast effect of the area to which the broadcast unit belongs is not up to standard; For areas where the broadcast effect is not up to standard, a broadcast enhancement strategy is generated.

10. The intelligent emergency early warning broadcast system according to claim 9, characterized in that, Generate broadcast reinforcement strategies, including: For areas where the broadcast effect is not up to standard, obtain detailed location information of the broadcast units corresponding to the area; Using the detailed location information as the center, search for other backup broadcast units within a preset radius in the broadcast unit location point layer of the geographic information database to form a backup broadcast unit list; Assess the overlap between the current available load and theoretical coverage of each backup broadcast unit in the backup broadcast unit list and the non-compliant area; Select one or more backup broadcast units with the best evaluation results as supplementary broadcast units; Based on the original formatted warning broadcast text content, generate a supplementary broadcast instruction; The reinforcement broadcast command is sent to the reinforcement broadcast unit, and the reinforcement broadcast is triggered.

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