Earthquake early warning device and method based on campus broadcasting

Abstract

The application discloses an earthquake early warning device and method based on campus broadcasting, which receives an early warning signal of an earthquake monitoring center, carries out building response coupling analysis to generate a magnitude-building coupling characteristic, and maps the magnitude-building coupling characteristic into an enhanced early warning source adapted to an acoustic environment; based on threatened area identification, an acoustic cavity is detected to construct a differentiated playing topology; a high-risk gathering area is extracted through personnel density scanning to generate personalized evacuation guide content and determine a playing priority; a synchronous reference anchor point technology is adopted to realize high-precision timing control, a multi-modal playing domain is constructed through carrier modulation and frequency division multiplexing; finally, an acoustic navigation field is used to guide accurate scheduling and playing of audio signals, and the application can realize differentiated playing and accurate coverage of early warning information according to the anti-seismic characteristics of different buildings and the acoustic environment characteristics, and provides an intelligent acoustic solution for campus earthquake early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of earthquake early warning and campus safety protection, in particular to an earthquake early warning device and method based on campus broadcasting. BACKGROUND

[0002] Earthquake, as a major natural disaster, poses a serious threat to the safety of campus personnel. An effective earthquake early warning broadcast system can provide valuable evacuation time for teachers and students before the arrival of seismic waves, and is an important technical means to reduce earthquake casualties. The campus environment has complex building distribution, large personnel density variation, and significant differences in acoustic environment, which puts higher requirements on the accuracy and adaptability of the early warning broadcast system.

[0003] Existing campus broadcast systems mostly use unified playback mode, lack of targeted adaptation to different building acoustic characteristics, and are difficult to differentiate early warning according to building seismic performance and personnel distribution. The traditional method has shortcomings in acoustic environment mapping, cannot fully utilize building response characteristics for personalized generation of early warning content, and has limited precision in multi-node synchronous playback control, which is prone to playback delay and information confusion, affecting evacuation efficiency and early warning effect. SUMMARY

[0004] The present application provides an earthquake early warning device and method based on campus broadcasting, aiming to realize precise, differentiated and intelligent control of campus early warning broadcasting through building response coupling analysis and acoustic environment adaptive mapping technology, and effectively improve the response speed and evacuation guidance effect of earthquake early warning.

[0005] The first aspect of the present application proposes an earthquake early warning method based on campus broadcasting, comprising the following steps:

[0006] Receiving the early warning signal sent by the earthquake monitoring center, performing building response coupling analysis on the early warning signal to generate magnitude-building coupling characteristics, and performing acoustic environment mapping on the magnitude-building coupling characteristics to generate an enhanced early warning source;

[0007] Identifying threatened campus areas based on the enhanced early warning source, detecting acoustic cavities through the threatened campus areas to form building acoustic distribution, and constructing a differentiated playback topology according to the building acoustic distribution;

[0008] Performing personnel density scanning on the differentiated playback topology to extract high-risk aggregation areas, generating evacuation guidance content based on the high-risk aggregation areas, and fusing the evacuation guidance content with the enhanced early warning source to determine the playback priority;

[0009] Generate timing synchronization parameters based on the synchronization demand analysis of the differentiated playback topology based on the playback priority, establish a synchronization reference anchor point based on the timing synchronization parameters, measure the echo delay using the synchronization reference anchor point to form a delay compensation parameter, and generate a broadcast scheduling table based on the delay compensation parameter;

[0010] Separate power control parameters from the broadcast scheduling table to form a carrier channel through carrier modulation, construct a multi-path focusing transmission to generate a composite transmission frame based on the carrier channel, and construct a multi-modal playback domain according to the composite transmission frame;

[0011] Analyze the sound field interference distribution using the multi-modal playback domain to form an acoustic navigation field, construct a frequency division multiplexing playback network based on the acoustic navigation field, and generate a playback instruction through audio signal scheduling through the frequency division multiplexing playback network to complete the earthquake warning of the campus broadcast.

[0012] The second aspect of the present application proposes an earthquake warning device based on campus broadcast, comprising:

[0013] The signal processing module is configured to receive an early warning signal sent by a seismic monitoring center, perform building response coupling analysis on the early warning signal to generate a magnitude-building coupling feature, and perform acoustic environment mapping on the magnitude-building coupling feature to generate an enhanced early warning source.

[0014] The area recognition module is configured to identify a threatened campus area based on the enhanced early warning source, perform acoustic cavity detection on the threatened campus area to form a building acoustic distribution, and construct a differentiated playback topology according to the building acoustic distribution.

[0015] The priority generation module is configured to perform personnel density scanning on the differentiated playback topology to extract a high-risk aggregation area, generate evacuation guide content based on the high-risk aggregation area, and fuse the evacuation guide content with the enhanced early warning source to determine a playback priority.

[0016] The synchronization control module is configured to perform synchronization demand analysis on the differentiated playback topology based on the playback priority to generate timing synchronization parameters, establish a synchronization reference anchor point based on the timing synchronization parameters, measure the echo delay using the synchronization reference anchor point to form a delay compensation parameter, and generate a broadcast scheduling table based on the delay compensation parameter.

[0017] The transmission coordination module is configured to separate power control parameters from the broadcast scheduling table to form a carrier channel through carrier modulation, construct a multi-path focusing transmission to generate a composite transmission frame based on the carrier channel, and construct a multi-modal playback domain according to the composite transmission frame.

[0018] The early warning output module is used for forming an acoustic navigation field by analyzing the sound field interference distribution of the multi-modal playing field, constructing a frequency division multiplexing playing network based on the acoustic navigation field, generating a playing instruction by audio signal scheduling through the frequency division multiplexing playing network, and completing the earthquake early warning of the campus broadcasting.

[0019] The beneficial effects of the present application are embodied in the following aspects: first, through the building response coupling analysis of the earthquake early warning signal and the acoustic cavity detection technology, the personalized adaptation of the early warning broadcast is realized. This technology can couple the earthquake magnitude information with the seismic characteristics of different buildings to generate targeted early warning content, and at the same time, according to the acoustic characteristics of different building spaces such as classrooms, auditoriums and dormitories, a differentiated playing topology structure is constructed to ensure that the early warning information can achieve the best propagation effect in various building environments. Secondly, the dynamic scanning of personnel density and high-precision timing synchronization control technology are adopted to improve the intelligent level of evacuation guidance. By real-time identification of the personnel gathering condition in the campus, personalized evacuation guidance content is automatically generated and the playing priority is reasonably arranged, and combined with the synchronous reference anchor point and delay compensation processing, the precise coordination of multiple playing nodes is realized to avoid the time disorder and information conflict of the evacuation instruction. Finally, the carrier modulation transmission and acoustic navigation field technology are used to enhance the coverage accuracy and transmission reliability of the early warning broadcast. The multi-modal playing field formed by multi-path focused transmission can accurately project sound energy to the target area, and the construction of the acoustic navigation field further optimizes the spatial distribution of the audio signal, which ensures the parallel transmission and accurate scheduling of multiple early warning information through the frequency division multiplexing playing network, and provides comprehensive and reliable acoustic protection for the campus earthquake early warning.

[0020] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0021] The drawings herein show specific examples of the technical solutions described in the present application, and constitute part of the specification together with the specific embodiments, for explaining the technical solutions, principles and effects of the present application.

[0022] Unless specifically stated, the same reference signs in different drawings represent the same or similar technical features, and different reference signs may also be used to represent the same or similar technical features.

[0023] Figure 1 is a flowchart of the earthquake early warning method based on campus broadcasting of the present application.

[0024] Figure 2 is a structural block diagram of the earthquake early warning equipment based on campus broadcasting of the present application. DETAILED DESCRIPTION

[0025] In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and

[0026] It is to be understood that the terminology "including", "comprising", "consisting" and "consisting essentially of" used in the specification and the appended claims, are used in the sense of open ended inclusion, that is, inclusion of unspecified elements, features, steps, operations, elements, components, and / or groups thereof, but not limited to the elements, features, steps, operations, elements, components, and / or groups thereof specifically recited.

[0027] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but can refer to different embodiments, although the phrases can be used to describe particular implementations. The terms "including", "comprising", "having", and the like are meant to be inclusive and mean that there can be additional

[0028] The technical solutions of the embodiments of the present application are described below.

[0029] As shown in Figure 1 The earthquake warning method based on campus broadcasting provided by the embodiments of the present application includes the following steps S110-S160:

[0030] In step S110, a warning signal sent by a seismic monitoring center is received, building response coupling analysis is performed on the warning signal to generate a magnitude-building coupling feature, and the magnitude-building coupling feature is subjected to acoustic environment mapping to generate an enhanced warning source.

[0031] Specifically, the early warning signal sent by the earthquake monitoring center is received. Through the special communication link of the China Earthquake Early Warning Network, the early warning signal data containing the epicenter location, epicenter latitude and longitude, earthquake time, estimated magnitude, focal depth, and epicenter intensity is received in real time. The early warning signal reception adopts a multi-channel redundant configuration, the main channel uses optical fiber special line transmission, the standby channel uses satellite communication and wireless network transmission, to ensure the reliability and real-time performance of signal reception. The signal integrity check confirms the accuracy and source reliability of the received signal through digital signature and check code technology. The early warning signal contains key parameters such as the P-wave and S-wave arrival time difference, magnitude estimate, epicenter distance, and predicted impact intensity. At the same time, the epicenter distance is calculated according to the epicenter latitude and longitude and the location of the campus buildings. The signal data is encoded in a standardized format, containing time stamp, geographic coordinates, magnitude value, and confidence interval structured information. The signal buffering function stores the received early warning signal in chronological order, supporting historical data backtracking and trend analysis. The signal quality monitoring function detects the signal-to-noise ratio, delay time, and packet loss rate of the signal in real time. The early warning signal processing adopts multiple decoding techniques to uniformly analyze and format convert signals of different formats and protocols.

[0032] The early warning signal is coupled with the building response to generate a magnitude-building coupling feature. The received early warning signal is coupled with the structural characteristics of the campus buildings to form a correlation between the ground motion parameters and the building response. When a magnitude 6.5 earthquake early warning signal with an epicenter distance of 15 kilometers is received, the device automatically analyzes the response characteristics of different buildings on campus: the 5-story brick-mixed structure old teaching building built in the 1990s is expected to have a maximum inter-story displacement of 15 mm and a top layer acceleration of 0.8g; while the 8-story frame structure new library built in 2015 is expected to have a maximum inter-story displacement of only 8 mm and a top layer acceleration of 0.4g. The building response coupling analysis uses structural dynamics response method, considering key parameters such as building natural frequency, damping ratio and structural stiffness. The magnitude-building response function R=f(M,H,D), where R is the building response intensity, M is the magnitude, H is the building height, and D is the epicenter distance. The coupling analysis converts the peak ground acceleration in the early warning signal into the displacement response and acceleration response of each layer of the building. The influence of soil conditions on seismic wave propagation is adjusted by the site effect correction coefficient to correct the ground motion parameters to reflect the influence of local geological conditions. The magnitude-building coupling feature includes building response parameters such as maximum inter-story displacement, top layer acceleration, torsion angle, and energy dissipation. The response characteristics database of different types of buildings covers typical campus building types such as frame structure, shear wall structure, and steel structure.

[0033] In some embodiments, the acoustic environment mapping of the magnitude-building coupling feature generates an enhanced early warning source, comprising: performing an environmental adaptation analysis on the magnitude-building coupling feature to obtain an acoustic response profile; combining a preset campus environmental noise baseline and the acoustic response profile to form an environment-response correlation network; extracting a stable propagation path in the environment-response correlation network; and constructing an enhanced early warning source based on the intensity value of the stable propagation path.

[0034] The environmental adaptation analysis on the magnitude-building coupling feature obtains an acoustic response profile. The generated magnitude-building coupling feature is adaptively analyzed with the campus acoustic environment characteristics to determine the propagation characteristics and auditory effects of the early warning sound under specific environmental conditions. The environmental adaptation analysis considers the acoustic characteristic differences of different areas of the campus, including the reverberation time, sound absorption coefficient, and sound propagation loss of open areas, building-dense areas, and indoor spaces. The frequency spectrum analysis of the acoustic response profile converts the physical response of the magnitude-building coupling feature into an acoustic frequency spectrum distribution. The acoustic response profile uses a 1 / 3 octave analysis method, covering the full frequency range of 20 Hz to 20 kHz, with a focus on the 1 kHz to 4 kHz frequency band sensitive to the human ear. The acoustic response curves corresponding to the building response under different magnitudes form a corresponding relationship between response intensity and frequency distribution. The acoustic response profile includes characteristic parameters such as sound pressure level distribution, frequency centroid, frequency band width, and peak frequency. The effects of the Doppler effect and sound reflection on the acoustic response are adapted to the dynamic acoustic environment by modifying the profile parameters. The time-varying characteristics of the profile describe the evolution process and decay law of the acoustic response over time. The spatial distribution characteristics of the acoustic response profile reflect the response differences and propagation characteristics at different positions.

[0035] For example, the combination of the preset campus environmental noise baseline and the acoustic response profile forms an environment-response correlation network, comprising: identifying an acoustic sensitive frequency band according to the acoustic response profile to determine an analysis interval, the acoustic sensitive frequency band including low frequency penetration, medium frequency clarity, and high frequency attenuation rate; taking the preset campus environmental noise baseline as a reference, tracking the noise variation process along the analysis interval to form a noise variation spectrum; extracting the frequency coordinates of each response point in the noise variation spectrum; and arranging the frequency coordinates according to the response intensity to generate an environment-response correlation network.

[0036] The analysis interval is determined according to the acoustic response profile to identify the acoustic sensitive frequency band. In the obtained acoustic response profile, a spectral analysis is performed to identify the sensitive frequency range that has a key influence on the propagation of the earthquake early warning signal. The identification of the acoustic sensitive frequency band uses the energy density analysis method, and the energy contribution and propagation efficiency of each frequency component are quantified. The low-frequency penetration analysis covers the 20Hz to 200Hz frequency band, which has strong penetration ability and can effectively penetrate buildings and obstacles, suitable for long-distance early warning signal propagation. The medium-frequency clarity analysis covers the 200Hz to 2kHz frequency band, which corresponds to the most sensitive area of the human ear, has good speech clarity and recognition, and is suitable for the propagation of voice early warning content. The high-frequency attenuation rate analysis covers the 2kHz to 20kHz frequency band, which although has a faster attenuation but has good directivity and detail performance, suitable for the design of emergency alarm sound. The quantitative indicators of the sensitive frequency band include penetration index, clarity index and attenuation index, etc. The determination of the analysis interval considers the characteristics and weights of each sensitive frequency band, and determines the final analysis frequency range by weighted average method. The importance of the frequency band is sorted according to the specific characteristics of the campus environment to adjust the priority and weight allocation of each frequency band.

[0037] With the preset campus environment noise baseline as a reference, the noise change process is tracked along the analysis interval to form a noise change map. In the determined analysis interval, the preset campus environment noise baseline is taken as the reference standard, and the change process of the environmental noise in the time and frequency dimensions is systematically tracked. The noise baseline data contains the statistical characteristics of the environmental noise of the campus at different times, including the noise level distribution in the morning, morning, noon, afternoon and night. The tracking process uses continuous monitoring method, and the noise level is sampled at fixed frequency interval in the analysis interval to form a time-frequency analysis matrix. The noise change map uses two-dimensional visualization, with frequency as the horizontal axis and time as the vertical axis, and color or gray value representing noise intensity level. The change process tracking considers the short-term fluctuations and long-term trends of the noise, and extracts the main change mode of the noise through the sliding average and trend analysis method. The analysis of the noise change gradient shows the change rate and mutation characteristics of the noise level in the time and frequency directions. The construction of the map uses high-resolution time-frequency analysis technology to ensure that the details and transient processes of the noise change can be captured. The statistical feature extraction of the map includes the mean, variance, kurtosis and skewness of the noise change.

[0038] The frequency coordinates of each response point are extracted within the noise variation map. A response point is defined as a key position in the noise variation map with special acoustic significance, including noise minimum points, rate of change mutation points, and spectral feature points. The frequency coordinate extraction uses peak detection and valley detection methods to automatically identify the local extreme points and inflection point positions in the map. The classification system of response points divides the response points into low-noise window points, noise jump points, and noise stable points, etc. according to the noise characteristics. The accuracy of coordinate extraction is improved by interpolation method, and the more accurate frequency coordinate values are obtained by interpolation processing between discrete sampling points. The importance of response points is determined by the weight coefficient according to the noise level, stability and duration of the point position. The clustering analysis method groups the extracted response points to identify response point groups with similar characteristics. The frequency coordinates are represented in a standardized format, including frequency value, time stamp, noise level, and response intensity attribute information. The quality control of the coordinates ensures the accuracy and reliability of the coordinate extraction through cross comparison and repeated measurement.

[0039] The environmental-response association network is generated by arranging the response intensity of the frequency coordinates. The response intensity quantization considers multiple factors such as noise level, signal clarity, and propagation efficiency to form a comprehensive index. The arrangement process uses a multi-key sorting method, first sorting by response intensity from high to low, and sorting by frequency coordinates when the response intensity is the same. The network generation uses an adjacency matrix representation method, and the matrix elements represent the association strength and connection weight between different frequency coordinates. The expression of association strength is C = W1S1 + W2S2 + W3S3, where S1 is the signal-to-noise ratio, S2 is the propagation efficiency, S3 is the frequency importance, and W1, W2, W3 are weight coefficients. The network topology uses small-world network characteristics, with high clustering degree and short path length, which facilitates fast information propagation and association analysis. The network update function adjusts the network structure and weight distribution according to real-time environmental changes. The environmental-response association network supports multi-level analysis, which can be performed on the whole network, subnetwork, and node level for feature analysis and optimization.

[0040] Stable propagation paths are extracted in the environment-response correlation network. The extraction of stable propagation paths uses a graph theory traversal algorithm, taking the frequency coordinate nodes with high correlation strength in the network as candidate path nodes, and constructing a path weight matrix by calculating the acoustic propagation loss and environmental interference impedance between nodes. The path stability evaluation quantifies the comprehensive indicators of propagation loss, signal-to-noise ratio, and frequency response. When the open area between the teaching building and the dormitory building has a low propagation loss, the path is marked as a high stability path; while the path through the dense building group has a lower stability due to multiple reflections and scattering. The multi-path identification algorithm extracts the main direct path and the secondary diffraction path, analyzes the phase relationship and interference effect of different paths, and when the reflection path of the library outer wall and the direct path produce constructive interference, the overall propagation effect is enhanced. Directionality analysis divides the campus into multiple sectors by angle, identifies the optimal propagation path in each direction, for example, the northward path facing the playground has the lowest attenuation coefficient, while the southeast path facing the high-rise building needs higher signal strength compensation. The extraction results form a path set containing path coordinates, propagation characteristics, and stability indicators, and realize dynamic switching and optimal adjustment of the path by real-time monitoring of environmental changes.

[0041] An enhanced warning source is constructed based on the intensity value of the stable propagation path. Using the extracted stable propagation path characteristic parameters, an enhanced warning source system suitable for the campus environment is designed and constructed. The sound intensity of the warning source is adjusted according to the attenuation characteristics of the stable propagation path to ensure that the warning signal can reach the target area with sufficient sound pressure level. The intensity value compensation uses the sound propagation loss compensation formula L = L0 + 20lg(r) + ar, where L is the compensated sound pressure level, L0 is the initial sound pressure level, r is the propagation distance, and a is the atmospheric absorption coefficient. The enhanced warning source uses adaptive power control technology to adjust the output power according to the real-time environmental noise level. The multi-frequency power distribution strategy allocates more power in the frequency band corresponding to the stable propagation path to improve the propagation effect of the warning signal. The frequency characteristics of the warning source are optimized according to the frequency selectivity of the stable propagation path, focusing on enhancing the frequency components with good propagation effect. The sound modulation technology improves the attention attracting ability of the warning signal through frequency modulation and amplitude modulation. The enhanced warning source contains sound directional control function, which uses phased array technology to concentrate sound energy to the area that needs warning.

[0042] Step S120, identify the threatened campus area based on the enhanced warning source, and perform acoustic cavity detection in the threatened campus area to form a building acoustic distribution, and construct a differentiated playback topology according to the building acoustic distribution.

[0043] Specifically, the threatened campus area is identified based on the enhanced early warning source. The magnitude information of the enhanced early warning source is matched and analyzed with the seismic grade of the campus building to form a building risk grade distribution map. The threatened campus area is divided into three levels of high-risk area, medium-risk area and low-risk area according to the threat degree. The high-risk area includes old buildings with low seismic grade, key facilities such as personnel-intensive teaching buildings and experimental buildings. The medium-risk area covers medium importance buildings such as general teaching buildings, office buildings and student dormitories. The low-risk area mainly includes newly-built high-standard buildings, sports venues and green areas and other open spaces. The area identification adopts GIS spatial analysis technology to superimpose and analyze the influence range of the enhanced early warning source and the distribution of the campus buildings. The threat identification considers multiple factors such as the structure type, construction year, personnel capacity and evacuation conditions of the building. The boundary of the threatened area is determined through the analysis of the isoseismal line, and different magnitudes correspond to different influence radius and threat range. The analysis of the personnel distribution density and evacuation path in the area provides an important reference for the early warning playing strategy.

[0044] The building acoustics distribution is formed by detecting the acoustic cavity in the threatened campus area. In the identified threatened campus area, the acoustic cavity characteristics of various buildings are detected, and the acoustic propagation characteristics and sound field distribution law of the building internal space are analyzed. For example, when the acoustic detection is carried out in a staircase classroom accommodating 200 people, it is found that the reverberation time of the classroom is 1.2 seconds, which is suitable for clear voice propagation; while in a large auditorium that can accommodate 1000 people, the reverberation time reaches 2.8 seconds, and special sound processing is needed to ensure the clarity of the early warning information. The corridor space of the student dormitory has a reverberation time of only 0.6 seconds, but the sound attenuates quickly, so the playing power needs to be increased. The acoustic cavity detection adopts the impulse response measurement method, emits acoustic test signals in the building, and records the propagation process and reflection characteristics of the sound in the cavity. The building acoustic distribution includes key acoustic parameters such as reverberation time, sound clarity, sound field uniformity and frequency response. The cavity detection considers the influence of the geometric shape, material properties and internal decoration of the building on the acoustic characteristics. The acoustic distribution measurement adopts a multi-point detection method, arranges acoustic sensors at key positions in the building to obtain detailed distribution information of the space sound field. The frequency response characteristic analysis covers the voice frequency band and the alarm audio band to ensure that the early warning signal can be effectively propagated in each frequency band.

[0045] In some embodiments, the construction of the differentiated playing topology according to the building acoustic distribution comprises: obtaining impedance distribution parameters based on acoustic impedance analysis of the building acoustic distribution; performing impedance matching processing on the playing equipment using the impedance distribution parameters to form a matching playing node; establishing a sound resistance balance relationship sequence through the matching playing node; and constructing a differentiated playing topology based on the sound resistance balance relationship sequence.

[0046] The impedance distribution parameters are obtained by analyzing the acoustic impedance based on the architectural acoustics distribution. The impedance analysis converts the architectural acoustics distribution data into electrical impedance parameters suitable for circuit matching design through electroacoustic conversion modeling. The conversion process uses electroacoustic analogy theory to map acoustic characteristics such as reverberation time and sound field uniformity into impedance characteristics of the equivalent circuit, establishing an impedance correlation model between the acoustic environment and the playback device interface. The frequency-dependent impedance parameters are extracted by dividing the warning signal frequency range (100 Hz-8 kHz) into multiple frequency bands, and the equivalent impedance value and phase characteristics are calculated in each frequency band. Taking a staircase classroom as an example, when the reverberation time is 1.2 seconds, the equivalent input impedance at 1 kHz is 8.2Ω, and the phase angle is -15 degrees. This value can be directly used for matching calculation with the standard playback device output impedance. The spatial impedance distribution is obtained by dividing the architectural cavity into acoustic zones, and the equivalent input impedance value of each zone is calculated independently to form a spatial impedance distribution matrix, providing a quantitative basis for the spatial optimization of the playback nodes. The impedance characteristic analysis considers the influence of different building materials and geometric structures on the impedance frequency response, and the hard surface area presents a higher equivalent impedance value, while the soft sound-absorbing area presents a lower impedance value. The final impedance distribution parameters include equivalent impedance values, phase characteristics, spatial distribution characteristics, and transmission line parameters in each frequency band.

[0047] The impedance matching process is used to optimize the sound transmission efficiency and sound quality performance of the playback device based on the extracted impedance distribution parameters. The impedance matching process uses transmission line theory to match the output impedance of the playback device with the input impedance of the architectural acoustics environment. The matching network design uses L-type, π-type or T-type circuit topology to achieve impedance transformation through inductance and capacitance elements. The selection of the playback device considers the matching degree of its output impedance characteristics with the target architectural environment impedance. The frequency characteristics of the impedance matching design cover the main frequency range of the warning signal to ensure the matching effect in the key frequency bands. The power transmission efficiency of the matched playback node is quantified by the matching degree index, and the higher the matching degree, the better the transmission efficiency. The spatial arrangement of the playback nodes is optimized based on the spatial variation characteristics of the impedance distribution. The multi-band impedance matching technology realizes efficient sound transmission in a wide frequency range. The matched playback nodes achieve the best adaptation with the architectural environment in terms of acoustic performance, improving the propagation quality and coverage effect of the warning signal.

[0048] The matching playback nodes are connected in series and parallel according to the acoustic impedance balance principle to form a network sequence structure of acoustic impedance balance. The acoustic impedance balance sequence follows the impedance matching law, and the impedance matching error between adjacent nodes is controlled within the allowable range. The sequence connection adopts a stepped impedance transformation method, and the smooth impedance transition from the source end to the load end is realized through multi-stage impedance transformation. The maintenance of the balance relationship is realized through an impedance compensation network, which automatically adjusts the compensation parameters when the impedance characteristics of a certain node change. The power distribution of each node in the sequence is optimized according to the impedance ratio and transmission loss. The acoustic impedance balance sequence supports bidirectional signal transmission, which can transmit signals from the master control end to each playback node, and can also feedback state information from the nodes to the master control end. The stability of the sequence is analyzed by the impedance stability criterion to ensure that the system can work stably under various working conditions. The topology structure of the balance relationship sequence adopts a tree or grid layout, and the optimal topology form is selected according to the building distribution and connection convenience. The physical realization of the sequence connection is completed through special acoustic transmission cables and connectors to ensure the quality and reliability of signal transmission.

[0049] Based on the acoustic impedance balance relationship sequence, a differentiated playback topology is constructed. The acoustic impedance balance relationship sequence is used as the basic network skeleton to further expand and optimize the differentiated playback topology structure that adapts to different regional needs. The differentiated playback topology adopts a hierarchical and partitioned network architecture, with the top layer being a unified control layer for the whole school, the middle layer being a regional differentiated control layer, and the bottom layer being a terminal playback execution layer. The topology construction considers the differentiated needs of different building types and use functions, with the teaching area adopting a high-definition voice playback topology and the living area adopting a large-volume alarm playback topology. The differentiation of the playback topology is reflected in the individualized configuration of multiple dimensions such as signal routing, power distribution, frequency selection, and playback timing. The redundant design of the topology network is realized through multi-path connection and backup node configuration to improve the reliability and fault tolerance of the system. The differentiated control strategy supports flexible switching between two working modes of independent playback in different regions and synchronous playback in the whole region. The extensibility design of the topology structure supports the convenient access of new playback nodes and regions, adapting to the needs of campus construction and reconstruction. The management interface of the playback topology provides graphical network monitoring and configuration functions, facilitating daily maintenance and troubleshooting by system administrators.

[0050] In step S130, the personnel density scanning is performed on the differentiated playback topology to extract high-risk gathering areas, and the evacuation guide content is generated based on the high-risk gathering areas. The evacuation guide content is fused with the enhanced warning source to determine the playback priority.

[0051] Specifically, personnel density scanning is performed on the differentiated playing topology to extract high-risk gathering areas. The personnel density scanning adopts multi-source data fusion technology, integrating access control system data, video monitoring data, mobile signal strength data, WiFi connection data and other information sources. The scanning system divides the campus into grids according to the network partitions of the playing topology, and each grid corresponds to a specific playing node coverage range. The personnel density data is updated in real time, and the scanning frequency is adjusted according to the time period characteristics, with an increased scanning frequency during class time and a decreased scanning frequency during rest time. The density threshold is set considering the accommodation capacity and evacuation difficulty of different building types, with a lower density threshold for teaching buildings and a higher density threshold for open areas. High-risk gathering areas are defined as areas with personnel density exceeding the safety threshold and relatively difficult evacuation conditions. The area risk level is divided into four levels: extremely high risk, high risk, medium risk and low risk. Personnel gathering feature analysis includes indicators such as gathering size, gathering duration, personnel mobility and evacuation path smoothness. The scanning results are associated and mapped with the playing topology nodes to form a correspondence between personnel distribution and playing equipment.

[0052] Based on the high-risk gathering areas, evacuation guidance content is generated. The evacuation guidance content adopts a hierarchical design strategy, including basic evacuation instructions, detailed path guidance and safety precautions. The basic evacuation instructions include concise and clear core information such as "earthquake warning, evacuate immediately", suitable for quick broadcast in emergency situations. The detailed path guidance generates personalized evacuation route instructions based on the specific location and evacuation path of the high-risk gathering area. The safety precautions cover behavior norms and safety requirements during evacuation, such as "remain calm, orderly evacuation, pay attention to safety" and other content. The language expression of the evacuation guidance content uses simple and clear colloquial expressions, avoiding the use of professional terms and complex sentence patterns. The content generation considers the understanding ability and language habits of different groups of people, providing both Chinese and English versions. The broadcast order of the evacuation instructions is determined according to the area risk level, with extremely high-risk areas being broadcast first and low-risk areas being broadcast later. The duration of the guidance content is controlled within a reasonable range to ensure that key information can be effectively communicated within a limited time. The content library contains evacuation guidance templates for various scenarios, supporting the rapid generation of customized guidance content according to actual conditions.

[0053] In some embodiments, the fusion of the evacuation guidance content and the enhanced warning source determines the playing priority, including: quantifying the emergency of the evacuation guidance content to generate a guidance emergency degree; using the enhanced warning source to evaluate the threat degree of the guidance emergency degree to form a threat correlation spectrum; weighting and fusing the threat correlation spectrum to generate a priority coefficient; and implementing level division according to the priority coefficient to generate a playing priority.

[0054] The emergency degree of the evacuation guidance content is quantified. The generated evacuation guidance content is quantified according to the emergency degree and importance to form a numerical index of the emergency degree that can be used for priority ranking. For example, the emergency degree of the core evacuation instruction "earthquake warning, evacuate immediately to the playground" is 0.95 (highest level) because it contains clear action instructions and target locations; the emergency degree of the behavior specification guidance "remain calm and orderly evacuate" is 0.7 (high-middle level); and the emergency degree of the safety reminder "pay attention to the safety of your feet and avoid trampling" is 0.5 (medium level). The emergency degree of the evacuation instruction for high-risk buildings such as old teaching buildings is additionally increased by 0.1-0.2. The emergency degree quantification adopts a multi-dimensional evaluation system, including time sensitivity, personnel safety impact, evacuation complexity, and information criticality. The time sensitivity is obtained quickly by searching for time keywords, with "immediately" and "soon" counting 1.0, "as soon as possible" and "quickly" counting 0.7, and others counting 0.4. The personnel safety impact is directly assigned according to the content type, with evacuation instructions counting 1.0, behavior specifications counting 0.6, and safety reminders counting 0.3. The evacuation complexity is preset according to the building attributes, with old buildings counting 0.8, standard buildings counting 0.5, and open areas counting 0.2. The information criticality is determined according to the position of the sentence in the broadcast, with the first sentence counting 1.0, the middle sentence counting 0.5, and the last sentence counting 0.2. The quantification formula of the guidance emergency degree E = 0.3T + 0.3S + 0.2C + 0.2Q, where E is the guidance emergency degree, T is the time sensitivity, S is the safety impact, C is the evacuation complexity, and Q is the information criticality, and the weight coefficients are 0.3, 0.3, 0.2, and 0.2, respectively, to balance the contribution of each dimension. Through this standardized scoring table, the emergency degree quantification of any evacuation guidance content can be completed within a few seconds.

[0055] The threat correlation spectrum is formed by analyzing the threat degree of the guiding emergency degree and the enhanced early warning source. The guiding emergency degree and the threat parameters in the enhanced early warning source are coupled and analyzed, and the comprehensive threat degree is determined through the interaction of the two. The threat degree analysis takes the guiding emergency degree as the modulation factor, and dynamically adjusts the parameters such as the magnitude intensity, influence range, arrival time and duration of the enhanced early warning source. The higher the guiding emergency degree, the more obvious the amplification effect on the threat parameters. The high emergency degree content of the core evacuation instruction class produces the maximum amplification coefficient, and the medium emergency degree content of the safety reminder class maintains the baseline amplification level. The construction of the threat correlation spectrum considers the coupling relationship between the guiding emergency degree and the threat parameters, and the threat degree rises sharply under the condition of short early warning time for high emergency degree content. The construction formula of the correlation spectrum is T(x,t)=A(x)×M×f(E)×exp(-α(t-t0)), where T(x,t) is the threat correlation intensity at spatial position x and time t, A(x) is a spatial attenuation function representing the attenuation of threat with distance, M is a magnitude threat coefficient reflecting the influence of earthquake intensity, f(E) is a modulation function of the guiding emergency degree E, α is a time attenuation coefficient, t0 is the time of earthquake occurrence, and exp is an exponential function representing the attenuation law of threat with time. The threat correlation intensity corresponding to the high-risk building area is correspondingly improved due to the additional weight adjustment of the guiding emergency degree. The spatial resolution of the threat correlation spectrum matches the node distribution of the playback topology, ensuring that each playback area has a threat degree value that considers the guiding emergency degree and the threat parameters.

[0056] The priority coefficient is generated by weighting and fusing the threat correlation spectrum. The constructed threat correlation spectrum and the guiding emergency degree are weighted and fused, and a combination of linear weighting and nonlinear weighting is used. The linear part mainly processes the threat and emergency degree factors, and the nonlinear part processes the interaction between the factors. The distribution of fusion weights is adjusted according to the threat type and the severity of the emergency, and the threat correlation spectrum obtains a higher weight in extreme threat situations, and the guiding emergency degree weight is relatively high in general situations. The generation formula of the priority coefficient is P=αE+βT(x,t)+γE×T(x,t), where P is the priority coefficient, E is the guiding emergency degree, T is the threat correlation intensity, α and β are linear weight coefficients, and γ is a nonlinear interaction coefficient. The coefficient standardization process maps the priority coefficient to a standard numerical interval, facilitating the comparison of priority in different areas and at different times. The fusion process considers the time-varying characteristics of the threat correlation spectrum and the guiding emergency degree, supporting real-time updating and adjustment of the priority coefficient. The selection of weighting parameters is determined by optimization method, the goal is to maximize the evacuation efficiency and minimize the risk of personnel. The stability of the fusion result is guaranteed by smoothing filter processing, avoiding the influence of the dramatic fluctuation of the priority coefficient on the playback scheduling.

[0057] The play priority is generated according to the priority coefficient implementation level division. Based on the obtained priority coefficient value, the level division and classification processing are performed to form a play priority sequence convenient for the play system to execute. The level division adopts the quantile method, and the priority coefficients are divided into five levels of special level, first level, second level, third level and fourth level according to the value size. The special level priority corresponds to the highest 10% area of the priority coefficient, and is suitable for the extremely urgent evacuation scene. The first level priority corresponds to the first 10%-30% area of the priority coefficient, and is suitable for the high emergency evacuation scene. The second level priority corresponds to the first 30%-60% area of the priority coefficient, and is suitable for the medium emergency evacuation scene. The third and fourth level priorities correspond to the subsequent lower emergency degree areas respectively, and are suitable for the general evacuation guiding scene. The numerical coding of the play priority adopts integer representation, the special level is 5, the first level is 4, the second level is 3, the third level is 2, and the fourth level is 1, which is convenient for the logical judgment and scheduling execution of the play system. The threshold setting of the level division considers the actual needs of campus evacuation and the processing capacity of the play equipment, so as to ensure the reasonable division of the play task quantity of each level. The play priority sequence supports multi-level preemption scheduling, and the high-level play task can interrupt the execution of the low-level task. The play order in the same level is finely sorted according to the specific value of the priority coefficient. The execution state of the play priority is tracked by a state marker, including state types such as waiting to play, playing, playing complete and playing interruption.

[0058] In step S140, the time sequence synchronization parameters are generated based on the synchronization demand analysis of the differential play topology based on the play priority, the synchronization reference anchor points are established based on the time sequence synchronization parameters, the echo delay measurement is performed by using the synchronization reference anchor points to form the delay compensation parameters, and the broadcast scheduling table is generated based on the delay compensation parameters.

[0059] Specifically, the timing synchronization parameters are generated based on the synchronization requirement analysis of the differentiated playing topology according to the playing priority. Using the playing priority sequence, the synchronization playing requirement and timing coordination requirement of each playing node in the differentiated playing topology are analyzed. For example, when the main teaching building detects a 5-level earthquake warning, the playing node of the building is marked as a special priority, requiring millisecond-level synchronization playing with the adjacent library and experimental building playing nodes to ensure that the "evacuate to the playground immediately" instruction is sounded simultaneously in the three buildings. The student dormitory area far away is marked as a second-level priority, allowing a 3-5 millisecond playing delay difference. The network delay characteristic analysis of the playing topology covers three components: signal transmission delay, device processing delay, and sound propagation delay. The timing synchronization parameters include key parameters such as reference clock frequency, synchronization accuracy requirement, delay tolerance, and jitter control range. The reference clock frequency is set to 48 kHz to ensure high-fidelity transmission of audio signals and accurate timing control. The synchronization accuracy requirement is hierarchically set according to the playing priority, with a microsecond-level synchronization accuracy requirement for a special priority and a millisecond-level synchronization error for a lower priority. The distance difference between the playing nodes causes natural delay of sound arrival time, which is pre-compensated by the timing synchronization parameters.

[0060] In some embodiments, the establishing a synchronization reference anchor based on the timing synchronization parameters comprises: decomposing the sound wave propagation delay by the timing synchronization parameters to generate direct wave timing and reflected wave timing; establishing a zero-delay reference point using the direct wave timing; performing phase correction processing on the reflected wave timing based on the zero-delay reference point to generate in-phase timing; and phase-locked synthesis of the direct wave timing and the in-phase timing to establish a synchronization reference anchor.

[0061] The direct wave time sequence and the reflected wave time sequence are generated by time delay decomposition of sound wave propagation based on the time synchronization parameters. Based on the obtained time synchronization parameters, the time delay characteristics of the sound propagation in the playing topology environment are decomposed, and the different time sequence characteristics of direct propagation and reflected propagation are distinguished. For example, in a standard classroom with a length of 12 meters, a width of 8 meters and a height of 3 meters, the loudspeaker is located in the center of the front wall. When playing the evacuation instruction, the student sitting in the first row directly hears the sound (direct wave), the propagation distance is about 2 meters, and the time is 5.8 milliseconds. In addition to the direct sound (propagation distance of 10 meters and time of 29 milliseconds), the student sitting in the last row near the wall will also hear the sound reflected from the back wall, side wall and ceiling (reflected wave), and the propagation distance of the reflected wave is 12-15 meters and the time is 35-44 milliseconds. The sound wave propagation time delay decomposition adopts a multipath propagation analysis method to identify all possible propagation paths of the sound from the playing source to the receiving point. The direct wave time sequence corresponds to the time sequence of the sound propagating along the shortest straight line path, without considering any reflection and diffraction effects. The calculation of the direct wave time sequence adopts the method of dividing the straight line distance by the sound speed, and the formula is Td=d / c, where Td is the direct wave time delay, d is the straight line distance, and c is the sound speed. The calculation of the reflected wave time sequence needs to consider the geometric relationship of the reflection path and the acoustic characteristics of the reflection interface. The propagation path of the first reflected wave follows the reflection law, and the incident angle is equal to the reflection angle. The propagation path of the multiple reflected wave is more complex and needs to be analyzed by the ray tracing method.

[0062] A zero delay reference point is established by using the direct wave time sequence. The direct wave time sequence obtained by decomposition is used as a time reference to establish a zero delay reference point in the playing topology network as the time origin of the entire synchronization system. The zero delay reference point is defined as a virtual reference position with a direct wave propagation time delay of zero, which actually corresponds to the emission source point of the playing signal. The time coordinate of the reference point is set as the starting point of the system time, and all other time sequences are calculated relative to this time. The linear characteristics of the direct wave time sequence make it suitable as a time reference, and the propagation time delay is proportional to the distance, which is convenient for accurate time calculation. The spatial position of the reference point is determined by the geometric center or control center of the playing topology, and the position with the optimal network coverage is selected. The clock accuracy of the reference point requires to reach the microsecond level, and a high stability quartz crystal oscillator or atomic clock is used as the time source. The time synchronization of the reference point is realized through the network time protocol or GPS time signal to ensure consistency with the standard time. The realization of the zero delay concept is realized through time offset and compensation technology, and the actual propagation time delay is offset to zero in the calculation. The distribution of the reference point signal adopts a star-shaped topology structure to distribute the time reference signal from the central reference point to each playing node.

[0063] For example, the step of performing phase correction processing on the reflected wave timing based on the zero-delay reference point to generate in-phase timing includes: performing phase deviation detection based on the zero-delay reference point to determine positive phase deviation and negative phase deviation; establishing a phase correction template using the positive phase deviation; performing phase compensation processing on the negative phase deviation using the phase correction template to generate a corrected phase; and synchronously locking the positive phase deviation and the corrected phase to generate in-phase timing.

[0064] Phase deviation detection based on a zero-delay reference point determines positive and negative phase deviations. Phase deviation detection employs phase comparator technology, comparing the reflected wave signal with the reference point signal in real time. The detection process is implemented using a digital phase detector, outputting a voltage or digital signal proportional to the phase difference. A positive phase deviation is defined as the amount by which the reflected wave phase leads the reference phase, and its value is positive. A negative phase deviation is defined as the amount by which the reflected wave phase lags the reference phase, and its value is negative. The measurement range of phase deviation is set to ±180 degrees; phase differences exceeding this range are processed using a phase unrolling algorithm. The frequency resolution of deviation detection employs FFT spectrum analysis technology, decomposing the broadband signal into multiple narrowband frequency components for separate detection. The classification of positive and negative phase deviations is based on the sign of the phase difference: positive values ​​are classified as positive phase deviations, and negative values ​​as negative phase deviations. Statistical analysis of the deviation data provides deviation distribution characteristics and anomaly identification capabilities.

[0065] A phase correction template is established using positive phase deviation. Detected positive phase deviation data is used as a reference standard to construct a phase correction template and compensation scheme for negative phase deviation correction. The phase correction template is stored in a lookup table format, containing a mapping relationship between frequency, positive phase deviation value, and corresponding correction parameters. The template is built based on the statistical characteristics and frequency response features of positive phase deviation, generating a continuous correction function through data fitting and interpolation techniques. The frequency sampling points of the correction template cover the entire frequency band of the audio signal, and the sampling interval is determined according to the smoothness of phase changes. The template data is organized using a piecewise linear interpolation method, performing linear interpolation calculations between sampling points. The representativeness of the positive phase deviation is determined through statistical analysis, selecting phase values ​​with high frequency and stable deviation as the template reference. The correction template supports multiple correction strategies, including linear correction, nonlinear correction, and adaptive correction methods. The timeliness and accuracy of the template are maintained. The template storage format uses compression encoding technology to reduce storage space usage and access time.

[0066] The negative phase deviation is phase compensated by a phase correction template to generate a correction phase. A combination of look-up table and interpolation is used for the phase compensation. The corresponding compensation parameters are looked up in the correction template according to the frequency and amplitude of the negative phase deviation. The compensation algorithm uses the inverse correction principle. The negative phase deviation is balanced by adding a positive phase correction. The calculation formula of the correction is Φc=-Φn+Φp, where Φc is the correction phase, Φn is the negative phase deviation, and Φp is the corresponding positive phase deviation reference. The phase compensation is implemented by a all-pass phase shifter or a digital phase shift algorithm, ensuring that the compensation process does not affect the amplitude characteristics of the signal. The compensation accuracy is controlled by an iterative correction method, which is corrected multiple times until the phase error meets the accuracy requirement. The quality of the correction phase is measured by the phase error and the frequency response flatness index. The real-time requirement of the compensation process is high, and parallel processing and hardware acceleration techniques are used to improve the processing speed.

[0067] The positive phase deviation is synchronized and locked with the correction phase to generate a same-phase timing. The synchronization and locking is implemented by a double-loop phase-locked loop architecture. The inner loop locks the correction phase, and the outer loop locks the positive phase deviation reference. The locking process is achieved by phase error detection and feedback control. When the phase error is less than the set threshold, it is determined that the locking is successful. The loop parameter design of the phase-locked loop considers the balance between locking speed and stability. Fast locking requires a larger loop bandwidth, and stable locking requires a moderate loop bandwidth. The generation of the same-phase timing is implemented by a phase synthesizer, which weights and synthesizes the phase information of two input signals. The monitoring of the locked state is achieved by a phase error detector and a locking indicator, which provides real-time feedback of the locked state. The synchronization accuracy is quantified by the phase jitter and the frequency stability index, which requires the phase jitter to be less than 1 degree RMS and the frequency stability to be better than 10^-9. The dynamic characteristics of the locking process include performance parameters such as capture time, locking time, and tracking accuracy. The output format of the same-phase timing includes data fields such as timestamp, phase information, and synchronization quality identifier. The fault tolerance of the locking system is achieved by lock loss detection and automatic relocking function, which ensures the reliable operation of the system.

[0068] The direct wave timing and the in-phase timing are phase-locked to establish a synchronization reference anchor. The phase-locked loop technology is used to ensure the strict synchronization of the phase and the consistency of the frequency of the two timing signals. In the process of phase-locked synthesis, the direct wave timing is used as the main reference signal, and the in-phase timing is used as the slave signal for phase tracking. The loop bandwidth of the phase-locked loop is designed to consider the stability and tracking accuracy of the system. If the bandwidth is too narrow, the response will be slow, and if the bandwidth is too wide, the stability will be poor. The amplitude distribution of the synthesized signal is realized by weighted summation. The direct wave signal is usually assigned a higher weight, and the reflected wave signal is assigned a lower weight. The signal quality of the synchronization reference anchor is measured by the signal-to-noise ratio and the phase noise index, which requires a signal-to-noise ratio greater than 60 dB and a phase noise less than -80 dBc / Hz. The digital implementation of phase-locked synthesis uses a digital signal processor to achieve high-precision phase control through a software phase-locked loop algorithm. The synthesized synchronization reference anchor has good time stability and anti-interference ability, and can provide a reliable time reference for the entire playback topology network. The output format of the anchor signal includes timestamp, phase information, and quality index metadata.

[0069] The echo delay measurement is performed using the synchronization reference anchor to form the delay compensation parameters. Based on the established synchronization reference anchor, the acoustic echo delay measurement is performed in the playback topology network to obtain the actual propagation delay data of each playback node. For example, the control center sends a test pulse to the A teaching building 100 meters away, and the pulse propagation time is 0.29 milliseconds (100 meters ÷ 343 meters / second). However, the actual measured echo delay is 0.35 milliseconds, and the additional 0.06 milliseconds of delay comes from the device processing time and the reflection path. Similarly, the student dormitory 200 meters away measures an echo delay of 0.64 milliseconds, which is 0.06 milliseconds more than the theoretical propagation time of 0.58 milliseconds. The echo delay measurement uses the impulse response measurement technique to transmit a standard test pulse from the synchronization reference anchor and record the pulse arrival time at each playback node. The measurement pulse uses a linear frequency modulation signal, covering the main frequency band of voice and alarm sound to ensure the frequency accuracy of the delay measurement. The echo path analysis includes direct path and reflection path propagation modes. The direct path reflects the shortest propagation time, and the reflection path reflects the environmental acoustic characteristics. The delay compensation parameters include propagation delay, device delay, network delay, and environmental delay. The propagation delay compensation formula is Td = d / v, where Td is the propagation delay, d is the propagation distance, and v is the corrected sound speed.

[0070] The broadcast schedule table is generated based on the delay compensation parameters. First, the delay compensation parameters of all the playback nodes in the playback topology network are summarized to establish a node delay parameter database. The database contains complete information such as node ID, physical location, propagation delay, device delay, network delay, etc. The schedule table generation process is batch-processed according to the playback priority, with special priority tasks being allocated time slices first, and subsequent priorities being queued in turn. The power allocation algorithm calculates the playback power parameters of each node according to the building type, personnel density, and emergency evacuation degree. The A teaching building is allocated 80% power due to high personnel density, the B experimental building is allocated 60% power, and the C dormitory building is allocated 40% power. The time synchronization algorithm uses a reverse calculation method, starting from the target playback time, and calculating the time point of starting playback for each node. Taking the earthquake warning broadcast as an example, the target synchronous playback time is set to 14:00:00.000, the main teaching building (node A01) has a delay compensation of 35 milliseconds, and the calculated start time is 13:59:59.965; the library (node B02) has a delay compensation of 28 milliseconds, and the start time is 13:59:59.972; the student dormitory (node C03) has a delay compensation of 64 milliseconds, and the start time is 13:59:59.936. The schedule table data structure uses a two-dimensional table form, with columns containing task ID, playback content, target playback time, node ID, calculated start time, playback power, gain coefficient, dynamic range, frequency response, priority level, playback duration, repetition number, etc. The scheduling algorithm also needs to handle the segmentation of the playback content, such as the evacuation instruction being divided into "immediate evacuation", "evacuation path", "safety precautions" three playback segments, each segment being allocated an independent time slice and corresponding power control parameters. The final generated broadcast schedule table takes the time axis as the main sequence, containing complete playback schedule, node allocation table, power control parameter table and content playback list, ensuring accurate synchronization of earthquake warning information propagation and differentiated power control.

[0071] Step S150, separate the power control parameters from the broadcast schedule table to form a carrier channel by carrier modulation, construct a multi-focus transmission based on the carrier channel to generate a composite transmission frame, and construct a multi-modal playback domain according to the composite transmission frame.

[0072] Specifically, power control parameters are extracted from the broadcast scheduling table and carrier modulated to form carrier channels. The power control data is extracted from the broadcast scheduling table, and the audio signal is modulated onto different carrier frequencies using carrier modulation technology, forming multiple independent carrier channels. For example, when the scheduling table shows that Building A needs 80% power, Building B needs 60% power, and Dormitory C needs 40% power, the system modulates these power parameters onto carrier frequencies f1=40kHz, f2=45kHz, and f3=50kHz respectively, forming three independent carrier channels. Each channel carries the warning audio signal and power control information for the corresponding building. The power control parameters include key control data such as playback power, gain coefficient, dynamic range, and frequency response. Carrier modulation uses a hybrid modulation method combining frequency modulation (FM) and amplitude modulation (AM). Frequency modulation carries the audio signal, and amplitude modulation carries the power control information. The carrier frequency is selected to avoid audio frequency bands and environmental interference bands, using ultrasonic frequency bands above 40kHz as the carrier. The modulation depth is optimized according to the signal dynamic range and transmission distance; a smaller modulation depth is used for short-distance transmission, and a larger modulation depth is used for long-distance transmission. The bandwidth allocation of the carrier channels is determined based on the spectral characteristics of the audio signal; an 8kHz bandwidth is allocated to speech signals, and a 20kHz bandwidth is allocated to music signals. Channel isolation is achieved through frequency spacing and filters, with the frequency spacing between adjacent carriers being greater than 1.5 times the signal bandwidth.

[0073] In some embodiments, the step of constructing a multi-path focused transmission to generate a composite transmission frame based on the carrier channel includes: performing multi-point carrier interferometry analysis on the carrier channel to obtain interferometric focusing parameters; establishing a carrier phase synchronization control matrix using the interferometric focusing parameters; performing spatial focusing processing on the carrier signal through the carrier phase synchronization control matrix to generate a focused carrier beam; and performing time-domain multiplexing and encapsulation of the focused carrier beam to form a composite transmission frame.

[0074] The carrier channel is subjected to multi-point carrier interference analysis to obtain interference focusing parameters. The carrier interference analysis is based on a spatial sampling method, and a plurality of measurement points are set in a playing coverage area to record the amplitude and phase information of each carrier channel signal at different spatial positions. The interference focusing parameters include phase difference, amplitude ratio, frequency offset, and spatial attenuation coefficient, and other key parameters between carriers. Synchronous sampling is used to ensure the time consistency and phase accuracy of the data of each measurement point. The mathematical basis of the interference analysis is the principle of wave superposition. When a plurality of carrier signals meet at a certain point in space, the amplitude and phase of the combined signal are determined by the vector sum of the carrier signals. Constructive interference occurs at spatial positions where the phases of the carrier signals are similar, and the amplitude of the combined signal is maximum. Destructive interference occurs at spatial positions where the phases of the carrier signals are opposite, and the amplitude of the combined signal is minimum. The extraction of focusing parameters is realized by signal processing algorithms, including phase difference measurement, amplitude ratio calculation, and spatial gradient analysis. The spatial distribution characteristics of the parameters reflect the interference mode and focusing characteristics of the carrier signals in three-dimensional space. The accuracy of the interference focusing parameters directly affects the quality of the subsequent focusing effect, and the phase measurement accuracy is required to be within 1 degree, and the amplitude measurement accuracy is required to be within 0.1 dB.

[0075] The interference focusing parameters are used to form a carrier phase synchronization control matrix. The obtained interference focusing parameters are used as input data to construct a matrix structure for carrier phase synchronization control, realizing the coordinated control of multi-carrier signals. The carrier phase synchronization control matrix adopts a complex matrix form, and the matrix elements include the phase control amount and amplitude weight coefficient of each carrier channel. The rows of the matrix correspond to different carrier channels, and the columns correspond to different focusing target positions. The matrix element aij represents the phase control parameter of the i-th carrier channel to the j-th focusing position. The generation of the phase control matrix adopts an optimization method, and the maximum focusing effect is used as the objective function to solve the optimal phase control amount of each carrier channel. The calculation formula of the matrix element is aij=Aij·exp(jφij), where Aij is the amplitude weight, φij is the phase control amount, and j is the imaginary unit. The constraint conditions of the synchronization control include power limitation, phase range limitation, and convergence condition constraints. The solution of the matrix adopts an iterative optimization algorithm to gradually approach the optimal solution through multiple iterations. The phase synchronization accuracy is guaranteed by the phase-locked loop technology and digital signal processing technology, and the synchronization error is controlled within the sub-degree level.

[0076] The focused carrier beams are formed by applying the constructed carrier phase synchronization control matrix to adjust the phase and amplitude of the signals of each carrier channel, so as to realize the focused transmission of the carrier signals at the specified spatial position. The spatial focusing processing is realized by digital beam forming, and the spatial pointing control of the beam is realized by adjusting the phase and amplitude of each carrier signal in real time. The formation of the focused carrier beam is based on the principle of phased array, and a plurality of carrier sources form coherent superposition at the target position after phase control. The weight vector of the beam forming is provided by the corresponding row vector of the phase synchronization control matrix, and different target positions correspond to different weight vectors. The main lobe of the focused carrier beam points to the target area, and the side lobe points to the non-target area, and the interference to the non-target area is reduced by the side lobe suppression technology. The focusing effect of the carrier signal is quantified by parameters such as focusing gain and beam width, and the focusing gain reflects the signal enhancement degree of the target position, and the beam width reflects the spatial precision of the focusing. The real-time requirement of the focusing processing is high, and the special digital signal processor and parallel computing technology are used to realize the fast processing.

[0077] The focused carrier beams are formed by applying the constructed carrier phase synchronization control matrix to adjust the phase and amplitude of the signals of each carrier channel, so as to realize the focused transmission of the carrier signals at the specified spatial position. The spatial focusing processing is realized by digital beam forming, and the spatial pointing control of the beam is realized by adjusting the phase and amplitude of each carrier signal in real time. The formation of the focused carrier beam is based on the principle of phased array, and a plurality of carrier sources form coherent superposition at the target position after phase control. The weight vector of the beam forming is provided by the corresponding row vector of the phase synchronization control matrix, and different target positions correspond to different weight vectors. The main lobe of the focused carrier beam points to the target area, and the side lobe points to the non-target area, and the interference to the non-target area is reduced by the side lobe suppression technology. The focusing effect of the carrier signal is quantified by parameters such as focusing gain and beam width, and the focusing gain reflects the signal enhancement degree of the target position, and the beam width reflects the spatial precision of the focusing. The real-time requirement of the focusing processing is high, and the special digital signal processor and parallel computing technology are used to realize the fast processing.

[0078] The multi-modal playing field is constructed according to the composite transmission frame. First, the received composite transmission frame is structurally parsed, the time reference information is extracted from the frame synchronization time slot, the playing mode instruction, the carrier beam configuration parameter and the power control data are parsed from the control information time slot. For example, when the mode field in the control information time slot shows "02", it indicates a partition playing mode, the power allocation field shows "A zone 80%, B zone 60%, C zone 40%", and the carrier beam pointing parameter shows "azimuth angle 045°, elevation angle 15°". The parsing of the carrier beam data time slot separates the audio data of different focused carrier beams through time division multiplexing technology, and each carrier beam corresponds to a specific playing area and acoustic coverage. The playing field construction algorithm selects the corresponding field configuration strategy according to the parsed playing mode instruction. The global playing mode broadcasts all carrier beam data to the entire campus area, and the partition playing mode transmits different audio content to the corresponding area according to the spatial pointing parameters of the carrier beams. The division of the spatial field is based on the carrier beam focusing parameters in the composite transmission frame. By parsing the main lobe pointing angle, beam width and focusing gain parameters of each carrier beam, multiple acoustic coverage sub-fields are established in three-dimensional space. The determination of the boundary between fields is calculated through the carrier beam side lobe suppression parameter and the spatial attenuation function, which ensures that the interference between adjacent playing sub-fields is controlled within the allowable range. Finally, a multi-modal playing system including global playing field, partition playing field, directional playing field and adaptive playing field is formed, and the activation and switching of each modal field are completely driven by the control instruction in the composite transmission frame.

[0079] In step S160, the sound field interference distribution is analyzed by using the multi-modal playing field to form an acoustic navigation field, a frequency division multiplexing playing network is constructed based on the acoustic navigation field, a playing instruction is generated by scheduling the audio signal through the frequency division multiplexing playing network, and the earthquake warning of the campus broadcast is completed.

[0080] In some embodiments, the analysis of the sound field interference distribution by using the multi-modal playing field to form an acoustic navigation field includes: performing acoustic modal separation on the multi-modal playing field to identify direct sound modalities and reflected sound modalities; performing phase interference analysis based on the direct sound modalities and the reflected sound modalities to generate an interference intensity distribution map; extracting a sound pressure enhancement area and a sound pressure attenuation area from the interference intensity distribution map to form a sound field distribution feature; and converting the sound field distribution feature into spatial acoustic guidance information to generate an acoustic navigation field.

[0081] Acoustic modal separation is performed on the multi-modal playing domain to identify direct sound and reflected sound. Modal decomposition is performed on the acoustic signals in the constructed multi-modal playing domain to separate the composite sound field into two basic acoustic modes of direct propagation and reflected propagation. For example, in the playing domain of a campus central square, when a loudspeaker plays a warning message, a student sitting in the center of the square mainly hears the sound directly transmitted from the loudspeaker (direct sound mode), while a student standing under the teaching building hears both the direct sound and the sound reflected from the building wall (reflected sound mode). The two modes differ by about 20-50 milliseconds in time and have obvious differences in frequency response. Acoustic modal separation uses blind source separation technology and independent component analysis method to extract independent acoustic components from the mixed sound field through signal processing algorithms. The direct sound mode corresponds to the acoustic path of sound directly propagating from the playing source to the receiving point, with the shortest propagation time and the highest signal strength. The reflected sound mode contains the acoustic path of sound reaching the receiving point after being reflected by various interfaces, with longer propagation time and attenuated signal strength. The mathematical basis of modal separation is the linear superposition principle of sound field, and the mixed sound field can be represented as a linear combination of independent modes. The separation process uses time-frequency analysis technology to identify and separate the modes in both time and frequency domains. The characteristics of the direct sound mode include the shortest propagation delay, the highest spectral fidelity, and the strongest spatial directivity. The characteristics of the reflected sound mode include longer propagation delay, changes in frequency spectrum, and stronger spatial diffusion.

[0082] Phase interference analysis is performed based on the direct sound mode and the reflected sound mode to generate an interference intensity distribution map. The separated direct sound mode and reflected sound mode are analyzed for phase relationship, and the spatial distribution map of interference intensity in the sound field is generated by calculating the phase difference and amplitude ratio between the two modes. Phase interference analysis is based on the principle of wave interference. When two or more sound waves meet at a point in space, their combined effect depends on the phase relationship of each wave. The calculation formula of interference intensity is I = I1 + I2 + 2(I1I2)^0.5×cos(Δφ), where I is the combined sound intensity, I1 and I2 are the intensities of direct sound and reflected sound, and Δφ is the phase difference. Constructive interference occurs at positions where the phase difference is 0 or an integer multiple of 2π, resulting in the maximum combined sound intensity. Destructive interference occurs at positions where the phase difference is an odd multiple of π, resulting in the minimum combined sound intensity. The interference intensity distribution map is represented by color coding or contour lines, with different colors or lines corresponding to different sound intensity levels. The spatial resolution of the distribution map is determined according to the control requirements of the playing domain, and a 1 meter x 1 meter grid is generally used. The frequency range of the phase analysis covers the main frequency band of the audio signal, and the interference patterns at different frequencies may differ. The generation of the distribution map uses numerical calculation and graphics rendering technology, supporting both two-dimensional and three-dimensional displays.

[0083] The sound pressure enhancement region and the sound pressure reduction region are extracted from the interference intensity distribution map to form the sound field distribution characteristics. In the generated interference intensity distribution map, the characteristic region identification and extraction are performed, and the sound field is divided into three regions of sound pressure enhancement, sound pressure reduction and normal sound pressure. The sound pressure enhancement region corresponds to the spatial position where constructive interference occurs, and the sound loudness in this region is obviously higher than that in the surrounding region. The sound pressure reduction region corresponds to the spatial position where destructive interference occurs, and the sound loudness in this region is obviously lower than that in the surrounding region. The feature extraction adopts the image processing technology, and the threshold segmentation and connected domain analysis method are used to identify the boundary and range of different characteristic regions. The threshold of the sound pressure enhancement region is set to the average sound pressure level plus 3 dB, and the threshold of the sound pressure reduction region is set to the average sound pressure level minus 3 dB. The morphological processing of region extraction includes opening operation and closing operation, which eliminates noise points and fills small cavities. The quantitative parameters of sound field distribution characteristics include region area, shape coefficient, distribution density and spatial connectivity and other geometric characteristics. The spatial distribution patterns of the enhancement region and the reduction region reflect the acoustic characteristics and optimization potential of the playing domain. The data storage of the sound field distribution characteristics adopts the vector format, which includes region boundary coordinates, characteristic type and intensity parameters and other information.

[0084] The sound field distribution characteristics are converted into spatial acoustic guidance information to generate an acoustic navigation field. The acoustic navigation field serves as the core data structure of spatial acoustic optimization, and guides the configuration of the playing network by converting static sound field distribution characteristics into dynamic guidance information. The generation process of the acoustic navigation field marks the sound pressure enhancement region as "standard coverage demand region", sets the audio quality to standard level, and allocates the bandwidth demand to 8-12 kHz; marks the sound pressure reduction region as "key coverage demand region", upgrades the audio quality to high definition level, and allocates the bandwidth demand to 12-20 kHz to compensate for the acoustic defects. Taking the square in front of the library on campus as an example, the acoustic navigation field marks the sound pressure reduction region in the southeast corner of the square as the key coverage region, sets the guide intensity to 0.8-1.0, and allocates the transmission power to 15-25 W; sets the guide intensity of the sound pressure enhancement region in the center of the square to 0.4-0.6, and the transmission power to 10-15 W to meet the requirements. The guide intensity of the acoustic navigation field reflects the importance of coverage demand, and the guide intensity of the region with obvious sound field characteristics is higher, with a value of 0.7-1.0, and the guide intensity of the region with unobvious sound field characteristics is lower, with a value of 0.3-0.5. The constructed acoustic navigation field contains key information such as coverage demand level, audio quality requirement, bandwidth allocation suggestion and guide intensity, which provides spatial optimization basis for frequency division multiplexing playing network.

[0085] The frequency division multiplexing (FDM) broadcasting network is constructed based on the acoustic navigation field. The FDM broadcasting network architecture is designed and constructed using the information of the acoustic navigation field, which realizes the parallel transmission and precise control of multiple audio signals. The FDM broadcasting network divides the available frequency spectrum into multiple sub-bands, each of which carries an independent audio signal, avoiding mutual interference between signals. The sub-bands are optimally allocated according to the spectral characteristics and transmission requirements of the audio signals, with voice signals allocated to narrower frequency bands and music signals allocated to wider frequency bands. The network topology structure combines star and ring topologies, with the star structure used for backbone transmission and the ring structure used for local branches. The carrier frequency of the FDM is selected to avoid the audio frequency band and environmental interference frequency band, and radio frequency or microwave frequency band is used as the carrier. The frequency synchronization of the network nodes is achieved through the master-slave clock synchronization protocol, ensuring the precise consistency of the carrier frequencies of the nodes. The bandwidth allocation of the broadcasting network is dynamically adjusted according to the coverage requirements of the acoustic navigation field and the audio quality requirements. The network control protocol adopts a hierarchical control structure, with the physical layer responsible for signal transmission, the data link layer responsible for frame synchronization, and the network layer responsible for routing selection. The modulation method of FDM uses orthogonal frequency division multiplexing (OFDM) technology to improve the spectral efficiency and anti-interference ability.

[0086] The audio signal scheduling is performed through the FDM broadcasting network to generate the broadcasting instructions. The FDM broadcasting network is used to uniformly schedule and distribute the audio signals to generate specific broadcasting instructions for different broadcasting areas and devices. The audio signal scheduling adopts a centralized scheduling strategy, and the scheduling center formulates the optimal scheduling scheme according to the warning level, regional priority, and network state. The scheduling algorithm considers multiple factors such as signal priority, bandwidth demand, transmission delay, and broadcasting synchronization, and solves the optimal scheduling sequence through a multi-objective optimization method. The broadcasting instructions contain control information such as broadcasting content, broadcasting time, broadcasting power, frequency parameters, and synchronization control. The broadcasting content includes core warning information such as epicenter location, estimated magnitude, warning time, local estimated intensity, epicenter distance, and warning level, which is divided into four levels of red, orange, yellow, and blue according to the threat level. The data format of the instructions uses structured coding, including instruction header, parameter field, and check field. The instruction header contains basic information such as instruction type, priority, target device, and timestamp. The parameter field contains specific broadcasting control parameters such as volume, equalizer settings, and spatial positioning. The real-time performance of the scheduling process is guaranteed through hardware acceleration and parallel processing technology, with a delay of milliseconds. The distribution of the broadcasting instructions uses multicast technology, and the instructions of the same broadcasting content can be sent to multiple target devices simultaneously. The confirmation and feedback mechanism of the instructions ensures that the broadcasting devices correctly receive and execute the broadcasting instructions. The scheduling state monitoring tracks the execution progress and completion of each broadcasting task, ultimately achieving the timely, accurate, and comprehensive broadcasting of earthquake warning information in the campus, providing effective earthquake warning services for campus personnel.

[0087] In order to perform the above-mentioned method embodiment corresponding to the campus broadcast-based earthquake early warning method, to realize the corresponding functions and technical effects. Referring to Figure 2 , Figure 2 The structure block diagram of the campus broadcast-based earthquake early warning device 200 provided by the embodiment of the application is shown. For ease of illustration, only the part related to the embodiment is shown. The campus broadcast-based earthquake early warning device 200 provided by the embodiment of the application comprises:

[0088] The signal processing module 201 is configured to receive an early warning signal sent by a seismic monitoring center, perform building response coupling analysis on the early warning signal to generate a magnitude-building coupling feature, perform acoustic environment mapping on the magnitude-building coupling feature to generate an enhanced early warning source.

[0089] The region identification module 202 is configured to identify a threatened campus region based on the enhanced early warning source, perform acoustic cavity detection through the threatened campus region to form a building acoustic distribution, and construct a differentiated playing topology according to the building acoustic distribution.

[0090] The priority generation module 203 is configured to perform personnel density scanning on the differentiated playing topology to extract a high-risk gathering region, generate evacuation guide content based on the high-risk gathering region, fuse the evacuation guide content with the enhanced early warning source to determine a playing priority.

[0091] The synchronization control module 204 is configured to perform synchronization demand analysis on the differentiated playing topology based on the playing priority to generate a timing synchronization parameter, establish a synchronization reference anchor point based on the timing synchronization parameter, perform echo delay measurement using the synchronization reference anchor point to form a delay compensation parameter, and generate a broadcast scheduling table based on the delay compensation parameter.

[0092] The transmission coordination module 205 is configured to separate a power control parameter from the broadcast scheduling table to form a carrier channel through carrier modulation, construct a multi-path focusing transmission to generate a composite transmission frame based on the carrier channel, and construct a multi-modal playing domain according to the composite transmission frame.

[0093] The early warning output module 206 is configured to analyze a sound field interference distribution using the multi-modal playing domain to form an acoustic navigation field, construct a frequency division multiplexing playing network based on the acoustic navigation field, perform audio signal scheduling through the frequency division multiplexing playing network to generate a playing instruction, and complete the campus broadcast-based earthquake early warning.

[0094] The campus broadcast-based earthquake warning device 200 described above can implement the campus broadcast-based earthquake warning method of the method embodiment described above. The optional items in the method embodiment described above are also applicable to this embodiment, and will not be described in detail here. The remaining content of the present embodiment can refer to the content of the method embodiment described above, and will not be described in detail in this embodiment.

[0095] The purpose of the above embodiments is to exemplarily reproduce and deduce the technical solutions of the present application, and to completely describe the technical solutions, purposes and effects of the present application. The purpose is to make the public understand the disclosure of the present application more thoroughly and comprehensively, and not to limit the protection scope of the present application.

[0096] The above embodiments are also not an exhaustive enumeration based on the present application, and there can be many other unlisted embodiments. Any replacement and improvement made without violating the concept of the present application is within the protection scope of the present application.

Claims

1. An earthquake early warning method based on campus broadcasting, characterized in that, include: The system receives early warning signals from an earthquake monitoring center, performs building response coupling analysis on the early warning signals to generate magnitude-building coupling features, and maps the magnitude-building coupling features to the acoustic environment to generate an enhanced early warning source. This includes: performing environmental adaptation analysis on the magnitude-building coupling features to obtain an acoustic response profile; forming an environment-response correlation network by combining a preset campus environmental noise baseline and the acoustic response profile; extracting stable propagation paths within the environment-response correlation network; and constructing an enhanced early warning source based on the intensity values ​​of the stable propagation paths. Based on the enhanced early warning source, threatened campus areas are identified. Acoustic cavity detection is performed on the threatened campus areas to form architectural acoustic distribution. A differentiated playback topology is constructed based on the architectural acoustic distribution, including: performing acoustic impedance analysis based on the architectural acoustic distribution to obtain impedance distribution parameters; using the impedance distribution parameters to perform impedance matching processing on playback devices to form matched playback nodes; establishing a sequence of acoustic impedance balance relationships through the matched playback nodes; and constructing a differentiated playback topology based on the acoustic impedance balance relationship sequence. The differentiated playback topology is scanned for personnel density to extract high-risk gathering areas. Evacuation guidance content is generated based on the high-risk gathering areas. The evacuation guidance content is then fused with the enhanced early warning source to determine the playback priority. Based on the playback priority, a synchronization requirement analysis is performed on the differentiated playback topology to generate timing synchronization parameters. A synchronization reference anchor point is then established based on these parameters, including: decomposing sound wave propagation delay using the timing synchronization parameters to generate direct wave timing and reflected wave timing; establishing a zero-delay reference point using the direct wave timing; performing phase correction processing on the reflected wave timing based on the zero-delay reference point to generate in-phase timing; phase-locking the direct wave timing with the in-phase timing to establish a synchronization reference anchor point; measuring echo delay using the synchronization reference anchor point to generate delay compensation parameters; and generating a broadcast scheduling table based on the delay compensation parameters. Power control parameters are separated from the broadcast scheduling table and carrier modulation is performed to form a carrier channel. Based on the carrier channel, a multi-channel focused transmission is constructed to generate a composite transmission frame. A multimodal playback domain is constructed based on the composite transmission frame. The method of forming an acoustic navigation field by analyzing the sound field interference distribution using the multimodal playback domain includes: performing acoustic mode separation and identification of direct sound mode and reflected sound mode in the multimodal playback domain; performing phase interference analysis based on the direct sound mode and the reflected sound mode to generate an interference intensity distribution map; extracting sound pressure enhancement regions and sound pressure reduction regions from the interference intensity distribution map to form sound field distribution features; converting the sound field distribution features into spatial acoustic guidance information to generate an acoustic navigation field; constructing a frequency division multiplexing playback network based on the acoustic navigation field; and using the frequency division multiplexing playback network to schedule audio signals and generate playback instructions to complete the earthquake early warning broadcast on campus.

2. The method according to claim 1, characterized in that, The step of fusing the evacuation guidance content with the enhanced early warning source to determine the playback priority includes: The evacuation guidance content is quantified to generate a guidance urgency level. The enhanced early warning source is used to assess the threat level of the guidance urgency to form a threat correlation spectrum; Priority coefficients are generated by weighted fusion of the threat correlation spectrum; Playback priority is generated by classifying levels based on the priority coefficients.

3. The method according to claim 1, characterized in that, The step of constructing a multi-path focused transmission to generate a composite transmission frame based on the carrier channel includes: Multi-point carrier interferometry analysis is performed on the carrier channel to obtain the interferometric focusing parameters; A carrier phase synchronization control matrix is ​​established using the aforementioned interferometric focusing parameters; The carrier signal spatial focusing process, performed by the carrier phase synchronization control matrix, generates a focused carrier beam. The focused carrier beam is time-domain multiplexed and encapsulated to form a composite transmission frame.

4. The method according to claim 1, characterized in that, The process of forming an environment-response correlation network by combining a preset campus environmental noise baseline and the acoustic response profile includes: The analysis interval is determined by identifying the acoustic sensitive frequency band based on the acoustic response profile, wherein the acoustic sensitive frequency band includes low-frequency penetration, mid-frequency clarity, and high-frequency attenuation rate; Using a preset campus environmental noise baseline as a reference, the noise change process is tracked along the analysis interval to form a noise change spectrum; Extract the frequency coordinates of each response point from the noise variation spectrum; An environment-response correlation network is generated by arranging the response intensities according to the frequency coordinates.

5. The method according to claim 1, characterized in that, The step of performing phase correction processing on the reflected wave timing based on the zero-delay reference point to generate in-phase timing includes: Positive and negative phase deviations are determined based on the zero-delay reference point; A phase correction template is established using the positive phase deviation. The negative phase deviation is compensated using the phase correction template to generate a corrected phase. The positive phase deviation is synchronized and locked with the corrected phase to generate in-phase timing.

6. An earthquake early warning device based on campus broadcasting, characterized in that, include: The signal processing module receives early warning signals sent by the earthquake monitoring center, performs building response coupling analysis on the early warning signals to generate magnitude-building coupling features, and maps the magnitude-building coupling features to the acoustic environment to generate an enhanced early warning source. This includes: performing environmental adaptation analysis on the magnitude-building coupling features to obtain an acoustic response profile; forming an environment-response correlation network by combining a preset campus environmental noise baseline and the acoustic response profile; extracting stable propagation paths within the environment-response correlation network; and constructing an enhanced early warning source based on the intensity values ​​of the stable propagation paths. The area identification module is used to identify threatened campus areas based on the enhanced early warning source, perform acoustic cavity detection on the threatened campus areas to form architectural acoustic distribution, and construct a differentiated playback topology based on the architectural acoustic distribution. This includes: performing acoustic impedance analysis based on the architectural acoustic distribution to obtain impedance distribution parameters; using the impedance distribution parameters to perform impedance matching processing on the playback devices to form matched playback nodes; establishing a sound impedance balance relationship sequence through the matched playback nodes; and constructing a differentiated playback topology based on the sound impedance balance relationship sequence. The priority generation module is used to perform personnel density scanning on the differentiated playback topology to extract high-risk gathering areas, generate evacuation guidance content based on the high-risk gathering areas, and fuse the evacuation guidance content with the enhanced early warning source to determine the playback priority. The synchronization control module is used to perform synchronization requirement analysis on the differentiated playback topology based on the playback priority to generate timing synchronization parameters, and to establish a synchronization reference anchor point based on the timing synchronization parameters. This includes: performing sound wave propagation delay decomposition using the timing synchronization parameters to generate direct wave timing and reflected wave timing; establishing a zero-delay reference point using the direct wave timing; performing phase correction processing on the reflected wave timing based on the zero-delay reference point to generate in-phase timing; phase-locking the direct wave timing with the in-phase timing to establish a synchronization reference anchor point; using the synchronization reference anchor point to perform echo delay measurement to form delay compensation parameters; and generating a broadcast scheduling table based on the delay compensation parameters. The transmission coordination module is used to separate power control parameters from the broadcast scheduling table, perform carrier modulation to form a carrier channel, construct multi-channel focused transmission based on the carrier channel to generate a composite transmission frame, and construct a multimodal playback domain based on the composite transmission frame; The early warning output module is used to analyze the sound field interference distribution using the multimodal playback domain to form an acoustic navigation field. This includes: performing acoustic mode separation and identification of direct and reflected sound modes in the multimodal playback domain; performing phase interference analysis based on the direct and reflected sound modes to generate an interference intensity distribution map; extracting sound pressure enhancement and reduction regions from the interference intensity distribution map to form sound field distribution features; converting the sound field distribution features into spatial acoustic guidance information to generate an acoustic navigation field; constructing a frequency division multiplexing playback network based on the acoustic navigation field; and using the frequency division multiplexing playback network to schedule audio signals and generate playback instructions to complete the earthquake early warning broadcast on campus.

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