An artificial intelligence automatic broadcasting method and system

Abstract

This invention discloses an automatic broadcasting method and system based on artificial intelligence, relating to the field of broadcasting technology. The key technical aspects include a sensing component, a resource library, a logic management component, a human-computer interaction component, and a front-end device. The sensing component is used to collect environmental and network data; the resource library is used to store and provide the data and algorithm resources required for system operation. The sensing component, resource library, logic management component, human-computer interaction component, and front-end device adopt a distributed architecture. The logic management component can automatically trigger the front-end device to broadcast based on the input from the sensing component and the resources in the resource library through computational reasoning. The effect is that the functional combination of different components meets the broadcasting needs of various scenarios, including daily notifications, safety warnings, and emergency dispatch.

Description

Technical Field

[0001] This invention relates to the field of broadcasting technology, and more specifically, to an artificial intelligence-based automatic broadcasting method and system. Background Technology

[0002] Currently, traditional analog or digital network broadcasting suffers from drawbacks such as limited functionality, limited scalability, and cumbersome configuration. IP network broadcasting, whether using proprietary protocols or the common SIP or DANTE protocols, also has functional limitations, requiring both debugging and operation personnel to possess strong technical expertise. Furthermore, frequent job changes necessitate repeated training from the manufacturer for new employees to accurately configure and operate the professional broadcasting system. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an artificial intelligence automatic broadcasting method and system.

[0004] To achieve the above objectives, the present invention provides the following technical solution: An artificial intelligence-based automated broadcasting system includes a sensing component, a resource library, a logic management component, a human-computer interaction component, and a front-end device. The sensing component is used to collect environmental data and network data; The resource library is used to store and provide the data and algorithm resources required for system operation; The logic management component is connected to the sensing component and the resource library respectively. The logic management component is used to analyze and reason about the data collected by the sensing component and then generate broadcast control commands. The human-computer interaction component is connected to the logic management component, and the human-computer interaction component is used to receive manual commands and display the system status. The front-end device is connected to the logic management component, and the front-end device is used to receive broadcast control commands and execute corresponding broadcast sounds. The sensing component, resource library, logic management component, human-computer interaction component, and front-end device adopt a distributed architecture. The logic management component can automatically trigger the front-end device to broadcast based on the input of the sensing component and the resources of the resource library through calculation and reasoning.

[0005] Preferably, the sensing component includes at least one of the following: a visual sensor, a human body sensor, a temperature and humidity sensor, a pressure sensor, a sound sensor, a gas sensor, an internet information classification sensor, and a ground vibration sensor.

[0006] Preferably, the visual sensor is used to identify specific behaviors and trigger alarm broadcasts; The human body sensor is used to detect when a person approaches a dangerous area and trigger an alarm broadcast. The temperature and humidity sensors are used to trigger a friendly reminder broadcast based on environmental data; The pressure sensor, combined with facial recognition technology, is used to manage user health characteristics and trigger targeted health alert broadcasts. The sound sensor is used to identify dangerous behavior and trigger corresponding broadcasts through sound pressure level, spectrum analysis, or speech recognition technology. The Internet information classification sensor is used to match network information with system location information to trigger relevant broadcasts; The ground vibration sensor is used to detect sudden events and trigger real-time emergency broadcasts.

[0007] Preferably, the resource library includes a language library, a program library, an artificial intelligence learning library, an algorithm library, a database, and a storage unit; The language library is used to convert text to speech and provide multilingual support; The program library is used to store background music, warning sound sources, and dispelling sound sources; The artificial intelligence learning library is used to improve the system's cognitive and reasoning abilities through sensor data; The algorithm library is used to provide the algorithms required for the system's computation; The database is used for high-speed storage and retrieval of system data; The storage unit is used to store calculation results and system operation logs.

[0008] Preferably, the human-computer interaction components include at least one of the following: touch screen and display screen, mouse and keyboard, microphone and speaker, and 3D holographic control device.

[0009] Preferably, the logic management component includes a logic analysis module, an operation and reasoning module, an event and log management module, a database management module, and a domain operation module; The logic analysis module is used to perform real-time data processing on the status of each component. The computational reasoning module is used to generate broadcast operation instructions based on scene conditions; The event and log management module is used to record the system's operation history; The database management module is used to handle database anomalies, corrections, and backups; The domain computation module is used to perform sound field spectrum analysis and compensation calculations.

[0010] Preferably, the front-end device includes at least one of the following: a speaker with phase compensation, a network speaker with DSP processing, a distributed network speaker, a network amplifier with detection function, an infrared flat panel network speaker, and a network area sound-emitting board.

[0011] Preferably, the speaker is used to compensate for sound based on acoustic sensor data to improve speech intelligibility; The network speaker has built-in anti-feedback, equalizer, high and low cut, and noise gate modules. The network speaker is used to dynamically compensate for audio effects according to different sound sources. The distributed network speaker achieves zero-latency distributed sound amplification through multiple speakers; The network power amplifier has self-detection and master / slave switching functions for temperature, voltage, current, signal strength, and decoding circuit. The infrared flat-panel network speaker is used to broadcast securely to a sound source via infrared radiation; The network area sound-emitting panel causes a sound pressure level difference of more than 20 dB between adjacent areas.

[0012] An automated broadcasting method based on artificial intelligence, comprising the following steps: Collect environmental and network data; Stores and provides the data and algorithm resources required for system operation; After analyzing and reasoning about the data collected by the sensing components, broadcast control commands are generated. Used to receive manual commands and display system status; Receive broadcast control commands and execute the corresponding broadcast sounds.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The human-computer interaction components and front-end devices of this invention, including the sensing component, resource library, and logic management component, adopt a distributed architecture. These components are deployed independently yet operate collaboratively, avoiding the single point of failure risk of centralized architectures and allowing for flexible adjustment of the component layout according to different scenario requirements. The sensing component automatically collects environmental and network data, while the logic management component analyzes and infers based on this data and the support of the resource library, autonomously generating broadcast control commands and triggering the front-end devices to broadcast, all without manual operation. The collaborative cooperation of each component allows the broadcast function to adapt to scenario requirements. The sensing component provides multi-dimensional data sources, the resource library supports the supply of data and algorithms, the logic management component accurately generates commands, the front-end devices execute corresponding broadcast operations, and the human-computer interaction component assists in manual intervention and status monitoring, forming a complete closed loop from data collection to broadcast execution. The functional combination of different components meets the broadcast needs of various scenarios, including daily notifications, safety alerts, and emergency dispatch. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the architecture of an embodiment of the present invention. Detailed Implementation

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0016] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0017] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0018] Reference Figure 1 As shown.

[0019] The embodiments further illustrate the artificial intelligence-based automatic broadcasting method and system proposed in this invention.

[0020] An artificial intelligence-based automated broadcasting system includes a sensing component, a resource library, a logic management component, a human-computer interaction component, and a front-end device. Sensing components are used to collect environmental and network data; The resource repository is used to store and provide the data and algorithm resources required for system operation; The logic management component is connected to the sensing component and the resource library respectively. The logic management component is used to analyze and reason about the data collected by the sensing component and then generate broadcast control commands. The human-computer interaction component is connected to the logic management component. The human-computer interaction component is used to receive manual commands and display the system status. The front-end device is connected to the logic management component. The front-end device is used to receive broadcast control commands and execute corresponding broadcast sounds. The sensing component, resource library, logic management component, human-computer interaction component, and front-end device adopt a distributed architecture. The logic management component can automatically trigger the front-end device to broadcast based on the input of the sensing component and the resources of the resource library through calculation and reasoning.

[0021] The sensing components include at least one of the following: visual sensor, human body sensor, temperature and humidity sensor, pressure sensor, sound sensor, gas sensor, internet information classification sensor, and ground vibration sensor.

[0022] Visual sensors are used to identify specific behaviors and trigger alarm broadcasts; Human body sensors are used to detect when a person approaches a dangerous area and trigger an alarm broadcast; Temperature and humidity sensors are used to trigger friendly reminder broadcasts based on environmental data; Pressure sensors, combined with facial recognition technology, are used to manage user health characteristics and trigger targeted health alert broadcasts; Sound sensors are used to identify dangerous behavior and trigger appropriate broadcasts through sound pressure level, spectrum analysis, or speech recognition technologies. Internet information classification sensors are used to match network information with system location information to trigger relevant broadcasts; Seismic sensors are used to detect sudden events and trigger real-time emergency broadcasts.

[0023] Visual sensors continuously collect visual information about the area they are in. When they detect a specific pre-defined behavior, they send a signal to the system's logic management component, triggering an alarm broadcast. For example, in a school setting, if a visual sensor detects physical conflict between students, it will automatically play an alarm broadcast.

[0024] Human body sensors detect human signals in the surrounding environment and monitor the distance between a person and a danger zone in real time. When a person is detected approaching a preset danger threshold, the sensor sends a message back to the system, triggering an alarm broadcast. For example, near a high-altitude work area in a factory, if the human body sensor detects someone approaching the edge of the work area, it will immediately play an alarm.

[0025] Temperature and humidity sensors collect temperature and humidity data from the surrounding environment. When these data reach a pre-set threshold, a friendly reminder broadcast is triggered. For example, in winter, if the temperature and humidity sensors detect that the ambient temperature in the office area is below 10 degrees Celsius, a reminder broadcast will be played automatically.

[0026] When a user appears within the sensor's detection range, their identity is confirmed via facial recognition, and their weight data is collected by a pressure sensor. This data, combined with the user's stored health information, is used to manage their health characteristics. If an abnormal trend in the user's health data is detected, a targeted health alert is broadcast. For example, at a community health monitoring point, if a pressure sensor combined with facial recognition identifies a resident and detects significant recent fluctuations in their weight, a targeted health alert will be broadcast to that resident.

[0027] Sound sensors collect ambient sound information and analyze it using sound pressure level detection, spectrum analysis, and speech recognition technology. When a sound is identified as being related to dangerous behavior, a corresponding broadcast is triggered. For example, in a public place, if a sound sensor detects a sudden and significant increase in sound pressure level and, through speech recognition, determines that the sound is related to dangerous behavior such as arguing or calling for help, then a broadcast will be played.

[0028] The internet information classification sensor collects various types of information from the internet in real time, while simultaneously acquiring the system's location information. It matches network information with location information, and when a match is found that relates to that location, a corresponding broadcast is triggered. For example, if the internet information classification sensor collects a warning of impending heavy rain in a city area and confirms that the system is within that city, it will broadcast the warning; if a match is found with local policy announcements, it will also promptly broadcast the corresponding policy information.

[0029] The seismic sensors continuously monitor vibrations in the area. When the detected vibration intensity reaches the criteria for a sudden event, they immediately transmit a signal to the system, triggering a real-time emergency broadcast. When the seismic sensors detect an earthquake, they play an emergency broadcast within a short time to provide timely guidance for people to take shelter.

[0030] The resource library includes a language library, a program library, an artificial intelligence learning library, an algorithm library, a database, and storage units; The language library is used to convert text to speech and provide multilingual support; The program library is used to store background music, warning sound sources, and dispelling sound sources; Artificial intelligence learning libraries are used to improve the cognitive and reasoning abilities of systems through sensor data; Algorithm libraries are used to provide the algorithms required for system computation; Databases are used for high-speed storage and retrieval of system data; The storage unit is used to store the calculation results and system operation logs.

[0031] The language library enables bidirectional conversion between text and speech, while also providing multilingual resource adaptation. When broadcast content in text form needs to be played, the language library converts the text into the corresponding speech signal; if the system needs to record speech commands or feedback, it converts the speech into text for storage. The program library stores various audio resources, covering background music, warning sounds, and dispersive sounds. The appropriate audio source is retrieved from the program library in different scenarios. The artificial intelligence learning library continuously receives data collected by the sensing components to continuously improve the system's cognitive and reasoning abilities. During system operation, the sensing components continuously collect various data about the environment and personnel; this data is transmitted to the artificial intelligence learning library, which analyzes, summarizes, and learns from this data. The algorithm library provides the algorithms required for various calculations. Corresponding algorithm support is needed for data processing, scene recognition, and audio optimization. For example, when analyzing audio data collected by sound sensors, the algorithm library provides spectrum analysis algorithms; when adjusting the audio effects of front-end speakers, the algorithm library provides sound compensation algorithms.

[0032] Databases enable high-speed storage and retrieval of system data. For example, environmental data collected by sensing components and user operation information are quickly stored in the database; when the system needs to access this data for analysis and reasoning, it can retrieve it from the database at high speed. For instance, when handling an alarm event, environmental data and historical broadcast records for that area can be quickly retrieved from the database.

[0033] The storage unit stores the system's computation results and operation logs. After the system completes data computation, the corresponding computation results are stored in the storage unit for easy viewing and retrieval later; at the same time, every operation of the system, such as initiating a broadcast or adjusting parameters, is recorded as an operation log and stored.

[0034] Human-computer interaction components include at least one of the following: touch screen and display screen, mouse and keyboard, microphone and speaker, and 3D holographic control device.

[0035] Touchscreens and displays enable two-way interaction through a visual interface. The display shows the system's real-time operating status, such as the current broadcast task, the working status of each component, and environmental data monitoring results. Touchscreens allow users to directly input commands via touch, such as adjusting broadcast volume, selecting specific audio sources, and setting sensor monitoring thresholds. A mouse and keyboard meet the needs for complex commands and information input. The mouse helps users locate options within the interface, while the keyboard supports rapid text input, such as editing custom broadcast content and entering new sensor monitoring rules.

[0036] Microphones and speakers enable human-computer interaction via voice. Microphones receive voice commands and convert them into recognizable operational signals. 3D holographic control devices utilize spatial stereoscopic technology to achieve immersive interaction. Motion recognition captures the user's stereoscopic movements and presents the system's interface in 3D holographic form. Users can control the system without touching the physical device, simply through 3D gestures.

[0037] The logic management components include a logic analysis module, a computation and reasoning module, an event and log management module, a database management module, and a domain computation module; The logic analysis module is used to perform real-time data processing on the status of each component; The computational reasoning module is used to generate broadcast operation instructions based on scene conditions; The event and log management module is used to record the system's operation history; The database management module is used to handle database exceptions, corrections, and backups; The domain computation module is used to perform sound field spectrum analysis and compensation calculations.

[0038] The logic analysis module performs real-time data processing on the status of each component of the system. It continuously receives environmental data collected by sensing components, resource call status of the resource library, and operating parameters of front-end devices, and filters, integrates, and parses this data to extract key and effective information.

[0039] The computational reasoning module generates broadcast operation instructions based on scene conditions. Using information processed by the logic analysis module and combined with algorithms and data provided by the resource library, it determines the type of demand in the current scene and generates corresponding broadcast instructions, including broadcast content, playback device, and sound pressure level.

[0040] The event and log management module records the system's operational history. Every start and end of a broadcast task, every input of a manual command, and every device status switch during system operation is fully recorded by this module, thus forming a traceable operation log.

[0041] The database management module handles database anomalies, corrections, and backups. It monitors the database's operational status in real time and automatically initiates error correction mechanisms to fix problems when anomalies such as data read errors or storage failures are detected. It also regularly backs up the database data to prevent data loss.

[0042] The domain computation module performs sound field spectrum analysis and compensation calculations. It collects sound output data from the front-end devices, determines the spectral distribution of the sound field, performs sound compensation calculations, and then optimizes the audio effect.

[0043] The front-end equipment includes at least one of the following: a speaker with phase compensation, a network speaker with DSP processing, a distributed network speaker, a network amplifier with detection function, an infrared flat panel network speaker, and a network area sound-emitting board.

[0044] The speaker is used to compensate for sound based on acoustic sensor data to improve speech intelligibility; The network speaker has built-in anti-feedback, equalizer, high and low cut, and noise gate modules. The network speaker is used to dynamically compensate for audio effects based on different sound sources. Distributed network speakers achieve zero-latency distributed sound reinforcement through multiple speakers; The network amplifier has self-detection and master / slave switching functions for temperature, voltage, current, signal strength, and decoding circuit. Infrared flat-panel network speakers are used for secure broadcasting to a sound source via infrared radiation; The network area sound-emitting panel causes a sound pressure level difference of more than 20 dB between neighboring areas.

[0045] Speakers with phase compensation improve speech intelligibility by combining data collected by acoustic sensors with phase compensation processing of the sound. When the speaker receives a broadcast audio signal, it simultaneously acquires the ambient sound field data detected by the acoustic sensors, analyzes the phase deviation during sound propagation using a built-in algorithm, and then adjusts and compensates the phase of the output audio.

[0046] The network speaker with DSP processing incorporates anti-feedback, equalizer, high / low cut, and noise gate modules to dynamically compensate for different sound sources. When the speaker receives different types of broadcast audio sources, the DSP module automatically identifies the source attributes and calls the corresponding function modules for processing: if it is playing a paging message, the equalizer will enhance the mid-frequency range to improve voice recognition, and the anti-feedback module will be activated to prevent sound feedback; if it is playing background music, the high / low cut module will optimize the high and low frequency performance of the audio, and the noise gate module will filter out ambient noise.

[0047] Distributed network speakers achieve zero-latency distributed sound reinforcement through the coordinated deployment of multiple speakers. These speakers synchronously receive broadcast commands and audio signals via a network, and utilize low-latency transmission technology to allow multiple speakers to output sound simultaneously, replacing traditional centralized loudspeaker amplification.

[0048] Network amplifiers with detection capabilities feature self-detection and master / slave switching functions for temperature, voltage, current, signal strength, and decoding circuitry. During operation, they monitor their hardware status parameters in real time, and automatically switch to backup components to continue operation when faults such as excessively high temperature, abnormal voltage, or insufficient signal strength are detected.

[0049] Infrared flat-panel network speakers transmit sound securely by directing it through infrared radiation to a specific source. Instead of transmitting sound directly through the air, they convert audio signals into infrared radiation signals, which can only be received and reproduced by a corresponding infrared receiver.

[0050] The network area sound-emitting panel uses a special sound-emitting structure design to make the sound pressure level difference between adjacent areas exceed 20 decibels. When emitting sound, the sound energy will be concentrated to cover the designated area, while the sound intensity of adjacent areas will be greatly attenuated.

[0051] An automated broadcasting method based on artificial intelligence, comprising the following steps: Collect environmental and network data; Stores and provides the data and algorithm resources required for system operation; After analyzing and reasoning about the data collected by the sensing components, broadcast control commands are generated. Used to receive manual commands and display system status; Receive broadcast control commands and execute the corresponding broadcast sounds.

[0052] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0053] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An artificial intelligence-based automatic broadcasting system, characterized in that, This includes sensing components, resource libraries, logic management components, human-computer interaction components, and front-end devices: The sensing component is used to collect environmental data and network data; The resource library is used to store and provide the data and algorithm resources required for system operation; The logic management component is connected to the sensing component and the resource library respectively. The logic management component is used to analyze and reason about the data collected by the sensing component and then generate broadcast control commands. The human-computer interaction component is connected to the logic management component, and the human-computer interaction component is used to receive manual commands and display the system status. The front-end device is connected to the logic management component, and the front-end device is used to receive broadcast control commands and execute corresponding broadcast sounds. The sensing component, resource library, logic management component, human-computer interaction component, and front-end device adopt a distributed architecture. The logic management component can automatically trigger the front-end device to broadcast based on the input of the sensing component and the resources of the resource library through calculation and reasoning.

2. The artificial intelligence-based automatic broadcasting system according to claim 1, characterized in that, The sensing components include at least one of the following: visual sensor, human body sensor, temperature and humidity sensor, pressure sensor, sound sensor, gas sensor, internet information classification sensor, and ground vibration sensor.

3. The artificial intelligence-based automatic broadcasting system according to claim 2, characterized in that, The visual sensor is used to identify specific behaviors and trigger alarm broadcasts; The human body sensor is used to detect when a person approaches a dangerous area and trigger an alarm broadcast. The temperature and humidity sensors are used to trigger a friendly reminder broadcast based on environmental data; The pressure sensor, combined with facial recognition technology, is used to manage user health characteristics and trigger targeted health alert broadcasts. The sound sensor is used to identify dangerous behavior and trigger corresponding broadcasts through sound pressure level, spectrum analysis, or speech recognition technology. The Internet information classification sensor is used to match network information with system location information to trigger relevant broadcasts; The ground vibration sensor is used to detect sudden events and trigger real-time emergency broadcasts.

4. The artificial intelligence-based automatic broadcasting system according to claim 1, characterized in that, The resource library includes a language library, a program library, an artificial intelligence learning library, an algorithm library, a database, and storage units; The language library is used to convert text to speech and provide multilingual support; The program library is used to store background music, warning sound sources, and dispelling sound sources; The artificial intelligence learning library is used to improve the system's cognitive and reasoning abilities through sensor data; The algorithm library is used to provide the algorithms required for the system's computation; The database is used for high-speed storage and retrieval of system data; The storage unit is used to store the calculation results and system operation logs.

5. The artificial intelligence-based automatic broadcasting system according to claim 1, characterized in that, The human-computer interaction components include at least one of the following: touch screen and display screen, mouse and keyboard, microphone and speaker, and 3D holographic control device.

6. The artificial intelligence-based automatic broadcasting system according to claim 1, characterized in that, The logic management component includes a logic analysis module, a computation and reasoning module, an event and log management module, a database management module, and a domain computation module; The logic analysis module is used to perform real-time data processing on the status of each component. The computational reasoning module is used to generate broadcast operation instructions based on scene conditions; The event and log management module is used to record the system's operation history; The database management module is used to handle database anomalies, corrections, and backups; The domain computation module is used to perform sound field spectrum analysis and compensation calculations.

7. An artificial intelligence-based automatic broadcasting system according to claim 6, characterized in that, The front-end device includes at least one of the following: a speaker with phase compensation, a network speaker with DSP processing, a distributed network speaker, a network amplifier with detection function, an infrared flat panel network speaker, and a network area sound emission board.

8. An artificial intelligence-based automatic broadcasting system according to claim 7, characterized in that, The speaker is used to compensate for sound based on acoustic sensor data to improve speech intelligibility; The network speaker has built-in anti-feedback, equalizer, high and low cut, and noise gate modules. The network speaker is used to dynamically compensate for audio effects according to different sound sources. The distributed network speaker achieves zero-latency distributed sound amplification through multiple speakers; The network power amplifier has self-detection and master / slave switching functions for temperature, voltage, current, signal strength, and decoding circuit. The infrared flat-panel network speaker is used to broadcast securely to a sound source via infrared radiation; The network area sound-emitting panel causes a sound pressure level difference of more than 20 dB between adjacent areas.

9. An artificial intelligence-based automatic broadcasting method, applied to an artificial intelligence-based automatic broadcasting system according to any one of claims 1-8, characterized in that, The method includes the following steps: Collect environmental and network data; Stores and provides the data and algorithm resources required for system operation; After analyzing and reasoning about the data collected by the sensing components, broadcast control commands are generated. Used to receive manual commands and display system status; Receive broadcast control commands and execute the corresponding broadcast sounds.

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