Multi-terminal linkage communication control method and platform based on smart station

Abstract

The present invention discloses a multi-terminal linkage communication control method and platform based on a smart station, and relates to the field of communication data processing technology. The method includes: constructing a multi-terminal linkage architecture of a smart station, including a data perception layer, a network communication layer, a data processing layer, and a device control layer. Obtain the regional distribution information of the target station, deploy a set of station monitoring sensor modules, and obtain a set of monitoring data streams through the data perception layer; obtain a list of smart station equipment, and obtain a set of equipment communication data through the data perception layer. The data set is transmitted to the data processing layer via the network communication layer for analysis and parsing, and a set of target linkage equipment and a set of control strategy parameters are obtained. The device control layer performs multi-terminal device communication control on the target station based on these sets. It solves the technical problems of data dispersion and independent operation of equipment in existing stations without linkage, and achieves the technical effect of improving the operating efficiency, service quality and management level of smart stations.

Description

Technical Field

[0001] This application relates to the field of communication data processing technology, and in particular to a multi-terminal linkage communication control method and platform based on smart stations. Background Art

[0002] In the context of station operation management, the issues of operational efficiency and service quality, especially during peak hours, are particularly prominent due to changes in passenger flow. Efficiently managing station resources and better meeting the needs of different areas are crucial for achieving efficient station operation. Traditional station management is often decentralized and independent, with data scattered across various independent systems and equipment operating in silos. It relies solely on the operation of a single device or localized area monitoring, resulting in insufficient control over the overall station operation. It lacks in-depth analysis and utilization of multi-terminal data, making it difficult to comprehensively and accurately assess the condition of various areas of the station and the operational risks of equipment. Furthermore, there are unreasonable resource allocations, leading to insufficient resources in some key areas while resources are idle in others. Management decisions rely heavily on experience and cannot effectively address the constantly changing realities of the station.

[0003] At present, the relevant technologies have technical problems such as data being scattered in stations, equipment operating independently without linkage, and decision-making lacking data support. Summary of the Invention

[0004] This application provides a multi-terminal linkage communication control method and platform based on smart stations. It adopts a multi-terminal linkage architecture for smart stations, including a data perception layer, a network communication layer, a data processing layer, and an equipment control layer. The method acquires the distribution information of the target station area, deploys a set of station monitoring sensor modules, and obtains a set of monitoring data streams through the data perception layer; it also acquires a list of smart station equipment and obtains a set of equipment communication data through the data perception layer. The data set is transmitted via the network communication layer to the data processing layer for analysis and parsing, yielding a set of target linkage equipment and a set of control strategy parameters. The equipment control layer uses these sets to perform multi-terminal equipment communication control of the target station. This multi-terminal linkage architecture enables centralized data management, coordinated equipment control, and provides strong support for scientific decision-making, thereby improving the operational efficiency, service quality, and management level of smart stations.

[0005] This application provides a multi-terminal linkage communication control method based on smart stations, including:

[0006] A multi-terminal linkage architecture for smart stations is constructed, comprising a data perception layer, a network communication layer, a data processing layer, and an equipment control layer. The architecture acquires regional distribution information of the target station, monitors and analyzes this information, deploys a set of station monitoring sensor modules, and obtains a set of station regional monitoring data streams from these sensor modules through the data perception layer. A list of smart station equipment is obtained, where each station device has a built-in sensor group that is communicatively connected to the data perception layer. The data perception layer then acquires a set of device communication data from the smart station equipment list. The station regional monitoring data streams and the device communication data sets are transmitted through the network communication layer to the data processing layer for multi-terminal data analysis and equipment linkage parsing, resulting in a target linkage station equipment set and a set of equipment control strategy parameters. The equipment control layer then performs multi-terminal device communication control on the target station based on these parameters.

[0007] This application also provides a multi-terminal linkage communication control platform based on smart stations, including:

[0008] The system includes a linkage architecture construction module for building a multi-terminal linkage architecture for smart stations, comprising a data perception layer, a network communication layer, a data processing layer, and an equipment control layer; a monitoring and analysis module for acquiring regional distribution information of the target station, monitoring and analyzing the regional distribution information, deploying a set of station monitoring sensor modules, and acquiring a set of station regional monitoring data streams from the station monitoring sensor module set through the data perception layer; and a communication connection module for acquiring a list of smart station equipment, wherein each station device in the smart station equipment list has a built-in sensor group, and... The sensor group is communicatively connected to the data perception layer, and obtains the device communication data set of the smart station device list through the data perception layer; the device linkage analysis module is used to transmit the station area monitoring data stream set and the device communication data set through the network communication layer to the data processing layer for multi-terminal data analysis and device linkage analysis, to obtain the target linkage station device set and device control strategy parameter set; the device communication control module is used by the device control layer to perform multi-terminal device communication control on the target station based on the target linkage station device set and device control strategy parameter set.

[0009] The multi - terminal linkage communication control method and platform based on a smart station proposed in this application first constructs a multi - terminal linkage architecture for the smart station, including a data perception layer, a network communication layer, a data processing layer, and a device control layer. Obtain the distribution information of the target station area, deploy a set of station monitoring sensor modules, and obtain a set of monitoring data streams through the data perception layer; obtain a list of smart station devices and obtain a set of device communication data through the data perception layer. Transmit the data sets through the network communication layer to the data processing layer for analysis and parsing, obtaining a set of target linkage devices and a set of control strategy parameters. The device control layer performs multi - terminal device communication control on the target station based on these sets, achieving the technical effects of centralized data management, device linkage control, and providing a strong basis for scientific decision - making by constructing a multi - terminal linkage architecture, thereby improving the operation efficiency, service quality, and management level of the smart station. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the platform according to the embodiments of this application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in order. On the contrary, according to needs, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0011] Figure 1 It is a schematic flowchart of the multi - terminal linkage communication control method based on a smart station provided by an embodiment of this application;

[0012] Figure 2 It is a schematic structural diagram of the multi - terminal linkage communication control platform based on a smart station provided by an embodiment of this application.

[0013] Description of reference numerals: Linkage architecture construction module 10, monitoring and analysis module 20, communication connection module 30, device linkage parsing module 40, device communication control module 50. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically lists the specific embodiments of this application.

[0015] In order to make the purpose, technical solutions, and advantages of this application clearer, the following will further describe this application in detail with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0016] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0017] This application provides a multi-terminal linkage communication control method based on smart stations, such as... Figure 1 As shown, the method includes:

[0018] Step S100: Construct a multi-terminal linkage architecture for the smart station. This architecture includes a data perception layer, a network communication layer, a data processing layer, and an equipment control layer. Specifically, the multi-terminal linkage architecture consists of a data perception layer, a network communication layer, a data processing layer, and an equipment control layer. The data perception layer determines monitoring needs based on different areas and equipment functions within the station, selects appropriate sensor types and deploys them in key locations, collects data in real time, and transmits it to the network communication layer via wired or wireless means. The network communication layer establishes high-speed and stable wired and wireless networks, selects appropriate data transmission protocols, performs encryption processing, sets access control policies, connects each layer, and transmits data. The data processing layer establishes a database to store data, uses big data analytics to process data, and provides decision support for station management and operation. When a specific situation is analyzed, the information and response strategies are transmitted to the equipment control layer, while simultaneously receiving feedback from the equipment control layer to optimize decisions. The equipment control layer integrates various station devices, formulates control commands based on the decision results of the data processing layer and preset control strategies, transmits them to the equipment for execution via the network communication layer, and feeds back the execution status to the data processing layer, forming a closed-loop linkage system to achieve efficient operation and management of the smart station.

[0019] Step S200: Obtain the regional distribution information of the target station, monitor and analyze the regional distribution information, deploy a set of station monitoring sensor modules, and obtain the station regional monitoring data stream set of the station monitoring sensor module set through the data perception layer. Specifically, obtaining the regional distribution information of the target station involves comprehensively understanding the division of various areas of the station, including the specific location and scope of different functional areas such as waiting areas, platform areas, equipment areas, and entrances / exits, through methods such as station design drawings, geographic information system data, or on-site surveys. Monitoring and analyzing the regional distribution information involves in-depth research into the characteristics and needs of each area. For example, for the waiting area, factors such as personnel density, temperature, and air quality need to be considered; for the equipment area, the focus is on the operating status and energy consumption of the equipment; and for entrances / exits, the focus is on passenger flow and direction. By analyzing these factors, the key parameters that need to be monitored in each area are determined, and a set of station monitoring sensor modules is deployed. Based on the monitoring and analysis results, sensors are selected and arranged accordingly. For different monitoring parameters, appropriate types of sensors are selected. For example, temperature sensors can be used to monitor temperature; infrared sensors or video analysis sensors can be used to count personnel density. When deploying sensors, it is necessary to ensure comprehensive coverage of all areas and accurate collection of the required data. For example, temperature sensors and air quality sensors are evenly distributed in different locations in the waiting area, and equipment operation status sensors are installed near key equipment in the equipment area. The station area monitoring data stream set of the station monitoring sensor module set is obtained through the data perception layer. As the foundation layer of the entire system, the data perception layer is responsible for receiving data from various sensors. The sensors collect data in real time and transmit it to the data perception layer. The data perception layer performs preliminary processing and summarization of the data to form a set of station area monitoring data streams. The set contains various monitoring data from each area, providing the original data source for subsequent processing and analysis.

[0020] In one possible implementation, the regional distribution information of the target station is obtained, the regional distribution information is monitored and analyzed, a set of station monitoring sensor modules is deployed, and a set of station regional monitoring data streams of the set of station monitoring sensor modules is obtained through the data perception layer. Step S200 further includes step S210, which analyzes the monitoring elements of each area information in the regional distribution information to obtain a set of station regional monitoring element parameters. The set of station regional monitoring element parameters includes traffic flow parameters and safety demand level parameters of each area information. Specifically, based on the regional distribution information of the target station, it is divided into different specific areas, such as waiting areas, platform areas, equipment areas, and entrances / exits. Monitoring element analysis is performed on each area separately. Traffic flow parameters are obtained by installing passenger flow statistics sensors, such as infrared sensors or video surveillance cameras, at entrances / exits to collect real-time statistics on the number and direction of people entering and exiting the station, thereby determining the traffic flow situation in different areas. For example, during peak hours, traffic flow in waiting areas and entrances / exits will increase significantly. This data will be used as part of the traffic flow parameters. Determining the safety demand level parameters requires a comprehensive analysis of multiple factors. On the one hand, the importance of the area is assessed; for example, platform areas involve train operation and passenger boarding / alighting, thus having relatively high safety demands. On the other hand, historical safety accident data is analyzed to identify areas with frequent safety accidents. For example, if a certain area has frequently experienced equipment failures or personnel accidents in the past period, then the safety demand level of that area will be correspondingly increased. Through these analyses, a set of station area monitoring element parameters, including traffic flow parameters and safety demand level parameters for each area, is finally obtained.

[0021] Step S220: Based on the station area monitoring element parameter set, perform a monitoring coverage analysis on the regional distribution information to determine the station area monitoring point layout parameter set. Specifically, after obtaining the station area monitoring element parameter set, a monitoring coverage analysis is performed. Based on the traffic flow parameters and safety requirement level parameters of different areas, the required monitoring level for each area is determined. For areas with high traffic flow, such as entrances / exits and waiting areas, the density of monitoring points needs to be increased to ensure accurate understanding of passenger flow and environmental changes. For example, a passenger flow statistics sensor can be installed at regular intervals at entrances / exits, and multiple temperature and humidity sensors can be evenly distributed in the waiting area. For areas with high safety requirements, such as equipment areas and platform areas, more attention needs to be paid to the selection of monitoring locations to ensure that critical equipment and hazardous areas are covered. For example, equipment operation status sensors can be installed next to important equipment in the equipment area, and safety protection sensors can be installed at the platform edge. Through analysis, the station area monitoring point layout parameter set, including monitoring location and monitoring point density, is determined to achieve comprehensive coverage of the monitoring area.

[0022] Step S230 involves analyzing the sensor requirements of the station area monitoring point deployment parameter set to obtain the station area monitoring point sensor parameter set. Specifically, after determining the monitoring point deployment parameter set, it is necessary to further analyze the sensor type and parameters required for each monitoring point. For different monitoring needs, appropriate sensors are selected. For example, to monitor temperature and humidity, high-precision, fast-response temperature and humidity sensors are needed; to monitor personnel density, infrared sensors or video analysis sensors can be used; to monitor equipment operating status, sensors matching the equipment type, such as pressure sensors and current sensors, are needed. The sensor parameter requirements are determined based on the specific conditions of the monitoring point. For example, in noisy areas, sensors with strong anti-interference capabilities are needed; in areas with limited space, small-sized, easy-to-install sensors may be required. By analyzing the requirements of each monitoring point, the station area monitoring point sensor parameter set is obtained.

[0023] Step S240: Based on the station area monitoring point deployment parameter set and the station area monitoring point sensor parameter set, sensors are deployed according to the area distribution information to obtain the station monitoring sensor module set. Specifically, according to the station area monitoring point deployment parameter set and the station area monitoring point sensor parameter set, actual sensor deployment work is carried out. Different types of sensors are installed in the corresponding areas according to the monitoring location requirements. For example, temperature and humidity sensors and personnel density sensors are installed at designated locations in the waiting area; specific equipment operation status sensors are installed on key equipment in the equipment area. During installation, it is necessary to ensure that the sensor density meets the monitoring point density requirements to achieve comprehensive coverage. The sensors are debugged and calibrated to ensure they can work normally and accurately collect data. Through the deployment process, the station monitoring sensor module set is finally obtained, providing a foundation for subsequent data collection and analysis.

[0024] Step S300: Obtain the smart station equipment list. Each station device in the smart station equipment list has a built-in sensor group, and the sensor group is communicatively connected to the data perception layer. The data perception layer obtains the device communication data set of the smart station equipment list. Specifically, the smart station equipment list is obtained by conducting a comprehensive equipment inventory and information organization of the smart station. This includes conducting on-site visits to various areas within the station to record all smart devices. For example, passenger guidance devices such as electronic displays and signs can be found in the waiting hall; elevator service equipment can be found in stairwells and passageways; audio and video call terminals may be installed at service counters and key locations. The station's equipment management system or archives are reviewed to obtain detailed information about the equipment, including equipment name, model, installation location, and performance parameters. For example, for elevator service equipment, its load-bearing capacity, operating speed, and fault history can be obtained. For passenger guidance equipment, information such as its display content and update frequency can be obtained. Communication with the station's equipment management department and technical support team is crucial to acquire their understanding of the equipment and its latest information, such as the maintenance status and upgrade status of a particular audio / video call terminal. A complete smart station equipment list should be established, covering various intelligent devices within the station and their performance parameters. The built-in sensor groups and communication connections with the data sensing layer should be identified. For each piece of station equipment in the smart station equipment list, the function and characteristics of each device should be analyzed to determine its built-in sensor groups; for example, elevator service equipment may have a built-in speed sensor. Position sensors, load sensors, etc., are used to monitor the elevator's operating status; passenger guidance equipment may have built-in brightness sensors, ambient light sensors, etc., to automatically adjust display brightness according to environmental conditions; audio and video call terminals may have built-in microphone sensors, camera sensors, etc., to collect sound and image information and establish a communication connection with the data perception layer. This ensures that each device's built-in sensor group can effectively communicate with the data perception layer. Based on the device type and sensor characteristics, an appropriate communication protocol is selected to ensure the data perception layer can recognize and receive signals from these sensors. If communication is via a network, the device's network parameters need to be set to enable connection with the network where the data perception layer resides. For wirelessly connected devices, wireless network configuration is required to ensure signal stability. After completing the communication connection setup, testing is conducted to ensure the sensor group can normally transmit data to the data perception layer. After establishing the communication connection between the device and the data perception layer, the device communication data set of the smart station device list is obtained through the data perception layer. The data perception layer continuously receives data from the built-in sensor groups of each device. This data can be real-time device operating status data, environmental parameter data, etc., such as the speed, position, and load data of elevator service equipment; and the brightness and ambient light data of passenger guidance equipment.The data perception layer initially organizes and classifies the collected audio and video call data, including sound and image data, storing data from different devices separately and organizing it according to a specific format for subsequent processing and analysis. For example, elevator service equipment data is stored in a specific data structure, including fields such as timestamp, device number, speed, location, and load; passenger guidance equipment data is stored in another data structure, including fields such as timestamp, device number, brightness, and ambient light intensity. After data organization, the data perception layer forms a collection containing communication data from all smart station devices. This collection serves as the basis for subsequent data analysis and processing, providing crucial data support for the multi-terminal linkage architecture of the smart station.

[0025] Step S400: The station area monitoring data stream set and the device communication data set are transmitted to the data processing layer through the network communication layer for multi-terminal data analysis and device linkage parsing to obtain the target linkage station device set and device control strategy parameter set. Specifically, the data perception layer continuously acquires data from monitoring sensors in various areas of the station to form the station area monitoring data stream set. Simultaneously, the built-in sensor groups of various devices in the smart station transmit device operating status information to the data perception layer to form the device communication data set. The network communication layer bridges the data transmission gap, using a combination of wired and wireless networks to transmit the station area monitoring data stream set and device communication data set from the data perception layer to the data processing layer. During transmission, data security and integrity are considered, and encryption technology and data verification methods are employed. The data processing layer performs multi-terminal data analysis and device linkage parsing on this data to obtain the target linkage station device set and device control strategy parameter set.

[0026] In one possible implementation, the station area monitoring data stream set and the device communication data set are transmitted to the data processing layer through the network communication layer for multi-terminal data analysis and device linkage parsing to obtain the target linkage station device set and the device control strategy parameter set. Step S400 further includes step S410, which involves encrypting and splitting the station area monitoring data stream set and the device communication data set to obtain a general station data stream set and an encrypted station data stream set. Specifically, the station area monitoring data stream set and the equipment communication data set are analyzed. The data set includes monitoring data from various areas of the station and communication data from equipment. Considering the security requirements of the data, it is necessary to perform data diversion processing. According to the sensitivity and security requirements of the data, the data is divided into a general station data stream set and an encrypted station data stream set. Data that does not involve sensitive information and has low security requirements can be classified into the general station data stream set. For example, some routine environmental monitoring data, such as temperature and humidity data in the waiting area, may not require high encryption. However, data that involves sensitive information or has high security requirements, such as key operating parameters of equipment or data involving passenger privacy, are classified into the encrypted station data stream set.

[0027] Step S420: A multi-dimensional evaluation of the encryption level of the encrypted station data stream set is performed to obtain a data encryption level parameter set. Based on this parameter set, it is matched with a data encryption algorithm list to determine the data transmission encryption algorithm set. Specifically, for the encrypted station data stream set, a multi-dimensional evaluation of the encryption level is required. Evaluation parameters may include the security level requirements of the monitoring area, equipment importance, and the type of data being monitored. If a monitoring area has high security requirements, such as an equipment area or an important control area, the data generated in that area may require a higher encryption level. The importance of the equipment is also an evaluation factor; the operational data of critical equipment may require stronger encryption protection. Different types of monitoring data also have different security requirements. For example, data involving passenger personal information requires strict encryption. Through a comprehensive evaluation of these factors, a data encryption level parameter set is obtained. This parameter set is then matched with a data encryption algorithm list, which contains encryption algorithms of various strengths. Based on the data encryption level requirements, a suitable encryption algorithm is selected. For example, for high-encryption-level data, a combination of complex symmetric and asymmetric encryption algorithms can be chosen; for medium-encryption-level data, a more commonly used symmetric encryption algorithm can be selected. Finally, the data transmission encryption algorithm set is determined.

[0028] Step S430: Based on the network communication layer, a general data transmission channel and an encrypted data transmission channel are obtained. The encrypted data transmission channel is configured for communication based on the data transmission encryption algorithm set to obtain a data encryption branch channel. Specifically, based on the network communication layer architecture, a general data transmission channel and an encrypted data transmission channel are determined. The general data transmission channel is used to transmit a general station data stream set; the data does not require high encryption and can use relatively conventional communication methods. For the encrypted data transmission channel, communication configuration is required based on the data transmission encryption algorithm set. The configuration process includes setting encryption algorithm parameters, key management, etc. By configuring the encrypted data transmission channel, a data encryption branch channel is established to ensure that the encrypted station data stream set can be transmitted securely in the channel.

[0029] Step S440: Based on the general data transmission channel and the data encryption branch channel, the general station data stream set and the encrypted station data stream set are mapped and distributed for transmission to the data processing layer. Specifically, using the general data transmission channel and the data encryption branch channel, the general station data stream set and the encrypted station data stream set are transmitted separately. The general station data stream set is transmitted to the data processing layer through the general data transmission channel, while the encrypted station data stream set is transmitted through the data encryption branch channel. During transmission, encryption is performed according to the configured encryption algorithm to ensure data security. By mapping and distributing the transmission, different types of data are transmitted to the data processing layer separately for subsequent data analysis and processing.

[0030] In one possible implementation, the station area monitoring data stream set and the device communication data set are transmitted to the data processing layer through the network communication layer for multi-terminal data analysis and device linkage parsing to obtain the target linkage station device set and the device control strategy parameter set. Step S400 further includes step S450, in which the data processing layer performs abnormal feature identification on the station area monitoring data stream set to obtain station abnormal feature parameters. Specifically, the data processing layer receives a set of station area monitoring data streams from the data perception layer. This set includes monitoring data from various areas of the station, such as temperature, humidity, personnel density, and equipment operating status. The data processing layer uses data analysis algorithms and models to identify abnormal features in this data. For example, it can set ranges and thresholds for normal data; when monitoring data exceeds these ranges, it is considered abnormal. Different anomaly detection methods are used for different types of monitoring data. For instance, for temperature data, anomalies can be determined by comparing the current temperature with the historical average temperature and the set temperature range. For personnel density data, anomalies can be identified based on the flow patterns during peak and off-peak hours. Through analysis of the station area monitoring data stream set, abnormal feature parameters are extracted, including anomaly type feature parameters (such as excessively high temperature, excessively high personnel density, etc.) and anomaly level feature parameters (such as mild anomaly, moderate anomaly, severe anomaly, etc.). These parameters provide crucial information for subsequent processing.

[0031] Step S460: Construct a smart station operation and maintenance strategy library. Match the station anomaly characteristic parameters with the smart station operation and maintenance strategy library to obtain target station operation and maintenance strategy information. Specifically, the smart station operation and maintenance strategy library stores various possible anomalies and corresponding handling strategies. For example, when an anomaly of excessively high temperature occurs, possible strategies include increasing the operating power of ventilation equipment and adjusting air conditioning temperature settings; when the personnel density is too high, strategies may include increasing the number of guide personnel and adjusting the indications of directional equipment. After obtaining the station anomaly characteristic parameters, the parameters are matched with the smart station operation and maintenance strategy library. Based on the anomaly type and anomaly level, the corresponding handling strategy is searched in the library. Through the matching process, the target station operation and maintenance strategy information is obtained, which will guide subsequent equipment linkage and control.

[0032] Step S470: Based on the target station operation and maintenance strategy information and the device communication data set, a linkage device mapping is performed with the smart station device list to obtain the target linkage station device set. Specifically, after obtaining the target station operation and maintenance strategy information, a linkage device mapping is performed by combining the device communication data set and the smart station device list. The device communication data set contains the operating status and parameter information of each device in the station. According to the device functions and requirements required in the target station operation and maintenance strategy information, the corresponding devices are searched in the smart station device list. For example, if the operation and maintenance strategy requires adjusting the ventilation equipment, all ventilation equipment is found in the device list, and the device status information in the device communication data set is combined to determine which ventilation equipment is in an available state. Through the mapping process, the devices participating in the linkage are determined, resulting in the target linkage station device set. The devices in the set will work collaboratively to cope with abnormal situations in the station.

[0033] Step S480: Based on the station anomaly characteristic parameters, perform linkage analysis on the target linked station equipment set to obtain the equipment control strategy parameter set. Specifically, based on the station anomaly characteristic parameters, perform linkage analysis on the target linked station equipment set, considering the anomaly type and anomaly level, and analyze how the equipment in the target linked station equipment set can work together to most effectively resolve the anomaly. For example, if the anomaly is excessively high temperature, for ventilation equipment, it is necessary to determine the amount of increased operating power; for air conditioning equipment, it is necessary to adjust the temperature setpoint, etc. Through linkage analysis of the target linked station equipment set, determine the specific control parameters of each device, thereby obtaining the equipment control strategy parameter set. The set will be transmitted to the equipment control layer for actual control of the station equipment operation to restore the station to its normal state.

[0034] In one possible implementation, the data processing layer performs anomaly feature identification on the station area monitoring data stream set to obtain station anomaly feature parameters. Step S450 further includes step S451, collecting a dataset of anomaly events in the station area of ​​the target station. Specifically, the anomaly event data of the target station area is collected through multiple channels. For example, video data of various anomaly events that have occurred in history can be obtained from the station's monitoring system, including situations such as overcrowding, equipment failure, and environmental anomalies. Anomaly event reports recorded by station staff can also be collected, which may include information such as the time, location, and specific description of the event. Anomaly event data recorded by the station's automatic equipment alarm system, such as excessively high equipment temperature and abnormal pressure, can be integrated to establish a dataset covering various types of anomaly events, providing a foundation for subsequent model training.

[0035] Step S452: Feature identification training is performed on the abnormal event dataset of the station area to obtain a station abnormal feature recognition model. This model includes an abnormality type feature recognition model and an abnormality level feature recognition model. Specifically, after obtaining the abnormal event dataset of the station area, feature identification training is performed. Each abnormal event in the dataset is analyzed in detail to extract features representative of the event. For example, for crowding events, features such as crowd density and flow speed can be extracted; for equipment failure events, features such as changes in equipment operating parameters and fault codes can be extracted. These features are then identified as abnormality type features and abnormality level features. Abnormality type features determine the specific category of the abnormal event, such as crowding, equipment failure, or adverse environmental conditions. Abnormality level features measure the severity of the abnormal event, such as mild, moderate, or severe. Machine learning algorithms, such as neural networks and support vector machines, are used to train the identified features to establish a station abnormal feature recognition model. This model includes an abnormality type feature recognition model and an abnormality level feature recognition model, used to identify the type and level of abnormal events, respectively.

[0036] Step S453: Based on the station anomaly feature recognition model, perform anomaly feature recognition on the station area monitoring data stream set, and output anomaly type feature parameters and anomaly level feature parameters. Specifically, after the model training is completed, the model is used to perform anomaly feature recognition on the station area monitoring data stream set. The monitoring data stream set contains real-time station area data, such as temperature, humidity, personnel density, equipment operating status, etc. The data in the monitoring data stream set is input into the anomaly feature recognition model. The model will analyze and judge the data according to the learned feature patterns. The anomaly type feature recognition model will output the type feature parameters of the anomaly event to determine the type of the current anomaly, such as crowding, equipment failure, etc. The anomaly level feature recognition model will output the level feature parameters of the anomaly event, indicating the severity of the current anomaly event, such as mild, moderate, severe, etc.

[0037] Step S454: Based on the anomaly type characteristic parameters and anomaly level characteristic parameters, determine the station's anomaly characteristic parameters. Specifically, based on the output anomaly type characteristic parameters and anomaly level characteristic parameters, determine the station's anomaly characteristic parameters. Integrate and analyze the anomaly type and anomaly level. For example, if the anomaly type is overcrowding and the level is moderate, then the corresponding anomaly characteristic parameter can be determined as "moderate overcrowding." These anomaly characteristic parameters will serve as an important basis for subsequent processing, triggering corresponding emergency measures and equipment linkage control to ensure the normal operation and safety of the station.

[0038] In one possible implementation, based on the target station operation and maintenance strategy information and the device communication data set, a linkage device mapping is performed with the smart station device list to obtain the target linkage station device set. Step S470 further includes step S471, which involves demand matching based on the target station operation and maintenance strategy information and the smart station device list to obtain a matching station device set. Specifically, the device requirements proposed by the target station operation and maintenance strategy information are clarified. The operation and maintenance strategy is determined based on the station's abnormal characteristic parameters and aims to solve specific abnormal situations. For example, if an abnormal situation of overcrowding occurs, the operation and maintenance strategy may include adding ventilation equipment, adjusting guide equipment, etc. The requirements are compared and matched with the smart station device list, which contains information on all intelligent devices in the station, such as device type, function, performance parameters, etc. By analyzing the functional characteristics of each device one by one, those devices that can meet the operation and maintenance strategy requirements are identified. For example, if the operation and maintenance strategy requires adding ventilation, all ventilation devices are identified from the device list and combined into a matching station device set. The devices in this set are all candidate devices that may participate in solving the current abnormal situation.

[0039] Step S472: Perform equipment status analysis on the matching station equipment set according to the equipment communication data set to determine the equipment usage status parameter set. Specifically, the equipment status analysis is performed on the equipment in the matching station equipment set using the equipment communication data set. The equipment communication data set contains real-time operating status information of each device, such as whether the device is running and whether the operating parameters are normal. For each device in the matching station equipment set, its current usage status is determined based on the equipment communication data. For example, it can be determined whether the device is in normal operation, fault state, or standby state. The operating parameters of the device are analyzed, such as the wind speed of ventilation equipment and the brightness of lighting equipment, to further understand the specific usage of the device. The equipment usage status information is organized into an equipment usage status parameter set to provide a basis for the subsequent optimization process. The parameter set includes the device's operating status identifier (such as normal, fault, standby, etc.) and specific operating parameter values.

[0040] Step S473: Based on the smart station equipment list, perform an operational cost analysis on the matching station equipment set to obtain a set of equipment operational cost parameters. Specifically, referring again to the smart station equipment list, perform an operational cost analysis on the equipment in the matching station equipment set. The equipment operational cost mainly includes the operating costs and operating consumption of each piece of equipment. Based on the information in the equipment list, determine the operating cost parameters for each piece of equipment, such as energy consumption costs and maintenance costs. Consider the operating consumption of the equipment, such as electricity consumption and consumable usage. Through a comprehensive analysis of these factors, calculate the operational cost of each piece of equipment. Organize the operational costs of all matching equipment into a set of equipment operational cost parameters. The set may include the operational cost value of each piece of equipment, as well as possible cost components, such as energy consumption costs and maintenance costs.

[0041] Step S474: Based on the set of equipment usage status parameters and the set of equipment operating cost parameters, the matching station equipment set is optimized to obtain the target linkage station equipment set. Specifically, the matching station equipment set is optimized by comprehensively analyzing the set of equipment usage status parameters and the set of equipment operating cost parameters. According to the set of equipment usage status parameters, equipment that is in a faulty state or is not suitable for the current situation is excluded. For example, if a piece of equipment is under maintenance or its operating parameters cannot meet the current requirements, it is removed from the candidate equipment. The set of equipment operating cost parameters is analyzed to select equipment with lower operating costs and the ability to effectively resolve abnormal situations. By setting certain optimization criteria, such as selecting equipment with operating costs within a certain range and good usage status, the final equipment participating in the linkage is determined, forming the target linkage station equipment set. The equipment in the set will work together to execute the target station operation and maintenance strategy and resolve abnormal situations at the station.

[0042] In one possible implementation, the set of target linked station equipment is analyzed based on the station anomaly characteristic parameters to obtain the set of equipment control strategy parameters. Step S480 further includes step S481, which analyzes the set of target linked station equipment based on the station anomaly characteristic parameters to obtain a set of station equipment control parameter selection thresholds. Specifically, based on the station anomaly characteristic parameters, the specific anomaly situation currently faced by the station is identified. The anomaly characteristic parameters may include anomaly type (such as overcrowding, equipment failure, etc.) and anomaly level (mild, moderate, severe, etc.). For each piece of equipment in the target linked station equipment set, the possible range of control parameters for dealing with the current anomaly situation is analyzed. For example, in the case of overcrowding, for ventilation equipment, possible control parameters include the range of wind speed adjustment; for guidance equipment, possible control parameters include the range of change in direction. By comprehensively considering and analyzing the control parameter range of each piece of equipment, a set of station equipment control parameter selection thresholds is determined. This set limits the value range of each piece of equipment's control parameters, providing a basis for subsequently selecting specific control parameters.

[0043] In one possible implementation, the set of target linked station equipment is analyzed based on the station anomaly feature parameters to obtain the set of equipment control strategy parameters. Step S480 further includes step S482, selecting multiple sets of equipment control parameters within the set of station equipment control parameter selection thresholds, and performing operation and maintenance effect simulation on the multiple sets of equipment control parameter sets to obtain the operation and maintenance effect of multiple sets of equipment control. Specifically, within a defined set of threshold values ​​for station equipment control parameters, multiple sets of different equipment control parameters are randomly selected. Each set contains the control parameters for each linked station device in the target linked station equipment set. Then, the selected sets of equipment control parameters are used to simulate the operation and maintenance effects. Using simulation models, the operation of the station system and the handling of abnormal situations are simulated under different equipment control parameters. For example, for a specific set of equipment control parameters, the simulation shows the degree of improvement in crowding and the stability of equipment operation when ventilation equipment operates at a specific wind speed and guidance equipment operates in a specific direction. Through simulation, the operation and maintenance effects of multiple sets of equipment control parameters are obtained. The effects can be measured by specific indicators, such as the time for personnel evacuation and the degree of reduction in equipment failure rate.

[0044] Step S483: Based on the operational and maintenance effects of the multiple sets of equipment control parameters, determine the optimization direction and perform global comparison optimization to obtain the equipment control strategy parameter set. Specifically, based on the operational and maintenance effects of the multiple sets of equipment control parameters, determine the optimization direction, analyze the operational and maintenance effects corresponding to different sets of equipment control parameters, and identify the characteristics and trends of parameter sets with better effects. For example, if certain parameter sets can solve the problem of overcrowding more quickly, it can be determined to further optimize the equipment control parameters in this direction. Perform global comparison optimization to comprehensively compare the multiple sets of equipment control parameters and their corresponding operational and maintenance effects, taking into account various factors such as equipment operation stability and energy consumption. Optimization algorithms, such as genetic algorithms and particle swarm optimization algorithms, can be used to find the optimal combination of equipment control parameters in the multiple sets of parameters. After the optimization process, the equipment control strategy parameter set is finally obtained. The parameters in the set will serve as the optimal strategy for controlling the linkage of various devices in the station equipment set to effectively cope with abnormal situations in the station and achieve efficient operation and management of the station.

[0045] In step S500, the equipment control layer performs multi-terminal equipment communication control on the target station based on the target linked station equipment set and the equipment control strategy parameter set. Specifically, the equipment control layer first receives the target linked station equipment set and the equipment control strategy parameter set determined by the data processing layer, establishes communication connections with each device in the target linked station equipment set through various communication methods, and then sends control commands to these devices according to the specific parameters in the equipment control strategy parameter set. After receiving the commands, the target linked station devices execute the corresponding operations and report the execution status information to the equipment control layer. The equipment control layer continuously monitors the equipment operating status and feedback information. If it finds that the results are inconsistent with expectations or unsatisfactory, it will adjust the equipment control strategy parameters.

[0046] This application's embodiment employs a multi-terminal linkage architecture for smart stations, comprising a data perception layer, a network communication layer, a data processing layer, and an equipment control layer. It acquires the distribution information of the target station area, deploys a set of station monitoring sensor modules, and obtains a set of monitoring data streams through the data perception layer; it also acquires a list of smart station equipment and obtains a set of equipment communication data through the data perception layer. The data set is transmitted via the network communication layer to the data processing layer for analysis and parsing, yielding a set of target linkage equipment and a set of control strategy parameters. The equipment control layer uses these sets to perform multi-terminal equipment communication control of the target station. This multi-terminal linkage architecture achieves centralized data management, coordinated equipment control, and provides strong support for scientific decision-making, thereby improving the operational efficiency, service quality, and management level of smart stations.

[0047] In the above text, refer to Figure 1This paper describes in detail a multi-terminal linkage communication control method based on a smart station according to an embodiment of the present invention. Next, reference will be made to... Figure 2 This invention describes a multi-terminal linkage communication control platform based on a smart station according to an embodiment of the present invention.

[0048] The multi-terminal linkage communication control platform based on smart stations according to embodiments of the present invention addresses the technical problems of data dispersion, lack of linkage in independent equipment operation, and lack of data support for decision-making in existing stations. By constructing a multi-terminal linkage architecture, it achieves centralized data management, linkage control of equipment, and provides strong evidence for scientific decision-making, thereby improving the operational efficiency, service quality, and management level of smart stations. The multi-terminal linkage communication control platform based on smart stations includes: a linkage architecture construction module 10, a monitoring and analysis module 20, a communication connection module 30, an equipment linkage analysis module 40, and an equipment communication control module 50.

[0049] The linkage architecture construction module 10 is used to construct a multi-terminal linkage architecture for smart stations. The multi-terminal linkage architecture for smart stations includes a data perception layer, a network communication layer, a data processing layer, and an equipment control layer.

[0050] The monitoring and analysis module 20 is used to acquire the regional distribution information of the target station, monitor and analyze the regional distribution information, deploy and obtain a set of station monitoring sensor modules, and acquire the station regional monitoring data stream set of the station monitoring sensor module set through the data perception layer.

[0051] The communication connection module 30 is used to obtain a list of smart station equipment. Each station equipment in the smart station equipment list has a built-in sensor group, and the sensor group is communicatively connected to the data perception layer. The data perception layer is used to obtain the set of device communication data of the smart station equipment list.

[0052] The equipment linkage analysis module 40 is used to transmit the station area monitoring data stream set and the equipment communication data set through the network communication layer to the data processing layer for multi-terminal data analysis and equipment linkage analysis, so as to obtain the target linkage station equipment set and the equipment control strategy parameter set.

[0053] The device communication control module 50 is used by the device control layer to perform multi-terminal device communication control on the target station based on the target linkage station device set and the device control strategy parameter set.

[0054] The specific configuration of the monitoring and analysis module 20 will be described in detail below. As described above, the monitoring and analysis module 20 further includes: a monitoring element analysis unit, which analyzes the regional information in the regional distribution information to obtain a set of station monitoring sensor modules, and obtains a set of station regional monitoring data streams of the station monitoring sensor module set through the data perception layer; a monitoring element analysis unit, which analyzes the monitoring elements of each region in the regional distribution information to obtain a set of station regional monitoring element parameters, which includes traffic flow parameters and safety demand level parameters of each region; a monitoring coverage analysis unit, which performs monitoring coverage analysis on the regional distribution information based on the set of station regional monitoring element parameters to determine a set of station regional monitoring point deployment parameters; a sensor demand analysis unit, which performs sensor demand analysis on the set of station regional monitoring point deployment parameters to obtain a set of station regional monitoring point sensor parameters; and a sensor deployment unit, which deploys sensors on the regional distribution information based on the set of station regional monitoring point deployment parameters and the set of station regional monitoring point sensor parameters to obtain the set of station monitoring sensor modules.

[0055] The specific configuration of the device linkage analysis module 40 will be described in detail below. As mentioned above, the station area monitoring data stream set and the device communication data set are transmitted to the data processing layer through the network communication layer for multi-terminal data analysis and device linkage analysis to obtain the target linkage station device set and the device control strategy parameter set. The device linkage analysis module 40 further includes: an encrypted data splitting unit, which is used to perform encrypted data splitting on the station area monitoring data stream set and the device communication data set to obtain a general station data stream set and an encrypted station data stream set; and a multi-dimensional evaluation unit, which is used to evaluate the encryption degree of the encrypted station data stream set in multiple dimensions to obtain data. Based on the encryption level parameter set, the data encryption level parameter set is matched with the data encryption algorithm list to determine the data transmission encryption algorithm set; a communication configuration unit is used to obtain a general data transmission channel and an encrypted data transmission channel according to the network communication layer, and to configure the communication of the encrypted data transmission channel based on the data transmission encryption algorithm set to obtain a data encryption branch channel; a mapping and splitting transmission unit is used to map and split the general station data stream set and the encrypted station data stream set to the data processing layer based on the general data transmission channel and the data encryption branch channel.

[0056] The device linkage analysis module 40 further includes: an anomaly feature identification unit, which is used to identify anomaly features in the station area monitoring data stream set through the data processing layer to obtain station anomaly feature parameters; an operation and maintenance strategy information acquisition unit, which is used to construct a smart station operation and maintenance strategy library, and match the station anomaly feature parameters with the smart station operation and maintenance strategy library to obtain target station operation and maintenance strategy information; a linkage device mapping unit, which is used to map linkage devices to the smart station device list according to the target station operation and maintenance strategy information and the device communication data set to obtain the target linkage station device set; and a device control strategy parameter set acquisition unit, which is used to perform linkage analysis on the target linkage station device set based on the station anomaly feature parameters to obtain the device control strategy parameter set.

[0057] The data processing layer performs anomaly feature identification on the station area monitoring data stream set to obtain station anomaly feature parameters. The anomaly feature identification unit further includes: an anomaly event dataset acquisition subunit, used to acquire the station area anomaly event dataset of the target station; a feature identifier training subunit, used to train feature identifiers on the station area anomaly event dataset to obtain a station anomaly feature identification model, which includes an anomaly type feature identification model and an anomaly level feature identification model; a feature parameter output subunit, used to perform anomaly feature identification on the station area monitoring data stream set based on the station anomaly feature identification model, and output anomaly type feature parameters and anomaly level feature parameters; and an anomaly feature parameter determination subunit, used to determine the station anomaly feature parameters based on the anomaly type feature parameters and anomaly level feature parameters.

[0058] The system includes: a target linked station equipment set, which is mapped to the smart station equipment list based on the target station operation and maintenance strategy information and the equipment communication data set. The linked equipment mapping unit further comprises: a demand matching subunit, used to match the target station operation and maintenance strategy information with the smart station equipment list to obtain a matched station equipment set; a status analysis subunit, used to perform equipment status analysis on the matched station equipment set according to the equipment communication data set to determine a set of equipment usage status parameters; a cost analysis subunit, used to perform operational cost analysis on the matched station equipment set based on the smart station equipment list to obtain a set of equipment operational cost parameters; and a set optimization subunit, used to optimize the matched station equipment set based on the set of equipment usage status parameters and the set of equipment operational cost parameters to obtain the target linked station equipment set.

[0059] The device control strategy parameter set acquisition unit further includes: a control parameter selection threshold set acquisition subunit, which performs linkage analysis on the target linked station equipment set based on the station anomaly feature parameters to obtain the equipment control strategy parameter set; a simulation subunit, which selects multiple sets of equipment control parameter sets within the station equipment control parameter selection threshold set and performs operation and maintenance effect simulation on the multiple sets of equipment control parameter sets to obtain the multiple sets of equipment control operation and maintenance effects; and a comparison and optimization subunit, which determines the optimization direction and performs global comparison and optimization on the multiple sets of equipment control parameter sets based on the multiple sets of equipment control operation and maintenance effects to obtain the equipment control strategy parameter set.

[0060] The multi-terminal linkage communication control platform based on smart stations provided in the embodiments of the present invention can execute the multi-terminal linkage communication control method based on smart stations provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0061] Although this application makes various references to certain modules in the platform according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0062] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A multi-terminal linkage communication control method based on a smart station, characterized in that: The method comprises: Construct a multi-terminal linkage architecture for a smart station, which includes a data perception layer, a network communication layer, a data processing layer, and a device control layer; Acquire regional distribution information of the target station, monitor and analyze the regional distribution information, deploy and obtain a set of station monitoring sensor modules, and obtain a set of station regional monitoring data streams of the station monitoring sensor modules through the data perception layer; Obtain a list of smart station equipment, each station equipment in the list of smart station equipment has a built-in sensor group, and the sensor group is communicatively connected with the data perception layer, and a device communication data set of the smart station equipment list is obtained through the data perception layer; The station area monitoring data stream set and the device communication data set are transmitted to the data processing layer through the network communication layer for multi-terminal data analysis and device linkage analysis to obtain a target linkage station device set and a device control strategy parameter set; The device control layer performs multi-terminal device communication control on the target station based on the target linkage station device set and the device control strategy parameter set; The step of obtaining the target linkage station equipment set and the equipment control strategy parameter set includes: The data processing layer identifies abnormal features of the station area monitoring data stream set to obtain abnormal feature parameters of the station; Constructing a smart station operation and maintenance strategy library, matching the abnormal characteristic parameters of the station with the smart station operation and maintenance strategy library to obtain the target station operation and maintenance strategy information; According to the target station operation and maintenance strategy information and the device communication data set, linkage device mapping is performed with the smart station device list to obtain the target linkage station device set; Perform linkage analysis on the target linkage station equipment set based on the abnormal characteristic parameters of the station to obtain the equipment control strategy parameter set; Wherein, the obtaining of abnormal characteristic parameters of the station includes: Collecting and obtaining a data set of abnormal events in the station area of ​​the target station; Performing feature identification training on the station area abnormal event data set to obtain a station abnormal feature recognition model, wherein the station abnormal feature recognition model includes an abnormal type feature recognition model and an abnormal level feature recognition model; Based on the station abnormal feature recognition model, the station area monitoring data stream set is subjected to abnormal feature recognition, and abnormal type feature parameters and abnormal level feature parameters are output; Determining the station abnormality characteristic parameter based on the abnormality type characteristic parameter and the abnormality level characteristic parameter; Wherein, obtaining the target linkage station equipment set includes: Based on the target station operation and maintenance strategy information and the smart station equipment list, demand matching is performed to obtain a matching station equipment set; Performing device status analysis on the matching station device set according to the device communication data set to determine a device usage status parameter set; Based on the smart station equipment list, an operation cost analysis is performed on the matching station equipment set to obtain an equipment operation cost parameter set; Based on the equipment usage status parameter set and the equipment operation cost parameter set, the matching station equipment set is optimized to obtain the target linkage station equipment set; Wherein, obtaining the device control strategy parameter set includes: Based on the abnormal characteristic parameters of the station, the target linkage station equipment set is analyzed in linkage to obtain a station equipment control parameter selection threshold set; Selecting multiple sets of equipment control parameter sets within the station equipment control parameter selection threshold set, performing operation and maintenance effect simulation on the multiple sets of equipment control parameter sets, and obtaining multiple sets of equipment control operation and maintenance effects; Based on the control operation and maintenance effects of the multiple groups of equipment, the optimization direction of the multiple groups of equipment control parameter sets is determined and globally compared and optimized to obtain the equipment control strategy parameter set.

2. The multi-terminal linkage communication control method based on a smart station as claimed in claim 1 is characterized in that: The station monitoring sensor module set is obtained by arranging, including: Performing monitoring element analysis on each area information in the area distribution information to obtain a station area monitoring element parameter set, wherein the station area monitoring element parameter set includes a traffic flow parameter and a safety demand level parameter of each area information; Based on the station area monitoring element parameter set, the monitoring coverage analysis of the regional distribution information is performed to determine the station area monitoring point layout parameter set; Performing sensor requirement analysis on the station area monitoring point deployment parameter set to obtain the station area monitoring point sensor parameter set; Based on the station area monitoring point layout parameter set and the station area monitoring point sensor parameter set, sensors are deployed for the area distribution information to obtain the station monitoring sensor module set.

3. The multi-terminal linkage communication control method based on a smart station as claimed in claim 1 is characterized in that: The step of transmitting the station area monitoring data stream set and the device communication data set to the data processing layer through the network communication layer includes: Performing encrypted data diversion on the station area monitoring data flow set and the device communication data set to obtain a general station data flow set and an encrypted station data flow set; Performing a multi-dimensional evaluation on the encryption degree of the encrypted station data stream set to obtain a data encryption level parameter set, and matching the data encryption algorithm list with the data encryption level parameter set to determine a data transmission encryption algorithm set; According to the network communication layer, a general data transmission channel and an encrypted data transmission channel are obtained, and communication configuration is performed on the encrypted data transmission channel based on the data transmission encryption algorithm set to obtain a data encryption branch channel; Based on the general data transmission channel and the data encryption branch channel, the general station data stream set and the encrypted station data stream set are mapped and transmitted to the data processing layer.

4. The multi-terminal linkage communication control platform based on the smart station is characterized by: The platform is used to implement the multi-terminal linkage communication control method based on a smart station according to any one of claims 1 to 3, and the platform includes: A linkage architecture building module, which is used to build a multi-terminal linkage architecture for a smart station. The multi-terminal linkage architecture for a smart station includes a data perception layer, a network communication layer, a data processing layer, and a device control layer; A monitoring and analysis module, the monitoring and analysis module is used to obtain regional distribution information of the target station, monitor and analyze the regional distribution information, deploy and obtain a set of station monitoring sensor modules, and obtain a set of station regional monitoring data streams of the station monitoring sensor module set through the data perception layer; A communication connection module, the communication connection module is used to obtain a list of smart station equipment, each station equipment in the list of smart station equipment has a built-in sensor group, and the sensor group is communicatively connected with the data perception layer, and a device communication data set of the smart station equipment list is obtained through the data perception layer; An equipment linkage analysis module, which is used to transmit the station area monitoring data stream set and the equipment communication data set to the data processing layer through the network communication layer for multi-terminal data analysis and equipment linkage analysis, and obtain a target linkage station equipment set and an equipment control strategy parameter set; The device communication control module is used by the device control layer to perform multi-terminal device communication control on the target station based on the target linkage station device set and the device control strategy parameter set.

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