Rail transit intelligent control bottom layer method and system based on arm architecture

Abstract

The application discloses a track traffic intelligent control bottom layer method and system based on an ARM architecture, relates to the track traffic technical field, and has the following steps: obtaining device operation parameters of a track traffic subway gate machine and ARM architecture control node information, and constructing a gate machine intelligent control bottom layer framework according to the device operation parameters and the ARM architecture control node information; the gate machine intelligent control bottom layer framework comprises a parameter benchmark area, an internet of things interaction area and a control execution area; wherein the parameter benchmark area comprises gate machine standard operation parameters and parameter weight coefficients; the internet of things interaction area comprises device interaction characteristic factors and data transmission diagnosis factors; real-time sensing data of the subway gate machine is obtained through an ARM architecture intelligent traffic internet of things service, and gate machine real-time operation factors are obtained according to the real-time sensing data; and the effect is to realize accurate intelligent regulation and control of the gate machine.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, specifically to a method and system for intelligent control of rail transit based on the ARM architecture. Background Technology

[0002] Against the backdrop of the continuous development of intelligent transportation IoT application services, the demand for intelligent gate control in the rail transit sector is becoming increasingly urgent. Traditional gate control methods mostly rely on simple local logic or basic communication, making it difficult to adapt to complex and ever-changing traffic scenarios and the need for massive data interaction. With the advancement of intelligent transportation IoT, more efficient and precise underlying control methods are needed to achieve deep integration of gates and IoT.

[0003] Against this backdrop, a new approach to intelligent control of rail transit based on the ARM architecture has emerged. Intelligent transportation IoT applications provide data acquisition, transmission, and interaction support, leveraging the high processing power of the ARM architecture to construct a complete framework encompassing parameter benchmarks, IoT interaction, and control execution. However, the management of equipment operating parameters lacks a standardized and systematic benchmark framework. Furthermore, the ARM-based approach to intelligent control of rail transit struggles to accurately determine the normal and abnormal operating states of turnstiles; during IoT interaction, there is a lack of effective correlation analysis between equipment interaction characteristics and communication diagnostics, making it difficult to identify and repair communication faults in a timely manner; and the generation of control commands does not fully integrate real-time operating data and IoT status, resulting in delayed responses from turnstiles to changes in passenger flow and equipment malfunctions, impacting traffic efficiency and operational stability. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for intelligent control of rail transit based on ARM architecture, so as to solve the problem in the prior art that there is no effective correlation analysis between the device interaction characteristics and communication diagnosis during the Internet of Things interaction process.

[0005] To solve the above-mentioned technical problems, the present invention specifically provides the following technical solution: A low-level method for intelligent control of rail transit based on ARM architecture, which includes the following steps: The system acquires the equipment operating parameters and ARM architecture control node information of the subway turnstiles, and constructs an intelligent control underlying framework for the turnstiles based on these parameters. This framework includes a parameter baseline area, an IoT interaction area, and a control execution area. The parameter baseline area includes standard operating parameters and parameter weight coefficients for the turnstiles. The IoT interaction area includes equipment interaction characteristic factors and data transmission diagnostic factors. The real-time sensor data of the subway turnstile is obtained through the ARM architecture intelligent transportation IoT service, and the real-time operation factor of the turnstile is obtained based on the real-time sensor data. Dynamic control parameters are obtained by processing and analyzing the real-time operating factors of the turnstile, the underlying framework of the intelligent control of the turnstile, the parameter weight coefficients, and the equipment operating parameters. The IoT communication status of the subway turnstile is obtained by processing and analyzing abnormal operation factors and equipment interaction characteristic factors. The dynamic control parameters and IoT communication status are transmitted to the control execution area to obtain the intelligent control execution command of the subway gate.

[0006] As a preferred embodiment of the present invention, dynamic control parameters are obtained by processing and analyzing the real-time operating factor of the turnstile, the underlying framework of the intelligent control of the turnstile, the parameter weighting coefficient, and the equipment operating parameters, specifically including the following steps: After inputting the real-time operation factor of the gate into the underlying framework of the gate intelligent control, the real-time operation factor of the gate is matched with the standard operation parameters of the gate in the parameter reference area to obtain the standard operation parameters of the gate that are consistent with the real-time operation factor of the gate in the parameter reference area, and the standard operation parameters of the gate are recorded as the target standard parameters. Compare the real-time parameter values ​​of the gate's real-time operation factors with the standard parameter range of the target standard parameters; If the real-time parameter value of the gate's real-time operation factor is not within the standard parameter range of the target standard parameter, then the gate's real-time operation factor is recorded as an abnormal operation factor, and the interval reference parameter of the standard parameter range is obtained. The abnormal parameter values ​​of the abnormal operation factor are obtained based on the interval benchmark parameters and real-time parameter values. The comprehensive abnormality coefficient of the subway gate is obtained by weighting the abnormal parameter values ​​of the abnormal operation factor with the parameter weight coefficient of the abnormal operation factor. The weight of the abnormal impact of the subway turnstiles is obtained based on the comprehensive abnormality coefficient and equipment operating parameters; The dynamic control parameters of the subway turnstiles are obtained based on the weight of abnormal impacts and the equipment operating parameters.

[0007] As a preferred embodiment of the present invention, the IoT communication status of the subway gate is obtained by processing and analyzing abnormal operation factors and equipment interaction characteristic factors, specifically including the following steps: Transmit abnormal operation factors to the IoT interaction area; By matching and analyzing the abnormal operation factors with the device interaction characteristic factors in the IoT interaction area, the target interaction factors and target transmission diagnostic factors corresponding to the subway gate are obtained. Obtain the communication diagnostic features corresponding to the target transmission diagnostic factors, and obtain the IoT communication status of the subway gate based on the communication diagnostic features corresponding to the target transmission diagnostic factors.

[0008] As a preferred embodiment of the present invention, the parameter reference area includes standard operating parameters of the gate and parameter weighting coefficients, specifically including the following steps: Obtain corresponding standard equipment operation data based on the equipment model and operating environment of the subway turnstiles; Based on standard equipment operating data, parameter features are extracted to obtain the corresponding standard operating feature parameters; Based on the standard operating characteristic parameters, corresponding standard operating parameters of the turnstile and standard parameter ranges corresponding to the standard operating parameters of the turnstile are generated, wherein the standard operating parameters of the turnstile correspond to the standard operating characteristic parameters. Set the parameter weighting coefficients for the corresponding standard operating parameters of the turnstiles based on the importance of the equipment operating parameters.

[0009] As a preferred embodiment of the present invention, the IoT interaction area includes device interaction characteristic factors and data transmission diagnostic factors, specifically including the following steps: Obtain historical device interaction data corresponding to ARM architecture IoT services; wherein, the historical device interaction data includes abnormal device interaction records and communication diagnostic results corresponding to the abnormal device interaction records; The abnormal device interaction records are used to extract the corresponding abnormal device interaction features. Based on the abnormal interaction characteristics of the device, generate device interaction characteristic factors and corresponding interaction parameter values ​​for the device interaction characteristic factors, wherein the device interaction characteristic factors correspond to the abnormal interaction characteristics of the device. The corresponding communication diagnostic features are obtained by extracting features from the communication diagnostic results. Based on the communication diagnostic features, a data transmission diagnostic factor and corresponding diagnostic parameter values ​​are generated, wherein the data transmission diagnostic factor corresponds to the communication diagnostic feature.

[0010] As a preferred embodiment of the present invention, obtaining the real-time operation factor of the gate based on the real-time sensing data specifically includes the following steps: The real-time operational characteristics of the subway turnstiles are obtained by extracting operational features from real-time sensor data. The real-time operation factor of the subway turnstile is generated based on the real-time operation characteristics, and the real-time parameter value corresponding to the real-time operation factor is generated. The real-time operation factor corresponds to the real-time operation characteristics.

[0011] As a preferred embodiment of the present invention, the abnormal operation factor is matched and analyzed with the device interaction characteristic factor in the Internet of Things interaction area to obtain the target interaction factor and target transmission diagnostic factor corresponding to the subway gate, specifically including the following steps: The abnormal operation factor is matched with the device interaction feature factor in the IoT interaction area to obtain the device interaction feature factor in the IoT interaction area that is consistent with the abnormal operation factor, and the device interaction feature factor is recorded as the target interaction factor. Connect the device interaction feature factor with the corresponding data transmission diagnostic factor to obtain the interaction diagnostic trajectory between the device interaction feature factor and the data transmission diagnostic factor. Based on the interactive diagnostic trajectory, the target transmission diagnostic factor corresponding to the target interaction factor is obtained.

[0012] As a preferred embodiment of the present invention, the intelligent control execution instructions include dynamic control parameters of the subway gate and adjustment instructions corresponding to the Internet of Things communication status.

[0013] The underlying system for intelligent control of rail transit based on ARM architecture includes: Acquisition Module: Acquires the equipment operating parameters and ARM architecture control node information of the rail transit subway turnstile, and constructs the underlying framework for intelligent control of the turnstile based on the equipment operating parameters and ARM architecture control node information; the underlying framework for intelligent control of the turnstile includes a parameter benchmark area, an IoT interaction area, and a control execution area; wherein, the parameter benchmark area includes the turnstile's standard operating parameters and parameter weight coefficients; the IoT interaction area includes equipment interaction characteristic factors and data transmission diagnostic factors; Processing module: Obtains real-time sensor data of the subway turnstiles through ARM architecture intelligent transportation IoT services, and obtains the real-time operation factor of the turnstiles based on the real-time sensor data; The first analysis module processes and analyzes the real-time operating factors of the turnstile, the underlying framework of the intelligent control of the turnstile, the parameter weight coefficients, and the equipment operating parameters to obtain dynamic control parameters; The second analysis module processes and analyzes abnormal operation factors and equipment interaction characteristic factors to obtain the IoT communication status of the subway gate. Control module: Transmits dynamic control parameters and IoT communication status to the control execution area to obtain intelligent control execution instructions for the subway turnstile.

[0014] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements a low-level method for intelligent control of rail transit based on an ARM architecture.

[0015] Compared with the prior art, the present invention has the following advantages: This invention establishes an intelligent control framework comprising a parameter baseline area, an IoT interaction area, and a control execution area by acquiring the operating parameters of subway turnstiles and information from ARM architecture control nodes. The parameter baseline area, using standard turnstile operating parameters and parameter weighting coefficients, establishes a clear and precise reference system for turnstile operation, providing a reliable basis for judging turnstile operating status. The IoT interaction area, using device interaction characteristic factors and data transmission diagnostic factors, clarifies the correlation between device interaction and communication diagnostics, laying a solid foundation for subsequent anomaly analysis and communication status judgment. By leveraging ARM architecture intelligent transportation IoT services to acquire real-time sensor data and extract real-time operating factors for the turnstiles, the perception of their operating status becomes more timely and accurate. Combining these real-time operating factors with the intelligent control framework yields dynamic control parameters, enabling the turnstiles to flexibly adjust their operation based on real-time conditions, improving traffic efficiency and equipment adaptability, and ensuring stable and efficient operation of the turnstiles in complex and changing scenarios. By analyzing abnormal operating factors and device interaction characteristics, the IoT communication status is accurately located. If communication anomalies occur, they can be quickly identified and directional adjustments can be made, reducing gate malfunctions and passage obstructions caused by communication problems, and ensuring the stability and reliability of the gate's interaction with the IoT. Dynamic control parameters and IoT communication status are transmitted to the control execution area to generate intelligent control execution commands, achieving precise and intelligent control of the gate. This ensures that the gate's operation is both in line with its own equipment status and adapted to the IoT communication environment, comprehensively improving the level of intelligent control from equipment operation to communication interaction. Attached Figure Description

[0016] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating the steps of the underlying method for intelligent control of rail transit based on ARM architecture proposed in this invention. Figure 2 This is a schematic diagram of the underlying module of the intelligent control system for rail transit based on the ARM architecture proposed in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The concepts involved in this application will first be described with reference to the accompanying drawings. It should be noted that the following descriptions of various concepts are only for the purpose of making the content of this application easier to understand and do not constitute a limitation on the scope of protection of this application; furthermore, the embodiments and features in the embodiments of this application can be combined with each other unless otherwise specified. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] like Figures 1-2 As shown in the embodiment, the underlying method and system for intelligent control of rail transit based on ARM architecture are provided, including the following steps: A low-level method for intelligent control of rail transit based on ARM architecture, which includes the following steps: The system acquires the equipment operating parameters and ARM architecture control node information of subway turnstiles, and constructs an intelligent control underlying framework for the turnstiles based on these parameters. This framework includes a parameter baseline area, an IoT interaction area, and a control execution area. The parameter baseline area includes standard operating parameters and parameter weighting coefficients for the turnstiles; the IoT interaction area includes equipment interaction characteristic factors and data transmission diagnostic factors. The real-time sensor data of the subway turnstiles is obtained through the ARM architecture intelligent transportation IoT service, and the real-time operation factor of the turnstiles is obtained based on the real-time sensor data. Dynamic control parameters are obtained by processing and analyzing the real-time operating factors of the turnstile, the underlying framework of the intelligent control of the turnstile, the parameter weight coefficients, and the equipment operating parameters. The IoT communication status of the subway turnstile is obtained by processing and analyzing abnormal operation factors and equipment interaction characteristic factors. The dynamic control parameters and IoT communication status are transmitted to the control execution area to obtain the intelligent control execution command of the subway gate.

[0021] First, the operational parameters of the rail transit subway turnstiles are obtained from the basic infrastructure level, such as the opening and closing time of the turnstiles and the motor power. The ARM architecture control node information includes the node's connection status and data processing capabilities. Based on these operational parameters and the ARM architecture control node information, a low-level intelligent control framework for the turnstiles is constructed. The parameter baseline area of ​​this framework stores the standard operating parameters of the turnstiles, i.e., the labeled values ​​of each indicator when the turnstiles are running normally and stably. These parameters, along with parameter weighting coefficients, are used to differentiate the importance of different parameters on the turnstiles' operation. The IoT interaction area incorporates device interaction characteristic factors, recording typical characteristics of the turnstiles' interactions with external devices and data transmission diagnostic factors. This helps determine whether data transmission is normal, laying the foundation for subsequent interactions and diagnostics.

[0022] Relying on ARM architecture intelligent transportation IoT services, the system collects real-time sensor data from subway turnstiles, such as personnel passage signals monitored by infrared sensors and turnstile door positions fed back by position sensors. From this data, the system extracts real-time operating factors of the turnstiles, transforming the physical operating status into analyzable factor forms to characterize whether the turnstiles are currently opening and closing normally or experiencing lag or other operational issues.

[0023] The real-time operating factors of the turnstile are input into a pre-built intelligent control framework. The influence of different parameters is considered using parameter weighting coefficients, and then correlated with device operating parameters. A comprehensive analysis is performed to determine whether the real-time operating factors meet the standards. If there are deviations, dynamic control parameters are calculated to determine how to adjust the turnstile's operating parameters to adapt it to real-time operating conditions. When abnormal operating factors occur, they are matched and analyzed with the device interaction characteristic factors in the IoT interaction area to determine whether the abnormality in data transmission is caused by interaction problems, thus determining the IoT communication status and clarifying whether it is a communication delay, interruption, or data error.

[0024] Dynamic control parameters and IoT communication status are transmitted to the control execution area. Based on this information, the control execution area generates intelligent control execution commands that directly affect the subway turnstiles, instructing them to adjust their operating status, such as adjusting the door opening speed and restarting the interaction after repairing communication anomalies. This ensures that the turnstiles operate intelligently, stably, and efficiently under the support of the ARM architecture, thereby adapting to the traffic management needs of rail transit.

[0025] The dynamic control parameters are obtained by processing and analyzing the real-time operating factors of the turnstile, the underlying framework of the intelligent control of the turnstile, the parameter weight coefficients, and the equipment operating parameters. The specific steps include: After inputting the real-time operation factor of the gate into the underlying framework of the gate intelligent control, the real-time operation factor of the gate is matched with the standard operation parameters of the gate in the parameter reference area to obtain the standard operation parameters of the gate that are consistent with the real-time operation factor of the gate in the parameter reference area, and the standard operation parameters of the gate are recorded as the target standard parameters. Compare the real-time parameter values ​​of the gate's real-time operation factors with the standard parameter range of the target standard parameters; If the real-time parameter value of the gate's real-time operation factor is not within the standard parameter range of the target standard parameter, then the gate's real-time operation factor is recorded as an abnormal operation factor, and the interval reference parameter of the standard parameter range is obtained. The abnormal parameter values ​​of the abnormal operation factor are obtained based on the interval baseline parameters and real-time parameter values; The comprehensive abnormality coefficient of the subway gate is obtained by weighting the abnormal parameter values ​​of the abnormal operation factor with the parameter weight coefficient of the abnormal operation factor. The weight of the abnormal impact of the subway turnstiles is obtained based on the comprehensive abnormality coefficient and equipment operating parameters; The dynamic control parameters of the subway turnstiles are obtained based on the weight of abnormal impacts and the equipment operating parameters.

[0026] This application inputs the real-time operation factor of the turnstile into the underlying intelligent control framework. It then uses the standard operation parameters stored in the parameter reference area of ​​the framework to perform parameter matching for the real-time operation factor, determining the completely corresponding standard operation parameters, i.e., the target standard parameters. The real-time parameter values ​​of the real-time operation factor are compared with the standard parameter range specified by the target standard parameters to determine whether the real-time operation value is within the normal range. If the real-time parameter value exceeds this range, it indicates that the turnstile's operating state is abnormal, thus marking this real-time operation factor as an abnormal operation factor, and simultaneously obtaining the interval reference parameter of the standard parameter range.

[0027] If the real-time parameter value is outside the normal range, the abnormal parameter value of the abnormal operation factor is calculated using the interval baseline parameter and the real-time parameter value, quantifying the degree of abnormality in the gate's operation. Because different parameters have different impacts on the overall operation of the gate, the abnormal parameter value and the parameter weight coefficient corresponding to the abnormal operation factor are weighted to obtain a comprehensive abnormality coefficient. The comprehensive abnormality coefficient can comprehensively reflect the overall abnormal condition of the gate and demonstrate the impact of different abnormal parameters on the overall abnormality.

[0028] Anomaly impact weights are derived based on a comprehensive anomaly coefficient and equipment operating parameters, taking into account the proportion of these anomalies' influence on the turnstile operation. Finally, dynamic control parameters are generated based on the anomaly impact weights and equipment operating parameters to guide the turnstiles in adjusting their operating status according to real-time anomaly conditions, enabling the turnstiles to intelligently and stably serve rail transit traffic management in complex and ever-changing real-world operating scenarios.

[0029] The IoT communication status of the subway turnstile is obtained by processing and analyzing abnormal operation factors and equipment interaction characteristic factors. The specific steps include: Transmit abnormal operation factors to the IoT interaction area; By matching and analyzing the abnormal operation factors with the device interaction characteristic factors in the IoT interaction area, the target interaction factors and target transmission diagnostic factors corresponding to the subway gate are obtained. Obtain the communication diagnostic features corresponding to the target transmission diagnostic factors, and obtain the IoT communication status of the subway gate based on the communication diagnostic features corresponding to the target transmission diagnostic factors.

[0030] This application transmits abnormal operation factors, which identify abnormal operation of the turnstile, to the IoT interaction area of ​​the IoT interaction management system. Once in the interaction area, the abnormal operation factors are matched and analyzed using the device interaction feature factors stored therein. These device interaction feature factors record various typical characteristics of normal or abnormal interactions between the turnstile and external devices. By matching and filtering, interaction-related factors corresponding to the current abnormal operation factors are identified, thereby finding the target interaction factors corresponding to the subway turnstile and simultaneously associating them with matching target transmission diagnostic factors.

[0031] Acquire the communication diagnostic characteristics corresponding to the target transmission diagnostic factors, including key information such as signal strength, transmission delay, and data packet loss. Based on these communication diagnostic characteristics corresponding to the target transmission diagnostic factors, determine the IoT communication status of the subway turnstiles, such as whether the communication is normal, has slight delay, or suffers from severe data packet loss. This allows operation and maintenance personnel or intelligent control systems to clearly understand the health of the communication between the turnstiles and the IoT, providing a basis for subsequent communication optimization and fault diagnosis, and ensuring the stability and reliability of remote control and data interaction of the turnstiles via the IoT.

[0032] The parameter baseline area includes the standard operating parameters of the turnstile and the parameter weighting coefficients, specifically including the following steps: Obtain corresponding standard equipment operation data based on the equipment model and operating environment of the subway turnstiles; Based on standard equipment operating data, parameter features are extracted to obtain the corresponding standard operating feature parameters; Based on the standard operating characteristic parameters, the corresponding standard operating parameters of the turnstile and the standard parameter range corresponding to the standard operating parameters of the turnstile are generated. The standard operating parameters of the turnstile correspond to the standard operating characteristic parameters. Set the parameter weighting coefficients for the corresponding standard operating parameters of the turnstiles based on the importance of the equipment operating parameters.

[0033] First, we need to combine the equipment model of the subway turnstile with the actual operating environment to obtain the standard equipment operation data that is compatible with it. Different models of turnstiles have differences in hardware performance and design logic, and the operating environment, such as temperature, humidity, and passenger flow, will also affect their operating performance.

[0034] After obtaining standard equipment operating data, parameter features are extracted to identify standard operating characteristic parameters that reflect the key operating states of the turnstile. These include the stable opening and closing time of the turnstile and the power range for efficient motor operation. These parameters are core indicators of normal turnstile operation. Based on the extracted standard operating characteristic parameters, corresponding standard operating parameters for the turnstile are generated. Simultaneously, a standard parameter range is defined for each standard operating parameter, ensuring a clear normal fluctuation range and guaranteeing a one-to-one correspondence between the standard operating parameters and the standard operating characteristic parameters, thus establishing a clear parameter correspondence relationship.

[0035] Based on the importance of equipment operating parameters to the overall operation of the turnstile, a parameter weighting coefficient is assigned to each standard operating parameter of the turnstile. For example, parameters related to the safe opening and closing of the turnstile have a higher weighting coefficient. This allows for the differentiation of the impact of different parameters when analyzing the operating status of the turnstile and judging the degree of abnormality. The entire parameter benchmark system not only includes the standard parameter range but also distinguishes the importance of parameters, providing an accurate and comprehensive reference for intelligent control of turnstiles.

[0036] The IoT interaction zone includes device interaction characteristic factors and data transmission diagnostic factors, specifically including the following steps: Obtain historical device interaction data corresponding to ARM architecture IoT services; the historical device interaction data includes abnormal device interaction records and communication diagnostic results corresponding to the abnormal device interaction records; The abnormal device interaction records are used to extract the corresponding abnormal device interaction features. Based on the abnormal interaction characteristics of the equipment, generate equipment interaction characteristic factors and corresponding interaction parameter values ​​for the equipment interaction characteristic factors. The equipment interaction characteristic factors correspond to the abnormal interaction characteristics of the equipment. The corresponding communication diagnostic features are obtained by extracting features from the communication diagnostic results. Based on communication diagnostic features, data transmission diagnostic factors and corresponding diagnostic parameter values ​​are generated, with the data transmission diagnostic factors corresponding to the communication diagnostic features.

[0037] First, obtain historical device interaction data corresponding to the ARM architecture IoT service. This data includes records of abnormal device interactions and corresponding communication diagnostic results, recording the problems and communication status diagnostic conclusions that occurred in past device interactions.

[0038] Feature extraction is performed on abnormal device interaction records. From specific abnormal interaction events, representative characteristics of the anomaly are extracted, such as typical durations of interaction delays and triggering conditions for data interaction failures. This transforms the anomaly records into analyzable feature information. Based on the extracted abnormal device interaction features, device interaction feature factors are generated, and corresponding interaction parameter values ​​are determined. This ensures a one-to-one correspondence between the device interaction feature factors and the abnormal device interaction features. In subsequent interactions, these factors can be matched to quickly identify the interaction status.

[0039] Feature extraction is performed on the communication diagnostic results to identify communication diagnostic features reflecting the type and severity of communication problems. Examples include characteristics of communication interruptions caused by weak signals and characteristics of transmission errors caused by network congestion. Based on these communication diagnostic features, data transmission diagnostic factors are generated, and corresponding diagnostic parameter values ​​are set to align these factors with the communication diagnostic features. These factors can then be used to determine the IoT communication status, providing a basis for ensuring stable interaction between the gate and IoT services, and enabling the entire IoT interaction area to identify abnormal interactions and diagnose communication problems.

[0040] The real-time operating factor of the gate is obtained based on real-time sensor data, specifically including the following steps: The real-time operational characteristics of the subway turnstiles are obtained by extracting operational features from real-time sensor data. Based on real-time operating characteristics, the real-time operating factors of the subway turnstiles are generated, along with the corresponding real-time parameter values. The real-time operating factors of the turnstiles correspond to the real-time operating characteristics.

[0041] This application first performs operational feature extraction on the collected real-time sensor data of the subway turnstiles. The real-time sensor data covers various physical signals during the operation of the turnstiles, such as changes in motor current, door position feedback, and infrared sensor detection signals. From this complex data, real-time operational features that reflect the essential state of the turnstiles at the moment of operation are extracted, such as the smoothness of door opening and closing and the stability of motor operation, transforming the raw data into key feature information that can be used for analysis.

[0042] Based on the extracted real-time operating features, corresponding real-time operating factors for the turnstile are generated, and the corresponding real-time parameter values ​​are determined. The real-time operating factor is an abstract and quantified representation of the real-time operating features. Different real-time operating features correspond to different operating factors; for example, gate opening / closing features correspond to gate operating factors, motor features correspond to motor operating factors, and so on. Each operating factor has specific real-time parameter values, used to accurately characterize the degree of operating state represented by that factor. This allows subsequent analysis and matching operations to be carried out based on these clear factors and parameter values, providing an intuitive and quantifiable basis for judging whether the turnstile is operating normally and whether the control strategy needs adjustment.

[0043] The abnormal operation factors are matched and analyzed with the device interaction characteristic factors in the IoT interaction area to obtain the target interaction factors and target transmission diagnostic factors corresponding to the subway turnstiles. The specific steps include: The abnormal operation factor is matched with the device interaction feature factor in the IoT interaction area to obtain the device interaction feature factor in the IoT interaction area that is consistent with the abnormal operation factor, and the device interaction feature factor is recorded as the target interaction factor. Connect the device interaction feature factor with the corresponding data transmission diagnostic factor to obtain the interaction diagnostic trajectory between the device interaction feature factor and the data transmission diagnostic factor. The target transmission diagnostic factor corresponding to the target interaction factor is obtained based on the interaction diagnostic trajectory.

[0044] The intelligent control execution instructions include the dynamic control parameters of the subway turnstiles and the control instructions corresponding to the IoT communication status.

[0045] First, the abnormal operation factor representing the abnormal operation status of the gate is input into the IoT interaction area and matched with the device interaction feature factors stored in the area. The device interaction feature factors in the IoT interaction area record the typical characteristics of various device interaction anomalies. By matching, the device interaction feature factors that match the characteristics of the current abnormal operation factor are identified and designated as the target interaction factor, thus clarifying the corresponding identifier of the anomaly at the interaction level.

[0046] Because there is a correlation between device interaction characteristic factors and data transmission diagnostic factors, the device interaction characteristic factors are connected with their corresponding data transmission diagnostic factors to trace their interaction diagnostic trajectory. This links abnormal interaction characteristics with communication diagnostic information. Based on this interaction diagnostic trajectory, the target transmission diagnostic factor corresponding to the target interaction factor is found, allowing for a precise correlation between the interaction characteristics and communication transmission diagnostic information of the gate's abnormal operation, thereby revealing the IoT communication diagnostic situation behind the abnormal operation.

[0047] The intelligent control execution command integrates the dynamic control parameters of the subway turnstile, which are the parameters that guide the turnstile to adjust its operating status, as well as the control commands corresponding to the IoT communication status, such as repair and optimization commands when communication is abnormal. Through such association and integration, the intelligent control execution command can comprehensively and accurately direct the turnstile to deal with abnormalities, ensuring the stable and intelligent operation of the turnstile in the IoT environment.

[0048] The underlying system for intelligent control of rail transit based on ARM architecture includes: Acquisition Module: Acquires the equipment operating parameters and ARM architecture control node information of the rail transit subway turnstiles, and constructs the underlying framework for intelligent control of the turnstiles based on the equipment operating parameters and ARM architecture control node information. The underlying framework for intelligent control of the turnstiles includes a parameter benchmark area, an IoT interaction area, and a control execution area. Among them, the parameter benchmark area includes the standard operating parameters of the turnstiles and parameter weight coefficients; the IoT interaction area includes equipment interaction characteristic factors and data transmission diagnostic factors. Processing module: Obtains real-time sensor data of the subway turnstiles through ARM architecture intelligent transportation IoT services, and obtains the real-time operation factor of the turnstiles based on the real-time sensor data; The first analysis module processes and analyzes the real-time operating factors of the turnstile, the underlying framework of the intelligent control of the turnstile, the parameter weight coefficients, and the equipment operating parameters to obtain dynamic control parameters; The second analysis module processes and analyzes abnormal operation factors and equipment interaction characteristic factors to obtain the IoT communication status of the subway gate. Control module: Transmits dynamic control parameters and IoT communication status to the control execution area to obtain intelligent control execution instructions for the subway turnstile.

[0049] Electronic devices, including memory, processor, and computer programs stored in memory and capable of running on the processor, wherein the processor executes the program to implement a low-level method for intelligent control of rail transit based on the ARM architecture.

[0050] The embodiments and / or implementation methods described above are merely preferred embodiments and / or implementation methods for implementing the technology of the present invention, and are not intended to limit the implementation methods of the technology of the present invention in any way. Any person skilled in the art can make some modifications or alterations to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as the technology or embodiments that are substantially the same as the present invention.

[0051] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. The above descriptions are only preferred embodiments of this application. It should be noted that due to the limitations of written expression, while there are objectively infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner. These improvements, modifications, changes, or combinations, or the direct application of the inventive concept and technical solution to other situations without modification, should all be considered within the scope of protection of this application.

Claims

1. A low-level method for intelligent control of rail transit based on ARM architecture, characterized in that, The method includes the following steps: The system acquires the equipment operating parameters and ARM architecture control node information of the subway turnstiles, and constructs an intelligent control underlying framework for the turnstiles based on these parameters. This framework includes a parameter baseline area, an IoT interaction area, and a control execution area. The parameter baseline area includes standard operating parameters and parameter weight coefficients for the turnstiles. The IoT interaction area includes equipment interaction characteristic factors and data transmission diagnostic factors. The real-time sensor data of the subway turnstile is obtained through the ARM architecture intelligent transportation IoT service, and the real-time operation factor of the turnstile is obtained based on the real-time sensor data. The dynamic control parameters are obtained by processing and analyzing the real-time operating factors of the turnstile, the underlying framework of the intelligent control of the turnstile, the parameter weight coefficients, and the equipment operating parameters. The specific steps include: After inputting the real-time operation factor of the gate into the underlying framework of the gate intelligent control, the real-time operation factor of the gate is matched with the standard operation parameters of the gate in the parameter reference area to obtain the standard operation parameters of the gate that are consistent with the real-time operation factor of the gate in the parameter reference area, and the standard operation parameters of the gate are recorded as the target standard parameters. Compare the real-time parameter values ​​of the gate's real-time operation factors with the standard parameter range of the target standard parameters; If the real-time parameter value of the gate's real-time operation factor is not within the standard parameter range of the target standard parameter, then the gate's real-time operation factor is recorded as an abnormal operation factor, and the interval reference parameter of the standard parameter range is obtained. The abnormal parameter values ​​of the abnormal operation factor are obtained based on the interval benchmark parameters and real-time parameter values. The comprehensive abnormality coefficient of the subway gate is obtained by weighting the abnormal parameter values ​​of the abnormal operation factor with the parameter weight coefficient of the abnormal operation factor. The weight of the abnormal impact of the subway turnstiles is obtained based on the comprehensive abnormality coefficient and equipment operating parameters; The dynamic control parameters of the subway turnstiles are obtained based on the weight of abnormal impacts and equipment operating parameters. The IoT communication status of the subway turnstile is obtained by processing and analyzing abnormal operation factors and equipment interaction characteristic factors. The specific steps include: Transmit abnormal operation factors to the IoT interaction area; The abnormal operation factors are matched and analyzed with the device interaction characteristic factors in the IoT interaction area to obtain the target interaction factors and target transmission diagnostic factors corresponding to the subway turnstiles. The specific steps include: The abnormal operation factor is matched with the device interaction feature factor in the IoT interaction area to obtain the device interaction feature factor in the IoT interaction area that is consistent with the abnormal operation factor, and the device interaction feature factor is recorded as the target interaction factor. Connect the device interaction feature factor with the corresponding data transmission diagnostic factor to obtain the interaction diagnostic trajectory between the device interaction feature factor and the data transmission diagnostic factor. Based on the interactive diagnostic trajectory, the target transmission diagnostic factor corresponding to the target interaction factor is obtained; Obtain the communication diagnostic features corresponding to the target transmission diagnostic factors, and obtain the IoT communication status of the subway gate based on the communication diagnostic features corresponding to the target transmission diagnostic factors; The dynamic control parameters and IoT communication status are transmitted to the control execution area to obtain the intelligent control execution command of the subway gate.

2. The underlying method for intelligent control of rail transit based on ARM architecture according to claim 1, characterized in that, The parameter reference area includes the standard operating parameters of the turnstile and parameter weighting coefficients, specifically including the following steps: Obtain corresponding standard equipment operation data based on the equipment model and operating environment of the subway turnstiles; Based on standard equipment operating data, parameter features are extracted to obtain the corresponding standard operating feature parameters; Based on the standard operating characteristic parameters, corresponding standard operating parameters of the turnstile and standard parameter ranges corresponding to the standard operating parameters of the turnstile are generated, wherein the standard operating parameters of the turnstile correspond to the standard operating characteristic parameters. Set the parameter weighting coefficients for the corresponding standard operating parameters of the turnstiles based on the importance of the equipment operating parameters.

3. The underlying method for intelligent control of rail transit based on ARM architecture according to claim 2, characterized in that, The IoT interaction area includes device interaction characteristic factors and data transmission diagnostic factors, specifically including the following steps: Obtain historical device interaction data corresponding to ARM architecture IoT services; wherein, the historical device interaction data includes abnormal device interaction records and communication diagnostic results corresponding to the abnormal device interaction records; The abnormal device interaction records are used to extract the corresponding abnormal device interaction features. Based on the abnormal interaction characteristics of the device, generate device interaction characteristic factors and corresponding interaction parameter values ​​for the device interaction characteristic factors, wherein the device interaction characteristic factors correspond to the abnormal interaction characteristics of the device. The corresponding communication diagnostic features are obtained by extracting features from the communication diagnostic results. Based on the communication diagnostic features, a data transmission diagnostic factor and corresponding diagnostic parameter values ​​are generated, wherein the data transmission diagnostic factor corresponds to the communication diagnostic feature.

4. The underlying method for intelligent control of rail transit based on ARM architecture according to claim 3, characterized in that, The real-time operation factor of the gate is obtained based on the real-time sensor data, specifically including the following steps: The real-time operational characteristics of the subway turnstiles are obtained by extracting operational features from real-time sensor data. The real-time operation factor of the subway turnstile is generated based on the real-time operation characteristics, and the real-time parameter value corresponding to the real-time operation factor is generated. The real-time operation factor corresponds to the real-time operation characteristics.

5. The underlying method for intelligent control of rail transit based on ARM architecture according to claim 4, characterized in that, The intelligent control execution instructions include the dynamic control parameters of the subway turnstile and the adjustment instructions corresponding to the IoT communication status.

6. A rail transit intelligent control underlying system based on ARM architecture, applied to the rail transit intelligent control underlying method based on ARM architecture as described in any one of claims 1 to 5, characterized in that, include: Acquisition Module: Acquires the equipment operating parameters and ARM architecture control node information of the rail transit subway turnstile, and constructs the underlying framework for intelligent control of the turnstile based on the equipment operating parameters and ARM architecture control node information; the underlying framework for intelligent control of the turnstile includes a parameter benchmark area, an IoT interaction area, and a control execution area; wherein, the parameter benchmark area includes the turnstile's standard operating parameters and parameter weight coefficients; the IoT interaction area includes equipment interaction characteristic factors and data transmission diagnostic factors; Processing module: Obtains real-time sensor data of the subway turnstiles through ARM architecture intelligent transportation IoT services, and obtains the real-time operation factor of the turnstiles based on the real-time sensor data; The first analysis module processes and analyzes the real-time operating factors of the turnstile, the underlying framework of the turnstile's intelligent control, parameter weight coefficients, and equipment operating parameters to obtain dynamic control parameters. This includes the following steps: After inputting the real-time operation factor of the gate into the underlying framework of the gate intelligent control, the real-time operation factor of the gate is matched with the standard operation parameters of the gate in the parameter reference area to obtain the standard operation parameters of the gate that are consistent with the real-time operation factor of the gate in the parameter reference area, and the standard operation parameters of the gate are recorded as the target standard parameters. Compare the real-time parameter values ​​of the gate's real-time operation factors with the standard parameter range of the target standard parameters; If the real-time parameter value of the gate's real-time operation factor is not within the standard parameter range of the target standard parameter, then the gate's real-time operation factor is recorded as an abnormal operation factor, and the interval reference parameter of the standard parameter range is obtained. The abnormal parameter values ​​of the abnormal operation factor are obtained based on the interval benchmark parameters and real-time parameter values. The comprehensive abnormality coefficient of the subway gate is obtained by weighting the abnormal parameter values ​​of the abnormal operation factor with the parameter weight coefficient of the abnormal operation factor. The weight of the abnormal impact of the subway turnstiles is obtained based on the comprehensive abnormality coefficient and equipment operating parameters; The dynamic control parameters of the subway turnstiles are obtained based on the weight of abnormal impacts and equipment operating parameters. The second analysis module processes and analyzes abnormal operation factors and equipment interaction characteristic factors to obtain the IoT communication status of the subway turnstiles. Specifically, it includes the following steps: Transmit abnormal operation factors to the IoT interaction area; The abnormal operation factors are matched and analyzed with the device interaction characteristic factors in the IoT interaction area to obtain the target interaction factors and target transmission diagnostic factors corresponding to the subway turnstiles. The specific steps include: The abnormal operation factor is matched with the device interaction feature factor in the IoT interaction area to obtain the device interaction feature factor in the IoT interaction area that is consistent with the abnormal operation factor, and the device interaction feature factor is recorded as the target interaction factor. Connect the device interaction feature factor with the corresponding data transmission diagnostic factor to obtain the interaction diagnostic trajectory between the device interaction feature factor and the data transmission diagnostic factor. Based on the interactive diagnostic trajectory, the target transmission diagnostic factor corresponding to the target interaction factor is obtained; Obtain the communication diagnostic features corresponding to the target transmission diagnostic factors, and obtain the IoT communication status of the subway gate based on the communication diagnostic features corresponding to the target transmission diagnostic factors; Control module: Transmits dynamic control parameters and IoT communication status to the control execution area to obtain intelligent control execution instructions for the subway turnstile.

7. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the underlying method for intelligent control of rail transit based on the ARM architecture as described in any one of claims 1 to 5.

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