A method and system for monitoring electromechanical equipment in a high-speed railway station

Abstract

The present invention belongs to the field of intelligent operation and maintenance management technology, and provides a method and system for monitoring electromechanical equipment in a high-speed railway station, which is used to solve the problem of low management level of electromechanical equipment in high-speed railway stations in the prior art. Among them, the real-time operation status data of each electromechanical equipment in the high-speed railway station is received; compared with the preset normal operation status data, the electromechanical equipment in abnormal state is automatically identified, and early warning information is generated; combined with the three-dimensional digital model of the high-speed railway station, the early warning information is mapped to the corresponding three-dimensional model position to form a visual monitoring interface; when the electromechanical equipment in abnormal state is detected, the preset linkage rules are triggered, and the operation mode of the associated equipment is automatically adjusted to reduce the impact of the fault; the occurrence time, processing process and results of each abnormal event are recorded to form a historical case database for subsequent fault analysis and prevention strategy formulation. The technical solution provided by the present invention improves the management level of electromechanical equipment in high-speed railway stations.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of intelligent operation and maintenance management technology, and in particular, to a method and system for monitoring electromechanical equipment in a high-speed railway station. Background Art

[0002] With the rapid development of my country's high-speed railway network, high-speed railway stations, as important transportation hubs, the stable operation of their internal electromechanical equipment is directly related to the safety of passengers and the quality of service.

[0003] At present, the operation and maintenance management of electromechanical equipment in high-speed railway stations is generally based on manual inspection and regular maintenance, supplemented by some simple automatic monitoring tools. For example, by installing sensors to collect basic operating data of equipment.

[0004] Although the existing operation and maintenance management model has met basic needs to a certain extent, there are still some significant deficiencies: relying on manual inspections makes it difficult to achieve all-weather monitoring without blind spots, and it is easy to miss early signs of failure, resulting in slow response. Traditional alarm systems can often only provide vague fault prompts, lacking specific fault locations and detailed fault information, which makes it difficult for maintenance personnel to find faults. Summary of the invention

[0005] The embodiment of the present invention provides a method and system for monitoring electromechanical equipment in a high-speed railway station, which are used to solve the problem of low management level of electromechanical equipment in a high-speed railway station in the prior art.

[0006] In a first aspect, an embodiment of the present invention provides a method for monitoring electromechanical equipment in a high-speed railway station, comprising:

[0007] Receive real-time operating status data from each electromechanical device in the high-speed railway station, wherein the real-time operating status data at least includes operating parameters, fault codes and environmental parameters of the device;

[0008] Automatically identify abnormal electromechanical equipment based on the comparison of the real-time operating status data with the preset normal operating status data, and generate warning information, wherein the warning information includes the location of the abnormal equipment, the abnormality type and recommended treatment measures;

[0009] Combined with the three-dimensional digital model of the high-speed railway station, the warning information is mapped to the corresponding three-dimensional model position to form a visual monitoring interface, so that the monitoring personnel can intuitively locate the abnormal state of the electromechanical equipment and the working status of the electromechanical equipment;

[0010] When abnormal state of electromechanical equipment is detected, the preset linkage rules are triggered to automatically adjust the operation mode of the associated equipment to reduce the impact of the fault;

[0011] Record the occurrence time, handling process and results of each abnormal event to form a historical case database for subsequent fault analysis and prevention strategy formulation.

[0012] In a second aspect, an embodiment of the present invention provides a high-speed railway station electromechanical equipment monitoring system, comprising:

[0013] A receiving module, used to receive real-time operating status data from various electromechanical equipment in the high-speed railway station, wherein the real-time operating status data at least includes working parameters, fault codes and environmental parameters of the equipment;

[0014] A generating module, used to automatically identify electromechanical equipment in abnormal state according to the comparison between the real-time operating state data and the preset normal operating state data, and generate warning information, wherein the warning information includes the location of the abnormal equipment, the abnormal type and the recommended treatment measures;

[0015] A module is formed, which is used to map the warning information to the corresponding three-dimensional model position in combination with the three-dimensional digital model of the high-speed railway station building, and form a visual monitoring interface, so that the monitoring personnel can intuitively locate the electromechanical equipment in abnormal state and the working state of the electromechanical equipment;

[0016] The adjustment module is used to trigger the preset linkage rules when abnormal state of electromechanical equipment is detected, and automatically adjust the operation mode of the associated equipment to reduce the impact of the fault;

[0017] The analysis and formulation module is used to record the occurrence time, handling process and results of each abnormal event, forming a historical case database for subsequent fault analysis and prevention strategy formulation.

[0018] In a third aspect, an embodiment of the present invention provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a high-speed railway station electromechanical equipment monitoring method as described in any one of the first aspects.

[0019] In a fourth aspect, an embodiment of the present invention provides a computer storage medium storing a computer program, which, when executed by a computer, implements a method for monitoring electromechanical equipment in a high-speed railway station as described in any one of the first aspects.

[0020] Beneficial effects of the present invention:

[0021] (1) In the embodiment of the present invention, real-time operating status data of each electromechanical device in a high-speed railway station is received; based on the comparison between the real-time operating status data and the preset normal operating status data, electromechanical devices in abnormal status are automatically identified, and warning information is generated; in combination with the three-dimensional digital model of the high-speed railway station, the warning information is mapped to the corresponding three-dimensional model position to form a visual monitoring interface, so that monitoring personnel can intuitively locate the electromechanical devices in abnormal status and the working status of the electromechanical devices.

[0022] (2) In the embodiment of the present invention, when an abnormal state of an electromechanical device is detected, a preset linkage rule is triggered to automatically adjust the operation mode of the associated device to reduce the impact of the fault.

[0023] (3) In the embodiment of the present invention, the occurrence time, processing process and results of each abnormal event are recorded to form a historical case database for subsequent fault analysis and prevention strategy formulation. The technical solution provided by the present invention improves the management level of electromechanical equipment in high-speed railway stations.

[0024] These and other aspects of the present invention will become more apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A flow chart of a method for monitoring electromechanical equipment in a high-speed railway station provided by an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of the structure of a high-speed railway station electromechanical equipment monitoring system provided by an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of the structure of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0030] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0032] Figure 1 A flowchart of a method for monitoring electromechanical equipment in a high-speed railway station is provided for an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0033] 101. Receive real-time operation status data from each electromechanical device in a high-speed railway station, wherein the real-time operation status data at least includes working parameters, fault codes and environmental parameters of the device;

[0034] In this step, the high-speed railway station building refers to the station building in the high-speed railway system, including waiting rooms, ticket offices, and entrance and exit facilities. Electromechanical equipment refers to equipment that combines mechanical and electrical functions, such as air-conditioning systems, elevators, lighting systems, and security monitoring systems. Real-time operating status data refers to the instant information generated by the equipment during operation, including operating parameters, fault codes, and environmental parameters. Operating parameters are specific indicators that describe the operating status of the equipment, such as temperature, humidity, current, and voltage. The fault code is a set of numbers or letters automatically generated by the system when a device fails, which is used to identify a specific fault type. Environmental parameters are external conditions that affect the operation of the equipment, such as temperature, humidity, and light intensity.

[0035] First, the real-time operating status data of electromechanical equipment is collected through various sensors and monitoring devices installed in the high-speed railway station.

[0036] Secondly, the collected data is transmitted to the central processing system using wired or wireless network technology.

[0037] In the example of the present invention, it is assumed that an intelligent monitoring system is deployed in a high-speed railway station, which can automatically collect real-time operating status data of all electromechanical equipment in the station, including the operating status of elevators, temperature adjustment of air-conditioning systems, and power consumption of lighting systems.

[0038] For example, a temperature and humidity sensor is installed in the air conditioning system, which can collect indoor temperature and humidity data once a minute and send it to the central processing system via a wireless network.

[0039] The use of 4G / 5G communication technology ensures reliable data transmission to the central processing center even when the network is unstable.

[0040] Through the implementation of the above solution, the operation status of electromechanical equipment in high-speed railway stations can be fully monitored, improving the travel experience of passengers. At the same time, the data-driven maintenance management method can effectively reduce operating costs, extend the service life of equipment, and provide a solid guarantee for the safe and stable operation of high-speed railway stations.

[0041] 102. Automatically identify electromechanical equipment in abnormal state according to the comparison between the real-time operation state data and the preset normal operation state data, and generate warning information, wherein the warning information includes the location of the abnormal equipment, the abnormal type and the recommended treatment measures;

[0042] In this step, after receiving the real-time data from the equipment, the central monitoring system compares and analyzes it with the preset normal operating status data. Once the deviation exceeds the set threshold, the equipment is considered to be in an abnormal state and an early warning message is generated. The early warning information includes the location of the abnormal equipment, the type of abnormality, and the recommended treatment measures.

[0043] In the embodiment of the present invention, the central server receives the temperature sensor data of the air conditioner unit and finds that the outlet temperature of a certain air conditioner is continuously higher than the normal range and lasts for more than 10 minutes. The system determines that the air conditioner may have problems such as refrigerant leakage, and then generates an early warning message: "Air conditioner A01 position: The outlet temperature is too high. It is recommended to check the refrigeration system immediately."

[0044] In the existing high-speed railway station electromechanical equipment monitoring system, although a large amount of real-time operating status data can be collected, due to the lack of an effective abnormality identification mechanism, even if the equipment fails or performance deteriorates, it is impossible to issue a timely and accurate warning. In this case, it may not only affect the service quality and passenger experience of the high-speed railway station, but also cause equipment damage or even safety accidents.

[0045] Therefore, the embodiment of the present invention proposes a more intelligent abnormal state identification and early warning solution, aiming to solve the problems caused by the lack of an effective abnormal state identification mechanism in the prior art and improve the reliability and security of the system.

[0046] The options are as follows:

[0047] Optionally, the method of automatically identifying electromechanical equipment in an abnormal state and generating warning information based on comparing the real-time operating status data with preset normal operating status data in step 102 includes: comparing the real-time operating status data with the preset normal operating status data, and when the real-time operating status data exceeds the preset normal operating status data, identifying the electromechanical equipment corresponding to the real-time operating status data as electromechanical equipment in an abnormal state, wherein the normal operating status data is a standard value provided by the equipment manufacturer or a standard value set based on long-term operating experience; determining the abnormality type corresponding to the electromechanical equipment in the abnormal state, wherein the abnormality type includes at least overheating, voltage fluctuation, and mechanical wear; generating corresponding warning information based on the determined abnormality type according to a preset knowledge base, wherein the warning information includes the location information, abnormality type, and recommended treatment measures of the electromechanical equipment in the abnormal state.

[0048] In this step, real-time operating status data refers to information such as operating parameters, fault codes, and environmental parameters collected in real time from electromechanical equipment. The preset normal operating status data is based on the standard values ​​provided by the equipment manufacturer or the standard values ​​set based on the summary of long-term operating experience, and is used to determine whether the equipment is in normal working condition. Abnormal status means that the actual operating status of the electromechanical equipment deviates from the preset normal operating status, and there may be a fault or performance degradation. The abnormal type is classified according to the specific manifestation of the abnormality, such as overheating, voltage fluctuations, mechanical wear, etc. The early warning information is the information automatically generated by the system when an abnormal state is detected, including the location of the abnormal equipment, the abnormality type, and recommended treatment measures.

[0049] First, the collected real-time operating status data is compared with the preset normal operating status data. For example, if the real-time temperature reading of an air conditioning system is continuously higher than the preset maximum normal temperature value, it is considered that the equipment may have an overheating problem.

[0050] Secondly, when the real-time operating status data exceeds the preset normal range, the system will identify the corresponding electromechanical equipment as an abnormal state electromechanical equipment. For example, if the current reading of an elevator exceeds the normal range, the system will mark it as abnormal.

[0051] Next, the system determines the type of anomaly based on how the anomaly manifests itself. For example, if the temperature sensor readings for an air conditioning system are abnormally high, the system will classify its anomaly type as "overheating." If the current in an elevator fluctuates greatly, the anomaly type may be "electrical problem."

[0052] Finally, based on the determined abnormality type, the system searches for the corresponding treatment measures from the preset knowledge base and generates an early warning message containing the location of the abnormal device, the abnormality type, and the recommended treatment measures. For example, if an air conditioning system at station A overheats, the early warning message may be: "Station A air conditioning system overheats, please check the cooling system and consider reducing the load."

[0053] In the embodiment of the present invention, assume that in a high-speed railway station, there is an intelligent monitoring system responsible for monitoring the operating status of all electromechanical equipment in the station. One day, the system detects that the current reading of an escalator located at platform B is abnormally high, exceeding the preset normal range. The system immediately executes the following steps:

[0054] The system compared the escalator's current reading with the preset normal current range and found that the current reading was 50A, while the normal range was 30A to 40A.

[0055] Because the current reading was outside the normal range, the system identified the escalator as an electromechanical device in an abnormal state.

[0056] The system analyzes the cause of the abnormally high current reading and, combined with other sensor data, determines the anomaly type to be an "electrical problem."

[0057] The system searches the preset knowledge base for recommended measures to deal with “electrical problems” and generates an early warning message: “The current of the escalator at platform B is too high. There may be an electrical problem. Please check the power line and motor, and consider powering off for inspection.”

[0058] Through the above solution, the system can automatically and timely identify abnormal conditions of electromechanical equipment and generate detailed early warning information to help maintenance personnel quickly locate problems and take corresponding measures. This not only improves the maintenance efficiency of equipment and reduces the risk of failure, but also improves the overall operational safety and service quality of high-speed railway stations.

[0059] In the existing high-speed railway station electromechanical equipment monitoring system, although a large amount of real-time operating status data can be collected, most systems rely on simple threshold judgments to identify abnormal status of equipment. This method has the following problems: different equipment may have different normal operating parameter ranges in different operating environments, and a single threshold is difficult to adapt to all situations. The abnormal status of equipment is often the result of the combined effect of multiple factors, and the threshold judgment of a single parameter cannot fully reflect the true status of the equipment. Most existing systems are post-alarm, lack the ability to predict the future status of the equipment, and cannot take preventive measures in advance.

[0060] In order to solve the above problems, an embodiment of the present invention proposes an abnormal state identification method based on a probability model. By comprehensively considering the working parameters, fault codes, environmental parameters and fault frequencies of the equipment, the probability of the equipment being in an abnormal state is calculated, thereby achieving more accurate and comprehensive abnormal state identification and early warning.

[0061] The options are as follows:

[0062] Optionally, the electromechanical device for automatically identifying abnormal conditions in step 102 further includes:

[0063] Use the following formula to calculate the time of electrical equipment The probability of being in abnormal state at all times:

[0064] ;

[0065] in, Indicates at time The probability that the device is in an abnormal state at any moment; Represents the time of the device Working parameters, fault codes and environmental parameters; It is a standard value or range within the preset normal operating status data; They represent the standard deviations of operating parameters, fault codes and environmental parameters respectively; It is a weight coefficient set according to the device characteristics and operating environment; Indicates the time at which the device The failure frequency.

[0066] In this step, real-time operation status data refers to information such as working parameters, fault codes and environmental parameters collected from electromechanical equipment in real time. The preset normal operation status data is a standard value set based on the standard value provided by the equipment manufacturer or the long-term operation experience summary, which is used to determine whether the equipment is in normal working condition. Abnormal state means that the actual operation state of the electromechanical equipment deviates from the preset normal operation state, and there may be a fault or performance degradation. The abnormal type is classified according to the specific manifestation of the abnormality, such as overheating, voltage fluctuation, and mechanical wear. Early warning information is information automatically generated by the system when an abnormal state is detected, including the location of the abnormal equipment, the abnormal type and recommended treatment measures. The probability model is a mathematical model used to calculate the probability of an event occurring. The working parameters are the various indicator values ​​of the equipment when it is operating normally, such as temperature, humidity, current, and voltage. The fault code is a specific code generated when the equipment is abnormal or fails, which is used to help technicians quickly locate the problem. Environmental parameters are the state of the environment in which the equipment is located, such as temperature, humidity, and air quality. The fault frequency is the number of times the equipment fails within a certain period of time.

[0067] First, collect working parameters from electromechanical equipment in real time , Fault code and environmental parameters .

[0068] Secondly, the real-time collected data is compared with the preset normal operation status data Perform standardization and calculate deviation.

[0069] Next, set the weight coefficient according to the device characteristics and operating environment. .

[0070] Further, the following formula is used to calculate the device at time The probability of being in an abnormal state at all times :

[0071] ;

[0072] in, Indicates at time The probability that the device is in an abnormal state at any moment; Represents the time of the device Working parameters, fault codes and environmental parameters; It is a standard value or range within the preset normal operating status data; They represent the standard deviations of operating parameters, fault codes and environmental parameters respectively; It is a weight coefficient set according to the device characteristics and operating environment; Indicates the time at which the device The failure frequency.

[0073] Finally, when When the preset threshold is exceeded, the device is identified as abnormal. Based on the determined abnormal type, an early warning message is generated containing the abnormal device location, abnormal type, and recommended treatment measures.

[0074] In the embodiment of the present invention, it is assumed that in a high-speed railway station, there is an intelligent monitoring system responsible for monitoring the operating status of all electromechanical equipment in the station. One day, the system detected the following real-time data of an air conditioning system located at platform A:

[0075] Working parameters: Temperature ;

[0076] Fault code: No fault code ;

[0077] Environmental parameters: humidity ;

[0078] Failure frequency: Failures occurred 2 times in the past week ;

[0079] The preset normal operating status data and parameters are as follows:

[0080] Normal temperature range: , standard deviation ;

[0081] Normal fault code: No fault code , standard deviation ;

[0082] Normal humidity range: , standard deviation ;

[0083] Weight coefficient: ;

[0084] Data Standardization:

[0085] ;

[0086] ;

[0087] ;

[0088] Probability calculation:

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] Assume that the preset abnormal probability threshold is 0.9, due to When the threshold is exceeded, the system identifies the air conditioning system as being in an abnormal state.

[0094] The system generates an alert message: "The temperature of the air conditioning system at platform A is too high. It may be overheated. Please check the cooling system and consider reducing the load."

[0095] Through the above scheme, the system can more accurately identify the abnormal state of electromechanical equipment and generate detailed early warning information. This method not only considers the threshold of a single parameter, but also integrates the influence of multiple factors, improving the accuracy and reliability of abnormal identification. In addition, by calculating the probability of abnormality, the system has a certain predictive ability, and can take preventive measures in advance before the equipment completely fails, reduce the risk of failure, and improve the operational safety and service quality of high-speed railway stations.

[0096] 103. In combination with the three-dimensional digital model of the high-speed railway station, the warning information is mapped to the corresponding three-dimensional model position to form a visual monitoring interface, so that the monitoring personnel can intuitively locate the abnormal state of the electromechanical equipment and the working state of the electromechanical equipment;

[0097] In this step, BIM technology is used to build a three-dimensional digital model of the high-speed railway station building, and the warning information is matched with the specific location in the model and displayed through a graphical interface, so that monitoring personnel can intuitively see the location of abnormal equipment and its working status.

[0098] In the embodiment of the present invention, the locations of all electromechanical equipment are marked in the BIM model of the high-speed railway station. When the central server generates warning information, the system automatically marks the information on the corresponding equipment icon. For example, the location icon of air conditioner A01 turns red, and a prompt box pops up to display specific abnormal information and recommended measures.

[0099] In the existing high-speed railway station electromechanical equipment monitoring system, although detailed warning information can be generated, this information is usually presented in the form of text or tables, lacking in intuitiveness and spatial sense. This causes monitoring personnel to spend extra time and energy in locating abnormal equipment, especially in large stations, where there are a large number of equipment and complex distribution, which can easily lead to inaccurate positioning or omissions.

[0100] Therefore, the embodiment of the present invention proposes a visual monitoring solution combined with a three-dimensional digital model, aiming to solve the problems in the prior art caused by the non-intuitive information presentation method and improve the monitoring efficiency and accuracy.

[0101] The options are as follows:

[0102] Optionally, the three-dimensional digital model of the high-speed railway station building in step 103 is combined to map the warning information to the corresponding three-dimensional model position to form a visual monitoring interface, including: constructing a three-dimensional digital model of the high-speed railway station building, the three-dimensional digital model is used to reflect the actual internal structure of the station building and the layout of electromechanical equipment, ensuring that the position of each electromechanical equipment in the three-dimensional space corresponds to the actual installation position; using a three-dimensional coordinate conversion algorithm to accurately map the position information of the electromechanical equipment in the abnormal state in the warning information to the corresponding position in the three-dimensional digital model; highlighting the electromechanical equipment in the abnormal state on the interface of the three-dimensional digital model by color coding and icon marking, and adding pop-up windows or labels around the electromechanical equipment in the abnormal state to display the corresponding abnormal type and suggested treatment measures.

[0103] In this step, the three-dimensional digital model is a digitized three-dimensional space model used to reflect the actual internal structure of the station building and the layout of the electromechanical equipment, ensuring that the position of each electromechanical equipment in the three-dimensional space corresponds to the actual installation position. The warning information is the information automatically generated by the system when an abnormal state is detected, which contains the location of the abnormal equipment, the type of abnormality and the recommended treatment measures. The three-dimensional coordinate conversion algorithm is a mathematical algorithm used to convert the location information of the equipment from one coordinate system to another coordinate system to ensure that the location information in the warning information can be accurately mapped to the three-dimensional digital model. Color coding is to represent equipment of different states or types by different colors for quick identification. Icon annotation is to use specific icons to identify equipment in the three-dimensional model to enhance the visual effect. Pop-up windows or labels are information display windows attached around the equipment to display detailed abnormality types and recommended treatment measures.

[0104] First, create a 3D digital model of the high-speed railway station to ensure that the position of each electromechanical device in 3D space corresponds to the actual installation location. The model should reflect the internal structure and equipment layout of the station in detail.

[0105] Secondly, the 3D coordinate conversion algorithm is used to accurately map the abnormal equipment location information in the warning information to the corresponding location in the 3D digital model. For example, if the warning information mentions that a certain air conditioning system is located at station A, the system needs to convert this location information into specific coordinates in the 3D model.

[0106] Finally, color coding and icon annotation are used to highlight abnormal electromechanical equipment on the interface of the 3D digital model. At the same time, pop-up windows or labels are attached around the abnormal equipment to display the corresponding abnormality type and recommended treatment measures.

[0107] In the embodiment of the present invention, it is assumed that in a high-speed railway station, there is an intelligent monitoring system responsible for monitoring the operating status of all electromechanical equipment in the station. One day, the system detected that an air conditioning system located at platform A was overheated and generated the following warning information:

[0108] Location: Platform A

[0109] Abnormal type: Overheating

[0110] Recommended action: Check cooling system and consider reducing load

[0111] The specific implementation steps are as follows:

[0112] Use 3D modeling software to create a 3D digital model of the high-speed railway station building, ensuring that the model reflects the internal structure and equipment layout of the station building in detail.

[0113] In the model, the location of each electromechanical device corresponds to the actual installation location. For example, the location of the air conditioning system of platform A in the model is .

[0114] Using the three-dimensional coordinate conversion algorithm, the location information "Platform A" in the warning information is converted into specific coordinates in the three-dimensional model. .

[0115] Assume that the location information in the warning information is the two-dimensional coordinates on the station floor plan It needs to be converted into three-dimensional coordinates. The conversion formula is as follows:

[0116] ;

[0117] ;

[0118] ;

[0119] in, is the rotation angle between the station floor plan and the 3D model. is the height of the device.

[0120] On the interface of the 3D digital model, the air conditioning system of Platform A is marked with red color coding and air conditioning icons to highlight its abnormal status.

[0121] Attach a pop-up window or label around the air conditioning system to display the detailed abnormality type and recommended treatment measures. For example, the pop-up window content is: "Station A air conditioning system overheated, please check the cooling system and consider reducing the load."

[0122] Through the above solution, monitoring personnel can intuitively see the location, status and treatment suggestions of abnormal equipment on the interface of the 3D digital model, without having to look for relevant information in complex floor plans or text information. This method not only improves monitoring efficiency and reduces the possibility of misoperation, but also enhances the visualization effect of the system, enabling monitoring personnel to make decisions faster and improve the operational safety and service quality of high-speed railway stations.

[0123] 104. When an abnormal state of electromechanical equipment is detected, the preset linkage rules are triggered to automatically adjust the operation mode of the associated equipment to reduce the impact of the fault;

[0124] In this step, the system has built-in multiple preset linkage rules. When a specific type of abnormality is detected, the corresponding emergency procedure will be automatically started to adjust the operating mode of the associated equipment, such as switching to backup power supply, shutting down non-critical loads in the affected area, etc., to minimize the impact of the fault on the overall system.

[0125] In the embodiment of the present invention, if a short circuit is detected in the lighting system of a certain area, the system will automatically cut off the main power supply of the area, activate the emergency lighting system, and notify the maintenance personnel to go and handle it. In addition, the system can also adjust the lighting brightness of adjacent areas to ensure the safety of passengers.

[0126] In the existing high-speed railway station electromechanical equipment monitoring system, when equipment abnormalities are detected, only early warning information can be generated, but there is a lack of automated response measures. In this case, even if the monitoring personnel can detect the abnormality in time, they still need to manually adjust the operation mode of the relevant equipment to reduce the impact of the fault. This manual operation is not only time-consuming and laborious, but may also delay processing due to human negligence, resulting in a larger impact of the fault.

[0127] Therefore, the embodiment of the present invention proposes an automatic adjustment solution based on preset linkage rules, aiming to solve the problems caused by the lack of automatic response measures in the prior art and improve the response speed and fault handling efficiency of the system.

[0128] The options are as follows:

[0129] Optionally, when an electromechanical device in an abnormal state is detected in step 104, a preset linkage rule is triggered, and the operating mode of the associated device is automatically adjusted to reduce the impact of the fault, including: defining the linkage rule corresponding to each abnormal type according to the characteristics and operating environment of the electromechanical equipment; when an electromechanical device in an abnormal state is detected, obtaining the linkage rule corresponding to the abnormal type of the electromechanical device in the abnormal state; based on the linkage rule corresponding to the abnormal type, automatically adjusting the operating mode of the associated device to reduce the impact of the fault.

[0130] In this step, abnormal state means that the actual operating state of the electromechanical equipment deviates from the preset normal operating state, and there may be a fault or performance degradation. The linkage rules are pre-defined rules that guide the system on how to automatically adjust the operating mode of the associated equipment when a specific abnormality type is detected to mitigate the impact of the fault. Associated equipment is other equipment related to the abnormal equipment, and the operating mode of these equipment needs to be adjusted according to the linkage rules. The operating mode is the working state or configuration of the equipment, such as on, off, and adjust power.

[0131] First, define the linkage rules corresponding to each abnormality type based on the characteristics and operating environment of the electromechanical equipment. These rules should describe in detail how the associated equipment should adjust its operating mode when a specific abnormality type is detected.

[0132] Secondly, when an abnormal state of electromechanical equipment is detected, the system obtains the corresponding linkage rules according to the abnormality type.

[0133] Finally, based on the acquired linkage rules, the system automatically adjusts the operation mode of the associated devices to mitigate the impact of the fault.

[0134] In the embodiment of the present invention, it is assumed that in a high-speed railway station, there is an intelligent monitoring system responsible for monitoring the operating status of all electromechanical equipment in the station. One day, the system detected that an air conditioning system located at platform A was overheated and generated the following warning information:

[0135] Location: Platform A

[0136] Abnormal type: Overheating

[0137] Recommended action: Check cooling system and consider reducing load

[0138] The specific implementation steps are as follows:

[0139] For the abnormal type of overheating of the air conditioning system, the following linkage rules are defined:

[0140] Reduce the power of your air conditioning system by 20%.

[0141] If platform A has a backup air conditioning system, start the backup air conditioning system to maintain the temperature of the platform.

[0142] Increase the air volume of the platform A ventilation system to help dissipate heat.

[0143] When the system detects that the air conditioning system of station A is overheated, the linkage rules defined above are obtained according to the abnormality type "overheat".

[0144] The system automatically reduces the air conditioning system power of Platform A from 100% to 80%.

[0145] The system detects that platform A has a backup air conditioning system and automatically starts the backup air conditioning system.

[0146] The system automatically increases the ventilation system air volume of Platform A from 5,000 cubic meters per hour to 7,000 cubic meters per hour.

[0147] Through the above scheme, when an equipment abnormality is detected, the system can automatically adjust the operating mode of the associated equipment to reduce the impact of the fault. This method not only improves the response speed of the system and reduces the fault handling time, but also reduces the risk of human error. Specifically: the system can take measures immediately after detecting an abnormality to avoid the expansion of the impact of the fault. Automated adjustment reduces the manual operation of monitoring personnel and improves work efficiency. By timely adjusting the operating mode of the associated equipment, the environment and equipment safety in the station building are ensured, and the comfort and satisfaction of passengers are improved.

[0148] In short, this solution achieves automated response to abnormal conditions through preset linkage rules, improving the intelligence level and fault handling capability of the electromechanical equipment monitoring system in high-speed railway stations.

[0149] 105. Record the occurrence time, handling process and results of each abnormal event to form a historical case database for subsequent fault analysis and prevention strategy formulation.

[0150] In this step, the system will record the relevant information of each abnormal event in detail, including the time of occurrence, the handling process, the measures taken and the final result, forming a historical case database. This data can be used for subsequent fault analysis, trend prediction and the formulation of preventive maintenance strategies.

[0151] In the embodiment of the present invention, after each abnormal event is handled, the system automatically stores the relevant information into the database, for example, "2024-10-16 14:30, air conditioner A01 had an overtemperature failure, the maintenance personnel arrived at the scene at 15:15, replaced the refrigerant, and resumed normal operation at 16:00." These data can be exported as a report for management review, and can also be used as a data source for training machine learning models to improve the accuracy of fault prediction.

[0152] In the existing high-speed railway station electromechanical equipment monitoring system, although it can generate detailed warning information and take corresponding treatment measures, it lacks systematic recording and analysis of abnormal events. In this case, the handling of each abnormal event is often isolated, and no effective historical data accumulation is formed, resulting in a lack of scientific basis for subsequent fault analysis and prevention strategy formulation.

[0153] Therefore, the embodiment of the present invention proposes a solution for recording and analyzing abnormal events, aiming to solve the problems caused by the lack of systematic recording and analysis in the prior art, and to improve the scientific nature of fault handling and the effectiveness of prevention.

[0154] The options are as follows:

[0155] Optionally, the recording of the occurrence time, processing process and result of each abnormal event in step 105 forms a historical case database for subsequent fault analysis and prevention strategy formulation, including: when an abnormal state of electromechanical equipment is detected, a corresponding abnormal event is created and a historical case database is formed, wherein the abnormal event includes basic information, occurrence time, processing process and result of the electromechanical equipment in the abnormal state; abnormal events are regularly extracted from the historical case database, and statistical methods and machine learning techniques are used to analyze the distribution patterns, common causes and processing efficiency of the abnormal events, so as to extract effective preventive measures and improvement plans, and form a prevention strategy document to guide subsequent fault analysis and prevention strategy formulation.

[0156] In this step, an abnormal event refers to a specific event in which an abnormal state occurs in an electromechanical device, including the basic information of the electromechanical device in the abnormal state, the time of occurrence, the processing process and the results. The historical case database is a database used to store detailed information on each abnormal event, which is convenient for subsequent fault analysis and prevention strategy formulation. The statistical method is to analyze the distribution patterns and common causes of abnormal events through statistical means to find out potential patterns and trends. Machine learning technology uses machine learning algorithms to conduct in-depth analysis of historical case data to extract effective preventive measures and improvement plans. The prevention strategy document is a document formed based on the analysis results, which is used to guide subsequent fault analysis and prevention strategy formulation.

[0157] First, when an abnormal state of electromechanical equipment is detected, a corresponding abnormal event is created, and its basic information, occurrence time, processing process and results are recorded.

[0158] Secondly, the detailed information of each abnormal event is stored in the historical case database to ensure the integrity and accuracy of the data.

[0159] Next, abnormal event data is regularly extracted from the historical case database and analyzed using statistical methods and machine learning techniques.

[0160] Furthermore, based on the analysis results, effective preventive measures and improvement plans are extracted to form a prevention strategy document.

[0161] Finally, the prevention strategy document is applied to subsequent fault analysis and prevention strategy formulation to improve the scientific nature of fault handling and the effectiveness of prevention.

[0162] In the embodiment of the present invention, it is assumed that in a high-speed railway station, there is an intelligent monitoring system responsible for monitoring the operating status of all electromechanical equipment in the station. The system records multiple abnormal events, and the specific implementation steps are as follows:

[0163] When the system detects that the air conditioning system at station A is overheated, an exception event is created:

[0164] Basic information: Equipment name: Air conditioning system; Equipment location: Platform A; Equipment number: AC001.

[0165] Time of occurrence: October 17, 2024 14:00.

[0166] Processing process:

[0167] 14:05 The system automatically reduced the air conditioning system power from 100% to 80%.

[0168] 14:10 Start the backup air conditioning system.

[0169] 14:15 Increase the air volume of the platform A ventilation system.

[0170] At 14:30, maintenance personnel arrived at the scene and checked the cooling system.

[0171] 15:00 The cooling system repair was completed and the air conditioning system returned to normal.

[0172] Processing result: The air-conditioning system resumed normal operation and the temperature of platform A returned to normal.

[0173] The detailed information of the above abnormal events is stored in the historical case database to ensure the integrity and accuracy of the data.

[0174] All abnormal event data are extracted from the historical case database every month and analyzed using statistical methods and machine learning techniques.

[0175] Calculate the total number of abnormal events each month, the proportion of each type of abnormal event, the distribution of processing time, etc.

[0176] Use clustering algorithms to classify abnormal events and identify common abnormal types and processing patterns; use regression analysis to predict the probability of occurrence and processing time of abnormal events.

[0177] Based on the analysis results, the following preventive measures and improvement plans are extracted:

[0178] Check and maintain the cooling system of the air conditioning system every month to prevent overheating.

[0179] During high temperature seasons, increase the startup frequency of the backup air-conditioning system appropriately to ensure stable platform temperature.

[0180] Provide regular training to maintenance personnel to improve their ability and efficiency in handling abnormal events.

[0181] The above-mentioned preventive measures and improvement plans are compiled into a preventive strategy document and applied to subsequent fault analysis and prevention strategy formulation to ensure the stable operation of the station's electromechanical equipment.

[0182] Through the above scheme, the system can systematically record and analyze each abnormal event to form a rich historical case database. These data not only provide a scientific basis for subsequent fault analysis, but also help to formulate effective preventive measures and improvement plans, improving the scientific nature of fault handling and the effectiveness of prevention. Specifically: systematically record each abnormal event and accumulate a large amount of valuable historical data. Use statistical methods and machine learning technology to conduct in-depth analysis of historical data to find potential patterns and trends. Based on the analysis results, extract effective preventive measures and improvement plans to form a prevention strategy document. Through the application of preventive measures and improvement plans, the efficiency of fault handling and the stability of equipment are improved, and the risk of faults is reduced.

[0183] In short, by recording and analyzing abnormal events, this solution achieves scientific management and prevention of faults, and improves the intelligence level and fault handling capability of the electromechanical equipment monitoring system in high-speed railway stations.

[0184] In the existing high-speed railway station electromechanical equipment monitoring system, although it can record the occurrence time, processing process and results of each abnormal event, it lacks in-depth analysis of these data, especially the evaluation of processing efficiency. In this case, it is difficult to accurately evaluate the effectiveness of existing processing measures and it is difficult to optimize future processing processes.

[0185] Therefore, the embodiment of the present invention proposes an abnormal event processing efficiency analysis solution based on statistical methods and machine learning technology, aiming to solve the problems caused by the lack of effective evaluation means in the prior art and improve the efficiency and quality of fault handling.

[0186] Optionally, the step 105 of analyzing the processing efficiency of the abnormal event by using statistical methods and machine learning techniques includes:

[0187] The optimized abnormal event processing efficiency is calculated using the following formula:

[0188] ;

[0189] in, Indicates the processing efficiency of optimized abnormal events; Indicates The total time from the occurrence to the resolution of an abnormal event; Indicates The cost consumed in the process of handling abnormal events; Indicates The quality score of abnormal event handling; denote the minimum and maximum costs respectively; They are the weights of time, cost and quality; is the attenuation coefficient; Indicates the total number of abnormal events recorded during the analysis period.

[0190] In this step, an abnormal event refers to a specific event in which an abnormal state occurs in an electromechanical device, including the basic information of the electromechanical device in the abnormal state, the time of occurrence, the processing process and the result. The historical case database is a database used to store detailed information on each abnormal event, which is convenient for subsequent fault analysis and prevention strategy formulation. Processing efficiency is a comprehensive indicator that measures the speed, cost and quality of abnormal events from occurrence to resolution.

[0191] First, when an abnormal state of electromechanical equipment is detected, a corresponding abnormal event is created, and its basic information, occurrence time, processing process and results are recorded.

[0192] Secondly, the detailed information of each abnormal event is stored in the historical case database to ensure the integrity and accuracy of the data.

[0193] Next, abnormal event data is regularly extracted from the historical case database and analyzed using statistical methods and machine learning techniques.

[0194] Furthermore, the following formula is used to calculate the optimized abnormal event processing efficiency:

[0195] ;

[0196] in, Indicates the processing efficiency of optimized abnormal events; Indicates The total time from the occurrence to the resolution of an abnormal event; Indicates The cost consumed in the process of handling abnormal events; Indicates The quality score of abnormal event handling; denote the minimum and maximum costs respectively; They are the weights of time, cost and quality; is the attenuation coefficient; Indicates the total number of abnormal events recorded during the analysis period.

[0197] Finally, based on the calculation results, effective preventive measures and improvement plans are extracted to form a preventive strategy document. The preventive strategy document is applied to subsequent fault analysis and prevention strategy formulation to improve the scientific nature of fault handling and the effectiveness of prevention.

[0198] In the embodiment of the present invention, it is assumed that in a high-speed railway station, there is an intelligent monitoring system responsible for monitoring the operating status of all electromechanical equipment in the station. The system records multiple abnormal events, and the specific implementation steps are as follows:

[0199] Create an exception event:

[0200] Abnormal event 1:

[0201] Basic information: Equipment name: Air conditioning system; Equipment location: Platform A; Equipment number: AC001.

[0202] Time of occurrence: October 17, 2024 14:00.

[0203] Processing process:

[0204] 14:05 The system automatically reduced the air conditioning system power from 100% to 80%.

[0205] 14:10 Start the backup air conditioning system.

[0206] 14:15 Increase the air volume of the platform A ventilation system.

[0207] At 14:30, maintenance personnel arrived at the scene and checked the cooling system.

[0208] 15:00 The cooling system repair was completed and the air conditioning system returned to normal.

[0209] Processing result: The air-conditioning system resumed normal operation and the temperature of platform A returned to normal.

[0210] Total time: 1 hour;

[0211] Cost: 1000 yuan;

[0212] Quality score: 85 points;

[0213] Abnormal event 2:

[0214] Basic information: Equipment name: Elevator system; Equipment location: Waiting room B; Equipment number: EL002.

[0215] Time of occurrence: 10:00 on October 18, 2024.

[0216] Processing process:

[0217] 10:05 The system automatically stops the elevator operation.

[0218] 10:10 Maintenance personnel arrived at the scene and checked the elevator control system.

[0219] 11:00 The control system was repaired and the elevator resumed normal operation.

[0220] Handling result: The elevator system resumed normal operation and the passengers were safe.

[0221] Total time: 1 hour;

[0222] Cost: 1,200 yuan;

[0223] Quality score: 90 points;

[0224] The detailed information of the above abnormal events is stored in the historical case database to ensure the integrity and accuracy of the data.

[0225] Assuming that the above two abnormal events are recorded during the analysis period, the following parameters are used for calculation:

[0226] ; ; ; ; ; ; ; ;

[0227] Computational processing efficiency:

[0228] For abnormal event 1:

[0229] ;

[0230] ;

[0231] ;

[0232] ;

[0233] ;

[0234] ;

[0235] ;

[0236] For abnormal event 2:

[0237] ;

[0238] ;

[0239] ;

[0240] ;

[0241] ;

[0242] ;

[0243] ;

[0244] Calculate the optimized processing efficiency:

[0245] ;

[0246] Based on the calculation results, the following preventive measures and improvement plans are proposed:

[0247] Optimize response time: Shorten the response time to abnormal events by improving the system automation.

[0248] Reduce costs: Optimize maintenance processes and reduce unnecessary cost expenditures.

[0249] Improve quality: Strengthen the training of maintenance personnel and improve the quality of handling abnormal events.

[0250] The above-mentioned preventive measures and improvement plans are compiled into a preventive strategy document and applied to subsequent fault analysis and prevention strategy formulation to ensure the stable operation of the station's electromechanical equipment.

[0251] Through the above scheme, the system can systematically record and analyze each abnormal event to form a rich historical case database. These data not only provide a scientific basis for subsequent fault analysis, but also help to formulate effective preventive measures and improvement plans, improving the scientific nature of fault handling and the effectiveness of prevention. Specifically: systematically record each abnormal event and accumulate a large amount of valuable historical data. Use statistical methods and machine learning technology to conduct in-depth analysis of historical data to find potential patterns and trends. By comprehensively evaluating time, cost and quality, calculate the optimized processing efficiency, and provide a scientific basis for improvement measures. Through the application of preventive measures and improvement plans, the efficiency of fault handling and the stability of equipment are improved, and the risk of failure is reduced.

[0252] In short, by recording and analyzing abnormal events, this solution achieves scientific management and prevention of faults, and improves the intelligence level and fault handling capability of the electromechanical equipment monitoring system in high-speed railway stations.

[0253] Figure 2 A structural diagram of a high-speed railway station electromechanical equipment monitoring system is provided for an embodiment of the present invention. Figure 2 As shown, the system includes:

[0254] The receiving module 21 is used to receive real-time operation status data from various electromechanical equipment in the high-speed railway station, and the real-time operation status data at least includes the working parameters, fault codes and environmental parameters of the equipment;

[0255] A generating module 22, for automatically identifying electromechanical equipment in abnormal state according to the comparison between the real-time operating state data and the preset normal operating state data, and generating warning information, wherein the warning information includes the location of the abnormal equipment, the abnormal type and the recommended treatment measures;

[0256] Forming module 23, for mapping the warning information to the corresponding three-dimensional model position in combination with the three-dimensional digital model of the high-speed railway station, forming a visual monitoring interface, so that the monitoring personnel can intuitively locate the electromechanical equipment in abnormal state and the working state of the electromechanical equipment;

[0257] The adjustment module 24 is used to trigger the preset linkage rules when an abnormal state of electromechanical equipment is detected, and automatically adjust the operation mode of the associated equipment to reduce the impact of the fault;

[0258] The analysis and formulation module 25 is used to record the occurrence time, processing process and results of each abnormal event, forming a historical case database for subsequent fault analysis and prevention strategy formulation.

[0259] Figure 2 The high-speed railway station electromechanical equipment monitoring system can perform Figure 1 The implementation principle and technical effect of the high-speed railway station electromechanical equipment monitoring method described in the illustrated embodiment will not be repeated. The specific way in which each module and unit performs operations in the high-speed railway station electromechanical equipment monitoring system in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0260] In one possible design, Figure 2 The high-speed railway station electromechanical equipment monitoring system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0261] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0262] The processing component 32 is used to: receive real-time operating status data from each electromechanical equipment in the high-speed railway station; automatically identify electromechanical equipment in abnormal state and generate early warning information based on the comparison between the real-time operating status data and the preset normal operating status data; map the early warning information to the corresponding three-dimensional model position in combination with the three-dimensional digital model of the high-speed railway station to form a visual monitoring interface, so that the monitoring personnel can intuitively locate the electromechanical equipment in abnormal state and the working status of the electromechanical equipment; when electromechanical equipment in abnormal state is detected, trigger the preset linkage rules and automatically adjust the operating mode of the related equipment to reduce the impact of the fault; record the occurrence time, processing process and results of each abnormal event to form a historical case database for subsequent fault analysis and prevention strategy formulation.

[0263] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0264] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0265] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0266] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0267] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0268] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0269] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 A method for monitoring electromechanical equipment in a high-speed railway station according to the embodiment shown.

[0270] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0271] The system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0272] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0273] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring electromechanical equipment in a high-speed railway station, characterized in that: include: Receive real-time operating status data from each electromechanical device in the high-speed railway station, wherein the real-time operating status data at least includes operating parameters, fault codes and environmental parameters of the device; Automatically identify abnormal electromechanical equipment based on the comparison of the real-time operating status data with the preset normal operating status data, and generate warning information, wherein the warning information includes the location of the abnormal equipment, the abnormality type and recommended treatment measures; Combined with the three-dimensional digital model of the high-speed railway station, the warning information is mapped to the corresponding three-dimensional model position to form a visual monitoring interface, so that the monitoring personnel can intuitively locate the abnormal state of the electromechanical equipment and the working status of the electromechanical equipment; When abnormal state of electromechanical equipment is detected, the preset linkage rules are triggered to automatically adjust the operation mode of the associated equipment to reduce the impact of the fault; Record the occurrence time, handling process and results of each abnormal event to form a historical case database for subsequent fault analysis and prevention strategy formulation; The automatic identification of abnormal electromechanical equipment based on the comparison of the real-time operating status data with the preset normal operating status data and the generation of warning information includes: Comparing the real-time operation status data with preset normal operation status data, and identifying the electromechanical equipment corresponding to the real-time operation status data as an electromechanical equipment in an abnormal state when the real-time operation status data exceeds the preset normal operation status data, wherein the normal operation status data is a standard value provided by the equipment manufacturer or a standard value set based on a summary of long-term operation experience; Determine the abnormality type corresponding to the electromechanical equipment in the abnormal state, wherein the abnormality type at least includes overheating, voltage fluctuation, and mechanical wear; According to the determined abnormality type, corresponding warning information is generated according to a preset knowledge base, wherein the warning information includes the location information of the electromechanical equipment in the abnormal state, the abnormality type and the recommended treatment measures; The recording of the occurrence time, handling process and results of each abnormal event forms a historical case database for subsequent fault analysis and prevention strategy formulation, including: When an abnormal state of electromechanical equipment is detected, a corresponding abnormal event is created and a historical case database is formed. The abnormal event includes basic information, occurrence time, processing process and results of the abnormal state of the electromechanical equipment; Regularly extract abnormal events from the historical case database, and use statistical methods and machine learning techniques to analyze the distribution patterns, common causes, and processing efficiency of the abnormal events, so as to extract effective preventive measures and improvement plans, and form prevention strategy documents to guide subsequent fault analysis and prevention strategy formulation; Wherein, the electromechanical device for automatically identifying abnormal conditions further comprises: Use the following formula to calculate the time of electrical equipment The probability of being in abnormal state at all times: ； in, Indicates at time The probability that the device is in an abnormal state at any moment; Represents the time of the device Working parameters, fault codes and environmental parameters; It is a standard value or range within the preset normal operating status data; They represent the standard deviations of operating parameters, fault codes and environmental parameters respectively; It is a weight coefficient set according to the device characteristics and operating environment; Indicates the time at which the device The failure frequency; The use of statistical methods and machine learning techniques to analyze the efficiency of handling the abnormal events includes: The optimized abnormal event processing efficiency is calculated using the following formula: ； in, Indicates the processing efficiency of optimized abnormal events; Indicates The total time from the occurrence to the resolution of an abnormal event; Indicates The cost consumed in the process of handling abnormal events; Indicates The quality score of abnormal event handling; denote the minimum and maximum costs respectively; They are the weights of time, cost and quality; is the attenuation coefficient; Indicates the total number of abnormal events recorded during the analysis period.

2. The method according to claim 1, characterized in that The three-dimensional digital model of the high-speed railway station is combined to map the warning information to the corresponding three-dimensional model position to form a visual monitoring interface, including: Construct a three-dimensional digital model of the high-speed railway station building, which is used to reflect the actual internal structure of the station building and the layout of electromechanical equipment, ensuring that the position of each electromechanical equipment in three-dimensional space corresponds to the actual installation position; Using a three-dimensional coordinate conversion algorithm, accurately mapping the position information of the abnormal electromechanical equipment in the warning information to a corresponding position in the three-dimensional digital model; The electromechanical equipment in the abnormal state is highlighted by color coding and icon marking on the interface of the three-dimensional digital model, and a pop-up window or label is attached around the electromechanical equipment in the abnormal state to display the corresponding abnormal type and recommended processing measures.

3. The method according to claim 1, characterized in that When an abnormal state of electromechanical equipment is detected, a preset linkage rule is triggered to automatically adjust the operation mode of the associated equipment to reduce the impact of the fault, including: Define linkage rules for each abnormality type based on the characteristics and operating environment of electromechanical equipment; When an electromechanical device in an abnormal state is detected, obtaining a linkage rule corresponding to the abnormal type of the electromechanical device in the abnormal state; Based on the linkage rules corresponding to the abnormality type, the operation mode of the associated equipment is automatically adjusted to reduce the impact of the fault.

4. A high-speed railway station electromechanical equipment monitoring system, used to execute a high-speed railway station electromechanical equipment monitoring method according to any one of claims 1 to 3, characterized in that: include: A receiving module, used to receive real-time operating status data from various electromechanical equipment in the high-speed railway station, wherein the real-time operating status data at least includes working parameters, fault codes and environmental parameters of the equipment; A generating module, used to automatically identify electromechanical equipment in abnormal state according to the comparison between the real-time operating state data and the preset normal operating state data, and generate warning information, wherein the warning information includes the location of the abnormal equipment, the abnormal type and the recommended treatment measures; A module is formed, which is used to map the warning information to the corresponding three-dimensional model position in combination with the three-dimensional digital model of the high-speed railway station building, and form a visual monitoring interface, so that the monitoring personnel can intuitively locate the electromechanical equipment in abnormal state and the working state of the electromechanical equipment; The adjustment module is used to trigger the preset linkage rules when abnormal state of electromechanical equipment is detected, and automatically adjust the operation mode of the associated equipment to reduce the impact of the fault; The analysis and formulation module is used to record the occurrence time, handling process and results of each abnormal event, forming a historical case database for subsequent fault analysis and prevention strategy formulation.

5. A computing device, characterized in that: It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a high-speed railway station electromechanical equipment monitoring method as described in any one of claims 1 to 3.

6. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a method for monitoring electromechanical equipment in a high-speed railway station building as described in any one of claims 1 to 3 is implemented.

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