High-sensitivity remote bus alarm controller

Abstract

The invention discloses a high-sensitivity remote bus alarm controller, which relates to the field of remote alarm and comprises a bus receiving module used for establishing a communication protocol with a sub-line point through a bus, receiving data and monitoring data flow on the bus in real time; the alarm notification module is used for receiving the alarm data and feeding back a detection result and early warning information to the bus receiving module; the signal processing unit is used for acquiring the initially received alarm signal, extracting a related transmission target and analyzing a floating coefficient in a target data transmission process in real time; through a high-sensitivity data processing algorithm, the controller can monitor and respond to an alarm signal in real time, reduce response delay, utilize machine learning and data analysis technologies to actively repair the occurred interruption and lagging conditions, and perform effective prediction and compensation under the condition of data interruption. And the integrity and accuracy of data transmission are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of remote alarms, in particular to a highly sensitive remote bus alarm controller. Background Art

[0002] Remote bus alarm controller is mainly used to monitor and manage alarm systems, and is usually used in fire protection, safety and industrial automation. Its main functions include: Real-time monitoring: It can monitor the status of various sensors in real time. Once an alarm signal occurs, it will be fed back to the control center immediately. With bus technology, it can realize centralized management of multiple alarm devices, which is convenient for operation and maintenance. It can also collect, analyze and store alarm data, provide a basis for subsequent troubleshooting and event analysis, and support information interaction between monitoring systems or fire protection systems and other systems for comprehensive management and control;

[0003] In the application process of the existing remote bus alarm controller, due to the various deployment environments of the controller, after receiving the alarm signal, if there are abnormal conditions such as system failure or transmission delay, it is easy to cause a lag in response time, and it is impossible to analyze and notify the system status in real time, which leads to an untimely response to the emergency. Especially in the remote bus alarm control system with multiple sub-line points, when the data is lost or interrupted during the transmission process due to such conditions, the existing system usually lacks effective self-repair capabilities for data loss, fails to effectively identify and handle the delay in data transmission, resulting in the system failing to correctly evaluate the actual transmission performance and status;

[0004] Many existing alarm controllers are unable to analyze the transmission characteristics and volatility of data in real time, and lack intelligent remediation mechanisms based on historical data. For existing interruptions and delays, existing technologies usually rely on simple linear interpolation and other methods to fill in, which cannot guarantee the quality and accuracy of the repaired data. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a highly sensitive remote bus alarm controller, which can effectively solve the problems of the prior art.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] The invention discloses a highly sensitive remote bus alarm controller, comprising:

[0010] The bus receiving module is used to establish a communication protocol with the sub-line point through the bus, receive data, and monitor the data flow on the bus in real time;

[0011] The alarm notification module is used to receive alarm data and feed back detection results and warning information to the bus receiving module;

[0012] A signal processing unit is used to obtain the alarm signal initially received, extract the relevant transmission target, and analyze the floating coefficient of the target data transmission process in real time;

[0013] The interrupt detection module is used to analyze the floating coefficient and check whether there is packet loss or transmission delay in the current periodic data transmission. If it is found that the data transmission interval exceeds the preset limit, it is judged as an interruption and outputs several interrupt signals;

[0014] The hysteresis detection module is used to analyze the floating coefficient, obtain the time delay of continuous data transmission in the current cycle, calculate the hysteresis coefficient, and if it exceeds the preset threshold, it is judged as hysteresis and outputs several hysteresis signals;

[0015] The event marking module is used to extract the data segments corresponding to several interruption signals and delay signals, label the data segments as "interruption" or "delay", and store the marked records in the log system at the same time;

[0016] A model building module, which is used to build a filling model based on historical data and its transmission behavior by applying machine learning algorithms;

[0017] The stagnation repair module analyzes the stagnation data segment and evaluates the stagnation degree. If the stagnation exceeds the limit, the model generates real-time filling data;

[0018] The interruption repair module is used to run the model to predict the interruption data at the interruption location of the data segment and fill in the missing values ​​according to the historical transmission trend.

[0019] Furthermore, the signal processing unit includes a signal capture module and a floating calculation module, and the signal capture module and the floating calculation module are interactively connected through an electrical medium, wherein:

[0020] The signal capture module is used to parse the data, identify the characteristics of the alarm signal, extract the transmission target from the alarm signal, and record the timestamp, signal type, data source address, signal strength, signal quality index, data dislocation information, data integrity information, channel congestion information and associated device status;

[0021] The floating calculation module is used to collect transmission data within a preset period, perform statistical analysis, calculate the mean and standard deviation of the data, obtain the floating coefficient, compare the floating coefficient with the preset standard floating threshold, and mark whether there is abnormal fluctuation.

[0022] Furthermore, the floating coefficient obtained by the signal processing unit is used to represent an indicator of the relative degree of change of data fluctuation, and the calculation formula of the floating coefficient is:

[0023]

[0024] In the formula, n represents the number of data points, x i represents each data point, and U represents the mean of the data set.

[0025] Furthermore, the bus receiving module is interactively connected to a data storage module via a wireless network, and the data storage module is used to store original alarm data, repaired data and model output, perform classified storage, and perform regular backup storage.

[0026] Furthermore, the alarm notification module receives real-time filling data from the delay repair module, fills the delay data segment in real time, and interrupts the transmission of the real delay data to be arrived that is not expressed as expected, and diverts it to the temporary storage area. If the interruption data of the interruption repair module continues to arrive in a certain period in the future, it is directly diverted to the temporary storage area, and the data in the temporary storage area is used as a correction reference for the filling model built by the model construction module.

[0027] Furthermore, the alarm notification module analyzes the data repaired by the slowdown repair module and the interruption repair module to determine the current system status, and for interruption and slowdown events, sends real-time warning information to the bus receiving module, updates the status in real time and records it.

[0028] Furthermore, the working logic of the event marking module is:

[0029] a. Receive the interrupt detection module and the delay detection module containing the array of timestamp, value and detection result;

[0030] b. Analyze the time range and specific data point index of the interruption signal of the interruption detection module, and the time range and index of the dynamic change of the delay detection module;

[0031] c. Create a new data list to store each data point and its corresponding label, traverse the data points, and determine whether it belongs to an interruption or delay segment;

[0032] d. Merge event tags and assign "interruption" or "delay" labels according to preset priorities;

[0033] e. Finally, an output result containing all data points and their corresponding labels is generated.

[0034] Furthermore, the filling model in the model building module is constructed by a time series algorithm, the collected data is preprocessed, and time series features, delayed label information, interrupted label information and external environment features are extracted from the preprocessed data.

[0035] Furthermore, the filling model in the model building module adopts the signal results of the historical slowdown repair module and the interruption repair module, marks the historical signals into several parts of the training set, records the start and end positions of the interruption and slowdown events, generates labels for training, and the input features of the filling model are the selected feature data, and the output label is the change coefficient caused by normal change, slowdown or interruption.

[0036] Furthermore, the bus receiving module is interactively connected with the alarm notification module and the signal processing unit through an electrical medium, the alarm notification module is interactively connected with the slowness repair module and the interruption repair module through an electrical medium, the signal processing unit is interactively connected with the interruption detection module and the slowness detection module through a wireless network, the event marking module is interactively connected with the interruption detection module, the slowness detection module and the model building module through a wireless network, and the model building module is interactively connected with the slowness repair module and the interruption repair module through a wireless network.

[0037] (III) Beneficial effects

[0038] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0039] 1. Through highly sensitive data processing algorithms, the controller can monitor and respond to alarm signals in real time, reduce response delays, and use machine learning and data analysis technologies to actively repair interruptions and delays. It can also make effective predictions and compensation in the event of data interruptions to ensure the integrity and accuracy of data transmission, thereby reducing the risk of system failures and the need for manual intervention. The system can automatically identify and process various types of signals, thereby improving monitoring efficiency.

[0040] 2. By analyzing past data, identifying abnormal patterns and trends, and calculating floating coefficients, the system can effectively evaluate the stability of data transmission, provide quantitative analysis of data fluctuations, thereby improving prediction accuracy, and immediately notify users when potential problems are detected. Combined with interruption and delay prediction and repair technology, in the event of data interruption or delay, the system can infer possible results through historical data, perform intelligent compensation, and ensure system operation.

[0041] 3. Accurately mark the detected interruptions and slowdown signals to ensure that subsequent data processing can be based on accurate event records, thereby improving the effectiveness of subsequent decisions. The complex model algorithm used by this controller can repair data more accurately than the simple linear interpolation method, thereby improving the quality and accuracy of the repaired data. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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 prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 It is a schematic diagram of the framework of the present invention.

[0044] The numbers in the figure represent, respectively, 1. bus receiving module; 2. alarm notification module; 3. signal processing unit; 301. signal capture module; 302. floating calculation module; 4. interruption detection module; 5. sluggishness detection module; 6. event marking module; 7. model building module; 8. sluggishness repair module; 9. interruption repair module; 10. data storage module. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0046] The present invention will be further described below in conjunction with the embodiments.

[0047] Example 1

[0048] A highly sensitive remote bus alarm controller of this embodiment, such as Figure 1 As shown, including:

[0049] The bus receiving module 1 is used to establish a communication protocol with the sub-line point through the bus, receive data, and monitor the data flow on the bus in real time. The bus receiving module 1 is interactively connected to the data storage module 10 through a wireless network. The data storage module 10 is used to store the original alarm data, the repaired data and the model output, and perform classified storage and regular backup storage;

[0050] Alarm notification module 2, used to receive alarm data and feed back detection results and warning information to bus receiving module 1;

[0051] The signal processing unit 3 is used to obtain the alarm signal initially received, extract the relevant transmission target, analyze the floating coefficient in the target data transmission process in real time, obtain and analyze the alarm signal of the transmission target in real time, and by extracting the floating coefficient, it can help the system determine the stability of data transmission and provide basic data for subsequent fault detection;

[0052] The interrupt detection module 4 is used to analyze the floating coefficient and check whether there is packet loss or transmission delay in the current periodic data transmission. If it is found that the data transmission interval exceeds the preset limit, it is judged as an interruption and outputs several interrupt signals to facilitate rapid adoption of corresponding measures to ensure data integrity;

[0053] The hysteresis detection module 5 is used to analyze the floating coefficient, obtain the time delay of continuous data transmission in the current cycle, calculate the hysteresis coefficient, and if it exceeds the preset threshold, it is judged as hysteresis and outputs several hysteresis signals. For the time delay analysis in continuous data transmission, the system can identify the hysteresis phenomenon and output relevant signals to ensure the effective monitoring of real-time data flow;

[0054] The event marking module 6 is used to extract the data segments corresponding to the interruption signals and the delay signals, label the data segments with "interruption" or "delay" respectively, and store the marked records in the log system at the same time; the working logic of the event marking module 6 is:

[0055] a. The receiving interrupt detection module 4 and the delay detection module 5 contain an array of timestamps, values ​​and detection results;

[0056] b. Analyze the time range and specific data point index of the interrupt signal of the interrupt detection module 4, and the time range and index of the dynamic change of the delay detection module 5;

[0057] c. Create a new data list to store each data point and its corresponding label, traverse the data points, and determine whether it belongs to an interruption or delay segment;

[0058] d. Merge event tags and assign "interruption" or "delay" labels according to preset priorities;

[0059] e. The output results containing all data points and their corresponding tags are finally generated. If a data point is marked as "interrupted", the delay tag is no longer checked. Through the system's tagging logic, different data segments can be effectively identified and recorded as "interrupted" or "delayed". At the same time, the merging of event tags ensures the traceability of the data and provides a clear tagging basis for subsequent data analysis.

[0060] The model building module 7 is used to construct a filling model by applying a machine learning algorithm based on historical data and its transmission behavior; the filling model in the model building module 7 is constructed by a time series algorithm, and the collected data is preprocessed, and time series features, label information of delays, label information of interruptions and external environment features are extracted from the preprocessed data; the filling model in the model building module 7 uses the signal results of the historical delay repair module 8 and the interruption repair module 9, marks the historical signals as several parts of the training set, records the start and end positions of the interruption and delay events, and generates labels for training. The input features of the filling model are the selected feature data, and the output labels are the coefficients of change caused by normal changes, delays or interruptions; based on historical data, the filling model constructed by the system using a machine learning algorithm can efficiently handle the problem of missing data, and through the extraction of time series features, it can realize the prediction and filling of future data to ensure the continuity and accuracy of the data flow;

[0061] The stagnation repair module 8 analyzes and evaluates the stagnation degree of the stagnation data segment. If the stagnation exceeds the limit, the model generates real-time filling data to ensure the integrity of the data and improve the reliability of the system.

[0062] The interruption repair module 9 is used to run the model to predict the interruption data at the interruption position of the data segment and fill in the missing values ​​according to the historical transmission trend. The alarm notification module 2 receives the real-time filling data of the delay repair module 8, fills the delay data segment in real time, and interrupts the transmission of the real delay data to be arrived that is not expressed as expected, and diverts it to the temporary storage area. If the interruption data of the interruption repair module 9 continues to arrive in a certain period in the future, it is directly diverted to the temporary storage area, and the data in the temporary storage area is used as a correction reference for the filling model built by the model construction module 7; the alarm notification module 2 analyzes the data repaired by the delay repair module 8 and the interruption repair module 9 to determine the current system status. For interruption and delay events, it sends real-time warning information to the bus receiving module 1, updates the status in real time and records it, predicts the interruption data with the historical transmission trend and fills it, effectively supplements the data missing caused by the interruption, and fills the delay data in real time, optimizes the data processing flow, and maintains the continuity of the data flow.

[0063] As a preferred implementation in this embodiment, Figure 1 As shown, the bus receiving module 1 is interactively connected with the alarm notification module 2 and the signal processing unit 3 through an electrical medium, the alarm notification module 2 is interactively connected with the slowness repair module 8 and the interruption repair module 9 through an electrical medium, the signal processing unit 3 is interactively connected with the interruption detection module 4 and the slowness detection module 5 through a wireless network, the event marking module 6 is interactively connected with the interruption detection module 4, the slowness detection module 5 and the model building module 7 through a wireless network, and the model building module 7 is interactively connected with the slowness repair module 8 and the interruption repair module 9 through a wireless network.

[0064] Compared with the existing technology, this system can monitor the data flow on the bus in real time, and through multi-level data analysis, timely feedback of detection results and early warning information, greatly improving the response speed. By extracting and analyzing the floating coefficient, the system can evaluate the stability of data transmission in real time, detect packet loss or transmission delay, and help to take quick measures to maintain data integrity and transmission quality. By establishing "interruption" and "delay" labels, it is convenient for subsequent analysis, and at the same time improves the traceability of data, helping users to clearly understand the system operation status;

[0065] The filling model built using machine learning algorithms can efficiently handle data missing problems, perform real-time prediction and filling based on historical data, ensure data continuity and accuracy, and improve data flow monitoring and filling mechanisms, so that the system can continue to operate stably in the face of various uncertainties and improve overall reliability.

[0066] Example 2

[0067] At other levels, the present embodiment provides a signal processing unit 3 that can obtain an alarm signal, extract a transmission target, and analyze a floating coefficient, such as Figure 1 As shown, the signal processing unit 3 includes a signal capture module 301 and a floating calculation module 302, and the signal capture module 301 and the floating calculation module 302 are interactively connected through an electrical medium, wherein:

[0068] The signal capture module 301 is used to parse the data, identify the characteristics of the alarm signal, extract the transmission target from the alarm signal, and record the timestamp, signal type, data source address, signal strength, signal quality index, data dislocation information, data integrity information, channel congestion information and associated device status;

[0069] The floating calculation module 302 is used to collect transmission data within a preset period, perform statistical analysis, calculate the mean and standard deviation of the data, obtain the floating coefficient, compare the floating coefficient with the preset standard floating threshold, mark whether there is abnormal fluctuation, collect a large amount of transmission data within the preset period, provide a rich data foundation, support deeper analysis, and calculate the mean and standard deviation of the collected data, which provides a basic indicator for judging the volatility of the data. Through the calculation of the floating coefficient, the system can dynamically monitor the data change trend and compare the floating coefficient with the standard floating threshold. If abnormal fluctuation is found, it will be marked immediately to provide prompts for subsequent abnormal processing.

[0070] As a preferred implementation in this embodiment, the floating coefficient obtained by the signal processing unit 3 is used to represent an indicator of the relative change degree of data fluctuation, thereby reflecting the stability of the data. It is defined as the ratio of the standard deviation to the mean. The calculation formula of the floating coefficient is:

[0071]

[0072] In the formula, n represents the number of data points, x i Represents each data point, x i -U represents the deviation between the data point and the mean, (x i -U) 2 represents the square of the deviation value, ensuring that all values ​​are non-negative, and U represents the mean of the data set, which is calculated as the sum of all data divided by the number of data points.

[0073] Compared with the existing technology, through the combination of the signal capture module 301 and the floating calculation module 302, the system can quickly identify and analyze alarm signals to ensure that potential problems are discovered and handled in a timely manner. Multi-dimensional information recording enables the system to have a more comprehensive understanding of the transmission status and reduce the blind spots of potential risks. The calculation of the floating coefficient and the comparison with the standard threshold enable the system to have flexible anomaly detection capabilities and can promptly identify and respond to unexpected situations.

[0074] Working principle: When the present invention is installed, the bus receiving module 1 performs overall control over each functional module, the alarm notification module 2 receives the initial alarm data of various sensors and detection equipment, the signal capture module 301 extracts the key features of the alarm data transmission process, the floating calculation module 302 calculates the floating coefficient in the transmission process, the interruption detection module 4 calculates the interruption signal in the data transmission, the hysteresis detection module 5 obtains the hysteresis signal, the model construction module 7 constructs a filling model for the interruption and hysteresis signal, the hysteresis repair module 8 generates hysteresis data filling for the hysteresis signal, the interruption repair module 9 generates missing data filling for the interruption signal, and the data storage module 10 stores all analysis data and collection data;

[0075] The present invention has more accurate real-time data monitoring and analysis capabilities, can quickly identify interruptions and delays in data transmission, and automatically fill in missing data through machine learning algorithms, thereby effectively improving the stability and reliability of data channels. At the same time, the system significantly enhances the response speed and decision-making support to potential risks through real-time early warning mechanisms and comprehensive status updates, thereby optimizing the fault management process as a whole and reducing operational risks.

[0076] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A highly sensitive remote bus alarm controller, characterized in that: include: A bus receiving module (1) is used to establish a communication protocol with a sub-line point through the bus, receive data, and monitor the data flow on the bus in real time; An alarm notification module (2), used for receiving alarm data and feeding back detection results and warning information to the bus receiving module (1); A signal processing unit (3) is used to obtain the alarm signal initially received, extract the relevant transmission target, and analyze the floating coefficient in the target data transmission process in real time; An interrupt detection module (4) is used to analyze the floating coefficient and check whether packet loss or transmission delay occurs in the current periodic data transmission. If it is found that the data transmission interval exceeds a preset limit, it is judged as an interruption and outputs a number of interrupt signals; A hysteresis detection module (5) is used to analyze the floating coefficient, obtain the time delay of continuous data transmission in the current cycle, calculate the hysteresis coefficient, and if it exceeds a preset threshold, it is judged as hysteresis and outputs a number of hysteresis signals; An event marking module (6) is used to extract data segments corresponding to a number of interruption signals and delay signals, label the data segments with "interruption" or "delay" respectively, and store the label records in a log system at the same time; A model building module (7), for building a filling model by applying a machine learning algorithm based on historical data and its transmission behavior; A stagnation repair module (8) analyzes the stagnation data segment to evaluate the stagnation degree, and if the stagnation exceeds the limit, generates real-time filling data through the model; The interruption repair module (9) is used to run the model to predict the interruption data at the interruption position of the data segment and fill in the missing values ​​according to the historical transmission trend.

2. A highly sensitive remote bus alarm controller according to claim 1, characterized in that: The signal processing unit (3) comprises a signal capture module (301) and a floating calculation module (302), wherein the signal capture module (301) and the floating calculation module (302) are interactively connected via an electrical medium, wherein: A signal capture module (301) is used to parse the data, identify the characteristics of the alarm signal, extract the transmission target from the alarm signal, and record the timestamp, signal type, data source address, signal strength, signal quality index, data misalignment information, data integrity information, channel congestion information and associated device status; The floating calculation module (302) is used to collect transmission data within a preset period, perform statistical analysis, calculate the mean and standard deviation of the data, obtain the floating coefficient, compare the floating coefficient with a preset standard floating threshold, and mark whether there is abnormal fluctuation.

3. A highly sensitive remote bus alarm controller according to claim 1, characterized in that: The floating coefficient obtained by the signal processing unit (3) is used to represent an index of the relative change degree of data fluctuation, thereby reflecting the stability of the data. It is defined as the ratio of the standard deviation to the mean. The calculation formula of the floating coefficient is: In the formula, n represents the number of data points, x i Represents each data point, x i -U represents the deviation between the data point and the mean, (x i -U) 2 represents the square of the deviation value, ensuring that all values ​​are non-negative, and U represents the mean of the data set, which is calculated as the sum of all data divided by the number of data points.

4. A highly sensitive remote bus alarm controller according to claim 1, characterized in that: The bus receiving module (1) is interactively connected to a data storage module (10) via a wireless network, and the data storage module (10) is used to store original alarm data, repaired data and model output, perform classified storage, and perform regular backup storage.

5. A highly sensitive remote bus alarm controller according to claim 1, characterized in that: The alarm notification module (2) receives the real-time filling data from the delay repair module (8), fills the delay data segment in real time, interrupts the transmission of the real delay data to be arrived that is not expressed as expected, and diverts it to the temporary storage area. If the interruption repair module (9) interrupts the data to continue to arrive in a certain period in the future, it is directly diverted to the temporary storage area, and the data in the temporary storage area is used as a correction reference for the filling model built by the model construction module (7).

6. A highly sensitive remote bus alarm controller according to claim 1, characterized in that: The alarm notification module (2) analyzes the data repaired by the delay repair module (8) and the interruption repair module (9) to determine the current system state, and for interruption and delay events, sends real-time warning information to the bus receiving module (1), updates the state in real time and records it.

7. A highly sensitive remote bus alarm controller according to claim 1, characterized in that: The working logic of the event marking module (6) is: a. The receiving interrupt detection module (4) and the delay detection module (5) contain an array of timestamps, values ​​and detection results; b. Analyze the time range and specific data point index of the interrupt signal of the interrupt detection module (4), and the time range and index of the dynamic change of the delay detection module (5); c. Create a new data list to store each data point and its corresponding label, traverse the data points, and determine whether it belongs to an interruption or delay segment; d. Merge event tags and assign "interruption" or "delay" labels according to preset priorities; e. Finally, an output result containing all data points and their corresponding labels is generated. If a data point is marked as "interrupted", the delay mark will no longer be checked.

8. A highly sensitive remote bus alarm controller according to claim 1, characterized in that: The filling model in the model building module (7) is constructed by a time series algorithm, and the collected data is preprocessed, and time series features, delayed label information, interrupted label information and external environment features are extracted from the preprocessed data.

9. A highly sensitive remote bus alarm controller according to claim 1, characterized in that: The filling model in the model building module (7) uses the signal results of the historical hysteresis repair module (8) and the interruption repair module (9), marks the historical signals into several parts of the training set, records the start and end positions of the interruption and hysteresis events, and generates labels for training. The input features of the filling model are selected feature data, and the output labels are the coefficients of change caused by normal changes, hysteresis or interruption.

10. A highly sensitive remote bus alarm controller according to claim 1, characterized in that: The bus receiving module (1) is interactively connected with the alarm notification module (2) and the signal processing unit (3) through an electrical medium; the alarm notification module (2) is interactively connected with the delay repair module (8) and the interruption repair module (9) through an electrical medium; the signal processing unit (3) is interactively connected with the interruption detection module (4) and the delay detection module (5) through a wireless network; the event marking module (6) is interactively connected with the interruption detection module (4), the delay detection module (5) and the model construction module (7) through a wireless network; and the model construction module (7) is interactively connected with the delay repair module (8) and the interruption repair module (9) through a wireless network.

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