Carbon emission monitoring management system and monitoring method based on Internet of Things

Abstract

The invention relates to the technical field of enterprise data acquisition, in particular to a carbon emission monitoring management system based on the Internet of Things and a monitoring method. Comprising a carbon emission data acquisition unit which comprises data acquisition sensors arranged at each carbon emission end and used for acquiring carbon emission data, a wireless transmission module used for data transmission and a data collection and arrangement terminal; the production data acquisition unit is used for acquiring the power production equipment and the power loss equipment corresponding to each carbon emission end and the production data corresponding to the carbon emission equipment based on the time sequence; the carbon emission data prediction unit receives the carbon emission data corresponding to different production data stored in the corresponding big data storage module, and analyzes the relationship between the production data and the carbon emission data through a data analysis module; according to the system, abnormal data acquired by the sensor can be processed, and the accuracy of the Internet of Things carbon data acquisition system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise data collection, and particularly relates to an Internet of Things-based carbon emission monitoring and management system and a monitoring method. Background Art

[0002] Internet of Things-based carbon emission data monitoring refers to the process of using Internet of Things technology to monitor carbon emissions in real time and collect data. The Internet of Things (IoT) realizes the intelligent identification, collection, transmission, and processing of carbon emission data through information sensing devices, network transmission, and data processing technologies. Internet of Things technology plays a key role in carbon emission data monitoring, mainly through the "cloud-pipe-edge-end" architecture. This architecture can effectively collect, process, and analyze data in the environment, thereby monitoring and managing carbon emissions. With the application of Internet of Things technology in the collection of carbon emission data, the data collected by carbon emission monitoring technology has become more comprehensive and accurate. However, there are also certain problems in the application of Internet of Things technology in carbon emission monitoring; for example, when the collection sensor devices of one or more links fail, it may cause deviations in the collected data, and we cannot discover it in time, which will affect the overall collection of carbon emission data; Based on the above problems, there is an urgent need for a method to prevent mistakes in the final overall data collection caused by the failure of collection sensors. Summary of the Invention

[0003] Aiming at the problem that in the current application of Internet of Things technology in carbon emission monitoring and management systems, mistakes in the final data collection are easily caused by the failure of sensor devices, this application provides an Internet of Things-based carbon emission monitoring and management system and a monitoring method to solve the above problems.

[0004] To achieve the above object, the present invention is realized through the following technical solutions: An embodiment of this application discloses an Internet of Things-based carbon emission monitoring and management system, including: Carbon emission data collection unit: including data collection sensors set at each carbon emission end for collecting carbon emission data, a wireless transmission module for data transmission, and a data collection and sorting terminal; Production data collection unit: used to collect production data corresponding to power production equipment, power loss equipment, and carbon emission equipment corresponding to each carbon emission end based on time series; Carbon emission data prediction unit: The carbon emission data prediction unit receives the carbon emission data corresponding to different production data stored in the corresponding big data storage module, and analyzes the relationship between production data and carbon emission data through a data analysis module; Carbon emission data anomaly detection unit: It is used to collect the data waveforms collected by each data acquisition sensor and determine whether there are sudden anomalies. Carbon emission data anomaly alarm unit: When there is a large deviation between the data predicted by the carbon emission data prediction unit and the actually collected carbon emission data, and the carbon emission data anomaly detection unit detects that the data waveform collected by the data acquisition sensor has a sudden anomaly, it will give a warning of the corresponding carbon emission data anomaly.

[0005] Adopting the above technical solution: The above solution provides a carbon emission data monitoring and management system. In addition to the carbon emission data acquisition unit, it is also equipped with a corresponding production data acquisition unit and a carbon emission data prediction unit. It can predict the carbon emission data based on the production data and can also judge the sudden anomaly through the carbon emission data anomaly detection unit. The two work together to further judge whether there is an anomaly in the carbon emission data acquisition and give a warning of the data anomaly.

[0006] Preferably, the carbon emission data prediction unit includes: Big data storage module: It is used to receive the carbon data emitted by different carbon emission ends collected by the carbon emission data acquisition unit and the production material data corresponding to the carbon emissions collected by the production data acquisition unit. Data analysis module: It is used to calculate the corresponding conversion coefficient based on the production material data corresponding to the carbon emissions collected by the production data acquisition unit and the corresponding carbon emission data. Prediction analysis module: Based on the conversion coefficient analyzed by the data analysis module, it estimates the carbon emission data for the collected production material data.

[0007] Adopting the above technical solution: The above solution details the carbon emission data prediction unit into a big data storage module, a data analysis module, and a prediction analysis module. It can obtain the corresponding conversion coefficient based on the corresponding relationship between the carbon emission data and the production material data. Through the conversion coefficient, only by obtaining the production material data, the carbon emission data can be estimated.

[0008] Preferably, the carbon emission data anomaly detection unit includes: Data waveform conversion module: It is used to convert the data collected by the carbon emission data acquisition unit into a corresponding carbon emission data waveform based on time change. Waveform threshold loading module: The operator sets the corresponding carbon emission mutation threshold according to the carbon emission data prediction unit. Carbon emission data anomaly confirmation module: It is used to judge whether the carbon emission data waveform converted by the data waveform conversion module exceeds the threshold, so as to judge whether there is an anomaly in the carbon emission data detection.

[0009] Adopting the above technical solution: The above solution can realize the judgment of the waveform of carbon emission data based on the waveform threshold to determine whether there is an abnormality in the carbon emission data.

[0010] Preferably, the carbon emission data abnormality alarm unit includes: Receiving module: Used to receive the carbon emission data collected and sorted by the carbon emission data collection unit, the carbon emission data predicted based on production data by the carbon emission data prediction unit, and whether the carbon emission data detection abnormality occurs confirmed by the carbon emission data abnormality detection unit; Judgment module: When the carbon emission data abnormality detection unit confirms that the carbon emission data is abnormal, further judge whether there is a large deviation between the carbon emission data predicted by the carbon emission data prediction unit and the data collected by the carbon emission data collection unit; if there is a large deviation, send a signal to the alarm module; Alarm module: Receive the judgment signal generated by the judgment module to perform corresponding alarms.

[0011] Adopting the above technical solution: When the carbon emission data abnormality detection unit confirms that the carbon emission data is abnormal, it may be due to changes in production material data or abnormalities in the acquisition sensors. The carbon emission data of this design can further predict the carbon emission data based on the production material data, confirm whether it is abnormal, and give an alarm.

[0012] Preferably, it further includes: an abnormal data processing module, which is used to, when the alarm module gives an alarm, based on the abnormally collected data detected by the carbon emission data detection unit, find the corresponding sensor module, replace the abnormal data with the data predicted by the carbon emission data prediction unit, and at the same time replace the abnormal data acquisition sensor with a normal data acquisition sensor.

[0013] Adopting the above technical solution: When the acquisition sensor fails, the carbon emission data predicted by the carbon emission data prediction unit in this design can be used as substitute data for abnormal data, which can further reduce the impact of abnormal data on data acquisition.

[0014] A monitoring method, applied to the above-mentioned Internet of Things-based carbon emission monitoring and management system, includes: S1: The data acquisition sensors set at each carbon emission end collect carbon emission data and transmit it to the data collection and sorting terminal through the wireless transmission module; S2: The production data acquisition unit collects the production data corresponding to the power production equipment, power loss equipment, and carbon emission equipment corresponding to each carbon emission end based on the time series; S3: The carbon emission data prediction unit receives the carbon emission data corresponding to different production data stored in the big data storage module, and analyzes the relationship between the production data and the carbon emission data through the data analysis module; S4: The carbon emission data anomaly detection unit collects the data waveforms collected by each data acquisition sensor and determines whether there are abnormal mutations; S5: When there is a large deviation between the data predicted by the carbon emission data prediction unit and the actually collected carbon emission data, and the data waveform collected by the data acquisition sensor detected by the carbon emission data anomaly detection unit shows a mutation anomaly, corresponding carbon emission data anomaly warning is carried out.

[0015] Further preferably, the S3 includes: S31: The big data storage unit receives the carbon data emitted by different carbon emission terminals collected by the carbon emission data collection unit and the production material data corresponding to the carbon emissions collected by the production data collection unit; S32: The data analysis module calculates the corresponding conversion coefficient based on the production material data corresponding to the carbon emissions collected by the production data collection unit and the corresponding carbon emission data; S33: The prediction analysis module estimates the carbon emission data for the collected production material data based on the conversion coefficient analyzed by the data analysis module; Further preferably, the S4 includes: S41: The data waveform conversion module converts the data collected by the carbon emission data collection unit into the corresponding carbon emission data waveform based on time variation; S42: The operator sets the corresponding carbon emission mutation threshold according to the carbon emission data prediction unit; S43: The carbon emission data anomaly confirmation module determines whether the carbon emission data waveform converted by the data waveform conversion module exceeds the threshold, and further determines whether there is an anomaly in the carbon emission data detection.

[0016] Further preferably, the S5 includes: S51: The receiving module receives the carbon emission data collected and sorted by the carbon emission data collection unit, the carbon emission data predicted based on the production data by the carbon emission data prediction unit, and whether the carbon emission data anomaly detection unit confirms that there is an anomaly in the carbon emission data detection; S52: When the carbon emission data anomaly detection unit confirms that the carbon emission data is abnormal, the judgment module further determines whether there is a large deviation between the carbon emission data predicted by the carbon emission data prediction unit and the data collected by the carbon emission data collection unit; if there is a large deviation, a signal is sent to the alarm module; S53: The alarm module receives the judgment signal generated by the judgment module and conducts corresponding alarms. Brief Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is the connection block diagram of the carbon emission monitoring and management system based on the Internet of Things of the present application; Figure 2 It is the flowchart of the monitoring method of the present application; Figure 3 For the present application Figure 2 It is the detailed flowchart of step S3 in the present application; Figure 4 For the present application Figure 2 It is the detailed flowchart of step S4 in the present application; Figure 5 For the present application Figure 2 It is the detailed flowchart of step S5 in the present application.

[0019] In the figure: 1. Carbon emission data collection unit; 2. Production data collection unit; 3. Carbon emission data prediction unit; 4. Carbon emission data anomaly detection unit; 5. Carbon emission data anomaly alarm unit; 6. Big data storage module; 7. Data analysis module; 8. Prediction analysis module; 9. Data waveform conversion module; 10. Waveform threshold loading module; 11. Carbon emission data anomaly confirmation module; 12. Receiving module; 13. Judgment module; 14. Alarm module; 15. Anomaly data processing module. Detailed Embodiments

[0020] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0021] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0022] Please refer to Figures 1-5, like the original Internet of Things-based carbon emission data monitoring and management system, since there are a large number of carbon data collection sensors installed at different carbon emission ends, once an abnormality occurs in a certain sensor, it may lead to errors in the finally collected data. Based on the above problems, the embodiment of the present application discloses an Internet of Things-based carbon emission monitoring and management system, including: Carbon emission data collection unit: including data collection sensors installed at each carbon emission end for collecting carbon emission data, a wireless transmission module for data transmission, and a data collection and collation terminal; Production data collection unit: used to collect production data corresponding to power production equipment, power loss equipment, and carbon emission equipment corresponding to each carbon emission end based on time series; Carbon emission data prediction unit: The carbon emission data prediction unit receives the carbon emission data corresponding to different production data stored in the corresponding big data storage module, and analyzes the relationship between production data and carbon emission data through the data analysis module; Carbon emission data anomaly detection unit: used to collect the data waveforms collected by each data collection sensor and judge whether there is a sudden anomaly; Carbon emission data anomaly alarm unit: When there is a large deviation between the data predicted by the carbon emission data prediction unit and the actually collected carbon emission data, and the carbon emission data anomaly detection unit detects a sudden anomaly in the data waveform collected by the data collection sensor, it will give a warning of the corresponding carbon emission data anomaly.

[0023] The above solution provides a carbon emission data monitoring and management system. In addition to the carbon emission data collection unit, it is also equipped with a corresponding production data collection unit and a carbon emission data prediction unit, which can predict carbon emission data based on production data and can also judge sudden anomalies through the carbon emission data anomaly detection unit. The combined effect of the two can further judge whether there is an anomaly in carbon emission data collection and give a warning of data anomaly.

[0024] The carbon emission data prediction unit includes: Big data storage module: used to receive carbon data emitted from different carbon emission ends collected by the carbon emission data collection unit and production data corresponding to carbon emissions collected by the production data collection unit; Data analysis module: used to calculate the corresponding conversion coefficient based on the production data corresponding to carbon emissions collected by the production data collection unit and the corresponding carbon emission data; Prediction analysis module: Based on the conversion coefficient analyzed by the data analysis module, estimate the carbon emission data for the collected production data.

[0025] The above solution refines the carbon emission data prediction unit into a big data storage module, a data analysis module, and a prediction analysis module, which can realize the corresponding relationship based on carbon emission data and production data, obtain the corresponding conversion coefficient, and through the conversion coefficient, only by obtaining the production data, the carbon emission data can be estimated.

[0026] The carbon emission data anomaly detection unit includes: Data waveform conversion module: used to convert the data collected by the carbon emission data collection unit into a carbon emission data waveform corresponding to time change; Waveform threshold loading module: The operator sets the corresponding carbon emission mutation threshold according to the carbon emission data prediction unit; Carbon emission data anomaly confirmation module: used to judge whether the carbon emission data waveform converted by the data waveform conversion module exceeds the threshold, so as to judge whether there is an anomaly in the carbon emission data detection.

[0027] The above solution can realize the judgment of the waveform of carbon emission data based on the waveform threshold, and judge whether there is an anomaly in the carbon emission data.

[0028] The carbon emission data anomaly alarm unit includes: Receiving module: used to receive the carbon emission data collected and sorted by the carbon emission data collection unit, the carbon emission data predicted based on production data by the carbon emission data prediction unit, and whether there is an anomaly in the carbon emission data confirmed by the carbon emission data anomaly detection unit; Judgment module: When the carbon emission data anomaly detection unit confirms that the carbon emission data is abnormal, further judge whether there is a large deviation between the carbon emission data predicted by the carbon emission data prediction unit and the data collected by the carbon emission data collection unit; if there is a large deviation, send a signal to the alarm module; Alarm module: Receive the judgment signal generated by the judgment module and perform corresponding alarms.

[0029] When the carbon emission data anomaly detection unit confirms that the carbon emission data is abnormal, it may be due to the change of production data, or it may be caused by the abnormality of the acquisition sensor. The carbon emission data of this design can further predict the carbon emission data based on the production data, confirm whether it is abnormal, and give an alarm.

[0030] It also includes: an abnormal data processing module, which is used to, when the alarm module gives an alarm, based on the abnormal data detected by the carbon emission data detection unit, find the corresponding sensor module, replace the abnormal data with the data predicted by the carbon emission data prediction unit, and at the same time replace the abnormal data acquisition sensor with a normal data acquisition sensor.

[0031] When the acquisition sensor fails, the carbon emission data predicted by the carbon emission data prediction unit in this design can be used as substitute data for abnormal data, which can further reduce the impact of abnormal data on data acquisition.

[0032] A monitoring method is applied to the Internet of Things-based carbon emission monitoring and management system described in any one of the above, and includes: S1: The data acquisition sensors set at each carbon emission end collect carbon emission data and transmit it to the data collection and collation terminal through a wireless transmission module; S2: The production data acquisition unit collects the production data corresponding to the power production equipment, power loss equipment, and carbon emission equipment corresponding to each carbon emission end based on time series; S3: The carbon emission data prediction unit receives the carbon emission data corresponding to different production data stored in the big data storage module, and analyzes the relationship between the production data and the carbon emission data through the data analysis module; S4: The carbon emission data anomaly detection unit collects the data waveforms collected by each data acquisition sensor and judges whether there is an abnormal mutation; S5: When there is a large deviation between the data predicted by the carbon emission data prediction unit and the actually collected carbon emission data, and the data waveform collected by the data acquisition sensor detected by the carbon emission data anomaly detection unit shows a mutation anomaly, corresponding carbon emission data anomaly warnings are issued.

[0033] Further preferably, the S3 includes: S31: The big data storage unit receives the carbon data emitted by different carbon emission ends collected by the carbon emission data acquisition unit and the production material data corresponding to the carbon emissions collected by the production data acquisition unit; S32: The data analysis module calculates the corresponding conversion coefficient based on the production material data corresponding to the carbon emissions collected by the production data acquisition unit and the corresponding carbon emission data; S33: The prediction analysis module estimates the carbon emission data for the collected production material data based on the conversion coefficient analyzed by the data analysis module; The S4 includes: S41: The data waveform conversion module converts the data collected by the carbon emission data acquisition unit into a corresponding carbon emission data waveform based on time variation; S42: The operator sets a corresponding carbon emission mutation threshold according to the carbon emission data prediction unit; S43: The carbon emission data anomaly confirmation module judges whether the carbon emission data waveform converted by the data waveform conversion module exceeds the threshold, and further judges whether there is an abnormal detection of carbon emission data.

[0034] The said S5 includes: S51: The receiving module receives the carbon emission data collected and sorted by the carbon emission data collection unit, the carbon emission data predicted based on production data by the carbon emission data prediction unit, and whether the carbon emission data detection anomaly occurs confirmed by the carbon emission data anomaly detection unit; S52: When the carbon emission data anomaly detection unit confirms that the carbon emission data is abnormal, the judgment module further judges whether there is a large deviation between the carbon emission data predicted by the carbon emission data prediction unit and the data collected by the carbon emission data collection unit; if there is a large deviation, a signal is sent to the alarm module; S53: The alarm module receives the judgment signal generated by the judgment module and conducts corresponding alarms.

[0035] In the above embodiments, the device elements involved, if not otherwise specified, are all conventional device elements, and the connection methods and control methods involved, if not otherwise specified, are all conventional connection methods and control methods.

[0036] The present invention has been described in detail above in conjunction with the embodiments. However, those skilled in the art can understand that without departing from the purpose of the present invention, various specific parameters in the above embodiments can be changed to form multiple specific embodiments, which are all within the common change range of the present invention and will not be elaborated here one by one.

Claims

1. An Internet of Things-based carbon emission monitoring and management system, characterized in that, Including: Carbon emission data collection unit: It includes data collection sensors set at each carbon emission end for collecting carbon emission data, a wireless transmission module for data transmission, and a data collection and collation terminal; Production data collection unit: It is used to collect production data corresponding to power production equipment, power loss equipment, and carbon emission equipment corresponding to each carbon emission end based on time series; Carbon emission data prediction unit: The carbon emission data prediction unit receives the carbon emission data corresponding to different production data stored in the corresponding big data storage module, and analyzes the relationship between production data and carbon emission data through the data analysis module; Carbon emission data anomaly detection unit: It is used to collect the data waveforms collected by each data collection sensor and judge whether there are sudden anomalies; Carbon emission data anomaly alarm unit: When there is a large deviation between the data predicted by the carbon emission data prediction unit and the actually collected carbon emission data, and the carbon emission data anomaly detection unit detects that the data waveform collected by the data collection sensor has a sudden anomaly, it will give a warning of the corresponding carbon emission data anomaly.

2. The carbon emission monitoring and management system based on the Internet of Things according to claim 1, characterized in that, The carbon emission data prediction unit includes: Big data storage module: It is used to receive the carbon data emitted by different carbon emission ends collected by the carbon emission data collection unit and the production material data corresponding to the carbon emissions collected by the production data collection unit; Data analysis module: It is used to calculate the corresponding conversion coefficient based on the production material data corresponding to the carbon emissions collected by the production data collection unit and the corresponding carbon emission data; Prediction analysis module: Based on the conversion coefficient analyzed by the data analysis module, it estimates the carbon emission data for the collected production material data.

3. An Internet of Things-based carbon emission monitoring and management system according to claim 1, characterized in that, The carbon emission data anomaly detection unit includes: Data waveform conversion module: It is used to convert the data collected by the carbon emission data collection unit into corresponding carbon emission data waveforms based on time changes; Waveform threshold loading module: The operator sets the corresponding carbon emission mutation threshold according to the carbon emission data prediction unit; Carbon emission data anomaly confirmation module: It is used to judge whether the carbon emission data waveform converted by the data waveform conversion module exceeds the threshold, so as to judge whether there is an anomaly in carbon emission data detection.

4. An Internet of Things-based carbon emission monitoring and management system according to claim 1, characterized in that, The carbon emission data anomaly alarm unit includes: Receiving module: It is used to receive the carbon emission data collected and collated by the carbon emission data collection unit, the carbon emission data predicted based on production data by the carbon emission data prediction unit, and whether the carbon emission data anomaly detection unit confirms that there is an anomaly in carbon emission data detection; Judgment module: When the carbon emission data anomaly detection unit confirms that there is an anomaly in carbon emission data, it further judges whether there is a large deviation between the carbon emission data predicted by the carbon emission data prediction unit and the data collected by the carbon emission data collection unit; if there is a large deviation, it sends a signal to the alarm module; Alarm module: It receives the judgment signal generated by the judgment module and gives a corresponding alarm.

5. An Internet of Things-based carbon emission monitoring and management system according to claim 1, characterized in that, It also includes: An abnormal data processing module, which is used to detect the abnormally collected data detected by the carbon emission data detection unit when the alarm module gives an alarm, find the corresponding sensor module, replace the abnormal data with the data predicted by the carbon emission data prediction unit, and at the same time replace the abnormal data collection sensor with a normal data collection sensor.

6. A monitoring method, applied to an Internet of Things-based carbon emission monitoring and management system according to any one of claims 1-5, comprising: S1: Data collection sensors arranged at each carbon emission end collect carbon emission data and transmit it to the data collection and collation terminal through a wireless transmission module; S2: The production data collection unit collects the production data corresponding to the power production equipment, power loss equipment, and carbon emission equipment corresponding to each carbon emission end based on time series; S3: The carbon emission data prediction unit receives the carbon emission data corresponding to different production data stored in the big data storage module, and analyzes the relationship between the production data and the carbon emission data through the data analysis module; S4: The carbon emission data abnormality detection unit collects the data waveforms collected by each data collection sensor and judges whether there is an abnormal mutation; S5: When there is a large deviation between the data predicted by the carbon emission data prediction unit and the actually collected carbon emission data, and the data waveform collected by the data collection sensor detected by the carbon emission data abnormality detection unit shows a mutation abnormality, corresponding carbon emission data abnormality early warning is carried out.

7. A monitoring method according to claim 6, characterized in that S3 includes: S31: The big data storage unit receives the carbon data emitted by different carbon emission ends collected by the carbon emission data collection unit and the production material data corresponding to the carbon emissions collected by the production data collection unit; S32: The data analysis module calculates the corresponding conversion coefficient based on the production material data corresponding to the carbon emissions collected by the production data collection unit and the corresponding carbon emission data; S33: The prediction analysis module estimates the carbon emission data for the collected production material data based on the conversion coefficient analyzed by the data analysis module.

8. A monitoring method according to claim 7, characterized in that, S4 includes: S41: The data waveform conversion module converts the data collected by the carbon emission data collection unit into a corresponding carbon emission data waveform based on time change; S42: The operator sets a corresponding carbon emission mutation threshold according to the carbon emission data prediction unit; S43: The carbon emission data abnormality confirmation module judges whether the carbon emission data waveform converted by the data waveform conversion module exceeds the threshold, and further judges whether there is an abnormal detection of the carbon emission data.

9. A monitoring method according to claim 8, characterized in that, S5 includes: S51: The receiving module receives the carbon emission data collected and sorted by the carbon emission data collection unit, the carbon emission data predicted based on the production data by the carbon emission data prediction unit, and whether the carbon emission data abnormality detection unit confirms that there is an abnormal detection of the carbon emission data; S52: When the judgment module determines that the carbon emission data is abnormal when the carbon emission data anomaly detection unit confirms the anomaly of the carbon emission data, it further determines whether there is a large deviation between the carbon emission data predicted by the carbon emission data prediction unit and the data collected by the carbon emission data collection unit; if there is a large deviation, it sends a signal to the alarm module; S53: The alarm module receives the judgment signal generated by the judgment module and gives corresponding alarms.

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