Remote monitoring and data analysis system and method for diesel engine controller

Abstract

The invention discloses a remote monitoring and data analysis system and method for a diesel engine controller, and the system comprises a data collection terminal, a data transmission network, a remote monitoring center and a user terminal, and relates to the technical field of diesel engine monitoring. According to the remote monitoring and data analysis system and method for the diesel engine controller, the operation data of the diesel engine can be collected and transmitted to the remote monitoring center in real time through the data collection terminal and the stable data transmission network. Management personnel can check the running state of the engine in real time through a user terminal (a PC terminal or a mobile terminal) at any place with network connection, such as the real-time change condition of parameters such as the rotating speed, the temperature and the pressure of the engine. By means of the real-time monitoring capacity, abnormal conditions such as sudden rotating speed fluctuation or temperature rise of the engine in the operation process of the engine can be found in time, and therefore measures can be taken in the first time, and further deterioration of faults is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of diesel engine monitoring, and in particular to a remote monitoring and data analysis system and method for a diesel engine controller. Background Art

[0002] At present, some diesel engines still use manual regular inspections to monitor their operating status. This method has obvious defects. Due to the time interval between manual inspections, it is impossible to achieve real-time monitoring of the engine's operating status. In the period between two inspections, the engine may have a fault or abnormality, but it cannot be discovered in time. For example, the engine may have coolant leakage, high oil temperature and other problems during the inspection interval. If these problems are not handled in time, they may cause engine damage, increase maintenance costs and downtime.

[0003] Although some diesel engines are equipped with local monitoring systems, these systems have problems with data isolation and limited analysis capabilities. Local monitoring systems can usually only store and display the operating data of the engine itself, and data between different engines cannot be effectively integrated and compared. Moreover, the data analysis algorithm of the local system is relatively simple, and it can often only perform basic threshold judgments, such as alarming when the temperature exceeds a certain set value. It is difficult to accurately predict some potential, progressive faults. For example, the wear of internal engine components is a gradual process, and the local monitoring system may not be able to detect the performance degradation caused by such wear in advance until a fault occurs.

[0004] Diesel engines generate a large amount of data during operation, which includes the operating status information of various engine components. Through in-depth analysis of these data, we can understand the health of the engine, predict possible failures, and take maintenance measures in advance. For example, comprehensive analysis of engine fuel consumption rate, exhaust temperature, vibration frequency and other data can determine whether the engine's combustion efficiency has decreased, whether there are problems such as wear of internal components.

[0005] However, data analysis in the field of diesel engines currently faces many challenges. On the one hand, engine operation data is diverse and complex, including real-time data (such as speed, pressure) and non-real-time data (such as cumulative operating time, maintenance records), etc. How to effectively collect and integrate these different types of data is a difficult problem. On the other hand, due to the lack of advanced data analysis models and algorithms, existing data analysis methods often cannot fully explore the potential information in the data and cannot accurately establish a relationship model between data and engine failures. Summary of the invention

[0006] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a remote monitoring and data analysis system and method for a diesel engine controller, which solves the problem that the existing monitoring method cannot remotely monitor the operating status of the diesel engine controller in real time, ensures that management personnel can obtain the engine operating information anytime and anywhere, and promptly discover abnormal situations, overcome the defects of isolated data in the local monitoring system, build a platform that can integrate the operating data of multiple diesel engines, and use advanced data analysis technology on this platform to conduct in-depth mining and analysis of the data. In view of the deficiency that the existing system is difficult to predict potential faults, an effective fault prediction model is developed. Through real-time analysis of the engine operating data, possible faults can be predicted in advance, providing an accurate basis for preventive maintenance, and reducing unplanned downtime and maintenance costs.

[0007] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a remote monitoring and data analysis system for a diesel engine controller, comprising a data acquisition terminal, a data transmission network, a remote monitoring center and a user terminal, wherein the data acquisition terminal comprises a sensor and a data acquisition module, wherein the sensor is responsible for real-time acquisition of various physical quantity data during the operation of the engine, and the data acquisition module performs pre-processing on the data collected by the sensor, such as amplification, filtering, analog-to-digital conversion and other operations, so as to convert the data into a digital signal suitable for transmission and processing; The data transmission network adopts a combination of wired and wireless modes to ensure stable data transmission. In terms of wired transmission, the local area network (LAN) where the engine is located is used for data transmission. In terms of wireless transmission, the corresponding wireless communication network is selected according to the actual application scenario. The data transmission network transmits the data collected by the data collection terminal to the remote monitoring center. The remote monitoring center is the core part of the whole system, including data receiving module, data storage module, data analysis module and monitoring platform. The data receiving module is responsible for receiving data from the data transmission network and passing it to the data storage module for storage; The user terminal includes a PC terminal and a mobile terminal. The user logs in to the remote monitoring platform through these terminal devices to realize remote monitoring and operation of the diesel engine. The user can view the real-time operating data, historical data, fault alarm information of the engine, and can also remotely adjust and control the operating parameters of the engine.

[0008] Preferably, the sensors include a temperature sensor, a pressure sensor, a rotation speed sensor and a vibration sensor.

[0009] Preferably, the mobile terminal is a smart phone or a tablet computer.

[0010] Preferably, the wireless communication network includes 4G mobile communication network, 5G mobile communication network, ZigBee and LoRa.

[0011] Preferably, the data analysis module uses big data analysis technology and machine learning algorithms to process and analyze the stored data, establish a fault prediction model, and generate a corresponding analysis report.

[0012] Preferably, the monitoring platform provides users with an intuitive operating interface for displaying the engine's operating status, fault alarm information and analysis results.

[0013] The present invention also provides a remote monitoring and data analysis method for a diesel engine controller, which specifically includes the following steps: S1. Data collection and preprocessing: At the data collection terminal, the collected data is preprocessed. First, the analog signal is amplified and filtered to remove noise interference and improve the signal quality. Then, the analog signal is converted into a digital signal through an analog-to-digital converter. For the converted digital signal, data verification and error correction are performed to ensure the accuracy of the data. For example, the cyclic redundancy check (CRC) algorithm is used to verify the data. If data errors are found, data re-collection or error correction algorithms are used for processing; S2. Data transmission mechanism: To ensure the accuracy and integrity of data during transmission, for example, when transmitting a large amount of historical data generated by long-term operation of the engine, a packet transmission method is used to ensure the efficiency and reliability of data transmission. In order to ensure the security of the data, encryption technology is used in the data transmission process to encrypt the collected engine operation data to prevent the data from being stolen or tampered with during transmission. For example, the Advanced Encryption Standard (AES) algorithm is used to encrypt the data and then decrypt it at the data receiving end. At the same time, the user login to the remote monitoring platform is authenticated, and the user identity is verified by user name, password, and digital certificate to ensure that only authorized users can access the engine's operating data and perform operations; S3. Data analysis model construction: In the remote monitoring center, the data analysis model is constructed using big data analysis technology. First, the large amount of collected engine operation data is cleaned to remove invalid data and abnormal data. Then, the cleaned data is subjected to feature extraction to extract key features that can reflect the engine operation status, such as the fluctuation range of the engine speed, the change trend of the oil temperature, etc. Based on these features, a machine learning algorithm (such as support vector machine, neural network, decision tree, etc.) is used to build a fault prediction model. For example, by learning the operating characteristics of the engine before the failure in the historical data, a model that can predict the engine failure is established. The fault prediction model is used to analyze the real-time operation data of the engine to predict possible failures. When the engine operation data changes abnormally, the fault prediction algorithm can issue a warning signal in time. For example, when the vibration frequency and amplitude of the engine increase abnormally, the fault prediction model is used to analyze and judge that it may be the looseness or wear of the internal components of the engine, and then a fault warning is issued to remind the management personnel to take corresponding maintenance measures; S4. Remote monitoring: The interface design of the remote monitoring platform is simple and intuitive, and it can display the operating status of multiple diesel engines in real time. On the monitoring interface, the key operating parameters of the engine, such as speed, temperature, pressure, etc., are displayed in the form of charts (such as bar charts, line charts, pie charts, etc.) and numbers. At the same time, the operating status of the engine is intuitively displayed through indicator lights of different colors (such as green for normal, yellow for warning, and red for fault). For example, when the engine coolant temperature exceeds the normal range, the corresponding temperature indicator on the monitoring interface will change from green to yellow or red, and the specific temperature value and alarm information will be displayed at the same time.

[0014] Preferably, in step S1, the frequency and accuracy of data collection are determined according to the operating characteristics and monitoring requirements of the diesel engine. For key parameters (such as engine speed, coolant temperature, oil pressure, etc.), a higher collection frequency (such as 1-10 times per second) and a higher accuracy (such as temperature measurement accuracy of ±0.5°C) are adopted to ensure that the changes in the operating state of the engine can be accurately captured. For non-critical parameters (such as the cumulative engine operating time, cumulative fuel consumption, etc.), a lower collection frequency (such as once per minute) is adopted.

[0015] Preferably, a dedicated data transmission protocol is used in step S2, which contains information such as the data type, source, and timestamp in the data packet header and specific operating data in the data packet body. During the transmission process, the data is sub-packetized and packaged. When the data volume is large, the data is divided into multiple small packets for transmission, and the packets are restored at the receiving end.

[0016] Preferably, in step S4, the user can remotely control the diesel engine through the user terminal. For example, in some special cases, the manager can remotely adjust the engine's speed, load and other operating parameters, or remotely stop and start the engine. When performing remote control operations, the system will verify the user's operating authority to ensure the safety of the operation. At the same time, the remote control operation is recorded for subsequent query and audit.

[0017] (III) Beneficial effects The present invention provides a remote monitoring and data analysis system and method for a diesel engine controller. Compared with the prior art, the system has the following beneficial effects: (1) The remote monitoring and data analysis system and method of the diesel engine controller can collect and transmit the operating data of the diesel engine to the remote monitoring center in real time through the data acquisition terminal and the stable data transmission network. The manager can view the operating status of the engine in real time through the user terminal (PC or mobile terminal) at any place with network connection, such as the real-time changes of parameters such as engine speed, temperature, pressure, etc. This real-time monitoring capability can timely detect abnormal conditions during the operation of the engine, such as sudden speed fluctuations or temperature increases of the engine, so that measures can be taken at the first time to avoid further deterioration of the fault. The user can operate remotely without going to the engine site. For example, when an emergency requires shutdown, the manager can immediately perform remote shutdown operation on the engine through the user terminal, reducing the time and risk of on-site operation. At the same time, for some situations where the operating parameters need to be adjusted, such as adjusting the engine speed according to load changes, it can also be conveniently operated remotely, improving the flexibility and efficiency of operation.

[0018] (2) The remote monitoring and data analysis system and method of the diesel engine controller. The system of the present invention can integrate the operating data of multiple diesel engines, breaking the situation of data isolation of the local monitoring system. By accumulating and analyzing a large amount of data, it is possible to discover patterns and problems that cannot be discovered by monitoring a single engine. For example, by comparing the operating data of multiple engines under the same working conditions, it is possible to find engines with large performance differences, and further analyze possible problems, such as differences in component quality or improper installation and debugging. The fault prediction model constructed using big data analysis technology and machine learning algorithms can accurately predict the potential faults of the engine. Through real-time analysis of engine operating data, the system can detect some progressive faults in advance, such as wear of internal engine components and blockage of the cooling system. For example, based on the changing trends of the engine's oil temperature, oil pressure and vibration data, combined with the fault prediction model, possible engine faults can be predicted hours or even days in advance, providing strong support for preventive maintenance.

[0019] (3) The remote monitoring and data analysis system and method of the diesel engine controller can arrange maintenance plans in advance and implement preventive maintenance based on accurate fault prediction. Compared with the traditional post-fault maintenance method, preventive maintenance can reduce unplanned downtime. For example, when it is predicted that the engine may fail, maintenance can be carried out during a period of time when the equipment downtime is relatively short (such as during regular equipment maintenance), avoiding long-term downtime caused by sudden engine failure during the production process, thereby improving production efficiency and reducing maintenance costs caused by sudden engine failure. Since faults can be discovered and maintained in advance, further deterioration of the fault can avoid causing greater damage to the engine, thereby reducing the cost of replacing parts and labor costs required for maintenance. At the same time, through centralized monitoring and data analysis of multiple engines, the allocation of maintenance resources can be optimized, unnecessary maintenance operations can be reduced, and maintenance costs can be further saved.

[0020] (4) The remote monitoring and data analysis system and method of the diesel engine controller ensures the security of the engine operation data by using encryption technology and identity authentication mechanism during the data transmission process. It prevents the data from being stolen or tampered with during the transmission process, and protects the company's business secrets and the safety of engine operation. For example, the company's diesel engine operation data may contain some sensitive information, such as engine performance parameters and operating conditions, etc. Data encryption can prevent this information from being obtained by competitors.

[0021] (5) The remote monitoring and data analysis system and method of the diesel engine controller improves the reliability of the entire remote monitoring and data analysis system by adopting redundant design and fault-tolerant mechanism. For example, in the data transmission network, when the wired network fails, the wireless communication network can continue to transmit data as a backup; in the remote monitoring center, the data storage module adopts redundant storage technology to prevent data loss. At the same time, the system has self-diagnosis and fault recovery capabilities. When a component of the system fails, it can be discovered in time and self-repaired or prompt the management personnel to repair it to ensure the normal operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention is a flowchart of a remote monitoring and data analysis method for a diesel engine controller. DETAILED DESCRIPTION

[0023] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 , an embodiment of the present invention provides a technical solution: a remote monitoring and data analysis system for a diesel engine controller, comprising a data acquisition terminal, a data transmission network, a remote monitoring center and a user terminal, wherein the data acquisition terminal comprises a sensor and a data acquisition module, wherein the sensor is responsible for real-time acquisition of various physical quantity data during the operation of the engine, and the data acquisition module performs pre-processing on the data collected by the sensor, such as amplification, filtering, analog-to-digital conversion and other operations, so as to convert the data into a digital signal suitable for transmission and processing; The data transmission network adopts a combination of wired and wireless modes to ensure stable data transmission. In terms of wired transmission, the local area network (LAN) where the engine is located is used for data transmission. In terms of wireless transmission, the corresponding wireless communication network is selected according to the actual application scenario. The data transmission network transmits the data collected by the data collection terminal to the remote monitoring center. The remote monitoring center is the core part of the whole system, including data receiving module, data storage module, data analysis module and monitoring platform. The data receiving module is responsible for receiving data from the data transmission network and passing it to the data storage module for storage; The user terminal includes a PC terminal and a mobile terminal. The user logs in to the remote monitoring platform through these terminal devices to realize remote monitoring and operation of the diesel engine. The user can view the real-time operating data, historical data, fault alarm information of the engine, and can also remotely adjust and control the operating parameters of the engine.

[0025] In the embodiment of the present invention, the sensor includes a temperature sensor, a pressure sensor, a rotation speed sensor and a vibration sensor.

[0026] In the embodiment of the present invention, the mobile terminal is a smart phone or a tablet computer.

[0027] In an embodiment of the present invention, the wireless communication network includes a 4G mobile communication network, a 5G mobile communication network, ZigBee and LoRa.

[0028] In the embodiment of the present invention, the data analysis module uses big data analysis technology and machine learning algorithms to process and analyze the stored data, establish a fault prediction model, and generate a corresponding analysis report.

[0029] In the embodiment of the present invention, the monitoring platform provides the user with an intuitive operation interface for displaying the operating status of the engine, fault alarm information and analysis results.

[0030] The embodiment of the present invention also provides a remote monitoring and data analysis method for a diesel engine controller, which specifically includes the following steps: S1. Data collection and preprocessing: At the data collection terminal, the collected data is preprocessed. First, the analog signal is amplified and filtered to remove noise interference and improve the signal quality. Then, the analog signal is converted into a digital signal through an analog-to-digital converter. For the converted digital signal, data verification and error correction are performed to ensure the accuracy of the data. For example, the cyclic redundancy check (CRC) algorithm is used to verify the data. If data errors are found, data re-collection or error correction algorithms are used for processing; S2. Data transmission mechanism: To ensure the accuracy and integrity of data during transmission, for example, when transmitting a large amount of historical data generated by long-term operation of the engine, a packet transmission method is used to ensure the efficiency and reliability of data transmission. In order to ensure the security of the data, encryption technology is used in the data transmission process to encrypt the collected engine operation data to prevent the data from being stolen or tampered with during transmission. For example, the Advanced Encryption Standard (AES) algorithm is used to encrypt the data and then decrypt it at the data receiving end. At the same time, the user login to the remote monitoring platform is authenticated, and the user identity is verified by user name, password, and digital certificate to ensure that only authorized users can access the engine's operating data and perform operations; S3. Data analysis model construction: In the remote monitoring center, the data analysis model is constructed using big data analysis technology. First, the large amount of collected engine operation data is cleaned to remove invalid data and abnormal data. Then, the cleaned data is subjected to feature extraction to extract key features that can reflect the engine operation status, such as the fluctuation range of the engine speed, the change trend of the oil temperature, etc. Based on these features, a machine learning algorithm (such as support vector machine, neural network, decision tree, etc.) is used to build a fault prediction model. For example, by learning the operating characteristics of the engine before the failure in the historical data, a model that can predict the engine failure is established. The fault prediction model is used to analyze the real-time operation data of the engine to predict possible failures. When the engine operation data changes abnormally, the fault prediction algorithm can issue a warning signal in time. For example, when the vibration frequency and amplitude of the engine increase abnormally, the fault prediction model is used to analyze and judge that it may be the looseness or wear of the internal components of the engine, and then a fault warning is issued to remind the management personnel to take corresponding maintenance measures; S4. Remote monitoring: The interface design of the remote monitoring platform is simple and intuitive, and it can display the operating status of multiple diesel engines in real time. On the monitoring interface, the key operating parameters of the engine, such as speed, temperature, pressure, etc., are displayed in the form of charts (such as bar charts, line charts, pie charts, etc.) and numbers. At the same time, the operating status of the engine is intuitively displayed through indicator lights of different colors (such as green for normal, yellow for warning, and red for fault). For example, when the engine coolant temperature exceeds the normal range, the corresponding temperature indicator on the monitoring interface will change from green to yellow or red, and the specific temperature value and alarm information will be displayed at the same time.

[0031] In the embodiment of the present invention, in step S1, the frequency and accuracy of data collection are determined according to the operating characteristics and monitoring requirements of the diesel engine. For key parameters (such as engine speed, coolant temperature, oil pressure, etc.), a higher collection frequency (such as 1-10 times per second) and a higher accuracy (such as temperature measurement accuracy of ±0.5°C) are adopted to ensure that the changes in the operating state of the engine can be accurately captured. For non-critical parameters (such as the cumulative engine operating time, cumulative fuel consumption, etc.), a lower collection frequency (such as once per minute) is adopted.

[0032] In an embodiment of the present invention, a dedicated data transmission protocol is used in step S2. The protocol includes information such as the data type, source, and timestamp in the data packet header, and includes specific operating data in the data packet body. During the transmission process, the data is sub-packetized and assembled. When the data volume is large, the data is divided into multiple small packets for transmission, and the packets are assembled and restored at the receiving end.

[0033] In the embodiment of the present invention, in step S4, the user can remotely control the diesel engine through the user terminal. For example, in some special cases, the manager can remotely adjust the engine's speed, load and other operating parameters, or remotely stop and start the engine. When performing remote control operations, the system will verify the user's operating authority to ensure the safety of the operation. At the same time, the remote control operation is recorded for subsequent query and audit.

[0034] 1. System installation and initialization: Data acquisition terminal installation: Select appropriate installation locations on the diesel engine to install various sensors. For example, temperature sensors are installed at the coolant outlet, oil pan and other locations of the engine to accurately measure the coolant temperature and oil temperature; pressure sensors are installed at oil filters, fuel filters and other locations to measure oil pressure and fuel pressure; speed sensors are installed at the crankshaft or flywheel of the engine to measure engine speed; vibration sensors are installed on the engine body to measure the vibration of the engine. When installing the sensor, ensure that the sensor is in good contact with the engine surface and can accurately collect the required physical quantity data. At the same time, install the data acquisition module at a suitable location near the engine, connect the sensor to the data acquisition module through a cable, and ensure a firm connection and stable signal transmission.

[0035] Data transmission network configuration: Select the appropriate data transmission module and communication method according to the application scenario of the engine. If it is in an area with good 4G / 5G network coverage, you can use the 4G / 5G wireless communication module; if it is in an area without ground network coverage such as a ship, you can use a satellite communication module. When configuring the data transmission module, you must set the communication parameters, such as IP address, port number, encryption key, etc., to ensure that the data can be transmitted to the remote monitoring center safely and stably.

[0036] Remote monitoring center construction: In the remote monitoring center, build a data receiving unit, data storage unit, data analysis unit and monitoring platform. The data receiving unit uses a high-performance server, configured with the corresponding network interface and data receiving software to ensure that the data from the data transmission module can be received in time. The data storage unit uses a large-capacity storage device, such as a disk array, to establish a reasonable data storage structure for storing the engine's operating data. The data analysis unit uses a server with powerful computing power, installed with big data analysis software and machine learning algorithm library for data analysis and processing. The monitoring platform adopts B / S architecture or C / S architecture, and develops a friendly user interface to facilitate user login and operation.

[0037] User terminal settings: Install remote monitoring software or applications on the user terminal (PC or mobile terminal). The user terminal is connected to the monitoring platform of the remote monitoring center through the network, and the user logs in with the assigned account and password. In the user terminal settings, configuration should be performed according to the user's permissions to ensure that different users can only perform operations within their permissions. For example, ordinary users can only view data, and administrator users can perform remote operations and system configuration.

[0038] (II) System operation process: Data collection and transmission: After the data collection module is started, the operating parameters of the diesel engine are collected according to the set collection frequency. After pre-processing, the collected data is sent to the remote monitoring center through the data transmission module. During the data transmission process, the data is encrypted to ensure security.

[0039] Data reception and storage: After receiving the data, the data receiving unit of the remote monitoring center decrypts the data and then passes the data to the data storage unit for storage. The data storage unit classifies and stores the data according to time sequence and data type to facilitate subsequent query and analysis.

[0040] Data analysis and fault prediction: The data analysis unit regularly reads data from the data storage unit, first cleans the data to remove invalid and abnormal data. Then it extracts features and inputs the extracted features into the fault prediction model for analysis. When the model predicts a possible fault, it sends a fault warning signal to the monitoring platform.

[0041] Remote monitoring and operation: Users log in to the monitoring platform of the remote monitoring center through the user terminal to view the real-time operating data, historical data and fault warning information of the engine. If they have the operation authority, users can remotely operate the diesel engine through the monitoring platform, such as adjusting the engine speed, load and other parameters. The system will record all operation processes for subsequent query and audit.

[0042] 3. System maintenance and upgrade: Hardware maintenance: Regularly inspect and maintain the sensors and data acquisition units of the data acquisition module to ensure that the sensors can collect data normally and the data acquisition units can work stably. Check the communication equipment of the data transmission module, such as whether the antenna is damaged and whether the signal strength is normal. In the remote monitoring center, regularly inspect the hardware such as servers and storage devices to detect and handle hardware failures in a timely manner.

[0043] Software upgrade: According to the needs of data analysis and fault prediction, the algorithms and models in the data analysis unit are continuously optimized and upgraded. For example, when a new type of engine fault appears, the fault prediction model is trained and updated to improve the model's ability to predict new faults. At the same time, the monitoring platform and user terminal software of the remote monitoring center are upgraded to fix software vulnerabilities and optimize the user interface and operating functions.

[0044] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0045] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0046] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote monitoring and data analysis system for a diesel engine controller, characterized in that: It includes a data acquisition terminal, a data transmission network, a remote monitoring center and a user terminal. The data acquisition terminal includes a sensor and a data acquisition module. The sensor is responsible for real-time acquisition of various physical quantity data during the operation of the engine. The data acquisition module pre-processes the data collected by the sensor. The data transmission network adopts a combination of wired and wireless modes to ensure stable data transmission. In terms of wired transmission, the local area network where the engine is located is used for data transmission. In terms of wireless transmission, the corresponding wireless communication network is selected according to the actual application scenario. The data transmission network transmits the data collected by the data collection terminal to the remote monitoring center. The remote monitoring center is the core part of the whole system, including data receiving module, data storage module, data analysis module and monitoring platform. The data receiving module is responsible for receiving data from the data transmission network and passing it to the data storage module for storage; The user terminal includes a PC terminal and a mobile terminal. The user logs in to the remote monitoring platform through these terminal devices to realize remote monitoring and operation of the diesel engine. The user can view the real-time operating data, historical data, fault alarm information of the engine, and can also remotely adjust and control the operating parameters of the engine.

2. A remote monitoring and data analysis system for a diesel engine controller according to claim 1, characterized in that: The sensors include a temperature sensor, a pressure sensor, a rotation speed sensor and a vibration sensor.

3. The remote monitoring and data analysis system for a diesel engine controller according to claim 1, characterized in that: The mobile terminal is a smart phone or a tablet computer.

4. A remote monitoring and data analysis system for a diesel engine controller according to claim 1, characterized in that: The wireless communication network includes 4G mobile communication network, 5G mobile communication network, ZigBee and LoRa.

5. The remote monitoring and data analysis system for a diesel engine controller according to claim 1, characterized in that: The data analysis module uses big data analysis technology and machine learning algorithms to process and analyze the stored data, establish a fault prediction model, and generate a corresponding analysis report.

6. A remote monitoring and data analysis system for a diesel engine controller according to claim 1, characterized in that: The monitoring platform provides users with an intuitive operation interface for displaying the engine's operating status, fault alarm information and analysis results.

7. The remote monitoring and data analysis method of a diesel engine controller according to claim 1, characterized in that: A remote monitoring and data analysis system using a diesel engine controller according to any one of claims 1 to 6 specifically comprises the following steps: S1. Data collection and preprocessing: At the data collection terminal, the collected data is preprocessed. First, the analog signal is amplified and filtered to remove noise interference and improve the signal quality. Then, the analog signal is converted into a digital signal through an analog-to-digital converter. For the converted digital signal, data verification and error correction are performed to ensure the accuracy of the data. For example, the cyclic redundancy check (CRC) algorithm is used to verify the data. If data errors are found, data re-collection or error correction algorithms are used for processing; S2. Data transmission mechanism: Encrypt the collected engine operation data to prevent the data from being stolen or tampered with during transmission. At the same time, authenticate the user's identity when logging into the remote monitoring platform through user name, password and digital certificate to ensure that only authorized users can access the engine's operation data and perform operations; S3. Data analysis model construction: In the remote monitoring center, a data analysis model is constructed using big data analysis technology. First, a large amount of collected engine operation data is cleaned to remove invalid and abnormal data. Then, feature extraction is performed on the cleaned data to extract key features that can reflect the engine operation status. Based on these features, a machine learning algorithm is used to construct a fault prediction model. The fault prediction model is used to analyze the real-time operation data of the engine to predict possible faults. When the engine operation data shows abnormal changes, the fault prediction algorithm can issue a warning signal in time. S4. Remote monitoring: The interface design of the remote monitoring platform is simple and intuitive, and it can display the operating status of multiple diesel engines in real time. On the monitoring interface, the key operating parameters of the engine are displayed in the form of charts and numbers, and the operating status of the engine is intuitively displayed through indicator lights of different colors. For example, when the engine coolant temperature exceeds the normal range, the corresponding temperature indicator light on the monitoring interface will change from green to yellow or red, and the specific temperature value and alarm information will be displayed at the same time.

8. The remote monitoring and data analysis method of a diesel engine controller according to claim 7, characterized in that: In step S1, the frequency and accuracy of data collection are determined according to the operating characteristics and monitoring requirements of the diesel engine. For key parameters, a higher collection frequency and higher accuracy are used to ensure that the operating state changes of the engine can be accurately captured. For non-key parameters, a lower collection frequency is used.

9. The remote monitoring and data analysis method of a diesel engine controller according to claim 7, characterized in that: In the step S2, a dedicated data transmission protocol is used, which contains information such as data type, source, timestamp, etc. in the data packet header, and contains specific operation data in the data packet body. During the transmission process, the data is sub-packetized and packaged. When the data volume is large, the data is divided into multiple small packets for transmission, and the packets are restored at the receiving end.

10. The remote monitoring and data analysis method of a diesel engine controller according to claim 7, characterized in that: In step S4, the user can remotely control the diesel engine through the user terminal, and record the remote control operation for subsequent query and audit.

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