Cloud platform data transmission device of new energy locomotive

Abstract

The invention relates to a cloud platform data transmission device of a new energy locomotive, and belongs to the technical field of rail transit transportation, and the device comprises a vehicle control unit which is used for collecting the operation state data of key parts of the new energy locomotive and sending the operation state data to an FBox terminal through Ethernet communication; the key components comprise a battery, a motor, an inverter, a DCDC converter and a fire extinguishing system; the FBox terminal is used for uploading the running state data to the monitoring platform; and the monitoring platform is used for carrying out analysis processing on the operation state data through edge calculation and monitoring the operation state of the new energy locomotive based on the analyzed data. According to the cloud platform data transmission device of the new energy locomotive, operation and maintenance personnel can find and predict potential faults in time, the fault rate and the maintenance cost are reduced, data support is provided for refined operation management of the locomotive, and real-time acquisition, safe transmission and efficient analysis of key data of the new energy locomotive are achieved.

Description

Technical Field

[0001] This invention relates to the field of rail transportation technology, and in particular to a cloud platform data transmission device for new energy locomotives. Background Technology

[0002] As the rail transit industry transforms towards green and intelligent development, new energy locomotives, with their advantages of low energy consumption and low emissions, are gradually becoming the mainstream development direction for rail transit equipment. However, the core components of new energy locomotives have complex structures and variable operating parameters, and their operating status directly affects the safety, reliability, and operating costs of the locomotives. Therefore, higher requirements are placed on the refined management of the entire life cycle of locomotives.

[0003] Currently, the existing operation and management model for new energy locomotives still has significant technical defects. It lacks an effective remote data transmission and monitoring mechanism, and cannot obtain key operating data, real-time location, speed and trajectory of the locomotive in real time. As a result, operation and management personnel have difficulty grasping the actual operating status of the locomotive and cannot detect potential safety hazards in a timely manner.

[0004] Therefore, there is an urgent need for a cloud platform data transmission device that can realize real-time acquisition, secure transmission, and efficient analysis of key data of new energy locomotives. Summary of the Invention

[0005] In view of this, it is necessary to provide a cloud platform data transmission device for new energy locomotives, so as to achieve the purpose of real-time collection, secure transmission and efficient analysis of key data of new energy locomotives.

[0006] To achieve the above objectives, the present invention provides a cloud platform data transmission device for new energy locomotives, comprising: Vehicle controller, FBox terminal, and monitoring platform; The vehicle controller is used to collect operating status data of key components of the new energy vehicle and send the operating status data to the FBox terminal via Ethernet communication; the key components include battery, motor, inverter, DC-DC converter and fire protection system; The FBox terminal is used to upload the operating status data to the monitoring platform; The monitoring platform is used to parse and process the operating status data through edge computing, and monitor the operating status of new energy locomotives based on the parsed data.

[0007] In one possible implementation, the monitoring platform includes: The system comprises an acquisition unit, a storage unit, a processing unit, and a report generation unit. The acquisition unit is used to read and parse the operating status data via the UDP communication protocol; The storage unit is used to store the running status data through an encrypted signature algorithm and an access control policy; The processing unit is used to preprocess the operating status data to obtain preprocessed data; The report generation unit is used to perform fault analysis and locomotive health status prediction based on the preprocessed data, and generate data reports.

[0008] In one possible implementation, the report generation unit is specifically used for: Based on locomotive operating condition data and locomotive maintenance system inspection data, a health model for the key components is established. Based on the health model of the key components, predict the health status of the key components; The health status of the locomotive is determined based on the health status of the key components.

[0009] In one possible implementation, the processing unit is specifically used for: The operational status data is summarized and cleaned to remove abnormal data that exceeds the normal range, resulting in cleaned data. The cleaned data is then converted to a unified format to obtain the preprocessed data.

[0010] In one possible implementation, the monitoring platform further includes: The visualization unit is used for program design and screen configuration through Visual Studio, and to visualize multi-dimensional data through bar charts, line charts or pie charts.

[0011] In one possible implementation, the monitoring platform further includes: The update unit is used to configure scheduled tasks via scripts or task scheduling tools.

[0012] In one possible implementation, the scheduled tasks include the automated execution of data acquisition, data storage, data processing, and report generation.

[0013] In one possible implementation, the access control policy includes: The system's functional modules and data are classified into different levels, and corresponding function access permissions and data access levels are configured for different roles.

[0014] In one possible implementation, the cryptographic signature algorithm is either the AES algorithm or the MD5 algorithm.

[0015] In one possible implementation, the runtime status data includes: Battery operating status data, motor operating status data, inverter operating status data, DC-DC converter operating status data, and fire protection system operating status data.

[0016] The beneficial effects of this invention are as follows: The cloud platform data transmission device for new energy locomotives provided by this invention, by installing an FBox terminal on the new energy locomotive, comprehensively collects the operating status data of key components through the VCU, obtains rich and real-time internal information of the locomotive, and then transmits the operating status data of key systems such as the locomotive battery and motor to the FBox via Ethernet communication, ensuring the stability and high efficiency of data transmission from the vehicle controller to the FBox terminal, avoiding the bandwidth limitations and data loss problems that may exist in traditional serial communication. The FBox then uploads the collected data to the new energy locomotive big data center monitoring platform. The FBox terminal, as a bridge for data uploading, realizes the seamless connection between the locomotive's local data and the remote monitoring platform, overcoming the limitation of difficult remote data acquisition in traditional solutions. The monitoring platform parses and processes the operating status data through edge computing, enabling some computing tasks to be completed near the data source, reducing data transmission latency and network bandwidth pressure, and improving the real-time performance and response speed of data processing. Ultimately, monitoring the operational status of new energy locomotives based on the analyzed data enables maintenance personnel to promptly identify and predict potential faults, reducing failure rates and maintenance costs. This provides data support for the refined operation and management of locomotives, enabling real-time collection, secure transmission, and efficient analysis of key data from new energy locomotives. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of an embodiment of the cloud platform data transmission device for new energy locomotives provided by the present invention; Figure 2 This is a schematic diagram of the monitoring platform provided by the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0021] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] This invention provides a cloud platform data transmission device for new energy locomotives, which will be described in detail below.

[0024] Figure 1 This is a schematic diagram of an embodiment of the cloud platform data transmission device for new energy locomotives provided by the present invention, as shown below. Figure 1 As shown, the cloud platform data transmission device for new energy locomotives includes: Vehicle controller 101, FBox terminal 102, and monitoring platform 103; The vehicle controller 101 is used to collect the operating status data of key components of the new energy vehicle and send the operating status data to the FBox terminal 102 via Ethernet communication; the key components include battery, motor, inverter, DC-DC converter and fire protection system; The FBox terminal 102 is used to upload the operating status data to the monitoring platform 103; The monitoring platform 103 is used to parse and process the operating status data through edge computing, and monitor the operating status of the new energy locomotive based on the parsed data.

[0025] The Vehicle Control Unit (VCU) is responsible for controlling the entire vehicle and collecting data from various sensors and major components.

[0026] The vehicle controller collects the following operational status data: battery operational status data, motor operational status data, inverter operational status data, DC-DC converter operational status data, and fire protection system operational status data.

[0027] FBox terminals are intelligent transmission terminal devices, which are industrial IoT data acquisition and transmission terminals. They typically have data caching, protocol conversion, and network communication functions, serving as a data bridge between field devices and remote cloud platforms.

[0028] The vehicle-mounted terminal device (FBox) is installed on the new energy locomotive. The vehicle controller sends the operating status data to the FBox terminal device via Ethernet communication, and then the FBox uploads the collected data to the new energy locomotive big data center monitoring platform.

[0029] The monitoring platform is a cloud computing-based software system used to receive, store, process, and analyze locomotive operating status data uploaded from the FBox terminal, and to provide services such as data visualization, status monitoring, fault diagnosis, and prediction.

[0030] The monitoring platform is configured to parse and process the aforementioned operational status data using edge computing, and monitor the operational status of the new energy locomotive based on the parsed data. The introduction of edge computing allows some data processing tasks to be completed on the FBox terminal or edge node of the monitoring platform, close to the data source. The monitoring platform can deploy a lightweight parsing module on the FBox terminal to perform preliminary format conversion, data verification, and outlier filtering on the raw data, thereby reducing the amount of data transmitted to the cloud. This pre-processed data is then transmitted to the core cloud of the monitoring platform for in-depth analysis using more powerful computing resources, such as integrating data from different sources, performing time-series analysis, or pattern recognition. Based on this parsed data, the monitoring platform can display various operational parameters of the locomotive in real time and assess the health status of key components.

[0031] For example, after receiving operational status data, the monitoring platform first performs preliminary parsing and processing at the edge nodes, standardizing the data format and filtering out initial outliers to ensure data validity. Subsequently, this pre-processed data is transmitted to the monitoring platform's cloud server for in-depth analysis, including analyzing battery charge and discharge curves, evaluating motor operating efficiency, and monitoring the status of the fire protection system in real time.

[0032] Based on this analyzed data, the monitoring platform can display various operating parameters of the locomotive in real time and identify potential fault risks. For example, when the battery temperature continues to rise or the motor efficiency drops abnormally, the system can issue a timely warning.

[0033] As a result, maintenance personnel can remotely monitor the locomotive's operating status and perform preventative maintenance based on data analysis results, thus effectively solving the problem that new energy locomotives cannot remotely monitor operating data, predict fault risks, and conduct refined cost control.

[0034] In summary, the cloud platform data transmission device for new energy locomotives provided in this embodiment of the invention, by installing an FBox terminal on the new energy locomotive and comprehensively collecting operational status data of key components through the VCU, obtains rich and real-time internal locomotive information. Then, the operational status data of key systems such as the locomotive battery and motor are transmitted to the FBox via Ethernet communication, ensuring the stability and high efficiency of data transmission from the vehicle controller to the FBox terminal. This avoids the bandwidth limitations and data loss problems that may exist in traditional serial communication. The FBox then uploads the collected data to the new energy locomotive big data center monitoring platform. The FBox terminal, acting as a bridge for data upload, achieves seamless connection between local locomotive data and the remote monitoring platform, overcoming the limitations of traditional solutions where data is difficult to obtain remotely. The monitoring platform uses edge computing to parse and process the operational status data, enabling some computational tasks to be completed near the data source, reducing data transmission latency and network bandwidth pressure, and improving the real-time performance and response speed of data processing. Ultimately, monitoring the operational status of new energy locomotives based on the analyzed data enables maintenance personnel to promptly identify and predict potential faults, reducing failure rates and maintenance costs. This provides data support for the refined operation and management of locomotives, enabling real-time collection, secure transmission, and efficient analysis of key data from new energy locomotives.

[0035] In some embodiments of the present invention, the operating status data includes: Battery operating status data, motor operating status data, inverter operating status data, DC-DC converter operating status data, and fire protection system operating status data.

[0036] Battery operating status data may include: voltage, current, cell temperature, BMS system status, fault information, cumulative charge and discharge amount, etc.

[0037] Motor operating status data can include: voltage, current, temperature, status, fault information, speed, and torque information.

[0038] Inverter operating status data can include: voltage, current, temperature, fault information, etc.

[0039] The operating status data of a DC-DC converter can include: output voltage, output current, temperature, fault information, etc.

[0040] Fire protection system operation status data can include: status, alarm information, etc.

[0041] Specifically, battery status data helps to accurately assess battery health and predict lifespan, and optimize charging management.

[0042] Motor operating status data can promptly detect abnormalities such as motor overload and wear, thus preventing sudden failures.

[0043] The operating status data of the inverter and DC-DC converter ensures the stability and efficiency of the power conversion system.

[0044] The operational status data of the fire protection system significantly improved the locomotive's safety early warning capabilities.

[0045] Therefore, this invention enables refined monitoring of the operating status of new energy locomotives, significantly improves the accuracy of fault risk prediction, and provides a solid data foundation for refined cost control of locomotives, thereby improving the operational reliability and safety of new energy locomotives.

[0046] In some embodiments of the present invention, the monitoring platform includes: The data acquisition unit 201, storage unit 202, processing unit 203, and report generation unit 204 are included. The acquisition unit 201 is used to read and parse the operating status data via the UDP communication protocol; The storage unit 202 is used to store the running status data through an encrypted signature algorithm and an access control policy; The processing unit 203 is used to preprocess the operating status data to obtain preprocessed data; The report generation unit 204 is used to perform fault analysis and locomotive health status prediction based on the preprocessed data, and generate data reports.

[0047] In some embodiments of the present invention, the processing unit is specifically used for: The operational status data is summarized and cleaned to remove abnormal data that exceeds the normal range, resulting in cleaned data. The cleaned data is then converted to a unified format to obtain the preprocessed data.

[0048] Figure 2 This is a schematic diagram of the monitoring platform provided by the present invention, as shown below. Figure 2 As shown, the monitoring platform includes: a data acquisition unit, a storage unit, a processing unit, and a report generation unit.

[0049] Data acquisition is the first step in cloud-based online data statistics, aggregation, and analysis. The vehicle controller and the cloud platform use the UDP communication protocol via Ethernet to read and parse the data.

[0050] Data storage is the second step in cloud-based online data statistics, aggregation, and analysis. Data is stored in Google Cloud Storage repositories, using encryption and access control policies to ensure security. Simultaneously, databases such as Google BigQuery are used to store structured data for subsequent querying and analysis.

[0051] In some embodiments of the present invention, the encryption signature algorithm is the AES algorithm or the MD5 algorithm.

[0052] The transmission of sensitive data employs encryption and signature algorithms to protect the data from tampering. For example, after a user logs into the system and enters their account and password, the user's input is encrypted and signed using algorithms such as AES / MD5. When the server receives the data, it verifies whether the data has been tampered with through the signature. If it has been tampered with, it will not be processed, thus ensuring the security of information transmission.

[0053] In some embodiments of the present invention, the access control policy includes: The system's functional modules and data are classified into different levels, and corresponding function access permissions and data access levels are configured for different roles.

[0054] Access permissions are controlled by classifying system functional modules and data within the system, and defining role permissions for different functional access permissions and data access levels. After logging into the system, users can only view and operate the modules and data within their authorized scope.

[0055] Data processing is a core component of cloud-based online data statistics, aggregation, and analysis. Data processing involves multiple steps, including data cleaning, data transformation, and data aggregation, utilizing the Apache Hadoop big data processing framework.

[0056] By using certain rules and algorithms (setting the data range for key data and discarding data when it is outside the normal range), noise and outliers in the data are removed, thus achieving the purpose of data cleaning and making the data cleaner and more standardized.

[0057] This also requires data transformation, converting data from different formats into a unified format required for analysis. Data aggregation involves summarizing and statistically analyzing data from multiple data sources for subsequent analysis.

[0058] In some embodiments of the present invention, the report generation unit is specifically used for: Based on locomotive operating condition data and locomotive maintenance system inspection data, a health model for the key components is established. Based on the health model of the key components, predict the health status of the key components; The health status of the locomotive is determined based on the health status of the key components.

[0059] After data visualization is complete, data analysis and report generation can be performed. Data analysis involves in-depth study of the visualization results to find patterns and trends in the data, and finally presenting them to users in the form of reports.

[0060] The specific process of data analysis is as follows: After receiving the data from the VCU, the data is processed through cleaning, merging, classification, storage, monitoring, resource extraction and calculation, fault analysis, and locomotive health prediction.

[0061] The cleaning process involves setting a range for critical data and discarding data that is outside the normal range.

[0062] Merging primarily involves merging continuous fault code data, combining multiple fault codes into a single fault code that includes both start and end times.

[0063] The data is categorized based on the source components, operating condition data, and fault data.

[0064] Storage involves classifying data and saving it to a structured database.

[0065] Monitoring involves two aspects: on the one hand, manual monitoring, with data being pushed to an in-memory database in real time for data monitoring via a visual interface; on the other hand, real-time data anomalies are captured and alerts are generated in the big data computing backend using specific algorithms.

[0066] Resource extraction calculations, using locomotive operating data, can calculate locomotive mileage, charging amount, energy feedback, and other data indicators, which can be further used for energy consumption analysis, locomotive operation analysis, etc., to provide decision support for production and operation.

[0067] Fault analysis involves two aspects: firstly, using simple algorithms (such as threshold methods and statistical methods) to calculate and capture fault warnings; and secondly, using fault alarms and operating condition data to build data models and employing artificial intelligence technologies such as neural networks to identify potential faults in advance, thereby significantly improving the safety of locomotive operation.

[0068] Locomotive health prediction combines locomotive operating condition data with data from the locomotive maintenance system, such as inspection, repair reports, and fault replacements, to comprehensively establish a health model for the locomotive's main components. This model dynamically calculates the replacement cycle of major components and predicts the health status of major components such as batteries, motors, electronic controls, auxiliary inverters, and air compressors, thereby comprehensively evaluating the overall health status of the locomotive.

[0069] In some embodiments of the present invention, the monitoring platform further includes: The visualization unit is used for program design and screen configuration through Visual Studio, and to visualize multi-dimensional data through bar charts, line charts or pie charts.

[0070] Data visualization is the final step in cloud-based online data statistical aggregation and analysis. Data visualization helps users intuitively understand data, thereby enabling them to make better decisions.

[0071] Visual Studio enables program design and screen configuration, supporting various chart types such as bar charts, line charts, and pie charts. Multiple charts can be integrated onto a single page to achieve multi-dimensional data display and analysis.

[0072] In some embodiments of the present invention, the monitoring platform further includes: The update unit is used to configure scheduled tasks via scripts or task scheduling tools.

[0073] In some embodiments of the present invention, the scheduled tasks include the automated execution of data acquisition, data storage, data processing, and report generation.

[0074] To improve the efficiency of data statistical aggregation and analysis, this invention introduces an automated and real-time update mechanism. Users can set the update frequency of the data source to ensure the real-time nature of the data.

[0075] Automation mechanisms can be implemented through scripts or task scheduling tools, allowing users to set up scheduled tasks to periodically collect, store, process, and visualize data. Automation and real-time updates can significantly improve the efficiency of data statistical summary and analysis, ensuring the timeliness and accuracy of the data.

[0076] Furthermore, data security and compliance are crucial factors to consider when conducting cloud-based online data statistical aggregation and analysis. Using encryption technology to protect data security prevents unauthorized access during transmission and storage. Ensuring data security and compliance effectively protects user data privacy and enhances the credibility of data analysis.

[0077] For example, the FBox terminal can be a basic version, a full-featured standard version, or a WiFi version, and its specific models and parameters are shown in the table below.

[0078]

[0079] This invention provides a cloud platform data transmission device for new energy locomotives. The new energy locomotive is equipped with an onboard terminal device. The vehicle controller transmits the operating status data of key systems such as the locomotive battery and motor to the onboard terminal device, which then uploads the collected data to the "New Energy Locomotive Big Data Center" monitoring platform. The monitoring platform manages the vehicle and users, providing safe operation services and management, and features functions such as operating data monitoring, fault analysis management, chart query and statistics, and location tracking management.

[0080] For example, the functional modules of the cloud platform data transmission device include: locomotive files, historical information, dictionary management, system management, data resources, energy consumption analysis, chart analysis, etc.

[0081] Locomotive Profile: Check the locomotive status, mileage, and other information of locomotives currently in operation.

[0082] Resume information: Check the technical parameters, certificate of conformity, operating instructions and other information of the locomotive.

[0083] Dictionary management: Query information such as locomotive model, application type, and power type.

[0084] System Management: Query the personal information of users who log in to the system.

[0085] Data resources: query information such as locomotive status, location, battery, motor, DC-DC converter, inverter, fire protection system, fault warning, status warning, daily mileage, monthly mileage, and annual mileage.

[0086] Energy consumption analysis: Query locomotive charging records to obtain information such as charging frequency, power changes, charging duration, and charging energy.

[0087] Chart Analysis: Select the required data and automatically generate trend charts to facilitate analysis of the locomotive's health status.

[0088] The cloud platform data transmission device for new energy locomotives provided by this invention is an information system that integrates data collection, analysis, and management. It realizes intelligent management of the entire life cycle of new energy locomotives through Internet of Things, cloud computing, and big data technologies.

[0089] The cloud platform data transmission device for new energy locomotives provided by this invention has the advantages of enabling real-time collection, transmission and intelligent monitoring of the operating status data of key components of new energy locomotives, thereby improving vehicle dispatching efficiency, reducing safety hazards and maintenance costs.

[0090] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0091] The cloud platform data transmission device for new energy locomotives provided by the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A cloud platform data transmission device of a new energy locomotive, characterized in that, The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system.

2. The cloud platform data transmission device of the new energy locomotive according to claim 1, characterized in that, The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system.

3. The cloud platform data transmission device of the new energy locomotive according to claim 2, characterized in that, The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system.

4. The cloud platform data transmission device of the new energy locomotive according to claim 2, characterized in that, The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system.

5. The cloud platform data transmission device of the new energy locomotive according to claim 2, characterized in that, The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system.

6. The cloud platform data transmission device of the new energy locomotive according to claim 2, characterized in that, The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system.

7. The cloud platform data transmission device of the new energy locomotive according to claim 6, characterized in that, The application relates to a new energy locomotive monitoring system.

8. The cloud platform data transmission device of the new energy locomotive according to claim 2, characterized in that, The application relates to a new energy locomotive monitoring system. The application relates to a new energy locomotive monitoring system.

9. The cloud platform data transmission device of the new energy locomotive according to claim 2, characterized in that, The application relates to a new energy locomotive monitoring system.

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