Network type electric control self-diagnosis system and method
By combining local and cloud-based fault analysis with a network-based electrical control self-diagnosis system, the problem of fault analysis and location when underground power supply equipment is used in combination has been solved, achieving more accurate and efficient fault diagnosis and supporting immediate response and long-term optimization.
Patent Information
- Application Number
- CN202411654145.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2026-05-19
AI Technical Summary
When underground power supply equipment is used in combination, fault analysis and location are difficult, and it is hard to obtain complete information, which makes it difficult to solve the problem.
Design a network-based electronic control self-diagnostic system, including a local data acquisition module, a processing module, an interaction module, and a cloud-based diagnostic module. The system performs fault analysis by combining local and cloud-based methods to obtain more complete fault-related information.
It reduces the difficulty of fault analysis and problem localization, improves the accuracy and efficiency of fault diagnosis, supports immediate response and long-term optimization, and enhances user experience.
Smart Images
Figure CN122064061A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of computer technology, and in particular to a network-based electronic control self-diagnosis system and method. Background Technology
[0002] With the development of intelligent coal mining, the number of underground power supply equipment is increasing, and this equipment is becoming more complex. Furthermore, underground power supply equipment is often used in combination. When a fault occurs in this combination of equipment, the complex and variable underground conditions, coupled with the significant limitations on the visual and auditory capabilities of personnel underground, often result in only partial information being obtained about the faulty equipment. This lack of complete information may lead to the omission of crucial details, increasing the difficulty of fault analysis, problem localization, and resolution. Summary of the Invention
[0003] To address the problems existing in the prior art, this invention provides a network-based electronic control self-diagnosis system and method.
[0004] This invention provides a network-based electronic control self-diagnostic system, the system comprising: A local data acquisition module, connected to the sensing device, is used to acquire the raw data sent by the sensing device; A local data processing module, connected to the local data acquisition module, is used to acquire the raw data sent by the local data acquisition module, perform local fault analysis on the raw data, and obtain local fault diagnosis results.
[0005] According to the present invention, a network-based electronic control self-diagnostic system further includes: A local data interaction module, connected to the local data processing module, is used to acquire the raw data sent by the local data processing module; The cloud-based diagnostic module is connected to the local data interaction module and is used to acquire the raw data sent by the local data interaction module, perform cloud-based fault analysis on the raw data, and obtain cloud-based fault diagnosis results.
[0006] According to the present invention, a network-based electronic control self-diagnostic system includes a cloud-based diagnostic module comprising: A cloud-based data interaction unit, connected to the local data interaction module, is used to acquire the raw data sent by the local data interaction module; The database, in conjunction with the cloud data interaction unit, is used to acquire and store the raw data sent by the cloud data interaction unit. The real-time fault diagnosis unit is connected to the database and is used to obtain target raw data from the database, perform real-time fault analysis on the target raw data in the cloud, and obtain real-time fault diagnosis results in the cloud.
[0007] According to the network-based electronic control self-diagnostic system provided by the present invention, the cloud-based diagnostic module further includes: The historical fault diagnosis unit is connected to the database and is used to obtain historical data within a specified time period from the database, perform cloud-based historical fault analysis on the historical data, obtain cloud-based historical fault diagnosis results, and store the cloud-based historical fault diagnosis results in the database.
[0008] According to the network-based electronic control self-diagnostic system provided by the present invention, the cloud-based diagnostic module further includes: A diagnostic result encapsulation module, connected to both the real-time fault diagnosis unit and the database, is used to obtain real-time fault diagnosis results from the cloud-based fault diagnosis unit, encapsulate the cloud-based real-time fault diagnosis results, and send the encapsulated cloud-based real-time fault diagnosis results to the front-end display module via a front-end interface; or The system retrieves historical fault diagnosis results from the database, encapsulates these results, and sends the encapsulated results to the front-end display module via a front-end interface.
[0009] According to the present invention, a network-based electronic control self-diagnosis system includes multiple local response modules, which are respectively connected to the local data interaction module; The local data interaction module is also used to receive the safety operation instructions sent by the cloud diagnostic module during the cloud fault analysis process, and to determine the target local response module based on the preset relationship between the safety operation instructions and the local response module. The target local response module is used to receive the security operation instruction sent by the local data interaction module, and to perform security operations based on the security operation instruction.
[0010] According to a network-based electronic control self-diagnosis system provided by the present invention, the real-time fault diagnosis unit is further configured to send the real-time fault diagnosis results from the cloud to the database and store them corresponding to the target original data.
[0011] According to a network-based electronic control self-diagnosis system provided by the present invention, the local data interaction module is further configured to acquire the local fault diagnosis result sent by the local data processing module; The cloud data interaction unit is also used to acquire the raw data sent by the local data interaction module and the corresponding local fault diagnosis results; The database is also used to obtain the raw data sent by the cloud data interaction unit and the corresponding local fault diagnosis results.
[0012] According to a network-based electronic control self-diagnosis system provided by the present invention, the local data acquisition module is further used to filter the raw data to obtain a smooth raw data curve that does not change the data properties.
[0013] The present invention also provides a network-based electronic control self-diagnosis method, applied to any of the network-based electronic control self-diagnosis systems described above, the method comprising: Acquire raw data sent by the sensing device; Perform local fault analysis on the raw data to obtain local fault diagnosis results; and / or The raw data is sent to the host computer, and cloud-based fault analysis is performed on the raw data to obtain cloud-based fault diagnosis results.
[0014] The network-based electronic control self-diagnosis system and method provided by the present invention connects a local data acquisition module to a sensing device and a local data processing module to a local data acquisition module. This enables the local data processing module to acquire the raw data sent by the sensing device, obtain more complete fault-related information, reduce the difficulty of performing local fault analysis on the raw data, obtain local fault diagnosis results, and locate and solve problems. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0016] Figure 1 This is one of the structural schematic diagrams of the network-type electronic control self-diagnosis system provided by the present invention.
[0017] Figure 2 This is the second schematic diagram of the network-type electronic control self-diagnosis system provided by the present invention.
[0018] Figure 3 This is the third schematic diagram of the network-type electronic control self-diagnosis system provided by the present invention.
[0019] Figure 4 This is a flowchart illustrating the network-based electronic control self-diagnosis method provided by the present invention. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0021] The following is combined Figures 1-4 The present invention describes a network-based electronic control self-diagnostic system and method.
[0022] Figure 1 This is one of the structural schematic diagrams of the network-type electronic control self-diagnostic system provided by the present invention, such as... Figure 1 As shown, this embodiment provides a network-based electronic control self-diagnosis system. This system mainly includes two units: a local data acquisition module 101 and a local data processing module 102. The following is a detailed description of each unit: The local data acquisition module 101 is connected to the sensing device and is used to acquire the raw data sent by the sensing device.
[0023] The local data acquisition module 101 refers to the module in a network-based electronic control self-diagnostic system that acquires data from sensing devices. For example, the local data acquisition module 101 can be connected to a single sensing device or multiple sensing devices.
[0024] Sensing equipment, also known as electrical control equipment, refers to the electrically powered equipment in the integrated mechanized equipment of underground coal mines. Sensing equipment can be hydraulic supports, or various other mining-use sensing devices such as pressure, tilt, and vibration sensors.
[0025] Raw data refers to the various data collected by sensors in sensing devices. Raw data can be analog signals, digital signals, or communication protocol data, etc. Analog signals refer to continuously changing signals such as temperature, pressure, and flow rate; digital signals refer to discrete signals such as switch states (on / off); and communication protocol data refers to signal data transmitted via protocols such as Modbus and EIP. For example, raw data may include sensor data, current data, voltage data, version information, action data, and hydraulic control data. Among these, action data and version information are behavioral data caused by humans.
[0026] For example, when the local data acquisition module 101 is connected to a single sensing device, the network-based electronic control self-diagnostic system provided in this embodiment can be applied to the controller of the sensing device. The controller can be electrically or communicatively connected to the target sensor in the sensing device, and exchange data through a network or other communication protocols to obtain various data collected by the target sensor and send it to the controller. After receiving the various data sent by the target sensor, the controller can forward it to the local data acquisition module 101.
[0027] In this embodiment, the connections between each module, the local data acquisition module 101 and the sensing device can be either electrical connections or communication connections, etc., which will not be described in detail below.
[0028] The local data processing module 102 is connected to the local data acquisition module 101 and is used to acquire the raw data sent by the local data acquisition module 101, perform local fault analysis on the raw data, and obtain local fault diagnosis results.
[0029] To facilitate comprehensive monitoring of the overall status of sensing devices, and to perform fault analysis and preventative maintenance, the raw data collected by the sensing devices and sent to the network-based electronic control self-diagnostic system includes both raw and normal raw data. Local fault analysis refers to the analysis of the raw data by the local data processing module 102, which can determine whether there is a fault in the sensing device corresponding to the raw data, so as to intervene as quickly as possible when a significant fault occurs in the sensing device.
[0030] Local fault diagnosis results refer to fault identification information, or fault diagnosis information, obtained through fault analysis by the local data processing module 102. Local fault diagnosis results can be directly sent to the sensing device so that the sensing device can perform corresponding operations and intervene locally based on the local fault diagnosis results; local fault diagnosis results can also be displayed to maintenance personnel so that they can perform corresponding operations based on the local fault diagnosis results, etc.
[0031] The network-based electronic control self-diagnosis system provided in this embodiment of the invention connects to the sensing device via a local data acquisition module 101 and a local data processing module 102 connects to the local data acquisition module 101. This enables the local data processing module 102 to acquire the raw data sent by the sensing device, obtain more complete fault-related information, reduce the difficulty of performing local fault analysis on the raw data, and obtain local fault diagnosis results to locate and solve problems.
[0032] In one embodiment, local fault analysis can be performed on the original data using simple calculation methods such as threshold judgment, switch judgment, and parameter judgment. When the local fault diagnosis results show that the sensing device corresponding to the original data has a fault, the simple fault of the sensing device can be obtained.
[0033] Based on the above embodiments, the system further includes: A local data interaction module, connected to the local data processing module 102, is used to acquire the raw data sent by the local data processing module 102; The cloud-based diagnostic module is connected to the local data interaction module and is used to acquire the raw data sent by the local data interaction module, perform cloud-based fault analysis on the raw data, and obtain cloud-based fault diagnosis results.
[0034] Cloud-based fault analysis refers to performing fault analysis on raw data through cloud-based diagnostic modules. For example, cloud-based fault analysis can be performed on a host computer using more complex data analysis techniques, such as machine learning or statistical analysis, which require more computational resources, to conduct complex fault analysis on the raw data.
[0035] Cloud-based fault diagnosis results refer to fault identification information, or fault diagnosis information, obtained through fault analysis using a cloud-based diagnostic module. These results can be directly sent to sensing devices, enabling them to perform corresponding operations and provide on-site intervention. Alternatively, the results can be displayed to maintenance personnel, allowing them to perform appropriate actions based on the findings.
[0036] For example, when the cloud-based fault diagnosis results show that the sensing device corresponding to the original data is faulty, the complex faults of the sensing device can be obtained.
[0037] In this embodiment, the local data interaction module is connected to the local data processing module 102, and the cloud diagnostic module is connected to the local data interaction module. This enables the cloud diagnostic module to obtain the raw data sent by the sensing device, obtain more complete fault-related information, reduce the difficulty of performing cloud fault analysis on the raw data, obtain cloud fault diagnosis results, and locate and solve the problem.
[0038] And, as Figure 2As shown, in this embodiment, local fault analysis of the raw data in the local data processing module 102 facilitates rapid response and handling of simple faults. Cloud-based fault analysis of the raw data in the cloud diagnostic module allows for more comprehensive real-time data analysis using greater computing resources, enabling complex fault analysis that the local data processing module 102 cannot handle. By separating fault analysis for simple and complex faults, different levels of gradient detection can be performed on faults, reducing communication load, improving system resource utilization, reducing the time required to detect and resolve faults, and enhancing user experience.
[0039] Based on any of the above embodiments, the cloud-based diagnostic module includes: A cloud-based data interaction unit, connected to the local data interaction module, is used to acquire the raw data sent by the local data interaction module; The database, in conjunction with the cloud data interaction unit, is used to acquire and store the raw data sent by the cloud data interaction unit. The real-time fault diagnosis unit is connected to the database and is used to obtain target raw data from the database, perform real-time fault analysis on the target raw data in the cloud, and obtain real-time fault diagnosis results in the cloud.
[0040] The target raw data is the raw data required for real-time fault analysis in the cloud. The target raw data can be selected from the raw data according to the business logic of real-time fault analysis in the cloud. For example, stress data from the most recent hour can be selected as the target raw data from the raw data according to the business logic of real-time fault analysis in the cloud.
[0041] A database refers to a collection of data that stores various raw data sent by sensing devices in a specific format and organization. In essence, a database supports operations such as management and retrieval of the various raw data sent by sensing devices.
[0042] Real-time cloud-based fault analysis refers to fault analysis that requires quick and efficient acquisition of fault diagnosis results, or emphasizes rapid response and immediate decision-making, in order to quickly identify and handle faults or anomalies in sensing devices. Examples include cloud-based fault analysis of scenarios such as network quality, parameter logic, motion sensing changes, data fluctuations, and data mutations.
[0043] Real-time fault diagnosis results in the cloud refer to fault identification information, or fault diagnosis information, obtained through real-time fault analysis in the cloud via a real-time fault diagnosis unit.
[0044] In this embodiment, the cloud data interaction unit is connected to the database, and the real-time fault diagnosis unit is connected to the database, enabling the real-time fault diagnosis unit to obtain the raw data sent by the sensing device and perform complex cloud real-time fault analysis on the raw data to obtain cloud real-time fault diagnosis results, so as to respond in real time when there are complex faults in the raw data.
[0045] Based on any of the above embodiments, the cloud-based diagnostic module further includes: The historical fault diagnosis unit is connected to the database and is used to obtain historical data within a specified time period from the database, perform cloud-based historical fault analysis on the historical data, obtain cloud-based historical fault diagnosis results, and store the cloud-based historical fault diagnosis results in the database.
[0046] Historical data, also known as historical raw data, is the raw data uploaded by sensing devices within a specified time period.
[0047] Cloud-based historical fault analysis refers to fault analysis of historical data within a specified time period using a specified algorithm. In other words, it focuses on identifying trends, patterns, and potential faults to identify and address potential faults or long-term performance trends in sensing devices. Cloud-based historical fault diagnosis results refer to fault identification information, or fault diagnosis information, obtained through cloud-based historical fault analysis using a historical fault diagnosis unit.
[0048] In this embodiment, the database is connected to the cloud data interaction unit, and the historical fault diagnosis unit is connected to the database. This enables the historical fault diagnosis unit to obtain historical data sent by the sensing device within a specified time period, and to perform complex cloud-based historical fault analysis on the historical data to obtain cloud-based historical fault diagnosis results. This allows for fault analysis of historical data, identification and processing of potential faults of the sensing device or cloud-based fault analysis of long-term performance trends, thereby improving the user experience.
[0049] Furthermore, in this embodiment, real-time fault analysis is performed in the cloud through the real-time fault diagnosis unit, and historical fault analysis is performed in the cloud through the historical fault diagnosis unit. This allows for full utilization of cloud computing resources to perform in-depth real-time fault analysis, supplementing the simple analysis of the original data performed by the local data processing module 102. At the same time, combined with historical data, fault prediction and preventive maintenance can be performed. This enables the provision of immediate response while using historical data to optimize the sensing device over the long term, thereby improving the stability and reliability of the sensing device.
[0050] In one embodiment, the historical fault diagnosis unit can perform cloud-based historical fault analysis based on one or more types of historical data within a specified time period in the database, and obtain cloud-based historical fault diagnosis results.
[0051] Based on any of the above embodiments, the cloud-based diagnostic module further includes a diagnostic result encapsulation module.
[0052] In one embodiment, the diagnostic result encapsulation module is connected to the real-time fault diagnosis unit, and is used to obtain the cloud-based real-time fault diagnosis result from the real-time fault diagnosis unit, encapsulate the cloud-based real-time fault diagnosis result, and send the encapsulated cloud-based real-time fault diagnosis result to the front-end display module through the front-end interface.
[0053] For example, after the diagnostic result encapsulation module obtains the real-time fault diagnosis results from the real-time fault diagnosis unit, it can convert the real-time fault diagnosis results from the cloud into a standardized format, such as JSON or XML, and then call the front-end interface of the front-end display module to pass the standardized cloud real-time fault diagnosis results to the front-end interface, and send them to the front-end display module through the front-end interface.
[0054] The real-time fault diagnosis results in the cloud need to be promptly notified to personnel. In this embodiment, the real-time fault diagnosis results in the cloud are directly obtained from the real-time fault diagnosis unit through the diagnosis result encapsulation module, so that the real-time fault diagnosis results in the cloud can be promptly sent to the front-end display module for display to personnel through the front-end interface.
[0055] In another embodiment, the diagnostic result encapsulation module is connected to the database and is used to obtain historical fault diagnosis results from the cloud from the database, encapsulate the historical fault diagnosis results from the cloud, and send the encapsulated historical fault diagnosis results from the cloud to the front-end display module through the front-end interface.
[0056] The process of encapsulating the historical fault diagnosis results in the cloud is basically the same as the process of encapsulating the real-time fault diagnosis results in the cloud, and will not be described in detail here. The difference is that the diagnosis result encapsulation module can retrieve the historical fault diagnosis results from the database after the historical fault diagnosis module in the cloud has stored them in the database, or it can retrieve the historical fault diagnosis results from the database periodically.
[0057] To facilitate cloud-based historical fault analysis of historical data, in some embodiments the target historical data and its cloud-based historical fault diagnosis results may be stored in a database (see the description of the embodiments below for details). The cloud-based historical fault diagnosis results can be used to remind personnel later. In this embodiment, the cloud-based historical fault diagnosis results are obtained from the database through the diagnosis result encapsulation module, rather than directly from the cloud-based historical fault diagnosis results, which simplifies the design of the network-based electronic control self-diagnosis system.
[0058] The front-end display module is responsible for the human-computer interaction function, which loads the obtained historical fault diagnosis results or real-time fault diagnosis results from the cloud into the corresponding display area for display.
[0059] like Figure 2 As shown, based on any of the above embodiments, the system includes multiple local response modules, each connected to the local data interaction module; The local data interaction module is also used to receive the safety operation instructions sent by the cloud diagnostic module during the cloud fault analysis process, and to determine the target local response module based on the preset relationship between the safety operation instructions and the local response module. The target local response module is used to receive the security operation instruction sent by the local data interaction module, and to perform security operations based on the security operation instruction.
[0060] Safety operation instructions refer to instructions used to instruct the target local response module to perform certain safety operations when cloud-based fault analysis determines that the raw data may correspond to a fault with a significant impact.
[0061] For example, the local data interaction module can preset a relationship mapping table between safety operation instructions and local response modules. After receiving multiple safety operation instructions sent by the cloud diagnostic module, each safety operation instruction can be sent to the corresponding target local response module based on the relationship mapping table, so that the target local response module can perform a series of preset safety operations such as emergency shutdown, overload protection, overheat protection, or switching to the backup system based on the safety operation instructions.
[0062] In the network-type electronic control self-diagnostic system, the local response module can be a component of the controller, or in other words, the network-type electronic control self-diagnostic system and the controller can share components.
[0063] In this embodiment, the cloud diagnostic module sends a safety operation command to the target local response module during the cloud fault analysis process. Based on the safety operation command, the target local response module is controlled to perform a safety operation, which can handle potential faults in a timely manner, reduce the duration of faults, and reduce the impact of faults on sensing devices.
[0064] Based on any of the above embodiments, the real-time fault diagnosis unit is further configured to send the cloud-based real-time fault diagnosis results to the database and store them corresponding to the target original data.
[0065] In this embodiment, in addition to sending the real-time fault diagnosis results from the cloud to the front-end display module, the real-time fault diagnosis results from the cloud are also stored in the database. This can enrich the historical data in the database and increase the amount of historical data within a specified time period. At the same time, by storing the real-time fault diagnosis results from the cloud and the target original data in association, it is easier to increase the accuracy of the historical fault diagnosis results from the cloud.
[0066] Based on any of the above embodiments, the local data interaction module is further configured to obtain the local fault diagnosis result sent by the local data processing module 102; The cloud data interaction unit is also used to acquire the raw data sent by the local data interaction module and the corresponding local fault diagnosis results; The database is also used to obtain the raw data sent by the cloud data interaction unit and the corresponding local fault diagnosis results.
[0067] In this embodiment, storing the original data and local fault diagnosis results in the database and storing the original data and real-time fault diagnosis results in the cloud in the database are essentially the same in terms of working principle and technical effect, and will not be described again here.
[0068] Furthermore, in this embodiment of the invention, the original data and local fault diagnosis results are stored in the database, the original data and real-time fault diagnosis results in the cloud are stored in the database, and the original data and historical fault diagnosis results in the cloud are stored in the database. In this way, by comprehensively storing the original data and the corresponding fault diagnosis results, it is possible to respond quickly to simple faults and increase the amount of information available for complex fault analysis, thereby improving the accuracy and reliability of fault diagnosis.
[0069] To focus on the raw data sent by the sensing devices and reduce the communication load between modules and units in the network-based electronic control self-diagnostic system, based on any of the above embodiments, the local data acquisition module 101 is further used to filter the raw data to obtain a smooth raw data curve that does not change the data properties. Here, "not changing the data properties" means that it does not affect the fault analysis of the raw data.
[0070] To illustrate the functionality of the network-based electronic control self-diagnostic system provided in this implementation, a specific example is given below.
[0071] like Figure 3 As shown in the figure, the embodiment of the present invention provides an overall design of a network-based electronic control self-diagnosis system, which is mainly divided into two parts: local diagnosis and cloud diagnosis. Local diagnosis is performed through a local data acquisition module, a local data processing module, a local data interaction module, and a local response module. Cloud diagnosis is performed through cloud diagnosis and a target local response module.
[0072] Local diagnostics mainly involves performing simple local fault analysis on the raw data: The local data acquisition module connects to the sensing device to collect raw data from the sensor. The local data acquisition module supports a variety of common protocols such as analog protocol, serial port protocol, Modbus protocol, and EIP protocol, and can perform filtering processing on the raw data. The local data processing module is connected to the local data acquisition module, receives the raw data sent by the local data acquisition module, and performs simple local fault analysis on the raw data based on simple calculation methods such as threshold judgment, switch judgment, and parameter judgment to obtain local fault diagnosis results. After obtaining the local fault diagnosis results, the local data processing module can send instructions to the corresponding local response module to perform on-site intervention.
[0073] After obtaining the local fault diagnosis results, the local data interaction module can send the raw data and the local fault diagnosis results to the database of the cloud diagnosis module.
[0074] Cloud-based diagnostics primarily involves performing complex cloud-based fault analysis on raw data. The cloud-based data interaction unit is connected to the data processing module, receives raw data sent by the data processing module, acquires the raw data sent by the data processing module, and sends the raw data to the database. The real-time fault diagnosis unit retrieves the target raw data from the database, performs real-time fault analysis on the target raw data in the cloud, and obtains real-time fault diagnosis results in the cloud. The historical fault diagnosis unit retrieves historical data within a specified time period from the database, performs cloud-based historical fault analysis on the historical data, obtains cloud-based historical fault diagnosis results, and stores the cloud-based historical fault diagnosis results in the database. In addition, historical fault diagnosis results from the cloud can be stored in a database, corresponding to the original target data. The diagnostic result encapsulation module is connected to both the real-time fault diagnosis unit and the database. It can obtain real-time fault diagnosis results from the cloud from the real-time fault diagnosis unit, encapsulate the real-time fault diagnosis results from the cloud, and send the encapsulated real-time fault diagnosis results from the cloud to the front-end display module through the front-end interface; or obtain historical fault diagnosis results from the cloud from the database, encapsulate the historical fault diagnosis results from the cloud, and send the encapsulated historical fault diagnosis results from the cloud to the front-end display module through the front-end interface.
[0075] The network-based electronic control self-diagnostic system provided in this embodiment of the invention has the ability to perform multi-point analysis. Both local detection and cloud detection can detect faults to different degrees. In addition, by separating the fault algorithm, front-end and back-end, the coupling is low, making the algorithm portable. It also supports a variety of mining sensing devices such as sensors, pressure sensors, tilt sensors, and vibration sensors, and supports multiple protocols such as proprietary, Modbus, and EIP.
[0076] The following describes the network-based electronic control self-diagnosis method provided by the present invention. The network-based electronic control self-diagnosis method described below can be referred to in correspondence with the network-based electronic control self-diagnosis system described above.
[0077] Figure 4 This is a flowchart illustrating the network-based electronic control self-diagnosis method provided by the present invention, applicable to any of the network-based electronic control self-diagnosis systems described above, such as... Figure 4 As shown, the method includes: S401. Obtain the raw data sent by the sensing device; S402. Perform local fault analysis on the raw data to obtain local fault diagnosis results; and / or send the raw data to the host computer to perform cloud fault analysis on the raw data to obtain cloud fault diagnosis results.
[0078] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; the method embodiments are applied to the device embodiments and can be modified by referring to each other; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A network-based electronic control self-diagnostic system, characterized in that, The system includes: A local data acquisition module, connected to the sensing device, is used to acquire the raw data sent by the sensing device; A local data processing module, connected to the local data acquisition module, is used to acquire the raw data sent by the local data acquisition module, perform local fault analysis on the raw data, and obtain local fault diagnosis results.
2. The network-based electronic control self-diagnostic system according to claim 1, characterized in that, The system also includes: A local data interaction module, connected to the local data processing module, is used to acquire the raw data sent by the local data processing module; The cloud-based diagnostic module is connected to the local data interaction module and is used to acquire the raw data sent by the local data interaction module, perform cloud-based fault analysis on the raw data, and obtain cloud-based fault diagnosis results.
3. The network-based electronic control self-diagnostic system according to claim 2, characterized in that, The cloud-based diagnostic module includes: A cloud-based data interaction unit, connected to the local data interaction module, is used to acquire the raw data sent by the local data interaction module; The database, in conjunction with the cloud data interaction unit, is used to acquire and store the raw data sent by the cloud data interaction unit. The real-time fault diagnosis unit is connected to the database and is used to obtain target raw data from the database, perform real-time fault analysis on the target raw data in the cloud, and obtain real-time fault diagnosis results in the cloud.
4. The network-type electronic control self-diagnostic system according to claim 3, characterized in that, The cloud-based diagnostic module also includes: The historical fault diagnosis unit is connected to the database and is used to obtain historical data within a specified time period from the database, perform cloud-based historical fault analysis on the historical data, obtain cloud-based historical fault diagnosis results, and store the cloud-based historical fault diagnosis results in the database.
5. The network-type electronic control self-diagnostic system according to claim 3 or 4, characterized in that, The cloud-based diagnostic module also includes: A diagnostic result encapsulation module, connected to both the real-time fault diagnosis unit and the database, is used to obtain real-time fault diagnosis results from the cloud-based fault diagnosis unit, encapsulate the cloud-based real-time fault diagnosis results, and send the encapsulated cloud-based real-time fault diagnosis results to the front-end display module via a front-end interface; or The system retrieves historical fault diagnosis results from the database, encapsulates these results, and sends the encapsulated results to the front-end display module via a front-end interface.
6. The network-type electronic control self-diagnostic system according to claim 2, characterized in that, The system includes multiple local response modules, which are respectively connected to the local data interaction module; The local data interaction module is also used to receive the safety operation instructions sent by the cloud diagnostic module during the cloud fault analysis process, and to determine the target local response module based on the preset relationship between the safety operation instructions and the local response module. The target local response module is used to receive the security operation instruction sent by the local data interaction module, and to perform security operations based on the security operation instruction.
7. The network-type electronic control self-diagnostic system according to claim 4, characterized in that, The real-time fault diagnosis unit is also used to send the real-time fault diagnosis results from the cloud to the database and store them in correspondence with the target original data.
8. The network-type electronic control self-diagnostic system according to claim 4, characterized in that, The local data interaction module is also used to obtain the local fault diagnosis result sent by the local data processing module; The cloud data interaction unit is also used to acquire the raw data sent by the local data interaction module and the corresponding local fault diagnosis results; The database is also used to obtain the raw data sent by the cloud data interaction unit and the corresponding local fault diagnosis results.
9. The network-based electronic control self-diagnostic system according to claim 1, characterized in that, The local data acquisition module is also used to filter the raw data to obtain a smooth raw data curve that does not change the properties of the data.
10. A network-based electronic control self-diagnosis method, characterized in that, Applied to the network-type electronic control self-diagnostic system according to any one of claims 1-9, the method includes: Acquire raw data sent by the sensing device; Perform local fault analysis on the raw data to obtain local fault diagnosis results; and / or The raw data is sent to the host computer, and cloud-based fault analysis is performed on the raw data to obtain cloud-based fault diagnosis results.