Meteorological equipment fault diagnosis method and system based on message queue remote sensing transmission

Abstract

The invention provides a meteorological equipment fault diagnosis method and system based on message queue remote sensing transmission, and the method comprises the steps: training a fault classification model through historical fault data and corresponding feature data, transmitting a processed real-time data set to the fault classification model, and outputting a fault diagnosis result; when the fault is identified as the fault capable of being remotely repaired, a repair instruction is sent to the to-be-repaired meteorological equipment through message queue remote sensing transmission based on a preset remote repair strategy, and the to-be-repaired meteorological equipment executes corresponding repair operation after receiving the instruction; and when the fault is identified as a fault which cannot be remotely repaired, generating an early warning message containing fault information, and sending the early warning message to related technicians. According to the application, the repair instruction is automatically generated and sent to the to-be-repaired meteorological equipment through remote sensing transmission of the message queue, and the equipment executes corresponding repair operation after receiving the instruction; the intelligent remote repair is realized, the manpower and time cost is saved, and the availability and stability of the equipment are improved.

Description

Technical Field

[0001] The present application relates to the technical field of meteorological equipment fault diagnosis, and in particular to a meteorological equipment fault diagnosis method and system based on message queue remote sensing transmission. Background Art

[0002] In the current complex environment of meteorological equipment operation and maintenance, fault diagnosis and troubleshooting are particularly important and challenging. This series of tasks mainly focuses on several core difficulties: potential fluctuations in the performance of meteorological equipment collectors, the accuracy of meteorological element measurement values, and data transmission interruptions caused by network instability; these problems not only test the accuracy and timeliness of meteorological data, but are also directly related to the accuracy and service level of meteorological forecasts.

[0003] Among the above problems, a particularly critical challenge is how to intelligently identify the type of fault and quickly determine whether it is possible to repair it remotely. Traditionally, such judgments rely on experienced technicians who determine the nature of the fault and the repair plan through on-site inspections, data analysis, and other means. However, with the increase in the number of meteorological equipment and the expansion of its distribution range, this manual-dependent approach is becoming increasingly inadequate. It is not only inefficient, but may also miss the best time for repair due to untimely response.

[0004] Therefore, exploring a technology that can intelligently identify fault types and automatically evaluate the feasibility of remote repairs has become an important issue that needs to be urgently addressed in the current field of meteorological equipment operation and maintenance. Summary of the invention

[0005] In order to overcome the above problems existing in the prior art, the present application provides a meteorological equipment fault diagnosis method and system based on message queue remote sensing transmission, which adopts the following technical solutions:

[0006] In a first aspect, the present application provides a method for diagnosing meteorological equipment faults based on message queue remote sensing transmission, comprising:

[0007] Real-time data collection, including meteorological equipment operating parameters, meteorological element data and network transmission status;

[0008] The fault classification model is trained using historical fault data and corresponding feature data to classify faults into two categories: remotely repairable and non-remotely repairable.

[0009] Process the real-time collected data, transfer the processed real-time data set to the fault classification model, identify the fault type in real time, judge the fault type based on the characteristics of the collected data, and output the fault diagnosis results;

[0010] When a fault is identified as remotely repairable, a repair instruction is sent to the meteorological equipment to be repaired through message queue telemetry transmission based on the preset remote repair strategy. After receiving the instruction, the meteorological equipment to be repaired performs the corresponding repair operation.

[0011] When a fault is identified as one that cannot be repaired remotely, an early warning message containing the fault information is generated and sent to relevant technicians.

[0012] Furthermore, the meteorological equipment operating parameters, meteorological element data and network transmission status collected in real time are transmitted to the central server through the message queue.

[0013] Furthermore, the fault classification model is trained through historical fault data and corresponding feature data to classify faults into two categories: remotely repairable and non-remotely repairable. The specific contents include:

[0014] Obtain historical data of meteorological equipment, including operating parameters of meteorological equipment, meteorological element data and network transmission status during the period when the fault occurred; obtain corresponding fault maintenance records at the same time, and use whether each fault event was repaired remotely or on-site as a label for fault classification;

[0015] Clean historical data, process missing values, and obtain preprocessed historical data;

[0016] Obtain the historical features of the extracted preprocessed historical data, including the statistical features of the operating parameters of the meteorological equipment, obtain the changing trend of the operating parameters of the meteorological equipment, obtain the statistical features of the meteorological element data, the correlation between different meteorological elements, and the spatial consistency features of the meteorological element data of multiple adjacent meteorological equipment; obtain the mean and variance of the packet loss rate of the network transmission status data, the distribution characteristics of the delay, and the fluctuation range of the signal strength;

[0017] Perform historical feature decomposition based on principal component analysis to obtain the main feature vectors; obtain the correlation between variables through the mutual information algorithm, filter out features with high correlation with the fault type, and obtain the processed historical data set;

[0018] The processed historical data set is divided into a training set and a test set. The fault classification model is trained with the training set to output the fault type and its probability. The performance of the fault classification model is evaluated with the test set, the parameters are adjusted, and the optimal fault classification model is selected for fault classification diagnosis.

[0019] Furthermore, the real-time collected data is processed, including:

[0020] Obtaining real-time data of meteorological equipment, including operating parameters of meteorological equipment, meteorological element data and network transmission status during the period when the fault occurred;

[0021] Clean the real-time data, process missing values, and obtain pre-processed real-time data;

[0022] Acquire the real-time features of the extracted preprocessed real-time data, including the statistical features of the operating parameters of the meteorological equipment, obtain the changing trend of the operating parameters of the meteorological equipment, obtain the statistical features of the meteorological element data, the correlation between different meteorological elements, and the spatial consistency features of the meteorological element data of multiple adjacent meteorological equipment; obtain the mean and variance of the packet loss rate of the network transmission status data, the distribution characteristics of the delay, and the fluctuation range of the signal strength;

[0023] Based on principal component analysis, real-time feature decomposition is performed to obtain the main feature vectors; the correlation between variables is obtained through the mutual information algorithm, the features with high correlation with the fault type are screened out, and the processed real-time data set is obtained.

[0024] Furthermore, the processed real-time data set is used as the input of the fault classification model. When the probability output by the fault classification model is greater than a preset threshold, it is judged that the fault can be repaired remotely. When the probability output by the fault classification model is less than the preset threshold, it is judged that the fault cannot be repaired remotely.

[0025] Furthermore, when a fault is identified as remotely repairable, a repair instruction is sent to the meteorological equipment to be repaired through message queue telemetry transmission based on the preset remote repair strategy. After receiving the instruction, the meteorological equipment to be repaired performs the corresponding repair operation, including:

[0026] Based on different types of faults, corresponding remote repair strategies are formulated in advance;

[0027] Classify and store remote repair strategies based on fault types;

[0028] When a fault is identified as remotely repairable, a corresponding strategy is selected from the stored remote repair strategies according to the fault type, and a specific repair instruction is generated;

[0029] Use the message queue remote sensing transmission protocol to send out the repair instructions, use the message queue remote sensing transmission client library to connect to the message queue remote sensing transmission proxy server, and publish the repair instructions as messages to the specified topic;

[0030] After a failure occurs, the meteorological equipment to be repaired subscribes to the message queue remote sensing transmission topic related to receiving repair instructions, and the meteorological equipment to be repaired continuously monitors the subscribed topic and waits to receive repair instructions;

[0031] When the meteorological equipment to be repaired receives the repair instruction message, the callback function provided by the message queue remote sensing transmission client library is used to parse the message and extract the type of instruction;

[0032] According to the type of instruction, the corresponding repair operation is performed.

[0033] Furthermore, when a fault is identified as one that cannot be repaired remotely, an early warning message containing fault information is generated and sent to relevant technical personnel. The specific content includes:

[0034] Obtain the location information, fault manifestation and fault cause analysis of the meteorological equipment to be repaired;

[0035] Integrate the location information, fault manifestation and fault cause analysis of the meteorological equipment to be repaired into the warning message based on the preset format;

[0036] Identify the relevant technicians responsible for maintenance and send warning information to them based on message queue telemetry transmission.

[0037] In a second aspect, the present application also provides a meteorological equipment fault diagnosis system based on message queue remote sensing transmission, comprising:

[0038] Real-time data collection module, used for real-time data collection, including meteorological equipment operating parameters, meteorological element data and network transmission status;

[0039] The model training module is used to train the fault classification model through historical fault data and corresponding feature data, and classify faults into two categories: remote repairable and non-remote repairable;

[0040] The fault result output module is used to process the real-time collected data, transmit the processed real-time data set to the fault classification model, identify the fault type in real time, judge the fault type based on the characteristics of the collected data, and output the fault diagnosis result;

[0041] The repairable fault module is used to send a repair instruction to the meteorological equipment to be repaired through message queue remote sensing transmission based on a preset remote repair strategy when the fault is identified as a remotely repairable fault. The meteorological equipment to be repaired performs the corresponding repair operation after receiving the instruction;

[0042] The warning message generation module is used to generate a warning message containing fault information when a fault is identified as one that cannot be repaired remotely, and send the warning message to relevant technicians.

[0043] In a third aspect, the present application provides an electronic device, including:

[0044] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, which, when executed by the device, cause the device to perform the method of the first aspect.

[0045] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored, and when the computer-readable storage medium is run on a computer, the computer executes the method of the first aspect.

[0046] In a fifth aspect, the present application provides a computer program, which, when executed by a computer, is used to execute the method of the first aspect.

[0047] In one possible design, the program in the fifth aspect may be stored in whole or in part on a storage medium packaged together with the processor, or may be stored in whole or in part on a memory not packaged together with the processor.

[0048] This application has the following beneficial effects:

[0049] 1. The present application trains a fault classification model through historical fault data and corresponding feature data, and divides faults into two categories: remotely repairable and non-remotely repairable. For remotely repairable faults, technicians can directly perform remote repairs through remote control without going to the site, and no professional technicians are required to troubleshoot the faults, which saves time for on-site troubleshooting, improves the efficiency of fault repair, and avoids unnecessary waste of manpower and material resources. For faults that cannot be repaired remotely, relevant technicians go to the site of the meteorological equipment fault to repair it, which reduces the judgment time in the face of meteorological equipment failures, saves a lot of time for meteorological equipment failure repair, and improves the efficiency of fault repair.

[0050] 2. This application processes the real-time collected data, transmits the processed real-time data set to the fault classification model, identifies the fault type in real time, judges the fault type based on the characteristics of the collected data, and outputs the fault diagnosis results. This application judges the fault type based on the characteristics of the collected data, makes full use of the rich information of the real-time data, and can respond as soon as the fault occurs.

[0051] 3. This application uses message queue-based remote sensing transmission technology to not only realize real-time transmission of meteorological data, but also provide effective fault diagnosis and remote repair functions when meteorological equipment fails, especially in remote or difficult-to-reach meteorological observation sites, reducing the need for on-site maintenance and greatly improving the reliability and maintenance efficiency of meteorological equipment, which is crucial to ensuring the continuity and accuracy of meteorological services.

[0052] 4. This application automatically generates repair instructions and sends them to the meteorological equipment to be repaired through message queue remote sensing transmission. After receiving the instructions, the equipment executes the corresponding repair operations, thereby realizing intelligent remote repair, saving manpower and time costs, and improving the availability and stability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is an exemplary system architecture diagram to which the embodiments of the present application can be applied;

[0054] Figure 2 This is a flow chart of a meteorological equipment fault diagnosis method based on message queue remote sensing transmission according to an embodiment of the present application;

[0055] Figure 3 This is a flow chart of model training in an embodiment of the present application;

[0056] Figure 4 This is a real-time data processing flow chart of an embodiment of the present application;

[0057] Figure 5 This is a flowchart of remote fault repair in an embodiment of the present application;

[0058] Figure 6 A flowchart for generating a warning message according to an embodiment of the present application;

[0059] Figure 7 This is a system flow chart of an embodiment of the present application;

[0060] Figure 8 It is a schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0062] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0063] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0064] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0065] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0066] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop computers and desktop computers, etc.

[0067] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0068] It should be noted that the meteorological equipment fault diagnosis method based on message queue remote sensing transmission provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the meteorological equipment fault diagnosis system based on message queue remote sensing transmission is generally set in the server / terminal device.

[0069] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0070] Continue to refer Figure 2 , the figure shows a flow chart of a meteorological equipment fault diagnosis method based on message queue remote sensing transmission of the present application, the method comprises the following steps:

[0071] Step 201, real-time data collection, the real-time data collection includes meteorological equipment operating parameters, meteorological element data and network transmission status;

[0072] In a possible implementation, the meteorological equipment operating parameters, meteorological element data and network transmission status collected in real time are transmitted to the central server via a message queue.

[0073] The meteorological equipment operating parameters include the voltage value, current value, equipment, operating frequency of each chip and running time of each component of the meteorological equipment collector; the collected data is transmitted to the local data collection terminal through the communication interface of the equipment, and a timestamp is added to each parameter at the data collection terminal, and the data is packaged in a preset format, and the data is published to the proxy server of the message queue remote sensing transmission protocol through the message queue remote sensing transmission protocol. For example, the collector power supply voltage data is published to the topic "device / collector / voltage", and the running time data is published to "device / collector / runtime".

[0074] The meteorological element data is collected by special meteorological sensors, such as temperature sensors, humidity sensors, air pressure sensors, wind speed and direction sensors, etc. These sensors convert physical quantities into electrical signals, and after analog-to-digital conversion, transmit the data to the data acquisition system. The data acquisition system also adds timestamps to the meteorological element data and publishes them to the proxy server through the message queue remote sensing transmission protocol with topics such as "weather / temperature", "weather / humidity", "weather / pressure", "weather / wind_speed", and "weather / wind_direction".

[0075] In the network communication module of the meteorological equipment (such as wireless communication module, wired network interface), the network transmission status data is obtained through the network management tool or the network monitoring function of the device itself. These data include the stability of the network connection (obtaining the packet loss rate and delay through the Ping command or similar mechanism), signal strength (for wireless networks), and traffic data of the network interface (the number of bytes sent and received, the number of data packets, etc.). The collected data is published to the proxy server through MQTT with topics such as "network / connectivity", "network / signal_strength", and "network / traffic".

[0076] Step 202: Train the fault classification model using historical fault data and corresponding feature data, and classify the faults into two categories: remotely repairable and non-remotely repairable.

[0077] In a possible implementation, the fault classification model is trained by using historical fault data and corresponding feature data to classify faults into two categories: remotely repairable and non-remotely repairable. Please refer to Figure 3 , the specific contents include:

[0078] Step 31, obtain historical data of meteorological equipment, where the historical data includes meteorological equipment operating parameters, meteorological element data and network transmission status during the period when the fault occurred; at the same time, obtain the corresponding fault maintenance records, and use whether each fault event is repaired remotely or on-site as a label for fault classification.

[0079] Step 32, clean the historical data, process missing values, and obtain pre-processed historical data.

[0080] Step 33, obtain the historical features of the extracted pre-processed historical data, the historical features include the statistical features of the meteorological equipment operating parameters, obtain the change trend of the meteorological equipment operating parameters, obtain the statistical features of the meteorological element data, the correlation between different meteorological elements, and the spatial consistency features of the meteorological element data of multiple adjacent meteorological equipment; obtain the mean and variance of the packet loss rate of the network transmission status data, the distribution characteristics of the delay, and the fluctuation range of the signal strength. The statistical features include the mean, median, standard deviation, maximum value, minimum value, etc.

[0081] Step 34, perform historical feature decomposition based on principal component analysis to obtain the main feature vectors; obtain the correlation between variables through the mutual information algorithm, screen out the features with high correlation with the fault type, and obtain the processed historical data set;

[0082] Step 35, divide the processed historical data set into a training set and a test set, train the fault classification model through the training set, and output the fault type and its probability; evaluate the performance of the fault classification model through the test set, adjust the parameters, and select the optimal fault classification model for fault classification diagnosis.

[0083] Step 203, processing the real-time collected data, transmitting the processed real-time data set to the fault classification model, identifying the fault type in real time, judging the fault type based on the characteristics of the collected data, and outputting the fault diagnosis result.

[0084] In a possible implementation, the real-time collected data is processed, please refer to Figure 4 , the specific contents include:

[0085] Step 41, obtaining real-time data of meteorological equipment, wherein the real-time data includes operating parameters of meteorological equipment, meteorological element data and network transmission status during the period when the fault occurs;

[0086] Step 42, clean the real-time data, process missing values, and obtain pre-processed real-time data.

[0087] Step 43, obtain the real-time features of the extracted pre-processed real-time data, the real-time features include the statistical features of the meteorological equipment operating parameters, obtain the change trend of the meteorological equipment operating parameters, obtain the statistical features of the meteorological element data, the correlation between different meteorological elements, and the spatial consistency features of the meteorological element data of multiple adjacent meteorological equipment; obtain the mean and variance of the packet loss rate of the network transmission status data, the distribution characteristics of the delay, and the fluctuation range of the signal strength. The statistical features include the mean, median, standard deviation, maximum value, minimum value, etc.

[0088] Step 44, perform real-time feature decomposition based on principal component analysis to obtain the main feature vectors; obtain the correlation between variables through the mutual information algorithm, screen out the features with high correlation with the fault type, and obtain the processed real-time data set.

[0089] In one possible implementation, the processed real-time data set is used as the input of a fault classification model. When the probability output by the fault classification model is greater than a preset threshold, the fault is judged to be remotely repairable. When the probability output by the fault classification model is less than the preset threshold, the fault is judged to be non-remotely repairable.

[0090] Step 204, when a fault is identified as being remotely repairable, a repair instruction is sent to the meteorological equipment to be repaired through message queue telemetry transmission based on a preset remote repair strategy, and the meteorological equipment to be repaired performs corresponding repair operations after receiving the instruction.

[0091] In a possible implementation, when a fault is identified as remotely repairable, a repair instruction is sent to the meteorological device to be repaired through message queue telemetry transmission based on a preset remote repair strategy. After receiving the instruction, the meteorological device to be repaired performs the corresponding repair operation. Please refer to Figure 5 , including:

[0092] Step 51, based on different types of faults, pre-formulate corresponding remote repair strategies; for example, if the fault is caused by a software parameter setting error, a strategy can be formulated to send specific parameter adjustment instructions; if it is a network connection problem, you can try to send a network reconfiguration instruction or an instruction to restart the network module.

[0093] Step 52, classify and store the remote repair strategies based on the fault type;

[0094] Step 53, when the fault is identified as remotely repairable, a corresponding strategy is selected from the stored remote repair strategies according to the fault type, and a specific repair instruction is generated;

[0095] Step 54, using the message queue remote sensing transmission protocol to send out the repair instruction, using the message queue remote sensing transmission client library to connect to the message queue remote sensing transmission proxy server, and publishing the repair instruction as a message to the specified topic;

[0096] Step 55, after a failure occurs, the meteorological device to be repaired subscribes to a message queue remote sensing transmission topic related to receiving repair instructions, and the meteorological device to be repaired continuously monitors the subscribed topic, waiting to receive repair instructions; for example, the device can use the message queue remote sensing transmission client library to subscribe to the "device_repair / [device_id]" topic, where [device_id] is its own unique identifier.

[0097] Step 56, when the meteorological device to be repaired receives the repair instruction message, the callback function provided by the message queue remote sensing transmission client library is used to parse the message and extract the type of instruction;

[0098] Step 57, according to the type of instruction, execute the corresponding repair operation. For example, if it is a parameter adjustment instruction, the device will modify the corresponding device parameters according to the parameter name and new value in the instruction; if it is a software update instruction, the device will start the software update program, download the new software version from the specified server and install it; if it is a restart instruction, the device will perform a restart operation to reinitialize each module of the device.

[0099] Step 205, when a fault is identified as one that cannot be repaired remotely, an early warning message containing fault information is generated and sent to relevant technicians, along with key information such as the location of the equipment, fault symptoms, possible causes, etc., so that the technicians can prepare appropriate repair tools and solutions.

[0100] In one possible implementation, when a fault is identified as one that cannot be repaired remotely, an early warning message containing fault information is generated and sent to relevant technicians, along with key information such as the location of the device, fault symptoms, possible causes, etc., so that the technicians can prepare appropriate repair tools and solutions. Please refer to Figure 6 , the specific contents include:

[0101] Step 61, obtain the location information, fault manifestation and fault cause analysis of the meteorological equipment to be repaired; combine the results of the fault type identification model, historical fault data and the characteristics of the currently collected data to analyze the possible causes of the fault. For example, if it is a collector failure, the possible causes include hardware aging, sensor damage, internal circuit failure, etc.; if it is a meteorological element measurement problem, it may be sensor contamination, calibration failure, external interference, etc.; for network problems, it may be network equipment failure, communication line damage, network configuration error, etc.

[0102] Step 62, integrating the location information, fault manifestation form and fault cause analysis of the meteorological equipment to be repaired into the warning message based on a preset format;

[0103] Step 63, determine the relevant technicians responsible for maintenance, and send warning information to the relevant technicians based on message queue telemetry transmission.

[0104] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0105] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0106] Continue to refer Figure 7 The meteorological equipment fault diagnosis system based on message queue remote sensing transmission of this embodiment includes:

[0107] The real-time data collection module 701 is used to collect data in real time, and the real-time collected data includes meteorological equipment operating parameters, meteorological element data and network transmission status;

[0108] Model training module 702, used to train the fault classification model through historical fault data and corresponding feature data, and classify the faults into two categories: remotely repairable and non-remotely repairable;

[0109] The fault result output module 703 is used to process the real-time collected data, transmit the processed real-time data set to the fault classification model, identify the fault type in real time, judge the fault type based on the characteristics of the collected data, and output the fault diagnosis result;

[0110] The repairable fault module 704 is used to send a repair instruction to the meteorological equipment to be repaired through message queue remote sensing transmission based on a preset remote repair strategy when the fault is identified as a remotely repairable fault, and the meteorological equipment to be repaired performs a corresponding repair operation after receiving the instruction;

[0111] The warning message generating module 705 is used to generate a warning message containing fault information when a fault is identified as a fault that cannot be repaired remotely, and send the warning message to relevant technicians.

[0112] To solve the above technical problems, the present application also provides a computer device. Figure 8 , Figure 8 This is a basic structural block diagram of the computer device in this embodiment.

[0113] The computer device 8 includes a memory 8a, a processor 8b, and a network interface 8c that are interconnected through a system bus. It should be noted that the figure only shows a computer device 8 with components 8a-8c, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0114] Computer devices can be computing devices such as desktop computers, notebooks, PDAs, and cloud servers. Computer devices can interact with users through keyboards, mice, remote controls, touch pads, or voice control devices.

[0115] The memory 8a includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 8a can be an internal storage unit of the computer device 8, such as a hard disk or memory of the computer device 8. In other embodiments, the memory 8a can also be an external storage device of the computer device 8, such as a plug-in hard disk equipped on the computer device 8, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 8a can also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 8a is generally used to store the operating system and various application software installed on the computer device 8, such as the program code of the meteorological equipment fault diagnosis method based on message queue remote sensing transmission, etc. In addition, the memory 8a can also be used to temporarily store various types of data that have been output or are to be output.

[0116] The processor 8b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 8b is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 8b is used to run the program code stored in the memory 8a or process data, such as running the program code of the meteorological equipment fault diagnosis method based on message queue remote sensing transmission.

[0117] The network interface 8c may include a wireless network interface or a wired network interface, and the network interface 8c is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0118] The present application also provides another embodiment, namely, providing a non-volatile computer-readable storage medium, which stores a program of a meteorological equipment fault diagnosis method based on message queue remote sensing transmission. The meteorological equipment fault diagnosis based on message queue remote sensing transmission can be executed by at least one processor, so that at least one processor executes the steps of the meteorological equipment fault diagnosis method based on message queue remote sensing transmission as described above.

[0119] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0120] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A meteorological equipment fault diagnosis method based on message queue remote sensing transmission, characterized in that: include: Real-time data collection, including meteorological equipment operating parameters, meteorological element data and network transmission status; The fault classification model is trained using historical fault data and corresponding feature data to classify faults into two categories: remotely repairable and non-remotely repairable. Process the real-time collected data, transfer the processed real-time data set to the fault classification model, identify the fault type in real time, judge the fault type based on the characteristics of the collected data, and output the fault diagnosis results; When a fault is identified as remotely repairable, a repair instruction is sent to the meteorological equipment to be repaired through message queue telemetry transmission based on the preset remote repair strategy. After receiving the instruction, the meteorological equipment to be repaired performs the corresponding repair operation. When a fault is identified as one that cannot be repaired remotely, an early warning message containing the fault information is generated and sent to relevant technicians.

2. The meteorological equipment fault diagnosis method based on message queue remote sensing transmission according to claim 1 is characterized in that: The meteorological equipment operating parameters, meteorological element data and network transmission status collected in real time are transmitted to the central server through the message queue.

3. The meteorological equipment fault diagnosis method based on message queue remote sensing transmission according to claim 2 is characterized in that: The fault classification model is trained through historical fault data and corresponding feature data to classify faults into two categories: remotely repairable and non-remotely repairable. The specific contents include: Obtain historical data of meteorological equipment, including operating parameters of meteorological equipment, meteorological element data and network transmission status during the period when the fault occurred; obtain corresponding fault maintenance records at the same time, and use whether each fault event was repaired remotely or on-site as a label for fault classification; Clean historical data, process missing values, and obtain preprocessed historical data; Obtain the historical features of the extracted preprocessed historical data, including the statistical features of the operating parameters of the meteorological equipment, obtain the changing trend of the operating parameters of the meteorological equipment, obtain the statistical features of the meteorological element data, the correlation between different meteorological elements, and the spatial consistency features of the meteorological element data of multiple adjacent meteorological equipment; obtain the mean and variance of the packet loss rate of the network transmission status data, the distribution characteristics of the delay, and the fluctuation range of the signal strength; Perform historical feature decomposition based on principal component analysis to obtain the main feature vectors; obtain the correlation between variables through the mutual information algorithm, filter out features with high correlation with the fault type, and obtain the processed historical data set; The processed historical data set is divided into a training set and a test set. The fault classification model is trained with the training set to output the fault type and its probability. The performance of the fault classification model is evaluated with the test set, the parameters are adjusted, and the optimal fault classification model is selected for fault classification diagnosis.

4. The meteorological equipment fault diagnosis method based on message queue remote sensing transmission according to claim 1 is characterized in that: Process the data collected in real time, including: Obtaining real-time data of meteorological equipment, including operating parameters of meteorological equipment, meteorological element data and network transmission status during the period when the fault occurred; Clean the real-time data, process missing values, and obtain pre-processed real-time data; Acquire the real-time features of the extracted preprocessed real-time data, including the statistical features of the operating parameters of the meteorological equipment, obtain the changing trend of the operating parameters of the meteorological equipment, obtain the statistical features of the meteorological element data, the correlation between different meteorological elements, and the spatial consistency features of the meteorological element data of multiple adjacent meteorological equipment; obtain the mean and variance of the packet loss rate of the network transmission status data, the distribution characteristics of the delay, and the fluctuation range of the signal strength; Based on principal component analysis, real-time feature decomposition is performed to obtain the main feature vectors; the correlation between variables is obtained through the mutual information algorithm, the features with high correlation with the fault type are screened out, and the processed real-time data set is obtained.

5. The meteorological equipment fault diagnosis method based on message queue remote sensing transmission according to claim 4 is characterized in that: The processed real-time data set is used as the input of the fault classification model. When the probability output by the fault classification model is greater than the preset threshold, it is judged that the fault can be repaired remotely. When the probability output by the fault classification model is less than the preset threshold, it is judged that the fault cannot be repaired remotely.

6. The meteorological equipment fault diagnosis method based on message queue remote sensing transmission according to claim 5 is characterized in that: When a fault is identified as remotely repairable, a repair instruction is sent to the meteorological equipment to be repaired through message queue telemetry transmission based on the preset remote repair strategy. After receiving the instruction, the meteorological equipment to be repaired performs the corresponding repair operation, including: Based on different types of faults, corresponding remote repair strategies are formulated in advance; Classify and store remote repair strategies based on fault types; When a fault is identified as remotely repairable, a corresponding strategy is selected from the stored remote repair strategies according to the fault type, and a specific repair instruction is generated; Use the message queue remote sensing transmission protocol to send out the repair instructions, use the message queue remote sensing transmission client library to connect to the message queue remote sensing transmission proxy server, and publish the repair instructions as messages to the specified topic; After a failure occurs, the meteorological equipment to be repaired subscribes to the message queue remote sensing transmission topic related to receiving repair instructions, and the meteorological equipment to be repaired continuously monitors the subscribed topic and waits to receive repair instructions; When the meteorological equipment to be repaired receives the repair instruction message, the callback function provided by the message queue remote sensing transmission client library is used to parse the message and extract the type of instruction; According to the type of instruction, the corresponding repair operation is performed.

7. The meteorological equipment fault diagnosis method based on message queue remote sensing transmission according to claim 5 is characterized in that: When a fault is identified as one that cannot be repaired remotely, an early warning message containing fault information is generated and sent to relevant technicians. The specific content includes: Obtain the location information, fault manifestation and fault cause analysis of the meteorological equipment to be repaired; Integrate the location information, fault manifestation and fault cause analysis of the meteorological equipment to be repaired into the warning message based on the preset format; Identify the relevant technicians responsible for maintenance and send warning information to them based on message queue telemetry transmission.

8. A meteorological equipment fault diagnosis system based on message queue remote sensing transmission, used to implement the meteorological equipment fault diagnosis method based on message queue remote sensing transmission of claims 1-7, characterized in that: include: Real-time data collection module, used for real-time data collection, including meteorological equipment operating parameters, meteorological element data and network transmission status; The model training module is used to train the fault classification model through historical fault data and corresponding feature data, and classify faults into two categories: remote repairable and non-remote repairable; The fault result output module is used to process the real-time collected data, transmit the processed real-time data set to the fault classification model, identify the fault type in real time, judge the fault type based on the characteristics of the collected data, and output the fault diagnosis result; The repairable fault module is used to send a repair instruction to the meteorological equipment to be repaired through message queue remote sensing transmission based on a preset remote repair strategy when the fault is identified as a remotely repairable fault. The meteorological equipment to be repaired performs the corresponding repair operation after receiving the instruction; The warning message generation module is used to generate a warning message containing fault information when a fault is identified as one that cannot be repaired remotely, and send the warning message to relevant technicians.

9. An electronic device, characterized in that: include: one or more processors; Memory; And one or more computer programs, wherein the one or more computer programs are stored in a memory, and the one or more computer programs include instructions, which, when executed by the device, enable the device to perform the steps of the meteorological equipment fault diagnosis method based on message queue remote sensing transmission as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored in a computer-readable storage medium, and when the computer-readable storage medium is run on a computer, the computer is caused to execute the steps of a meteorological equipment fault diagnosis method based on message queue remote sensing transmission as claimed in any one of claims 1 to 7.

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