Online monitoring analysis method, system, electronic device and storage medium

Abstract

The application provides an online monitoring analysis method and system, electronic equipment and a storage medium, and relates to the field of cloud computing. The method comprises the following steps: collecting data of a patrol area to obtain corresponding original input data; the original input data comprises a plurality of sub-data; selectively extracting data from the original input data according to a preset information extraction rule; the selective data comprises judgment data obtained by judging the original input data and correction data for supplementing deviations of the judgment data; and analyzing and judging the selective data to obtain a diagnosis result. The application solves the problem of low data processing efficiency in the related art.

Description

Technical Field

[0001] This invention relates to the field of cloud computing, and more specifically, to an online monitoring and analysis method, system, electronic device, and storage medium. Background Technology

[0002] Currently, in the field of cloud computing, cloud-based online detection is an inevitable trend. Its main advantages include integrating hardware devices such as servers to reduce construction costs; and cloud platforms can collect massive amounts of data from multiple substations for deep learning, accelerating the learning efficiency of artificial intelligence.

[0003] However, cloud platform solutions also have drawbacks: if a cloud platform is to directly analyze data such as videos and high-definition photos provided by online monitoring terminals, its network construction costs will be very high. At the same time, irrelevant data such as noise will greatly increase the platform load, thereby reducing data processing efficiency.

[0004] As can be seen from the above, the problem of how to improve data processing efficiency still needs to be solved. Summary of the Invention

[0005] This application provides an online monitoring and analysis method, system, electronic device, and storage medium, which can solve the problem of low data processing efficiency in related technologies. The technical solution is as follows:

[0006] According to one aspect of the embodiments of this application, an online monitoring and analysis method is provided, the method comprising: collecting data from an inspection area to obtain corresponding raw input data; the raw input data including several sub-data; selectively extracting data from the raw input data according to preset information extraction rules; the selective data including judgment data obtained after judging the raw input data and corrective data supplementing the deviation of the judgment data; and analyzing and judging the selective data to obtain a diagnostic result.

[0007] According to one aspect of the embodiments of this application, an online monitoring and analysis system is provided, the system comprising: an intelligent inspection robot, an edge computing platform, and a cloud computing platform, wherein: the intelligent inspection robot is used to collect data from an inspection area, obtain corresponding raw input data, and send it to the edge computing platform; the edge computing platform is used to selectively extract data from the raw input data according to the information extraction rules preset by the cloud computing platform, and send the extracted selective data to the cloud computing platform; the cloud computing platform is used to analyze and diagnose the selective data sent by the edge computing platform to obtain diagnostic results, and send the diagnostic results to the edge computing platform and the intelligent inspection robot to achieve early warning.

[0008] In an exemplary embodiment, the intelligent inspection robot includes a controller, sensors, and a wireless communication unit, wherein: the controller is used to implement one or more functions of the intelligent inspection robot, such as autonomous path planning, obstacle avoidance, and motion control; the sensors are used to monitor the inspection area in all directions and capture raw input data related to the inspection area; and the wireless communication unit is used to transmit the raw input data and receive diagnostic results.

[0009] In one exemplary embodiment, the edge computing platform includes: a first processor, the first processor being configured to extract the selective data from the raw input data.

[0010] In one exemplary embodiment, the cloud computing platform includes a second processor, which processes the selective data, analyzes and determines the fault type based on the processed selective data to obtain a diagnostic result, and sends the diagnostic result to the edge computing platform and the intelligent inspection robot to achieve early warning.

[0011] According to one aspect of the embodiments of this application, an electronic device includes at least one processor and at least one memory, wherein program instructions or code are stored in the memory; the program instructions or code are loaded and executed by the processor, causing the electronic device to implement the online monitoring and analysis method as described above.

[0012] According to one aspect of the embodiments of this application, a storage medium stores program instructions or code thereon, which are loaded and executed by a processor to implement the online monitoring and analysis method as described above.

[0013] According to one aspect of the embodiments of this application, a computer program product includes program instructions or code, which are stored in a storage medium. The processor of an electronic device reads the program instructions or code from the storage medium, loads and executes the program instructions or code, thereby enabling the electronic device to implement the online monitoring and analysis method as described above.

[0014] The beneficial effects of the technical solution provided in this application are:

[0015] In the above technical solution, data is collected from the inspection area to obtain the corresponding raw input data; selective data extraction is performed on the raw input data according to preset information extraction rules; and diagnostic results are obtained by analyzing and judging the selective data. Only key information is uploaded, redundant data is filtered out, reducing the communication bandwidth between the edge computing platform and the cloud platform, and reducing the amount of data uploaded to the cloud computing platform, thereby ensuring the data processing efficiency of the cloud computing platform and ensuring the accuracy of the uploaded data, thus improving the accuracy of cloud computing judgments. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0017] Figure 1 This is a flowchart illustrating an online monitoring and analysis method according to an exemplary embodiment;

[0018] Figure 2 yes Figure 1 A flowchart of step 330 in one embodiment corresponds to the following example;

[0019] Figure 3 This is a flowchart illustrating another online monitoring and analysis method according to an exemplary embodiment;

[0020] Figure 4 This is a structural block diagram of an online monitoring and analysis system according to an exemplary embodiment;

[0021] Figure 5 This is a hardware structure diagram of an electronic device according to an exemplary embodiment;

[0022] Figure 6 This is a structural block diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0023] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0024] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any of the elements and all combinations thereof of one or more associated listed items.

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0026] Please see Figure 1 This application provides an online monitoring and analysis method, which may include the following steps:

[0027] Step 310: Collect data from the inspection area to obtain the corresponding raw input data.

[0028] The raw input data includes several sub-data sets, such as illumination, temperature, noise, images, and videos. This raw input data is collected from the inspection area using various sensor devices, including photosensors, thermal sensors, acoustic sensors, and vision sensors. These sensor devices can be deployed on an intelligent inspection robot, which collects data within the inspection area to obtain the raw input data.

[0029] Step 330: Selectively extract data from the original input data according to the preset information extraction rules.

[0030] The preset information extraction rules include vectorizing the original input data using algorithms such as "word embedding layer" to reduce the data size; the selective data includes judgment data obtained after judging the original input data, and correction data to supplement the deviation of the judgment data.

[0031] In one possible implementation, selective data extraction from the raw input data is based on an AI model trained on a neural network, capable of extracting selective data from the raw input data. The training materials for this AI model include core scene images corresponding to the fault types of the inspection targets and fault status information from the intelligent inspection robot.

[0032] In one possible implementation, such as Figure 2 As shown, step 330 above may include the following steps:

[0033] Step 331: Randomly generate a data matrix of all sub-data in the original input data.

[0034] Specifically, the selected data is vectorized, and a data matrix is ​​obtained by concatenating the vectors corresponding to several sub-data points, where each row of the data matrix corresponds to a vector of a sub-data point.

[0035] Step 333: For any vector of the central sub-data, perform mean processing based on the vectors of the surrounding data in the data matrix to obtain the concatenated vector of the central sub-data.

[0036] Specifically, for a certain central sub-data A, its corresponding vector is a. Vectors b and c of the surrounding data of sub-data A are extracted from the data matrix. Based on vectors a, b, and c, the mean vector of the sub-data a' = (a + b + c) / 3 is calculated, thus obtaining the concatenated vector a' of the central sub-data A.

[0037] Step 335: Train the concatenated vector using logistic regression, and use the softmax function as the activation function to estimate the probability of the concatenated vector after regression training, thus obtaining the probability vector.

[0038] Step 357: Based on the matching of probability vectors with true vectors, selective data is obtained from the original input data.

[0039] By performing similarity matching between probability vectors and true vectors, and obtaining selective data from the original input data based on the similarity result being greater than a set similarity threshold, the system can obtain the most representative key data from the original input data, thereby minimizing data transmission and / or data processing costs.

[0040] Step 350: Analyze and judge the extracted selective data to obtain the diagnostic results.

[0041] In one possible implementation, such as Figure 3 As shown, after step 350 above, the method may further include the following steps:

[0042] Step 370: Based on the fault type indicated by the diagnostic results, respond to the warning corresponding to the fault type.

[0043] Through the above process, by selectively extracting data from the original input data, filtering redundant data, and processing only key information, the efficiency of data processing is greatly improved, while ensuring the accuracy of the data, thereby improving the accuracy of judgment.

[0044] The following are system embodiments of this application, which can be used to execute the online monitoring and analysis method involved in this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of the online monitoring and analysis method involved in this application.

[0045] Please see Figure 4 This application provides an online monitoring and analysis system 100, including but not limited to an intelligent inspection robot 200, an edge computing platform 300, and a cloud computing platform 400.

[0046] Among them, the intelligent inspection robot 200 is used to collect data from the inspection area, obtain the corresponding raw input data, and send it to the edge computing platform 300.

[0047] The edge computing platform 300 is used to selectively extract data from the original input data according to the information extraction rules preset by the cloud computing platform 400, and then send the extracted selective data to the cloud computing platform 400.

[0048] The cloud computing platform 400 is used to analyze and diagnose selective data sent by the edge computing platform 300 to obtain diagnostic results, and then send the diagnostic results to the edge computing platform 300 and the intelligent inspection robot 200 to achieve early warning.

[0049] In one exemplary embodiment, the intelligent inspection robot 200 includes a controller, sensors, and a wireless communication unit.

[0050] The controller is used to realize one or more functions of the intelligent inspection robot, including autonomous path planning, obstacle avoidance, and motion control.

[0051] Sensors are used to perform omnidirectional detection of the inspection area and capture raw input data related to the inspection area.

[0052] The wireless communication unit is used to transmit raw input data and receive diagnostic results.

[0053] In one exemplary embodiment, the edge computing platform 300 includes: a first processor for extracting selective data from raw input data.

[0054] In an exemplary embodiment, the cloud computing platform 400 includes: a second processor, configured to process selective data, determine the fault type based on the processed selective data to obtain a diagnostic result, and send the diagnostic result to the edge computing platform 300 and the intelligent inspection robot 200 to achieve early warning.

[0055] Figure 5 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment.

[0056] It should be noted that this electronic device is merely an example adapted to this application and should not be construed as providing any limitation on the scope of use of this application. Furthermore, this electronic device should not be interpreted as requiring or depending on any specific feature. Figure 5 One or more components of the exemplary electronic device 2000 shown.

[0057] The hardware structure of electronic devices 2000 can vary significantly due to differences in configuration or performance, such as... Figure 5 As shown, the electronic device 2000 includes: a power supply 210, an interface 230, at least one memory 250, and at least one central processing unit (CPU) 270.

[0058] Specifically, power supply 210 is used to provide operating voltage for various hardware devices on electronic device 2000.

[0059] Interface 230 includes at least one wired or wireless network interface 231 for interacting with external devices. Of course, in other examples adapted in this application, interface 230 may further include at least one serial-to-parallel conversion interface 233, at least one input / output interface 235, and at least one USB interface 237, etc. Figure 5 As shown, this does not constitute a specific limitation.

[0060] The memory 250 serves as a carrier for resource storage and can be a read-only memory, random access memory, disk, or optical disk, etc. The resources stored on it include the operating system 251, application programs 253, and data 255, etc., and the storage method can be temporary storage or permanent storage.

[0061] The operating system 251 is used to manage and control the various hardware devices and application programs 253 on the electronic device 2000, so as to enable the central processing unit 270 to perform calculations and processing on the massive data 255 in the memory 250. It can be Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0062] Application 253 is a program instruction or code based on operating system 251 that performs at least one specific task, and may include at least one module. Figure 5 (Not shown), each module can contain program instructions or code for the electronic device 2000. For example, the online monitoring and analysis system can be considered as an application program 253 deployed on the electronic device 2000.

[0063] Data 255 can be photos, pictures, etc. stored on a disk, or selective data, etc., stored in memory 250.

[0064] The central processing unit 270 may include one or more processors and is configured to communicate with the memory 250 via at least one communication bus to read program instructions or code stored in the memory 250, thereby enabling the computation and processing of massive amounts of data 255 in the memory 250. For example, an online monitoring and analysis method can be implemented by the central processing unit 270 reading a series of program instructions or code stored in the memory 250.

[0065] Furthermore, this application can also be implemented through hardware circuits or a combination of hardware circuits and software. Therefore, the implementation of this application is not limited to any specific hardware circuit, software, or combination thereof.

[0066] Please see Figure 6 This application provides an electronic device 4000, which may include: a desktop computer, a laptop computer, a server, etc.

[0067] exist Figure 6 In this context, the electronic device 4000 includes at least one processor 4001 and at least one memory 4003.

[0068] The data interaction between the processor 4001 and the memory 4003 can be achieved through at least one communication bus 4002. This communication bus 4002 may include a path for transmitting data between the processor 4001 and the memory 4003. The communication bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0069] Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this application.

[0070] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0071] The memory 4003 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program instructions or code in the form of instructions or data structures and accessible by the electronic device 400, but not limited thereto.

[0072] The memory 4003 stores program instructions or code, and the processor 4001 can read the program instructions or code stored in the memory 4003 through the communication bus 4002.

[0073] When the program instructions or code are executed by the processor 4001, the online monitoring and analysis methods in the above embodiments are implemented.

[0074] Furthermore, this application embodiment provides a storage medium storing program instructions or code, which is loaded and executed by a processor to implement the online monitoring and analysis method described above.

[0075] This application provides a computer program product, which includes program instructions or code. The program instructions or code are stored in a storage medium. The processor of the electronic device reads the program instructions or code from the storage medium, loads and executes the program instructions or code, so that the electronic device can implement the online monitoring and analysis method as described above.

[0076] Compared with related technologies, this application selectively extracts data from the original input data, filters redundant data, and processes only key information. This reduces the communication bandwidth between the edge computing platform and the cloud computing platform, reduces the amount of data uploaded to the cloud computing platform, thereby ensuring the data processing efficiency of the cloud computing platform and ensuring the accuracy of the uploaded data, thus improving the judgment accuracy of the cloud computing platform.

[0077] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0078] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An online monitoring and analysis method, characterized in that, The method includes: Data is collected from the inspection area to obtain the corresponding raw input data; According to preset information extraction rules, selective data extraction is performed on the original input data; wherein the steps for extracting the selective data include: Randomly generate a data matrix of all sub-data in the original input data; For any vector of the central sub-data, the average value of the vectors of the surrounding data of the central sub-data in the data matrix is ​​applied to obtain the concatenated vector of the central sub-data. The concatenated vector is trained using logistic regression, and the softmax function is used as the activation function to estimate the probability of the concatenated vector after regression training, thus obtaining a probability vector. Based on the matching of the probability vector with the true vector, selective data is obtained from the original input data; wherein, the selective data includes judgment data obtained after judging the original input data and correction data to supplement the deviation of the judgment data; Based on the analysis and judgment of the selective data, a diagnostic result is obtained.

2. The method as described in claim 1, characterized in that, The selective data extraction of the original input data is implemented based on an AI model, which is obtained by training a neural network and has the ability to extract selective data from the original input data.

3. The method as described in claim 1, characterized in that, After obtaining the diagnostic result, the method further includes: Based on the fault type indicated by the diagnostic results, a warning corresponding to the fault type is triggered.

4. An online monitoring and analysis system, characterized in that, The system includes: an intelligent inspection robot, an edge computing platform, and a cloud computing platform, wherein: The intelligent inspection robot is used to collect data from the inspection area, obtain the corresponding raw input data, and send it to the edge computing platform; the raw input data includes several sub-data. The edge computing platform is used to selectively extract data from the original input data according to the information extraction rules preset by the cloud computing platform, and send the extracted selective data to the cloud computing platform. The step of extracting the selective data includes: randomly generating a data matrix of all sub-data in the original input data; for any central sub-data vector, performing mean processing based on the vectors of the surrounding data in the data matrix to obtain a concatenated vector of the central sub-data; training the concatenated vector using logistic regression, and using the softmax function as the activation function to perform probability estimation on the concatenated vector after regression training to obtain a probability vector; and obtaining selective data from the original input data by matching the probability vector with the true vector. The selective data includes judgment data obtained after judging the original input data and correction data to supplement the deviation of the judgment data. The cloud computing platform is used to analyze and diagnose the selective data sent by the edge computing platform to obtain diagnostic results, and send the diagnostic results to the edge computing platform and the intelligent inspection robot to realize early warning.

5. The system as described in claim 4, characterized in that, The intelligent inspection robot includes: a controller, sensors, and a wireless communication unit, wherein: The controller is used to realize one or more functions of the intelligent inspection robot, including autonomous path planning, obstacle avoidance, and motion control. The sensor is used to perform omnidirectional detection of the inspection area and capture raw input data related to the inspection area; The wireless communication unit is used to transmit the raw input data and receive the diagnostic results.

6. The system as described in claim 4, characterized in that, The edge computing platform includes: a first processor, which is used to extract the selective data from the original input data.

7. The system as described in claim 4, characterized in that, The cloud computing platform includes a second processor, which processes the selective data, analyzes and determines the fault type based on the processed selective data to obtain a fault result, and sends the diagnostic result to the edge computing platform and the intelligent inspection robot to achieve early warning.

8. An electronic device, characterized in that, include: At least one processor and at least one memory, wherein program instructions or code are stored in the memory; The program instructions or code are loaded and executed by the processor, enabling the electronic device to implement the online monitoring and analysis method as described in any one of claims 1 to 3.

9. A storage medium storing program instructions or code thereon, characterized in that, The program instructions or code are loaded and executed by the processor to implement the online monitoring and analysis method as described in any one of claims 1 to 3.

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