Cloud edge collaborative information processing device and method

Abstract

The invention discloses a cloud edge collaborative information processing device and method. The device comprises an edge layer sub-module used for obtaining edge side data collected by an edge device, preprocessing the edge side data, and sending the preprocessed edge side data to a cloud layer sub-module; and the cloud layer sub-module is used for receiving the edge side data sent by the edge layer sub-module, reprocessing the edge side data according to a preset processing logic to obtain cloud side interaction data, storing the edge side data and the cloud side interaction data, and sending the edge side data and the cloud side interaction data to the cloud layer sub-module. Performing data interaction with the cloud computing center based on the edge side data and the cloud edge interaction data; and the anomaly detection sub-module is used for carrying out anomaly detection on the program operation data of the edge operation module to obtain anomaly diagnosis information, and carrying out anomaly early warning based on the anomaly diagnosis information, so that the data processing efficiency and the response speed are improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a cloud-edge collaborative information processing device and method. Background Art

[0002] With the rapid development of Internet of Things technology, the demand for data collection and processing is increasing.

[0003] In related technologies, cloud computing models have latency and bandwidth limitations when processing real-time, large-scale data. Edge computing, as an extension of cloud computing, can effectively reduce latency and improve data processing efficiency by sinking computing tasks to the edge of the network. However, how to achieve the collaborative work of edge computing and cloud computing to give full play to the advantages of both is a problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a cloud-edge collaborative information processing device and method to solve the problems of low data processing and transmission efficiency in cloud computing mode and excessive cloud load.

[0005] According to one aspect of the present invention, a cloud-edge collaborative information processing device is provided, comprising: an edge computing module, an edge device and a cloud computing center; the edge computing module comprises an edge layer submodule, a cloud layer submodule and a cloud layer anomaly detection submodule; wherein,

[0006] The edge layer submodule is used to obtain edge side data collected by the edge device, preprocess the edge side data, and send the preprocessed edge side data to the cloud layer submodule;

[0007] The cloud layer submodule is used to receive the edge side data sent by the edge layer submodule, reprocess the edge layer submodule according to a preset processing logic to obtain cloud-edge interaction data, store the edge side data and the cloud-edge interaction data, and perform data interaction with the cloud computing center based on the edge side data and the cloud-edge interaction data;

[0008] The abnormality detection submodule is used to perform abnormality detection on the program running data of the edge computing module to obtain abnormality diagnosis information, and to issue an abnormality warning based on the abnormality diagnosis information.

[0009] According to another aspect of the present invention, a cloud-edge collaborative information processing method is provided, comprising: an edge computing module, an edge device and a cloud computing center; the edge computing module comprises an edge layer submodule, a cloud layer submodule and a cloud layer anomaly detection submodule; wherein the cloud-edge collaborative information processing method comprises:

[0010] Obtain edge-side data collected by the edge device through the edge layer submodule, pre-process the edge-side data, and send the pre-processed edge-side data to the cloud layer submodule;

[0011] Receiving the edge-side data sent by the edge-layer submodule through the cloud-layer submodule, reprocessing the edge-layer submodule according to a preset processing logic to obtain cloud-edge interaction data, storing the edge-side data and the cloud-edge interaction data, and performing data interaction with the cloud computing center based on the edge-side data and the cloud-edge interaction data;

[0012] The abnormality detection submodule is used to perform abnormality detection on the program running data of the edge computing module to obtain abnormality diagnosis information, and an abnormality warning is performed based on the abnormality diagnosis information.

[0013] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the cloud-edge collaborative information processing method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the cloud-edge collaborative information processing method described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention is as follows: first, the edge-side data collected by the edge device is obtained through the edge layer submodule, the edge-side data is preprocessed, and the preprocessed edge-side data is sent to the cloud layer submodule, thereby ensuring the quality and consistency of the data and reducing the amount of data transmitted to the cloud layer submodule; then, the edge-side data sent by the edge layer submodule is received through the cloud layer submodule, and the edge-side data is reprocessed according to a preset processing logic to obtain cloud-edge interaction data, which can reduce the computing burden of the edge device, and the edge-side data and the cloud-edge interaction data are stored, and based on the edge-side data and the cloud-edge interaction data, The cloud-edge interaction data interacts with the cloud computing center, which can strengthen the protection of edge-side data and cloud-edge interaction data; finally, through the anomaly detection submodule, the program running data of the edge computing module is detected for anomalies to obtain abnormal diagnosis information, and abnormal warnings are issued based on the abnormal diagnosis information. Potential abnormal problems can be identified and early warnings can be issued to improve security, solve the problems of low data processing and transmission efficiency in the cloud computing mode and excessive cloud load, reduce data transmission volume and delay through edge-side processing, and the cloud provides powerful reprocessing and storage capabilities. Anomaly detection ensures system stability and improves data processing efficiency and response speed.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a structural diagram of a cloud-edge collaborative information processing device provided according to Embodiment 1 of the present invention;

[0022] Figure 2 is a flow chart of a cloud-edge collaborative information processing method provided according to Embodiment 2 of the present invention;

[0023] Figure 3 It is a structural diagram of an electronic device that implements the cloud-edge collaborative information processing method of an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0027] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0028] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0029] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0030] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0031] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0032] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0033] Embodiment 1

[0034] Figure 1 A flowchart of a cloud-edge collaborative information processing device is provided for embodiment 1 of the present invention. This embodiment can be applied to processing large amounts of real-time data, reducing latency and reducing bandwidth occupancy. The device can execute a cloud-edge collaborative information processing method. The cloud-edge collaborative information processing device can be implemented in the form of hardware and / or software.

[0035] like Figure 1 As shown, the device may specifically include: an edge computing module, an edge device and a cloud computing center; the edge computing module includes an edge layer submodule 110, a cloud layer submodule 120 and a cloud layer anomaly detection submodule 130; wherein the edge layer submodule 110 is used to obtain the edge side data collected by the edge device, pre-process the edge side data, and send the pre-processed edge side data to the cloud layer submodule; the cloud layer submodule 120 is used to receive the edge side data sent by the edge layer submodule, re-process the edge side data according to a preset processing logic to obtain cloud-edge interaction data, and store the edge side data and the cloud-edge interaction data, and, based on the edge side data and the cloud-edge interaction data, perform data interaction with the cloud computing center; the anomaly detection submodule 130 is used to perform anomaly detection on the program running data of the edge computing module to obtain anomaly diagnosis information, and perform anomaly warning based on the anomaly diagnosis information.

[0036] Among them, the edge computing module can be understood as a computing node located between the data source and the cloud server, which is used to receive data from the edge device, perform necessary preprocessing on it, and determine which data needs to be sent to the cloud for further processing or storage. The edge device can be understood as a physical device deployed at the edge of the network, which is used to collect environmental and / or operating data, etc., and transmit the data to the edge computing module 110 for processing. The cloud computing center can be understood as a data center, which is used to receive data from the edge computing module, perform tasks such as data analysis and machine learning model training, and feedback the results. The edge layer submodule can be understood as a part of the edge computing module, which is used for local data processing. Specifically, after the edge device obtains the edge side data, the data is preprocessed (such as filtering, aggregation and format conversion, etc.), and then the processed data is sent to the cloud layer submodule. The cloud layer submodule can be understood as the part of the edge computing module that interacts with the cloud computing environment. Specifically, the cloud layer submodule can receive the edge side data from the edge layer submodule, further process it according to the preset processing logic, generate cloud-edge interaction data that can be used for interaction, and store data, and exchange data with the cloud computing center. The cloud layer anomaly detection submodule can be understood as a component that detects whether the data of the edge computing module program is normal. Specifically, the cloud layer anomaly detection submodule can detect the operating status of the edge computing module, detect abnormal conditions that may affect performance or security, and generate abnormal diagnostic information and trigger an early warning mechanism when a problem is found, so that abnormal corrective measures can be taken in a timely manner.

[0037] The edge-side data may be understood as data collected by various sensors or electronic devices at the edge of the network close to the data source or user end.

[0038] Among them, the preset processing logic can be understood as the rules, algorithms or programs pre-defined by the cloud layer submodule in the edge computing module, which are used to indicate how to process the edge side data received from the edge layer submodule. The cloud-edge interaction data can be understood as the data interacted between the edge computing module and the cloud computing center. Specifically, the cloud-edge interaction data may include information processed by the edge layer submodule and the cloud layer submodule, as well as data used for collaboration between the edge layer submodule and the cloud layer submodule.

[0039] The abnormal diagnosis information can be understood as the diagnostic information obtained by the abnormal detection submodule when the program running data of the edge computing module exhibits abnormal behavior. The abnormal warning can be understood as an alarm issued based on the abnormal diagnosis information, which can be used to notify possible abnormalities in advance.

[0040] On the basis of the above scheme, optionally, the edge layer submodule includes a data acquisition unit and a data preprocessing unit; wherein the data acquisition unit is used to collect edge-side data through a data sensor pre-deployed at a preset detection position of the edge device, parse the device operation log of the edge device to obtain the edge-side data, and use an image acquisition device to capture an image of the device operation area of ​​the edge device to obtain the edge-side data, and send the obtained edge-side data to the data preprocessing unit; the data preprocessing unit is used to receive the edge-side data sent by the data acquisition unit, preprocess the edge-side data, and send the preprocessed edge-side data to the cloud layer submodule.

[0041] Among them, the data acquisition unit can be understood as a component or functional block for collecting edge-side data, and various types of edge-side data can be obtained by sensors deployed on edge devices, parsing device operation logs, and using image acquisition devices. The edge-side data may include but is not limited to at least one of physical quantities (such as temperature or humidity), event records, or visual information, and the edge-side data is sent to the data preprocessing unit for further processing. The preset detection position can be understood as a predetermined location where a data sensor is placed, which is used to collect edge-side data. The device operation log can be understood as a log file generated by the edge device, recording the operation history, error report, or other relevant information of the edge device. The image acquisition device can be understood as a device for capturing video or static images, which is used to take images or videos of the relevant area of ​​the edge device to detect the physical environment and operation of the edge device. The data preprocessing unit can be understood as a component that preprocesses the edge-side data received from the data acquisition unit to ensure the quality and consistency of the data. The preprocessing may include but is not limited to at least one of data cleaning processing, outlier correction processing, data aggregation processing, and data format conversion processing. The data cleaning processing can be understood as a method for removing noise, filling missing values, or correcting errors. The outlier correction process can be understood as a method for identifying and correcting data points in edge data that are out of the normal range. The data aggregation process can be understood as a method for merging multiple related data points in edge data into a higher-level summary data. The data format conversion can be understood as an operation for converting the representation of edge data to adapt to different application requirements, ensuring that edge data can be compatible and exchanged between different platforms while keeping its semantics unchanged.

[0042] The above scheme collects edge-side data through the data acquisition unit using data sensors at preset detection positions, and also parses the equipment operation log and uses image acquisition equipment to detect the equipment operation area to ensure the capture of operation status and environmental changes. The data preprocessing unit performs data cleaning, outlier correction, data aggregation and data format conversion on the edge-side data, which improves the quality and consistency of the data, reduces the amount of data transmitted to the cloud sub-module, reduces bandwidth requirements, and improves the efficiency and accuracy of subsequent data analysis.

[0043] Based on the above scheme, optionally, the edge computing module includes a visualization processing submodule and a display submodule; wherein the visualization processing submodule is used to convert the data to be displayed into target display data of a preset display style, and transmit the target display data to the display submodule; the display submodule is used to receive the target display data transmitted by the visualization processing submodule, and display the target display data.

[0044] Among them, the visualization processing submodule can be understood as a component for converting data into a visual representation form. The display submodule can be understood as a component for visualizing target display data. The data to be displayed can be understood as data for visual display. The data to be displayed may include but is not limited to at least one of sensor readings, log analysis results, or image recognition conclusions. The preset display style can be understood as a predefined visual mode for displaying the data to be displayed. The preset display style may include but is not limited to at least one of a text style, a table style, and a graphic style. The target display data can be understood as data processed according to the preset display style and used for visual display.

[0045] Said

[0046] The above scheme converts the data to be displayed into target display data of a preset display style (such as text, table or graphic, etc.) through the visualization processing submodule, and transmits the target display data to the display submodule for display, so that the data to be displayed can be displayed more intuitively and timely.

[0047] On the basis of the above scheme, optionally, the cloud layer submodule also includes a resource management unit, a data processing unit and a data storage unit; wherein the resource management unit is used to determine the computing resource requirement information of the edge-side data, the schedulable resource information of the edge device and the schedulable resource information of the cloud computing center, generate at least one data processing task according to the edge-side data, the computing resource requirement information and the schedulable resource information, and allocate the data processing task to the data processing unit of the edge device and the computing resources of the cloud computing center; the data processing unit is used to receive the data processing task allocated by the resource management unit, and execute the data processing task, obtain local processing results, and when executing the data processing task alone, send the local processing results as cloud-edge interaction data to the data storage unit, and, when executing the data processing task in collaboration with the cloud computing center, receive the cloud processing results fed back by the cloud computing center, so as to obtain cloud-edge interaction data based on the local processing results and the cloud processing results, and send the cloud-edge interaction data to the data storage unit; the data storage unit is used to receive the edge-side data sent by the edge layer sub-module, and receive the cloud-edge interaction data sent by the data processing unit, and store the edge-side data and the cloud-edge interaction data.

[0048] Among them, the resource management unit can be understood as a component responsible for managing and allocating computing resources. The computing resource demand information can be understood as information about computing resources required to process edge-side data. The computing resource demand information may include but is not limited to at least one of CPU / GPU time, memory size, and network bandwidth. The schedulable resource information can be understood as the resource status allocated for performing data processing tasks. The schedulable resource information may include details of computing resources currently available to edge devices and cloud computing centers, including but not limited to at least one of the number of idle CPU cores and / or remaining storage space. The data processing task can be understood as being created by the resource management unit, including but not limited to at least one of data processing instructions and parameter settings. The data processing unit can be understood as a component that performs a specific data processing task. The local processing result can be understood as the result obtained after completing data processing alone on the edge device. Specifically, when the data processing task can be completed alone by the data processing unit, the data processing unit will generate a local processing result and send the local processing result as part of the cloud-edge interaction data to the data storage unit. The cloud processing result can be understood as the result returned by the cloud computing center after executing the data processing task. In this embodiment, the data processing task may require the powerful computing power of the cloud computing center to assist in completion. At this time, the cloud processing results will be sent back to the data processing unit and merged with the local processing results to form complete cloud-edge interaction data. The data storage unit can be understood as a component for storing edge-side data and cloud-edge interaction data.

[0049] The above scheme, through the resource management unit, accurately matches the computing resource demand information of the edge data with the schedulable resource information, generates and allocates data processing tasks, thereby ensuring the optimal configuration of computing resources. The data processing unit executes the data processing tasks, generates local processing results or cloud processing results, and integrates them into cloud-edge interaction data. Finally, the data storage unit stores the edge data and the cloud-edge interaction data, thereby ensuring the security and availability of the data, accelerating the data processing process, and reducing delays.

[0050] On the basis of the above scheme, optionally, the cloud layer sub-module also includes a task scheduling unit, and the task scheduling unit includes at least one of a polling scheduling sub-unit, a shortest job scheduling sub-unit and a time slice round-robin scheduling sub-unit; wherein the polling scheduling sub-unit is used to assign the data processing tasks to be scheduled to the edge devices according to the preset scheduling order of multiple edge devices, so that the edge devices execute the assigned data processing tasks; the shortest job scheduling sub-unit is used to determine the task with the shortest execution time among the multiple data processing tasks to be scheduled as the target task, and execute the target task; the time slice round-robin scheduling sub-unit is used to assign execution time slices to each of the data processing tasks to be scheduled, and execute the data processing tasks according to the preset execution order corresponding to the data processing tasks and the execution time slices.

[0051] Among them, the polling scheduling subunit can be understood as a component of a scheduling mechanism that cyclically allocates tasks in a preset order. The data processing task to be scheduled can be understood as a data processing task that has been created but has not yet started to be executed. In this embodiment, the data processing task to be scheduled contains processing logic and required resource information, waiting for the polling scheduling subunit to allocate it to the edge device in a preset scheduling order. The preset scheduling order can be understood as a predefined allocation or execution order of data processing tasks, etc., which can be used to guide the polling scheduling subunit to allocate data processing tasks to each edge device in sequence, or to guide the time slice round-robin scheduling subunit to arrange the execution order of data processing tasks. The shortest job scheduling subunit can be understood as an algorithm scheduling component that prioritizes tasks based on the length of task execution time. The time slice round-robin scheduling subunit can be understood as a scheduling component that allocates a time slice of a fixed length to each task. The execution time slice can be understood as a fixed execution time period allocated to each data processing task. In this embodiment, the time slice round-robin scheduling subunit sets a time slice for each data processing task, limits the maximum duration of each execution, ensures that all data processing tasks have the opportunity to obtain CPU time, and avoids the situation where a single task occupies too many resources and other tasks cannot be processed.

[0052] In the above scheme, the polling scheduling subunit ensures that multiple edge devices execute data processing tasks in accordance with the preset scheduling order, avoiding resource idleness or overload. The shortest job scheduling subunit optimizes the execution order of data processing tasks, reduces the average waiting time, and improves the response speed. The time slice round-robin scheduling subunit allocates execution time slices to each data processing task, ensuring multi-task concurrent processing, ensuring efficient and fast execution of data processing tasks, and enhancing resource utilization.

[0053] On the basis of the above scheme, optionally, the data processing unit is used to obtain target processing data corresponding to the data processing task from the edge side data after receiving the data processing task, and determine the data processing algorithm corresponding to the data processing task, and obtain a local processing result based on the data processing algorithm and the target processing data.

[0054] Among them, the target processing data can be understood as edge-side data related to the data processing task. The data processing unit screens out the data to be processed from all edge-side data according to the requirements of the data processing task as the target processing data. The data processing algorithm can be understood as an algorithm used to guide the processing of edge-side data. The data processing algorithm may include but is not limited to at least one of a heuristic algorithm, a greedy algorithm, and a dynamic programming algorithm. The heuristic algorithm can be understood as an algorithm for solving problems based on empirical rules or intuitive judgments rather than strict mathematical proofs, which is used to quickly find approximate solutions to data processing tasks. The greedy algorithm can be understood as an optimization algorithm based on local optimality, which is used to find the local optimality of data processing tasks. The dynamic programming algorithm can be understood as an optimization algorithm based on a state transition equation, which is used to find the optimal solution to a data processing task.

[0055] The above scheme, after receiving the data processing task through the data processing unit, accurately extracts the target processing data from the edge side data, and selects the most appropriate data processing algorithm (such as heuristic algorithm, greedy algorithm and dynamic programming algorithm) for processing according to the task requirements, ensuring that each data processing task can be processed in the most optimized way, which not only improves the quality of local processing results, but also accelerates the speed of data processing, reducing resource consumption while ensuring performance.

[0056] On the basis of the above scheme, optionally, the cloud layer submodule includes a data compression unit, a data encryption unit and a data transmission unit; wherein the data transmission unit is used to determine the target interaction data corresponding to the cloud computing center based on the edge-side data and the cloud-edge interaction data when receiving a data interaction request with the cloud computing center, and send the target interaction data to the data compression unit; the data compression unit is used to receive the target interaction data sent by the data transmission unit, losslessly compress the target interaction data, and send the compressed target interaction data to the data encryption unit; the data encryption unit is used to receive the compressed target interaction data sent by the data compression unit, encrypt the received target interaction data according to a preset encryption method to obtain the target transmission data corresponding to the data interaction request, and send the target transmission data to the data transmission unit; the data transmission unit is also used to receive the target transmission data corresponding to the data interaction request sent by the data encryption unit, and transmit the target transmission data to the cloud computing center.

[0057] Among them, the data transmission unit can be understood as a component responsible for processing data interaction requests with the cloud computing center. The data interaction request can be understood as a request initiated from the cloud computing center to exchange data with the edge computing module. The target interaction data can be understood as data filtered out from the edge side data and the cloud edge interaction data according to the data interaction request, which is determined in response to the data interaction request, and is intended to ensure that only the required information will be transmitted to the cloud computing center to reduce bandwidth occupancy and improve transmission efficiency. The data compression unit can be understood as a component for compressing data to reduce data size. The lossless compression can be understood as a compression method that can completely restore the target interaction data after decompression without any data loss. The data encryption unit can be understood as a component responsible for encrypting and protecting the target interaction data. The preset encryption method can be understood as a predetermined encryption algorithm or technology to ensure that the target interaction data will not be read or tampered with by unauthorized parties during transmission. The target transmission data can be understood as the target interaction data that has been compressed and encrypted.

[0058] In the above scheme, when receiving a data interaction request from the cloud computing center, the data transmission unit extracts the target interaction data, and the data compression unit and the data encryption unit process the data through lossless compression and preset encryption methods, ensuring that the target interaction data reduces bandwidth occupancy while maintaining integrity and confidentiality during transmission. The unprocessed target interaction data is then sent to the cloud computing center through the data transmission unit, which not only speeds up the data transmission speed and reduces the network load, but also enhances the security protection capability.

[0059] On the basis of the above scheme, optionally, the data transmission unit is specifically used to receive the target transmission data corresponding to the data interaction request sent by the data encryption unit, convert the target transmission data into a target transmission data stream through a preset streaming data processing algorithm, and transmit the target transmission data stream to the cloud computing center.

[0060] Among them, the streaming data processing algorithm can be understood as an algorithm for processing continuously arriving data streams. The streaming data processing algorithm may include but is not limited to at least one of a data filtering algorithm, a data aggregation algorithm, and a data format conversion algorithm. The data filtering algorithm can be understood as a method for selectively retaining or discarding certain data items from the target transmission data, which is used to remove unnecessary or redundant target transmission data and reduce bandwidth occupancy. The data aggregation algorithm can be understood as an algorithm for merging related data points in multiple target transmission data into higher-level summary data. By combining similar data points together through the data aggregation algorithm, the amount of data transmitted can be reduced while retaining the target information, making the target transmission data more compact and easy to manage. The data format conversion algorithm can be understood as an operation that changes the representation of the target transmission data to adapt to different system or application requirements, ensuring that the target transmission data can be compatible and exchanged between different platforms or tools, while the semantics of the target transmission data remains unchanged, so that the cloud computing center can seamlessly parse and use the received target transmission data. The target transmission data stream can be understood as converting the target transmission data into a continuous data stream through a streaming data processing algorithm. The target transmission data stream allows the cloud computing center to gradually receive and process the target transmission data without waiting for the entire data packet to arrive.

[0061] The above scheme receives the encrypted target transmission data through the data transmission unit, and converts it into the target transmission data stream using the preset streaming data processing algorithm, thereby realizing efficient and secure data transmission. The data filtering, aggregation and format conversion technologies in the streaming data processing algorithm ensure that only necessary information is transmitted, reducing bandwidth occupancy and improving data processing efficiency. Moreover, through continuous data stream transmission, the cloud computing center can quickly obtain the latest edge-side data, thereby improving the flexibility of the overall system.

[0062] On the basis of the above scheme, optionally, the anomaly detection submodule is used to perform anomaly detection on the program running data of the edge computing module using a preset program tracking algorithm to obtain anomaly diagnosis information, and perform anomaly warning based on the anomaly diagnosis information.

[0063] Among them, it can be understood that the abnormal detection submodule is a component used to detect whether the internal operation of the edge computing module is running normally. The program tracking algorithm can be understood as an algorithm for tracking and recording the program execution process of the edge computing module. The program tracking algorithm may include but is not limited to a tracking algorithm that inserts tracking points in a function. In this embodiment, the program tracking algorithm can capture the behavior of the program at runtime by inserting tracking points at the target location of the edge computing module, including but not limited to function calls or variable value changes, thereby helping to identify potential problem areas or abnormal behavior patterns. Inserting tracking points in the function can be understood as inserting code snippets at the target location inside the function to record the execution of the function, and can collect at least one of the detailed information including but not limited to function call frequency, parameter values, and return results, which helps to gain a deeper understanding of the dynamic behavior of the program running.

[0064] The above solution uses a preset program tracking algorithm through the anomaly detection submodule to detect the program running data of the edge computing module, capture abnormal behavior and generate abnormal diagnosis information, and trigger abnormal warning based on the abnormal diagnosis information to ensure that the problem is handled quickly, greatly improving the stability and security of the system.

[0065] The technical solution of the embodiment of the present invention is as follows: first, the edge-side data collected by the edge device is obtained through the edge layer submodule, the edge-side data is preprocessed, and the preprocessed edge-side data is sent to the cloud layer submodule, thereby ensuring the quality and consistency of the data and reducing the amount of data transmitted to the cloud layer submodule; then, the edge-side data sent by the edge layer submodule is received through the cloud layer submodule, and the edge-side data is reprocessed according to a preset processing logic to obtain cloud-edge interaction data, which can reduce the computing burden of the edge device, and the edge-side data and the cloud-edge interaction data are stored, and based on the edge-side data and the cloud-edge interaction data, The cloud-edge interaction data interacts with the cloud computing center, which can strengthen the protection of edge-side data and cloud-edge interaction data; finally, through the anomaly detection submodule, the program running data of the edge computing module is detected for anomalies to obtain abnormal diagnosis information, and abnormal warnings are issued based on the abnormal diagnosis information. Potential abnormal problems can be identified and early warnings can be issued to improve security, solve the problems of low data processing and transmission efficiency in the cloud computing mode and excessive cloud load, reduce data transmission volume and delay through edge-side processing, and the cloud provides powerful reprocessing and storage capabilities. Anomaly detection ensures system stability and improves data processing efficiency and response speed.

[0066] Embodiment 2

[0067] Figure 2A flow chart of a cloud-edge collaborative information processing method provided in Embodiment 2 of the present invention, which can be applied to a cloud-edge collaborative information processing device. Optionally, the method can be implemented by an electronic device, which can be a mobile terminal, a PC, or a server. The cloud-edge collaborative information processing device may specifically include: an edge computing module, an edge device, and a cloud computing center; the edge computing module includes an edge layer submodule, a cloud layer submodule, and a cloud layer anomaly detection submodule.

[0068] like Figure 2 As shown, the cloud-edge collaborative information processing method may specifically include:

[0069] S210, obtaining edge-side data collected by the edge device through the edge layer submodule, preprocessing the edge-side data, and sending the preprocessed edge-side data to the cloud layer submodule;

[0070] S220, receiving the edge-side data sent by the edge-layer submodule through the cloud-layer submodule, reprocessing the edge-side data according to a preset processing logic to obtain cloud-edge interaction data, storing the edge-side data and the cloud-edge interaction data, and performing data interaction with the cloud computing center based on the edge-side data and the cloud-edge interaction data;

[0071] S230. Perform anomaly detection on the program running data of the edge computing module through the anomaly detection submodule to obtain anomaly diagnosis information, and perform an anomaly warning based on the anomaly diagnosis information.

[0072] The technical solution of the embodiment of the present invention is as follows: first, the edge-side data collected by the edge device is obtained through the edge layer submodule, the edge-side data is preprocessed, and the preprocessed edge-side data is sent to the cloud layer submodule, thereby ensuring the quality and consistency of the data and reducing the amount of data transmitted to the cloud layer submodule; then, the edge-side data sent by the edge layer submodule is received through the cloud layer submodule, and the edge-side data is reprocessed according to a preset processing logic to obtain cloud-edge interaction data, which can reduce the computing burden of the edge device, and the edge-side data and the cloud-edge interaction data are stored, and based on the edge-side data and the cloud-edge interaction data, The cloud-edge interaction data interacts with the cloud computing center, which can strengthen the protection of edge-side data and cloud-edge interaction data; finally, through the anomaly detection submodule, the program running data of the edge computing module is detected for anomalies to obtain abnormal diagnosis information, and abnormal warnings are issued based on the abnormal diagnosis information. Potential abnormal problems can be identified and early warnings can be issued to improve security, solve the problems of low data processing and transmission efficiency in the cloud computing mode and excessive cloud load, reduce data transmission volume and delay through edge-side processing, and the cloud provides powerful reprocessing and storage capabilities. Anomaly detection ensures system stability and improves data processing efficiency and response speed.

[0073] On the basis of the above scheme, optionally, receiving the edge side data sent by the edge layer submodule through the cloud layer submodule, reprocessing the edge side data according to a preset processing logic to obtain cloud-edge interaction data, and storing the edge side data and the cloud-edge interaction data, including:

[0074] The resource management unit determines the computing resource requirement information of the edge data, the schedulable resource information of the edge device, and the schedulable resource information of the cloud computing center, generates at least one data processing task according to the edge data, the computing resource requirement information, and the schedulable resource information, and allocates the data processing task to the data processing unit of the edge device and the computing resources of the cloud computing center; the data processing unit receives the data processing task allocated by the resource management unit, executes the data processing task, obtains a local processing result, sends the local processing result as cloud-edge interaction data to the data storage unit when the data processing task is executed alone, and receives the cloud processing result fed back by the cloud computing center when the data processing task is executed in collaboration with the cloud computing center, so as to obtain cloud-edge interaction data based on the local processing result and the cloud processing result, and sends the cloud-edge interaction data to the data storage unit; the data storage unit receives the edge data sent by the edge layer submodule, receives the cloud-edge interaction data sent by the data processing unit, and stores the edge data and the cloud-edge interaction data.

[0075] On the basis of the above scheme, optionally, the cloud-edge collaborative information processing method includes: allocating the data processing tasks to be scheduled to the edge devices according to the preset scheduling order of multiple edge devices through the polling scheduling subunit, so that the edge devices execute the assigned data processing tasks; determining the task with the shortest execution time among the multiple data processing tasks to be scheduled as the target task through the shortest job scheduling subunit, and executing the target task; allocating execution time slices to each of the data processing tasks to be scheduled through the time slice round-robin scheduling subunit, and executing the data processing tasks according to the preset execution order corresponding to the data processing tasks and the execution time slices.

[0076] On the basis of the above scheme, optionally, the cloud-edge collaborative information processing method includes: after receiving the data processing task, the data processing unit obtains the target processing data corresponding to the data processing task from the edge side data, and determines the data processing algorithm corresponding to the data processing task, and obtains the local processing result based on the target processing data of the data processing algorithm, wherein the data processing algorithm includes at least one of a heuristic algorithm, a greedy algorithm and a dynamic programming algorithm.

[0077] On the basis of the above scheme, optionally, the cloud-edge collaborative information processing method includes: when the data transmission unit receives a data interaction request with the cloud computing center, determining the target interaction data corresponding to the cloud computing center based on the edge-side data and the cloud-edge interaction data, and sending the target interaction data to the data compression unit; receiving the target interaction data sent by the data transmission unit through the data compression unit, losslessly compressing the target interaction data, and sending the compressed target interaction data to the data encryption unit; receiving the compressed target interaction data sent by the data compression unit through the data encryption unit, encrypting the received target interaction data according to a preset encryption method to obtain the target transmission data corresponding to the data interaction request, and sending the target transmission data to the data transmission unit; receiving the target transmission data corresponding to the data interaction request sent by the data encryption unit through the data transmission unit, and transmitting the target transmission data to the cloud computing center.

[0078] On the basis of the above scheme, optionally, the cloud-edge collaborative information processing method includes: receiving the target transmission data corresponding to the data interaction request sent by the data encryption unit through the data transmission unit, converting the target transmission data into a target transmission data stream through a preset streaming data processing algorithm, and transmitting the target transmission data stream to the cloud computing center, wherein the streaming data processing algorithm includes at least one of a data filtering algorithm, a data aggregation algorithm, and a data format conversion algorithm.

[0079] On the basis of the above scheme, optionally, the edge layer submodule obtains edge side data collected by the edge device, preprocesses the edge side data, and sends the preprocessed edge side data to the cloud layer submodule, including:

[0080] The data acquisition unit collects edge-side data through a data sensor pre-deployed at a preset detection position of the edge device, parses the device operation log of the edge device to obtain the edge-side data, and uses an image acquisition device to acquire an image of the device operation area of ​​the edge device to obtain the edge-side data, and sends the obtained edge-side data to the data preprocessing unit; the data preprocessing unit receives the edge-side data sent by the data acquisition unit, preprocesses the edge-side data, and sends the preprocessed edge-side data to the cloud layer sub-module, wherein the preprocessing includes at least one of data cleaning processing, outlier correction processing, data aggregation processing, and data format conversion processing.

[0081] On the basis of the above scheme, optionally, the program running data of the edge computing module is detected for abnormality by the abnormality detection submodule to obtain abnormality diagnosis information, and abnormality warning is issued based on the abnormality diagnosis information, including: the program running data of the edge computing module is detected for abnormality by the abnormality detection submodule using a preset program tracking algorithm to obtain abnormality diagnosis information, and abnormality warning is issued based on the abnormality diagnosis information, wherein the program tracking algorithm includes a tracking algorithm for inserting tracking points in a function.

[0082] Based on the above solution, optionally, the cloud-edge collaborative information processing method further includes:

[0083] The data to be displayed is converted into target display data of a preset display style through the visualization processing submodule, and the target display data is transmitted to the display submodule; the preset display style includes at least one of a text style, a table style and a graphic style; the target display data transmitted by the visualization processing submodule is received through the display submodule, and the target display data is displayed.

[0084] The cloud-edge collaborative information processing method provided by an embodiment of the present invention can be executed by the cloud-edge collaborative information processing device provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution device.

[0085] Embodiment 3

[0086] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computing machines, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computing machines. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0087] like Figure 3As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0088] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0089] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a cloud-edge collaborative information processing method.

[0090] In some embodiments, a cloud-edge collaborative information processing method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of a cloud-edge collaborative information processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute a cloud-edge collaborative information processing method in any other appropriate manner (e.g., by means of firmware).

[0091] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0093] In the context of the present invention, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. More specific examples of machine readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0094] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0095] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0096] The computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0097] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0098] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A cloud-edge collaborative information processing device, characterized in that: include: Edge computing module, edge device and cloud computing center; the edge computing module includes edge layer submodule, cloud layer submodule and cloud layer anomaly detection submodule; wherein, The edge layer submodule is used to obtain edge side data collected by the edge device, preprocess the edge side data, and send the preprocessed edge side data to the cloud layer submodule; The cloud layer submodule is used to receive the edge side data sent by the edge layer submodule, reprocess the edge side data according to a preset processing logic to obtain cloud-edge interaction data, store the edge side data and the cloud-edge interaction data, and perform data interaction with the cloud computing center based on the edge side data and the cloud-edge interaction data; The abnormality detection submodule is used to perform abnormality detection on the program running data of the edge computing module to obtain abnormality diagnosis information, and to issue an abnormality warning based on the abnormality diagnosis information.

2. The cloud-edge collaborative information processing device according to claim 1, characterized in that: The cloud layer submodule also includes a resource management unit, a data processing unit and a data storage unit; wherein, The resource management unit is used to determine the computing resource demand information of the edge side data, the schedulable resource information of the edge device, and the schedulable resource information of the cloud computing center, generate at least one data processing task according to the edge side data, the computing resource demand information, and the schedulable resource information, and allocate the data processing task to the data processing unit of the edge device and the computing resources of the cloud computing center; The data processing unit is used to receive the data processing task assigned by the resource management unit, execute the data processing task, obtain a local processing result, and send the local processing result as cloud-edge interaction data to the data storage unit when executing the data processing task alone, and receive the cloud processing result fed back by the cloud computing center when executing the data processing task in collaboration with the cloud computing center, so as to obtain cloud-edge interaction data based on the local processing result and the cloud processing result, and send the cloud-edge interaction data to the data storage unit; The data storage unit is used to receive the edge-side data sent by the edge layer submodule, and to receive the cloud-edge interaction data sent by the data processing unit, and to store the edge-side data and the cloud-edge interaction data.

3. The cloud-edge collaborative information processing device according to claim 2, characterized in that: The cloud layer submodule further includes a task scheduling unit, which includes at least one of a polling scheduling subunit, a shortest job scheduling subunit, and a time slice round-robin scheduling subunit; wherein, The polling scheduling subunit is used to assign the data processing tasks to be scheduled to the edge devices according to a preset scheduling order of multiple edge devices, so that the edge devices perform the assigned data processing tasks; The shortest job scheduling subunit is used to determine the task with the shortest execution time among the multiple data processing tasks to be scheduled as the target task, and execute the target task; The time slice round-robin scheduling subunit is used to allocate an execution time slice to each of the data processing tasks to be scheduled, and execute the data processing tasks according to the preset execution order corresponding to the data processing tasks and the execution time slices.

4. The cloud-edge collaborative information processing device according to claim 2, characterized in that: The data processing unit is used to obtain target processing data corresponding to the data processing task from the edge side data after receiving the data processing task, and determine the data processing algorithm corresponding to the data processing task, and obtain a local processing result based on the data processing algorithm and the target processing data, wherein the data processing algorithm includes at least one of a heuristic algorithm, a greedy algorithm and a dynamic programming algorithm.

5. The cloud-edge collaborative information processing device according to claim 1, characterized in that: The cloud layer submodule includes a data compression unit, a data encryption unit and a data transmission unit; wherein, The data transmission unit is used to determine the target interaction data corresponding to the cloud computing center based on the edge-side data and the cloud-edge interaction data when receiving a data interaction request with the cloud computing center, and send the target interaction data to the data compression unit; The data compression unit is used to receive the target interaction data sent by the data transmission unit, perform lossless compression on the target interaction data, and send the compressed target interaction data to the data encryption unit; The data encryption unit is used to receive the compressed target interaction data sent by the data compression unit, encrypt the received target interaction data according to a preset encryption method to obtain target transmission data corresponding to the data interaction request, and send the target transmission data to the data transmission unit; The data transmission unit is also used to receive target transmission data corresponding to the data interaction request sent by the data encryption unit, and transmit the target transmission data to the cloud computing center.

6. The cloud-edge collaborative information processing device according to claim 5, characterized in that: The data transmission unit is specifically used to receive the target transmission data corresponding to the data interaction request sent by the data encryption unit, convert the target transmission data into a target transmission data stream through a preset streaming data processing algorithm, and transmit the target transmission data stream to the cloud computing center, wherein the streaming data processing algorithm includes at least one of a data filtering algorithm, a data aggregation algorithm, and a data format conversion algorithm.

7. The cloud-edge collaborative information processing device according to claim 1, characterized in that: The edge layer submodule includes a data acquisition unit and a data preprocessing unit; wherein, The data acquisition unit is used to collect edge-side data through a data sensor pre-deployed at a preset detection position of the edge device, parse the device operation log of the edge device to obtain the edge-side data, and collect images of the device operation area of ​​the edge device through an image acquisition device to obtain the edge-side data, and send the obtained edge-side data to the data preprocessing unit; The data preprocessing unit is used to receive the edge side data sent by the data acquisition unit, preprocess the edge side data, and send the preprocessed edge side data to the cloud layer sub-module, wherein the preprocessing includes at least one of data cleaning processing, outlier correction processing, data aggregation processing and data format conversion processing.

8. The cloud-edge collaborative information processing device according to claim 1, characterized in that: The anomaly detection submodule is used to use a preset program tracing algorithm to perform anomaly detection on the program running data of the edge computing module to obtain anomaly diagnosis information, and to perform anomaly warning based on the anomaly diagnosis information, wherein the program tracing algorithm includes a tracing algorithm for inserting tracing points in a function.

9. The cloud-edge collaborative information processing device according to claim 1, characterized in that: The edge computing module includes a visualization processing submodule and a display submodule; wherein, The visualization processing submodule is used to convert the data to be displayed into target display data of a preset display style, and transmit the target display data to the display submodule; the preset display style includes at least one of a text style, a table style and a graphic style; The display submodule is used to receive the target display data transmitted by the visualization processing submodule and display the target display data.

10. A cloud-edge collaborative information processing method, characterized in that: include: Edge computing modules, edge devices, and cloud computing centers; The edge computing module includes an edge layer submodule, a cloud layer submodule and a cloud layer anomaly detection submodule; wherein the cloud-edge collaborative information processing method includes: Obtain edge-side data collected by the edge device through the edge layer submodule, pre-process the edge-side data, and send the pre-processed edge-side data to the cloud layer submodule; Receiving the edge-side data sent by the edge-layer submodule through the cloud-layer submodule, reprocessing the edge-side data according to a preset processing logic to obtain cloud-edge interaction data, storing the edge-side data and the cloud-edge interaction data, and performing data interaction with the cloud computing center based on the edge-side data and the cloud-edge interaction data; The abnormality detection submodule is used to perform abnormality detection on the program running data of the edge computing module to obtain abnormality diagnosis information, and an abnormality warning is performed based on the abnormality diagnosis information.

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