Integrated end-side large-scale intelligent edge computing system
By integrating a large-scale intelligent edge computing system, the problem of high hardware requirements at the edge in traditional road monitoring systems has been solved, achieving the effects of reducing configuration costs and improving data processing efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ANHUI INSTANT INTELLIGENT TECH CO LTD
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-01
AI Technical Summary
Traditional road monitoring systems have high hardware requirements at the edge, making it difficult to efficiently differentiate data processing functions, resulting in high configuration costs.
By adopting an integrated large-scale intelligent edge computing system, node monitoring data is acquired at the edge, and the data is classified and processed. The integrated end is used for data verification and integration, thereby reducing the configuration requirements of the edge end.
This approach achieves the goal of reducing the cost of edge hardware configuration while ensuring the ability to process large-scale road monitoring data, thereby improving data processing efficiency and resource utilization.
Smart Images
Figure CN121963467A_ABST
Abstract
Description
A large-scale integrated intelligent edge computing system Technical Field
[0001] This invention relates to the field of edge computing technology, specifically to an integrated large-scale intelligent edge computing system. Background Technology
[0002] Road monitoring plays a crucial role in ensuring traffic safety, optimizing traffic flow, and urban planning. With the rapid development of sensor technology, numerous sensors are deployed at key road locations, such as intersections, bridges, and tunnels, to collect various types of road monitoring data in real time. This data covers multiple dimensions, including traffic flow, vehicle speed, vehicle type, and road conditions. Traditional road monitoring data processing models face numerous challenges. Currently, most road monitoring systems employ a centralized data processing architecture, where data collected by sensors is transmitted via network to a remote data center for unified processing and analysis. This approach often places high demands on edge hardware, especially in terms of data processing types, requiring edge devices to simultaneously handle different data types. The challenge lies in how to differentiate data processing functions, enabling large-scale edge devices to utilize these differentiated functions for resource allocation and adaptation between devices, thereby reducing edge device configuration while still achieving large-scale data edge processing and lowering hardware costs. To address this, we present an integrated large-scale intelligent edge computing system. Summary of the Invention
[0003] The purpose of this invention is to provide an integrated, large-scale intelligent edge computing system.
[0004] The objective of this invention can be achieved through the following technical solution: a large-scale intelligent edge computing system integrating the terminal side, comprising a central control center, wherein the central control center is communicatively connected to an edge terminal and an integration terminal; the edge terminal is used to acquire node monitoring data of edge nodes, and generate data processing tasks according to the data types of the node monitoring data, process the node monitoring data of different data types according to the data processing tasks, and upload the processed node monitoring data to the integration terminal; the integration terminal is used to perform data verification on the received node monitoring data, and integrate the node monitoring data that has completed data verification.
[0005] Furthermore, the process of acquiring node monitoring data of edge nodes by the edge terminal includes: dividing each road in the target monitoring area into several road segments, setting up corresponding monitoring nodes in each road segment, deploying corresponding edge terminals for each monitoring node, and acquiring node monitoring data of the corresponding location through the deployed edge terminals; setting up edge nodes, wherein the edge nodes communicate with several edge terminals, and the edge terminals that communicate with the edge nodes communicate with at least one other edge terminal.
[0006] Furthermore, based on the data types of the node monitoring data, the process of generating data processing tasks includes: classifying the node monitoring data obtained from each edge terminal, and obtaining different data types based on the data classification results; constructing data storage spaces associated with each data type within the edge terminal, and importing node monitoring data of different data types into the corresponding data storage spaces; constructing task nodes associated with each data storage space, and empowering each task node with task processing requirements; and summarizing all task nodes to obtain the data processing task corresponding to that edge terminal.
[0007] Furthermore, the process of processing node monitoring data of different data types according to the data processing task and uploading the processed node monitoring data to the integration end includes: matching each task node of the data processing task with the data processing function of the edge end; if a task node matches the data processing function of the edge end, the data storage space associated with the task node is marked as the local processing space, and a task processing request is generated according to the data processing function required by other task nodes, and the task processing request is uploaded to the edge node; the edge node matches the data processing function of other edge ends according to the received task processing request, generates the corresponding data allocation instruction according to the matching result, sends the data allocation instruction to the corresponding edge end, and marks the edge end to which the data allocation instruction is sent as the scheduling edge end; the edge end sends the data of the corresponding data type stored in the data storage space and the task node to the local processing space of the corresponding scheduling edge end according to the corresponding data type of the remaining task nodes; and the task nodes that match the data processing function in each edge end are summarized to establish the corresponding edge data processing task.
[0008] Furthermore, the process of processing node monitoring data at the edge end includes: converting the node monitoring data corresponding to the edge data processing task into a data stream using the data processing function of the edge end; dividing the node monitoring data into several fixed-length time periods according to the acquisition time, and summarizing the data streams belonging to the same time period to obtain data stream segments corresponding to each time period; setting a rule check code, which is composed of several binary unit codes; comparing the length of the rule check code with the length of the data stream segment; if the length of the data stream segment is an integer multiple of the length of the rule check code, no operation is performed on the data stream segment; if the length of the data stream segment is not an integer multiple of the length of the rule check code, "0" is padded at the end of the data stream segment until the length of the data stream segment is an integer multiple of the length of the rule check code; performing a cyclic XOR operation on the data stream segment and the rule check code, obtaining the encryption key of the data stream segment according to the cyclic XOR operation, inserting the obtained encryption key into the end of the data stream segment to complete the data encryption of the data stream segment, and uploading the encrypted data stream segment to the integration end.
[0009] Furthermore, the process of performing a cyclic XOR operation on the data stream segment and the rule check code is as follows: A data stream calibration ruler is set, the length of which is the same as the length of the rule check code; calibration is performed starting from the first unit code of the data stream segment using the data stream calibration ruler, and the calibrated portion is XORed with the rule check code, with the XOR result used as the first sequence; the data stream calibration ruler is shifted by the corresponding length according to the length of the rule check code and recalibrated, and the calibrated portion is XORed with the first sequence, with the XOR result used as the second sequence, and so on, until the cyclic XOR operation on the data stream segment is completed.
[0010] Furthermore, the process of the integration end verifying the received node monitoring data includes: extracting the encryption key from each received data stream segment, performing a cyclic XOR operation on the data stream segment and the rule check code, and matching the cyclic XOR result with the encryption key. If the cyclic XOR result is exactly the same as the encryption key, it indicates that there is no abnormality in the data stream segment during transmission; if the cyclic XOR result is different from the encryption key, it indicates that there is an abnormality in the data stream segment during transmission. If there is an abnormality, the integration end generates an information anomaly feedback to the corresponding edge end, and the edge end re-uploads the corresponding data stream segment; if there is no abnormality, the node monitoring data uploaded by each edge end is integrated.
[0011] Furthermore, the process of integrating node monitoring data includes: obtaining the time periods corresponding to the data stream segments uploaded by each edge terminal, and summarizing the node monitoring data within the same time period; constructing a time axis corresponding to each edge terminal, mapping the node monitoring data of the same edge terminal within the same time period onto the time axis, and obtaining a node monitoring data change map corresponding to that edge terminal; constructing a time axis corresponding to the data type, mapping the node monitoring data of the same data type from different edge terminals at the same time onto the time axis, and obtaining a road data change map corresponding to that data type, thereby completing the data integration process of node monitoring data.
[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: By acquiring node monitoring data of the location of each edge node of the road through the edge terminal, classifying the node monitoring data, constructing different task nodes according to different types of node monitoring data, and matching them with the data processing functions of the edge terminal, the obtained different types of node monitoring data are allocated to the corresponding edge terminals according to the matching results of data type and data processing functions of the edge terminal. This allows the edge terminal to complete large-scale edge terminal data processing without having data processing functions for all data types, reducing the configuration requirements of the edge terminal. In large-scale road monitoring, the deployment cost of the edge terminal can be minimized while ensuring the data processing capabilities of each edge terminal. Attached Figure Description
[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.
[0014] Figure 1 is a schematic diagram of the present invention. Detailed Implementation
[0015] As shown in Figure 1, a large-scale intelligent edge computing system with integrated end-side includes a central control center, which is communicatively connected to an edge end and an integration end. The edge end is used to acquire node monitoring data of edge nodes, generate data processing tasks according to the data types of the node monitoring data, process node monitoring data of different data types according to the data processing tasks, and upload the processed node monitoring data to the integration end. The integration end is used to verify the received node monitoring data and integrate the node monitoring data that has completed data verification.
[0016] It should be further explained that, in the specific implementation process, the process of the edge terminal acquiring the node monitoring data of the edge node includes: dividing each road in the target monitoring area into several road segments, setting up corresponding monitoring nodes in each road segment, deploying corresponding edge terminals for each monitoring node, and acquiring node monitoring data of the corresponding location through the deployed edge terminals. The node monitoring data includes vehicle data, meteorological data, and video data. The edge node is set up, and the edge node communicates with several edge terminals, and the edge terminal communicating with the edge node communicates with at least one other edge terminal.
[0017] It should be further explained that, in the specific implementation process, the process of generating data processing tasks based on the data types of node monitoring data includes: classifying the node monitoring data obtained from each edge terminal, and obtaining different data types based on the data classification results; constructing data storage spaces associated with each data type within the edge terminal, and importing node monitoring data of different data types into the corresponding data storage spaces; constructing task nodes associated with each data storage space, and empowering each task node with task processing requirements; and summarizing all task nodes to obtain the data processing task corresponding to that edge terminal.
[0018] It should be further explained that the process of processing node monitoring data of different data types according to the data processing task and uploading the processed node monitoring data to the integration end includes: matching each task node of the data processing task with the data processing function of the edge terminal; it should be noted that each edge terminal has at least one data processing function to process one data type, and different edge terminals can have the same data processing function or different data processing functions, and the data processing functions of each edge terminal are backed up within the edge node; if a task node matches the data processing function of the edge terminal, the data storage space associated with the task node is marked as the local processing space, and tasks are generated according to the data processing functions required by other task nodes. The process involves uploading the task processing request to the edge node. The edge node matches the received task processing request with the data processing functions of other edge terminals, generates a corresponding data allocation instruction based on the matching result, and sends the data allocation instruction to the corresponding edge terminal, marking the edge terminal that sent the data allocation instruction as the scheduling edge terminal. The edge terminal then sends the remaining task nodes, based on their corresponding data types, the data stored in its data storage space, and the task nodes themselves to the local processing space within the corresponding scheduling edge terminal. The process aggregates the task nodes within each edge terminal that match the data processing functions, establishing corresponding edge data processing tasks. Finally, the edge terminal processes the corresponding node monitoring data according to the edge data processing tasks.
[0019] It should be further explained that, in the specific implementation process, the process of the edge terminal processing the node monitoring data includes: converting the node monitoring data corresponding to the edge data processing task into a data stream using the data processing function of the edge terminal; it should be noted that the data stream is a binary data stream composed of several binary unit codes; dividing the node monitoring data into several fixed-length time periods according to the acquisition time, and summarizing the data streams belonging to the same time period to obtain data stream segments corresponding to each time period; setting a rule check code, which is composed of several binary unit codes; comparing the length of the rule check code with the length of the data stream segment; if the length of the data stream segment is an integer multiple of the length of the rule check code, no operation is performed on the data stream segment; if the length of the data stream segment is not an integer multiple of the length of the rule check code, "0" is padded at the end of the data stream segment until the length of the data stream segment is an integer multiple of the length of the rule check code; and then processing the data stream segment... A cyclic XOR operation is performed with the rule checksum. The encryption key for this data stream segment is obtained from the cyclic XOR operation and inserted into the end of the data stream segment to complete the data encryption. The encrypted data stream segment is then uploaded to the integration terminal. The specific process of performing the cyclic XOR operation with the rule checksum is as follows: A data stream calibration ruler is set, with a length equal to the rule checksum length. The data stream segment is calibrated starting from the first unit code using the calibration ruler, and the calibrated portion is XORed with the rule checksum. The XOR result is used as the first sequence. The data stream calibration ruler is shifted by the corresponding length according to the rule checksum length and recalibrated. The calibrated portion is XORed with the first sequence, and the XOR result is used as the second sequence. This process is repeated until the cyclic XOR operation of the data stream segment is complete. Example: Setting data stream segment 1010... Given 0100 1101 and a rule checksum of 1010, the process of performing a cyclic XOR operation between the data stream segment and the rule checksum is as follows: The length of the data stream calibration ruler is set to "4". The first four units of the data stream segment are calibrated, with the calibrated portion being 1010. This calibrated portion is then XORed with the rule checksum 1010 to obtain the first sequence result 1111. The data stream calibration ruler is then shifted four units, resulting in a calibrated portion of 0100. This calibrated portion is then XORed with the first sequence 1111 to obtain the second sequence result 0100. This cyclic XOR operation on the data stream segment is then completed.
[0020] It should be further explained that, in the specific implementation process, the process of the integration end verifying the received node monitoring data includes: extracting the encryption key from each received data stream segment, performing a cyclic XOR operation on the data stream segment and the rule check code, and matching the cyclic XOR result with the encryption key. If the cyclic XOR result is exactly the same as the encryption key, it indicates that there is no abnormality in the data stream segment during transmission; if the cyclic XOR result is different from the encryption key, it indicates that there is an abnormality in the data stream segment during transmission. If there is an abnormality, the integration end generates an information anomaly feedback to the corresponding edge end, and the edge end re-uploads the corresponding data stream segment; if there is no abnormality, the node monitoring data uploaded by each edge end is integrated.
[0021] It should be further explained that the process of integrating node monitoring data includes: obtaining the time periods corresponding to the data stream segments uploaded by each edge terminal, and summarizing the node monitoring data within the same time period; constructing a time axis corresponding to each edge terminal, mapping the node monitoring data of the same edge terminal within the same time period onto the time axis, and obtaining a node monitoring data change map corresponding to that edge terminal; constructing a time axis corresponding to the data type, mapping the node monitoring data of the same data type from different edge terminals at the same time onto the time axis, and obtaining a road data change map corresponding to that data type, thereby completing the data integration process of node monitoring data, and uploading the road data change map and node monitoring data change map obtained from the data integration to the central control center.
[0022] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications or equivalent substitutions made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A large-scale integrated intelligent edge computing system, comprising a central control center, characterized in that, The central control center has communication connections to an edge terminal and an integration terminal. The edge terminal is used to acquire node monitoring data of edge nodes, generate data processing tasks according to the data types of node monitoring data, process node monitoring data of different data types according to the data processing tasks, and upload the processed node monitoring data to the integration terminal. The integration terminal is used to verify the received node monitoring data and integrate the node monitoring data that has completed data verification.
2. The integrated large-scale intelligent edge computing system according to claim 1, characterized in that, The process of acquiring node monitoring data of edge nodes by the edge terminal includes: dividing each road in the target monitoring area into several road segments, setting up corresponding monitoring nodes in each road segment, deploying corresponding edge terminals for each monitoring node, and acquiring node monitoring data of the corresponding location through the deployed edge terminals; setting up edge nodes, wherein the edge nodes communicate with several edge terminals, and the edge terminals that communicate with the edge nodes communicate with at least one other edge terminal.
3. The integrated large-scale intelligent edge computing system according to claim 2, characterized in that, Based on the data types of node monitoring data, the process of generating data processing tasks includes: classifying the node monitoring data obtained from each edge terminal and obtaining different data types based on the classification results; constructing data storage spaces associated with each data type within the edge terminal and importing node monitoring data of different data types into the corresponding data storage spaces; constructing task nodes associated with each data storage space and empowering each task node with task processing requirements; and summarizing all task nodes to obtain the data processing task corresponding to that edge terminal.
4. The integrated large-scale intelligent edge computing system according to claim 3, characterized in that, The process of processing node monitoring data of different data types according to data processing tasks and uploading the processed node monitoring data to the integration end includes: matching each task node of the data processing task with the data processing function of the edge end; if a task node matches the data processing function of the edge end, the data storage space associated with the task node is marked as the local processing space, and a task processing request is generated according to the data processing functions required by other task nodes, and the task processing request is uploaded to the edge node; the edge node matches the data processing functions of other edge ends according to the received task processing request, generates the corresponding data allocation instruction according to the matching result, sends the data allocation instruction to the corresponding edge end, and marks the edge end to which the data allocation instruction is sent as the scheduling edge end; the edge end sends the data of the corresponding data type stored in the data storage space and the task node to the local processing space of the corresponding scheduling edge end according to the corresponding data type of the remaining task nodes; and the task nodes that match the data processing functions in each edge end are summarized to establish the corresponding edge data processing task.
5. The integrated large-scale intelligent edge computing system according to claim 4, characterized in that, The process of edge terminal processing node monitoring data includes: converting node monitoring data corresponding to edge data processing tasks into data streams using the data processing function of the edge terminal; dividing the node monitoring data into several fixed-length time periods based on the acquisition time of the node monitoring data, and summarizing the data streams belonging to the same time period to obtain data stream segments corresponding to each time period; setting a rule check code, which is composed of several binary unit codes; comparing the length of the rule check code with the length of the data stream segment; if the length of the data stream segment is an integer multiple of the length of the rule check code, no operation is performed on the data stream segment; if the length of the data stream segment is not an integer multiple of the length of the rule check code, "0"s are padded at the end of the data stream segment until the length of the data stream segment is an integer multiple of the length of the rule check code; performing a cyclic XOR operation on the data stream segment and the rule check code, obtaining the encryption key of the data stream segment based on the cyclic XOR operation, inserting the obtained encryption key into the end of the data stream segment to complete the data encryption of the data stream segment, and uploading the encrypted data stream segment to the integration terminal.
6. The integrated large-scale intelligent edge computing system according to claim 5, characterized in that, The process of performing a cyclic XOR operation between a data stream segment and a rule checksum is as follows: A data stream calibration ruler is set, with a length equal to the length of the rule checksum; calibration is performed starting from the first unit code of the data stream segment using the calibration ruler, and the calibrated portion is XORed with the rule checksum, with the XOR result used as the first sequence; the data stream calibration ruler is shifted by the corresponding length according to the length of the rule checksum and recalibrated, and the calibrated portion is XORed with the first sequence, with the XOR result used as the second sequence, and so on, until the cyclic XOR operation of the data stream segment is completed.
7. The integrated large-scale intelligent edge computing system according to claim 6, characterized in that, The process by which the integration end verifies the received node monitoring data includes: extracting the encryption key from each received data stream segment, performing a cyclic XOR operation on the data stream segment and the rule check code, and matching the cyclic XOR result with the encryption key. If the cyclic XOR result is exactly the same as the encryption key, it indicates that there is no abnormality in the data stream segment during transmission; if the cyclic XOR result is different from the encryption key, it indicates that there is an abnormality in the data stream segment during transmission. If there is an abnormality, the integration end generates an information anomaly feedback to the corresponding edge end, and the edge end re-uploads the corresponding data stream segment; if there is no abnormality, the node monitoring data uploaded by each edge end is integrated.
8. The integrated large-scale intelligent edge computing system according to claim 7, characterized in that, The process of integrating node monitoring data includes: obtaining the time periods corresponding to the data stream segments uploaded by each edge terminal, and summarizing the node monitoring data within the same time period; constructing a time axis corresponding to each edge terminal, mapping the node monitoring data of the same edge terminal within the same time period onto the time axis, and obtaining a change map of node monitoring data corresponding to that edge terminal; constructing a time axis corresponding to the data type, mapping the node monitoring data of the same data type from different edge terminals at the same time onto the time axis, and obtaining a change map of road data corresponding to that data type, thereby completing the data integration process of node monitoring data.