Gateway data acquisition optimization system based on edge computing

Abstract

The invention discloses a gateway data acquisition optimization system based on edge computing, which relates to the technical field of gateway data acquisition optimization, and comprises a data acquisition range acquisition module, an interaction range acquisition module, an acquisition range category setting module and an implementation server judgment module. The system can adopt a differentiated and intelligent decision strategy, avoids blind task allocation, selects an optimal server through distance judgment in an overlapping region, reduces delay, improves acquisition efficiency, avoids redundant acquisition through accurate server judgment, ensures that only one most suitable server is responsible for each data point, and improves the acquisition efficiency. Therefore, the utilization rate of edge computing resources is maximized, the edge server responsible for acquisition can be quickly and accurately positioned, the decision time and invalid data transmission are reduced, the overall response speed of the system is improved, and unnecessary resource scrambling and repeated operation are avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of gateway data acquisition and optimization, and specifically relates to a gateway data acquisition and optimization system based on edge computing. Background Art

[0002] With the rapid development of the Internet of Things (IoT) and edge computing, edge computing has become a key way to process and store data, especially in data collection and analysis scenarios. By pushing data processing to the edge of the network, edge computing can reduce data transmission latency, lower bandwidth requirements, and improve system response speed. In practical applications, multiple edge nodes are often responsible for collecting data from different areas or devices. However, when multiple edge nodes are deployed relatively close to each other, their data collection ranges will overlap. In these overlapping areas, multiple edge nodes may repeatedly collect the same data, resulting in repeated processing of data collection tasks, data redundancy, and waste of computing resources. Based on this, a gateway data collection optimization system based on edge computing is proposed. Summary of the Invention

[0003] The purpose of the present invention is to provide a gateway data acquisition optimization system based on edge computing, which solves the technical problem that when the deployment locations of multiple edge nodes are relatively close, the data acquisition range will overlap. In these overlapping areas, multiple edge nodes may repeatedly collect the same data.

[0004] The gateway data collection and optimization system based on edge computing includes: The data collection range acquisition module obtains the data collection range corresponding to each edge node according to the rated data collection distance of each edge server corresponding to the edge node; The collection range category setting module sets the independent range as the first collection range, the dual-area interactive range as the second collection range, and the multi-area interactive range as the third collection range; The interaction range acquisition module analyzes the interaction ranges between the data collection ranges corresponding to each edge node to obtain independent ranges, dual-area interaction ranges, and multi-area interaction ranges; The implementation server judgment module obtains the collection position coordinates corresponding to the data point to be collected when data needs to be collected, and judges the pre-collection range type corresponding to the data point to be collected according to the location of the collection position coordinates corresponding to the data point to be collected. According to the pre-collection range type, the distance between the data point to be collected and the corresponding edge nodes within the interaction range of the collection range type is analyzed, and the implementation server corresponding to the data point to be collected is determined, and the implementation server is used as the edge server for data collection of the data point to be collected.

[0005] As a further solution of the present invention, the specific method of obtaining the data collection range corresponding to each edge node is: Each edge server is regarded as an edge node, and the rated data collection distance corresponding to each edge server is used as the preset radius of the data collection range corresponding to each edge node. The edge nodes corresponding to each edge server are used as circles. According to the preset radius corresponding to the data collection range of each edge server, the corresponding spheres corresponding to each edge server are drawn, and the spheres corresponding to each edge server are used as the data collection range corresponding to each edge server.

[0006] As a further solution of the present invention: the specific way to obtain the independent range is: The data collection range corresponding to each edge node is marked as an independent range Wi, where i refers to the edge node corresponding to each different edge server, i=1, 2, ..., a1, a1 refers to the total number of edge servers, i.e., edge nodes, a1 is a positive integer, and a1 satisfies a1>2.

[0007] As a further solution of the present invention: the specific method of obtaining the dual-region interaction range is: The edge nodes are combined in pairs, and the combinations with interaction ranges are obtained as double interaction combinations. The interaction ranges between the two edge node data collection ranges corresponding to each double interaction combination are used as the double-area interaction ranges corresponding to each double interaction combination. The double-area interaction range is the three-dimensional space of the intersection between the corresponding spheres between the two edge node data collection ranges.

[0008] As a further solution of the present invention: a specific method of obtaining the multi-region interaction range is: Combine the edge nodes in groups of three, and obtain a combination of data collection ranges of the three edge nodes with interactive ranges as a multi-interaction combination, obtain the three edge nodes corresponding to the multi-interaction combination, mark the three edge nodes as O1, O2 and O3 respectively, connect the three edge nodes O1, O2, O3 in sequence to obtain a triangular plane J, project the three edge nodes O1, O2, O3 to obtain projection points O1", O2" and O3", connect the projection points O1", O2" and O3" to obtain a drawing plane H, and draw a two-dimensional plane circle C1 with the projection points O1", O2" and O3" as the center on the drawing plane H. C2 and C3, connect the boundary points A1, A2 and A3 of the intersection area between the two-dimensional plane circles C1, C2 and C3 to obtain the top interaction shape, connect the corresponding intersection points A1, A2, A3 and edge nodes O1, O2, O3 in the triangular plane J and the top interaction shape one by one, and then obtain the first interaction range F1, perform a symmetrical transformation on the first interaction range F1 to obtain the second interaction range F2, and use the range between the first interaction range F1 and the second interaction range F2 as the multi-region interaction range of the corresponding interaction combination. By traversing the interaction range of each multi-interaction combination, the multi-region interaction range corresponding to all combinations is finally obtained.

[0009] As a further solution of the present invention, the specific method of projecting to obtain the projection points O1″, O2″ and O3″ is as follows: The three edge nodes O1, O2 and O3 are projected to positions with projection distances E1, E2 and E3 respectively from the normal vector direction of the triangle plane J. The projection distances E1, E2 and E3 are respectively the preset radii corresponding to the three edge nodes.

[0010] As a further solution of the present invention, the specific method of drawing two-dimensional plane circles C1, C2 and C3 with the projection points O1", O2" and O3" as the centers on the drawing plane H is as follows: In the drawing plane H, with the projection points O1″, O2″ and O3″ as the center of the circle, and the projection distances E1, E2 and E3 corresponding to the three edge nodes O1, O2 and O3 as the radius, draw two-dimensional plane circles C1, C2 and C3 with the projection points O1″, O2″ and O3″ as the center of the circle.

[0011] As a further solution of the present invention, the specific method of performing symmetrical transformation on the first interaction range F1 to obtain the second interaction range F2 is: The second interaction range F2 is obtained by performing a symmetrical transformation on the first interaction range F1 based on the triangular plane J. The first interaction range F1 and the second interaction range F2 are vertically symmetrical with respect to the triangular plane J.

[0012] As a further solution of the present invention: the specific method of determining the type of pre-collection range corresponding to the data point to be collected is: According to the collection position coordinates corresponding to the data point to be collected, the various collection range types corresponding to the location of the data point to be collected are obtained. If there are three types of collection ranges, the corresponding three types of collection ranges will be used as the pre-collection range types corresponding to the data point to be collected. If there are no three types of collection ranges but there are two types of collection ranges, the corresponding two types of collection ranges will be used as the pre-collection range type corresponding to the data point to be collected. If there is only one type of collection range, the one type of collection range will be used as the pre-collection range type corresponding to the data point to be collected. If no collection range type exists, no processing will be performed.

[0013] As a further solution of the present invention, the specific method for determining the implementation server corresponding to the collected data point is: If the pre-collection range type corresponding to the data point to be collected is a first-class collection range, the edge node corresponding to the independent range is determined to be the implementation server of the data point to be collected; if the data point to be collected does not exist in any collection range type, the distance between the collection data point and the edge nodes of each edge server is obtained, and the edge server corresponding to the edge node with the smallest distance is determined to be the implementation server of the data point to be collected; if the pre-collection range type corresponding to the data point to be collected is a second-class collection range, the distance between the data point to be collected and each edge node corresponding to the dual-interaction combination of the dual-region interaction range to which it belongs is calculated, and the edge server corresponding to the edge node with the smallest distance is determined to be the implementation server of the data point to be collected; if the pre-collection range type corresponding to the data point to be collected is a third-class collection range, the distance between the data point to be collected and each edge node corresponding to the multi-interaction combination of the multi-region interaction range to which it belongs is calculated, and the edge server corresponding to the edge node with the smallest distance is determined to be the implementation server of the data point to be collected; If there are multiple edge nodes with equal distances, one of the edge nodes with equal distances is randomly selected as the implementation server corresponding to the data point to be collected.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention, through geometric calculation, can accurately identify the overlapping data collection areas between different edge nodes. As the basis for optimizing data collection, the overlapping areas are divided into dual-region and multi-region. When calculating the multi-region interaction range, not only the existence of the intersection is identified, but also its internal structure is revealed through geometric modeling, providing richer spatial information for subsequent collaborative processing and data distribution. (2) The present invention can adopt differentiated and intelligent decision-making strategies based on the different types of areas where the data points are located, avoiding blind task allocation. In overlapping areas, the optimal server is selected by distance judgment, which reduces delays and improves collection efficiency. Accurate server judgment avoids redundant collection and ensures that each data point is only responsible for one most suitable server, thereby maximizing the utilization of edge computing resources. Since the edge server responsible for collection can be quickly and accurately located, the decision-making time and invalid data transmission are reduced, thereby improving the overall response speed of the system, avoiding unnecessary resource competition and repeated operations, and helping to improve the stability and reliability of the system. It effectively solves the overlapping and redundant problem of data collection in the edge computing environment, greatly optimizes resource allocation, improves the efficiency, robustness and scalability of the overall system, and ensures that the edge computing system is more efficient and accurate in the data collection process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the system framework structure of the present invention; Figure 2 This is a schematic diagram of the structure of the dual-zone interaction range of the present invention; Figure 3 Draw a schematic diagram of the structure of the plane and top interactive shapes for the present invention; Figure 4 It is a structural diagram of the top interactive shape of the present invention. DETAILED DESCRIPTION

[0016] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] Example 1: Please refer to Figure 1 ,This application provides a gateway data acquisition optimization system based on edge computing, including; The data collection range acquisition module uses each edge server as an edge node and obtains the data collection range corresponding to each edge server according to the rated data collection distance corresponding to each edge server; Each edge server is regarded as an edge node i, and the rated data collection distance corresponding to each edge server is regarded as the preset data collection range radius Ri corresponding to each edge server, where i refers to the edge node corresponding to each different edge server, i=1, 2, ..., a1, a1 refers to the total number of edge servers or edge nodes, a1 is a positive integer, and a1 satisfies a1>2; The preset radius Ri corresponding to the data collection range of each edge server is obtained according to the rated data collection distance corresponding to each edge server. The specific acquisition method is obtained from the data center of the edge server and the manual of each edge server. They are all mature and technical, so no further description is given; Take the edge node i corresponding to each edge server as the circle point, and draw the corresponding sphere corresponding to each edge server according to the preset radius Ri corresponding to the data collection range of each edge server. The sphere corresponding to each edge server is used as the data collection range corresponding to each edge server (such as Figure 2 ); Each edge server deployed in different geographical locations is abstracted as an edge node in three-dimensional space. The rated data collection distance is obtained from the edge server's data center or product manual and set as the preset radius corresponding to each edge node. A sphere is drawn in three-dimensional space with the coordinates of each edge node i as the center and its preset radius as the radius. This sphere represents the theoretical data collection range of the edge server. The irregular actual collection capabilities are uniformly abstracted into a standardized three-dimensional sphere model, which simplifies subsequent geometric calculations and logical judgments, and provides an accurate geometric basis and parameters for the subsequent calculation of independent range, dual-area interaction range, and multi-area interaction range. It can accurately reflect the coverage capability of the edge server in the actual three-dimensional space and enhance the accuracy and universality of the model.

[0018] The independent range marking module marks the data collection range corresponding to the edge nodes corresponding to each edge server as an independent range Wi; The data collection range of each edge server determined by the initialization module is simply and directly marked as a type of collection range, providing the most basic classification for subsequent collection range type judgment, clarifying the exclusive coverage area of ​​each edge node, and when the collected data points are clearly within a certain independent range, the implementation server can be directly determined, simplifying the decision-making process.

[0019] The interaction range acquisition module is used to analyze the interaction ranges between the data collection ranges corresponding to each edge node, and obtain the dual-area interaction range corresponding to each dual-interaction combination and the multi-area interaction range corresponding to each multi-interaction combination. The specific method is as follows: The edge nodes are combined in pairs to obtain a combination with an interaction range as a double interaction combination. The interaction range between the two edge node data collection ranges corresponding to each double interaction combination is the double-region interaction range Bb corresponding to each double interaction combination. From a geometric point of view, the double-region interaction range Bb is the three-dimensional space of the intersection between the corresponding spheres between the two edge node data collection ranges (such as Figure 2 ), where b refers to different double interaction combinations, b=1, 2, ..., a2, a2 ​​refers to the total number of double interaction combinations, a2 is a positive integer, and a2 satisfies a1-1≥a2; It should be noted that the interaction range between the data collection ranges of two edge nodes refers to the three-dimensional space portion of the intersection between the corresponding spheres between the data collection ranges of the two edge nodes. The interaction range formed by the intersection of the data collection ranges of the two edge nodes may be in the shape of a spherical cap or an ellipsoid, representing the shared collection space between the two nodes. A specific method for obtaining the multi-region interaction range corresponding to each multi-interaction combination; for combining the edge nodes in groups of three, and obtaining a combination having interactive ranges between the data collection ranges of the three edge nodes as a multi-interaction combination; It should be noted that the interactive range between the data collection ranges of the three edge nodes here refers to the three-dimensional space of the intersection between the spheres corresponding to the collection ranges of the three edge nodes; Obtain three edge nodes corresponding to a multi-interaction combination, mark the three edge nodes as O1, O2 and O3 respectively, and connect the three edge nodes O1, O2 and O3 in sequence to obtain a triangular plane J (such as Figure 4 ); Obtain the preset radius corresponding to the three edge nodes as the projection distance E1, E2 and E3, respectively, and project the three edge nodes O1, O2 and O3 to the positions with the projection distance E1, E2 and E3 in the direction of the normal vector of the triangle plane J, and then obtain the projection points O1", O2" and O3" corresponding to the three edge nodes O1, O2 and O3 respectively. Connect the projection points O1", O2" and O3" to obtain the drawing plane H (as shown in Figure 1). Figure 3 ), in the drawing plane H, with the projection points O1″, O2″ and O3″ as the circle centers, and according to the preset radii (i.e., projection distances E1, E2 and E3) corresponding to the three edge nodes O1, O2 and O3, draw two-dimensional plane circles C1, C2 and C3 with the projection points O1″, O2″ and O3″ as the circle centers; The boundary points of the intersection area between the two-dimensional plane circles C1, C2 and C3 are marked as A1, A2 and A3 respectively. The boundary points of the intersection area refer to the outer edge points of the intersection area. At the same time, the intersection points A1, A2 and A3 are connected respectively to obtain the top interaction shape. The corresponding intersection points A1, A2, A3 and edge nodes O1, O2 and O3 in the triangular plane J and the top interaction shape are connected one by one to obtain the first interaction range F1 (as shown in FIG. Figure 4), taking the triangular plane J as the reference, obtain the second interaction range F2 by performing a symmetric transformation on the first interaction range F1. The first interaction range F1 and the second interaction range F2 are symmetrical with respect to the triangular plane J. The range between the first interaction range F1 and the second interaction range F2 is used as the multi-region interaction range of the corresponding interaction combination. By traversing the interaction ranges of each multi-region interaction combination, the multi-region interaction range Dc corresponding to all combinations is finally obtained, where c refers to different dual interaction combinations, c=1, 2, ..., a3, a3 refers to the total number of dual interaction combinations, a3 is a positive integer, and a2 satisfies a1-2≥a3; Through geometric calculations, we can accurately identify overlapping data collection areas between different edge nodes. As a basis for optimizing data collection, we divide overlapping areas into "dual areas" and "multi-areas." When calculating the interaction range of multiple areas, we not only identify the existence of intersections, but also reveal their internal structure through geometric modeling, providing richer spatial information for subsequent collaborative processing and data distribution. The subsequent implementation of the server judgment module provides key "map" information, enabling the system to make more intelligent server selection based on the specific area type of the data point. In edge computing, multiple edge nodes are usually responsible for collecting data from different areas or devices. The data collection ranges of these edge nodes may overlap, and the overlapping area is the data collection space they share. The multi-area interaction range represents this shared space. It is obtained by calculating the intersection area between the data collection ranges of multiple edge nodes. The multi-area interaction range represents the effective area for shared data collection between multiple edge nodes. By accurately calculating the intersection area and considering symmetry, the multi-area interaction range can optimize data distribution and sharing in the edge computing environment. The multi-area interaction range can help the system understand which data between edge nodes is shared, thereby optimizing data collection distribution. The multi-area interaction range can be analyzed to help determine the task allocation strategy, especially for large-scale edge computing systems, which can effectively avoid repeated calculations in overlapping areas.

[0020] The collection range category setting module sets the independent range as the first collection range, the dual-area interactive range as the second collection range, and the multi-area interactive range as the third collection range; The implementation server judgment module obtains the collection location coordinates corresponding to the data point to be collected when data collection is required. Based on the location of the collection location coordinates corresponding to the data point to be collected, the pre-collection range type corresponding to the data point to be collected is determined. Based on the pre-collection range type, the distance between the data point to be collected and each edge node corresponding to the interaction range of the collection range type is analyzed. The implementation server corresponding to the data point to be collected is determined, and the implementation server is used as the edge server for data collection of the data point to be collected. The specific method is as follows: Obtain the collection position coordinates corresponding to the data point to be collected, and obtain the various collection range types corresponding to the location of the data point to be collected based on the collection position coordinates corresponding to the data point to be collected. If there are three types of collection ranges, the corresponding three types of collection ranges are used as the pre-collection range types corresponding to the data point to be collected. If there are no three types of collection ranges but there are two types of collection ranges, the corresponding two types of collection ranges are used as the pre-collection range types corresponding to the data point to be collected. If there is only one type of collection range, the first type of collection range is used as the pre-collection range type corresponding to the data point to be collected. If no collection range type exists, no processing is performed. The specific method for determining the implementation server corresponding to the collected data point is: First, the collection location coordinates corresponding to the collected data points are marked as K (Kx, Ky, Kz), and the coordinates of the edge nodes corresponding to each edge server are marked as Gi (Gxi, Gyi, Gzi); If the data point to be collected does not exist in any collection range type, the distance between the collection data point and the edge nodes of each edge server will be obtained, and the edge server corresponding to the edge node with the smallest distance will be determined as the implementation server of the data point to be collected. If there are multiple edge nodes with equal distances, one of the edge nodes with equal distances will be randomly selected as the implementation server corresponding to the data point to be collected; The specific method for calculating the distance between the collected data point and the edge node of each edge server is: pass ,calculate the distance between the collected data point and the edge node of each edge server; The following calculation method for obtaining the distance between the position coordinates of the data points to be collected and each edge node is the same. It is an existing and mature technology, so it will not be repeated in the following description. If the pre-collection range type corresponding to the data point to be collected is a type-one collection range, it means that the location coordinates corresponding to the data point to be collected are within the independent range, and the edge node of the independent range is determined to be the implementation server of the data point to be collected; If the pre-collection range type corresponding to the data point to be collected is a second-class collection range, it means that the location coordinates corresponding to the data point to be collected are within the dual-area interaction range. Then, the edge nodes corresponding to the dual-interaction combinations of each dual-area interaction range to which it belongs are obtained, and the distances between the location coordinates corresponding to the data point to be collected and each edge node are calculated. The edge server corresponding to the edge node with the smallest distance is determined as the implementation server for the data point to be collected. If there are multiple edge nodes with equal distances, one of the edge nodes with equal distances is randomly selected as the implementation server for the data point to be collected. If the pre-collection range type corresponding to the data point to be collected is within the three-category collection range, it means that the location coordinates corresponding to the data point to be collected are within the multi-region interaction range. Then, the edge nodes corresponding to the multi-interaction combinations of each multi-region interaction range to which it belongs are obtained, and the distances between the location coordinates corresponding to the data point to be collected and each edge node are calculated. The edge server corresponding to the edge node with the smallest distance is determined as the implementation server for the data point to be collected. If there are multiple edge nodes with equal distances, one of the edge nodes with equal distances is randomly selected as the implementation server for the data point to be collected. During data collection, the system determines which edge node is responsible for collecting the data point based on the distance between the location coordinates of the data point to be collected and each edge node. If the data point does not belong to any interaction range, the system selects the edge node with the smallest distance as the implementation server. If the data point belongs to a certain interaction range, such as a dual-region interaction range or a multi-region interaction range, the system selects the edge node with the closest distance as the implementation server. According to the different types of areas where the data points are located, the system can adopt differentiated and intelligent decision-making strategies to avoid blind task allocation. In overlapping areas, the optimal server is selected by distance judgment, which reduces latency and improves collection efficiency. Accurate server judgment avoids redundant collection and ensures that each data point is only responsible for one most suitable server, thereby maximizing the utilization of edge computing resources. Since the edge server responsible for collection can be located quickly and accurately, the decision-making time and invalid data transmission are reduced, thereby improving the overall response speed of the system, avoiding unnecessary resource competition and repeated operations, and helping to improve the stability and reliability of the system. It effectively solves the overlapping and redundant problems of data collection in edge computing environments, greatly optimizes resource allocation, and improves the efficiency, robustness and scalability of the overall system. It has significant engineering practice value and innovation.

[0021] By analyzing the multi-region interaction range, the system can determine which edge nodes share the data collection space, which can optimize data collection distribution and avoid repeated data collection. Especially in large-scale edge data collection systems, it can improve the efficiency of data collection and save data collection resources; by optimizing data collection tasks, it ensures that the edge computing system is more efficient and accurate in the data collection process.

[0022] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0023] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The gateway data acquisition and optimization system based on edge computing is characterized by: include: The data collection range acquisition module obtains the data collection range corresponding to each edge node according to the rated data collection distance of each edge server corresponding to the edge node; The interaction range acquisition module analyzes the interaction ranges between the data collection ranges corresponding to each edge node to obtain independent ranges, dual-area interaction ranges, and multi-area interaction ranges; The collection range category setting module sets the independent range as the first collection range, the dual-area interactive range as the second collection range, and the multi-area interactive range as the third collection range; The implementation server judgment module obtains the collection position coordinates corresponding to the data point to be collected when data needs to be collected, and judges the pre-collection range type corresponding to the data point to be collected according to the location of the collection position coordinates corresponding to the data point to be collected. According to the pre-collection range type, the distance between the data point to be collected and the corresponding edge nodes within the interaction range of the collection range type is analyzed, and the implementation server corresponding to the data point to be collected is determined, and the implementation server is used as the edge server for data collection of the data point to be collected.

2. The gateway data acquisition and optimization system based on edge computing according to claim 1 is characterized in that: The specific method of obtaining the data collection range corresponding to each edge node is: Each edge server is regarded as an edge node, and the rated data collection distance corresponding to each edge server is used as the preset radius of the data collection range corresponding to each edge node. The edge nodes corresponding to each edge server are used as circles. According to the preset radius corresponding to the data collection range of each edge server, the corresponding spheres corresponding to each edge server are drawn, and the spheres corresponding to each edge server are used as the data collection range corresponding to each edge server.

3. The gateway data acquisition and optimization system based on edge computing according to claim 1 is characterized in that: The specific way to obtain an independent scope is: The data collection range corresponding to each edge node is marked as an independent range Wi, where i refers to the edge node corresponding to each different edge server, i=1, 2, ..., a1, a1 refers to the total number of edge servers, i.e., edge nodes, a1 is a positive integer, and a1 satisfies a1>2.

4. The gateway data acquisition and optimization system based on edge computing according to claim 2 is characterized in that: The specific method to obtain the dual-area interaction range is: The edge nodes are combined in pairs, and the combinations with interaction ranges are obtained as double interaction combinations. The interaction ranges between the two edge node data collection ranges corresponding to each double interaction combination are used as the double-area interaction ranges corresponding to each double interaction combination. The double-area interaction range is the three-dimensional space of the intersection between the corresponding spheres between the two edge node data collection ranges.

5. The gateway data acquisition and optimization system based on edge computing according to claim 1 is characterized in that: The specific method to obtain the multi-region interaction range is: Combine the edge nodes in groups of three, and obtain a combination of data collection ranges of the three edge nodes with interactive ranges as a multi-interaction combination, obtain the three edge nodes corresponding to the multi-interaction combination, mark the three edge nodes as O1, O2 and O3 respectively, connect the three edge nodes O1, O2, O3 in sequence to obtain a triangular plane J, project the three edge nodes O1, O2, O3 to obtain projection points O1", O2" and O3", connect the projection points O1", O2" and O3" to obtain a drawing plane H, and draw a two-dimensional plane circle C1 with the projection points O1", O2" and O3" as the center on the drawing plane H. C2 and C3, connect the boundary points A1, A2 and A3 of the intersection area between the two-dimensional plane circles C1, C2 and C3 to obtain the top interaction shape, connect the corresponding intersection points A1, A2, A3 and edge nodes O1, O2, O3 in the triangular plane J and the top interaction shape one by one, and then obtain the first interaction range F1, perform a symmetrical transformation on the first interaction range F1 to obtain the second interaction range F2, and use the range between the first interaction range F1 and the second interaction range F2 as the multi-region interaction range of the corresponding interaction combination. By traversing the interaction range of each multi-interaction combination, the multi-region interaction range corresponding to all combinations is finally obtained.

6. The gateway data acquisition and optimization system based on edge computing according to claim 5 is characterized in that: The specific method of projecting to obtain the projection points O1″, O2″ and O3″ is as follows: The three edge nodes O1, O2 and O3 are projected to positions with projection distances E1, E2 and E3 respectively from the normal vector direction of the triangle plane J. The projection distances E1, E2 and E3 are respectively the preset radii corresponding to the three edge nodes.

7. The gateway data acquisition and optimization system based on edge computing according to claim 5 is characterized in that: The specific method of drawing two-dimensional plane circles C1, C2 and C3 with projection points O1", O2" and O3" as the centers on the drawing plane H is as follows: In the drawing plane H, with the projection points O1″, O2″ and O3″ as the centers, respectively, and the projection distances E1, E2 and E3 corresponding to the three edge nodes O1, O2 and O3 respectively as the radii, draw two-dimensional plane circles C1, C2 and C3 with the projection points O1″, O2″ and O3″ as the centers.

8. The gateway data acquisition and optimization system based on edge computing according to claim 5 is characterized in that: The specific method of performing symmetrical transformation on the first interaction range F1 to obtain the second interaction range F2 is: The second interaction range F2 is obtained by performing a symmetrical transformation on the first interaction range F1 based on the triangular plane J. The first interaction range F1 and the second interaction range F2 are vertically symmetrical with respect to the triangular plane J.

9. The gateway data acquisition and optimization system based on edge computing according to claim 1 is characterized in that: The specific method for determining the pre-collection range type corresponding to the data point to be collected is: According to the collection position coordinates corresponding to the data point to be collected, the various collection range types corresponding to the location of the data point to be collected are obtained. If there are three types of collection ranges, the corresponding three types of collection ranges will be used as the pre-collection range types corresponding to the data point to be collected. If there are no three types of collection ranges but there are two types of collection ranges, the corresponding two types of collection ranges will be used as the pre-collection range type corresponding to the data point to be collected. If there is only one type of collection range, the one type of collection range will be used as the pre-collection range type corresponding to the data point to be collected. If no collection range type exists, no processing will be performed.

10. The gateway data acquisition and optimization system based on edge computing according to claim 9 is characterized in that: The specific method for determining the implementation server corresponding to the collected data point is: If the pre-collection range type corresponding to the data point to be collected is a type-one collection range, the edge node of the independent range is determined to be the implementation server of the data point to be collected; If the data point to be collected does not exist in any collection range type, the distance between the collection data point and the edge nodes of each edge server will be obtained, and the edge server corresponding to the edge node with the smallest distance will be determined as the implementation server of the data point to be collected; if the pre-collection range type corresponding to the data point to be collected is a second-class collection range, the distance between the data point to be collected and each edge node corresponding to the dual interaction combination of the dual-region interaction range to which it belongs will be calculated, and the edge server corresponding to the edge node with the smallest distance will be determined as the implementation server of the data point to be collected; If the pre-collection range type corresponding to the data point to be collected is within the third collection range, calculate the distance between the data point to be collected and each edge node corresponding to the multi-interaction combination of the multi-region interaction range to which it belongs, and determine the edge server corresponding to the edge node with the smallest distance as the implementation server of the data point to be collected; If there are multiple edge nodes with equal distances, one of the edge nodes with equal distances is randomly selected as the implementation server corresponding to the data point to be collected.

Images