Gateway data acquisition optimization system based on edge computing

Abstract

This invention discloses a gateway data acquisition optimization system based on edge computing, belonging to the field of gateway data acquisition optimization technology. It includes a data acquisition range acquisition module, an interaction range acquisition module, an acquisition range category setting module, and an implementation server judgment module. By considering the different area types where data points are located, the system can adopt differentiated and intelligent decision-making strategies, avoiding blind task allocation. In overlapping areas, it selects the optimal server based on distance judgment, reducing latency and improving acquisition efficiency. Precise server judgment avoids redundant acquisition, ensuring that each data point is handled by only one most suitable server, thereby maximizing the utilization of edge computing resources. Because it can quickly and accurately locate the edge server responsible for acquisition, it reduces decision-making time and invalid data transmission, thereby improving the overall system response speed and avoiding unnecessary resource contention and repetitive operations.

Description

Technical Field

[0001] This invention belongs to the field of gateway data acquisition optimization technology, specifically a gateway data acquisition optimization system based on edge computing. Background Technology

[0002] With the rapid development of the Internet of Things (IoT) and edge computing, edge computing has become a key method for processing and storing data, especially in data acquisition 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 typically responsible for collecting data from different areas or devices.

[0003] However, when multiple edge nodes are deployed in relatively close locations, their data collection ranges may overlap. In these overlapping areas, multiple edge nodes may repeatedly collect the same data, leading to redundant processing of data collection tasks, data redundancy, and waste of computing resources. Based on this, an edge computing-based gateway data collection optimization system is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a gateway data acquisition optimization system based on edge computing, which solves the technical problem that when multiple edge nodes are deployed in relatively close locations, the data acquisition ranges overlap, and in these overlapping areas, multiple edge nodes may repeatedly collect the same data.

[0005] The gateway data acquisition optimization system based on edge computing includes:

[0006] The data acquisition range acquisition module obtains the data acquisition range corresponding to each edge node based on the rated data acquisition distance of each edge server corresponding to the edge node.

[0007] The data collection range category setting module allows you to set independent ranges as Class I collection ranges, dual-area interactive ranges as Class II collection ranges, and multi-area interactive ranges as Class III collection ranges.

[0008] The interaction range acquisition module analyzes the interaction range between the data collection ranges corresponding to each edge node to obtain independent range, dual-region interaction range, and multi-region interaction range.

[0009] The implementation server judgment module obtains the collection location coordinates corresponding to the data point to be collected when data needs to be collected. Based on the location of the collection location coordinates of the data point to be collected, it determines the type of the pre-collection range corresponding to the data point to be collected. Based on the type of the pre-collection range, it analyzes the distance between the data point to be collected and each corresponding edge node within the interaction range of the type of the collection range, and determines the implementation server corresponding to the data point to be collected, and uses the implementation server as the edge server for data collection of the data point to be collected.

[0010] As a further aspect of the present invention, the specific method for obtaining the data acquisition range corresponding to each edge node is as follows:

[0011] Each edge server is taken as an edge node, and the rated data acquisition distance of each edge server is taken as the preset radius of the data acquisition range of each edge node. Taking the edge node of each edge server as the point, and according to the preset radius of the data acquisition range of each edge server, the corresponding sphere of each edge server is drawn. The sphere of each edge server is taken as the data acquisition range of each edge server.

[0012] As a further aspect of the present invention, the specific method for obtaining the independent scope is as follows:

[0013] The data collection range corresponding to each edge node is marked as an independent range Wi, where i represents the edge node corresponding to each edge server, i=1,2,...,a1, a1 represents the total number of edge servers, i.e., edge nodes, a1 is a positive integer, and a1 satisfies a1>2.

[0014] As a further aspect of the present invention, the specific method for obtaining the dual-region interaction range is as follows:

[0015] Each edge node is paired up to obtain a pair with an interactive range as a double-interaction combination. The interactive range between the data acquisition ranges of the two edge nodes in each double-interaction combination is taken as the double-region interactive range corresponding to each double-interaction combination. The double-region interactive range is the three-dimensional space of the intersection between the corresponding spheres between the data acquisition ranges of the two edge nodes.

[0016] As a further aspect of the present invention, the specific method for obtaining the multi-region interaction range is as follows:

[0017] Each edge node is combined in triplicate. The combination of three edge nodes with interactive ranges is taken as a multi-interaction combination. The three edge nodes corresponding to a multi-interaction combination are then labeled O1, O2, and O3. Connecting the three edge nodes O1, O2, and O3 sequentially yields a triangular plane J. Projecting the three edge nodes O1, O2, and O3 yields projection points O1″, O2″, and O3″. Connecting the projection points O1″, O2″, and O3″ yields a drawing plane H. A two-dimensional plane circle C1 is drawn on drawing plane H with the projection points O1″, O2″, and O3″ as centers. C2 and C3. Connect the boundary points A1, A2 and A3 of the intersection region between the two-dimensional plane circles C1, C2 and C3 to obtain the top interactive shape. Connect the intersection points A1, A2 and A3 of the triangle plane J and the top interactive shape, and the edge nodes O1, O2 and O3 respectively to obtain the first interactive range F1. Perform a symmetrical transformation on the first interactive range F1 to obtain the second interactive range F2. The range between the first interactive range F1 and the second interactive range F2 is used as the multi-region interactive range of the corresponding interactive combination. By traversing the interactive range of each multi-interactive combination, the multi-region interactive range corresponding to all combinations is finally obtained.

[0018] As a further aspect of the present invention: the specific method for obtaining projection points O1″, O2″ and O3″ is as follows:

[0019] The three edge nodes O1, O2 and O3 are projected to positions E1, E2 and E3 respectively, which are distances from the direction of the normal vector of the triangle plane J. The projection distances E1, E2 and E3 are the preset radii corresponding to the three edge nodes.

[0020] As a further aspect of the present invention: the specific method for drawing two-dimensional planar circles C1, C2, and C3 with projection points O1″, O2″, and O3″ as centers on the drawing plane H is as follows:

[0021] Within the drawing plane H, with projection points O1″, O2″, and O3″ as centers, and the projection distances E1, E2, and E3 corresponding to the three edge nodes O1, O2, and O3 as radii, draw two-dimensional plane circles C1, C2, and C3 with projection points O1″, O2″, and O3″ as centers.

[0022] As a further aspect of the present invention, the specific method for obtaining the second interaction range F2 by performing a symmetrical transformation on the first interaction range F1 is as follows:

[0023] Using the triangular plane J as a reference, the second interactive range F2 is obtained by performing a symmetrical transformation on the first interactive range F1. The first interactive range F1 and the second interactive range F2 are symmetrical about the triangular plane J.

[0024] As a further aspect of the present invention: the specific method for determining the type of pre-collection range corresponding to the data point to be collected is as follows:

[0025] Based on the coordinates of the data point to be collected, the various collection range types corresponding to the location of the data point are obtained. If there are three types of collection ranges, the corresponding three types of collection ranges are used as the pre-collection range type 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 type corresponding to the data point to be collected. If there is only one type of collection range, the one type of collection range is used as the pre-collection range type corresponding to the data point to be collected. If there are no collection range types, no processing is performed.

[0026] As a further aspect of the present invention, the specific method for determining the implementation server corresponding to the data point to be collected is as follows:

[0027] If the pre-collection range type corresponding to the data point to be collected is a type I collection range, then the edge node corresponding to that independent range is determined as the implementation server of the data point to be collected; if the data point to be collected does not have any collection range type, then the distance between the 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 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 type II collection range, then 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 is calculated, and the edge server corresponding to the edge node with the smallest distance is 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 type III collection range, then 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 is calculated, and the edge server corresponding to the edge node with the smallest distance is determined as the implementation server of the data point to be collected.

[0028] If there are multiple edge nodes that are equidistant, then one of the edge nodes that are equidistant is randomly selected as the implementation server corresponding to the data point to be collected.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] (1) This invention can accurately identify the overlapping data acquisition areas between different edge nodes through geometric calculation. As the basis for optimizing data acquisition, when calculating the multi-region interaction range of the overlapping area into dual-region and multi-region, it not only identifies the existence of intersection, but also reveals its internal structure through geometric modeling, providing richer spatial information for subsequent collaborative processing and data distribution.

[0031] (2) According to the different types of regions where the data points are located, the system can adopt differentiated and intelligent decision-making strategies, avoiding blind task allocation. In overlapping areas, the optimal server is selected by distance judgment, which reduces latency and improves collection efficiency. Precise server judgment avoids redundant collection and ensures that each data point is only handled by the most suitable server, thereby maximizing the utilization rate of edge computing resources. Since the edge server responsible for collection can be located quickly and accurately, decision-making time and invalid data transmission are reduced, thereby improving the overall response speed of the system. It avoids unnecessary resource contention and repeated operations, which helps to improve the stability and reliability of the system. It effectively solves the problem of overlapping and redundant 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. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the system framework structure of the present invention;

[0033] Figure 2 This is a schematic diagram of the dual-region interaction range of the present invention;

[0034] Figure 3 A structural schematic diagram of the interactive shape of the plane and the top is drawn for this invention;

[0035] Figure 4 This is a schematic diagram of the top interactive shape of the present invention. Detailed Implementation

[0036] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1: Please refer to Figure 1 This application provides a gateway data acquisition optimization system based on edge computing, including:

[0038] The data acquisition range acquisition module takes each edge server as an edge node and obtains the data acquisition range corresponding to each edge server based on the rated data acquisition distance of each edge server.

[0039] Each edge server is designated as edge node i, and the rated data collection distance of each edge server is designated as the preset radius Ri of the data collection range of each edge server. Here, i refers to the edge node corresponding to each 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.

[0040] The preset radius Ri corresponding to the data collection range of each edge server is obtained according to the rated data collection distance of each edge server. The specific method of obtaining the radius Ri is to obtain it from the data center of the edge server and the instruction manual of each edge server. These are mature technologies, so no further details will be provided.

[0041] Using the edge node i corresponding to each edge server as the center, and based on the preset radius Ri corresponding to the data collection range of each edge server, draw the corresponding sphere for each edge server. Use the sphere corresponding to each edge server as the data collection range for each edge server (e.g., ...). Figure 2 );

[0042] Each edge server deployed in a different geographical location is abstracted as an edge node in three-dimensional space. The rated data acquisition distance of the edge server is obtained from its data center or product manual and set as the preset radius for each edge node. With the coordinates of each edge node i as the center and its preset radius as the radius, a sphere is drawn in three-dimensional space. This sphere represents the theoretical data acquisition range of the edge server. The irregular actual acquisition capabilities are uniformly abstracted into a standardized three-dimensional sphere model, which simplifies subsequent geometric calculations and logical judgments. It provides an accurate geometric basis and parameters for subsequent calculations of independent range, dual-region interaction range, and multi-region interaction range, which can accurately reflect the coverage capability of the edge server in actual three-dimensional space and enhance the accuracy and universality of the model.

[0043] The independent range marking module marks the data collection range corresponding to each edge node of each edge server as an independent range Wi;

[0044] The initialization module simply and directly labels the data collection range of each edge server as a collection range, providing the most basic classification for subsequent collection range type judgment. It clarifies the exclusive coverage area of ​​each edge node. When the data points to be collected are clearly within a certain independent range, the implementation server can be directly determined, simplifying the decision-making process.

[0045] The interaction range acquisition module is used to analyze the interaction range between the data acquisition ranges corresponding to each edge node, and to obtain the dual-region interaction range corresponding to each dual-interaction combination and the multi-region interaction range corresponding to each multi-interaction combination. Specifically:

[0046] Each edge node is paired up to obtain a combination with an interactive range, which is called a double-interaction combination. The interactive range between the data acquisition ranges of the two edge nodes in each double-interaction combination is defined as the double-region interactive range Bb of each double-interaction combination. Geometrically, the double-region interactive range Bb is the three-dimensional space where the corresponding spheres between the data acquisition ranges of the two edge nodes intersect (e.g., ...). Figure 2 ), where b represents different double-interaction combinations, b = 1, 2, ..., a2, a2 ​​represents the total number of double-interaction combinations, a2 is a positive integer, and a2 satisfies a1-1≥a2;

[0047] It should be noted that the interaction range between the data acquisition ranges of two edge nodes refers to the three-dimensional space of the intersection between the corresponding spheres of the data acquisition ranges of the two edge nodes. The interaction range formed by the intersection of the data acquisition ranges of the two edge nodes may be a spherical or ellipsoidal shape, representing the shared acquisition space between the two nodes.

[0048] The specific methods for obtaining the multi-area interaction range corresponding to each multi-interaction combination;

[0049] This is used to combine each edge node in groups of three, and obtain the combination of the data collection ranges of the three edge nodes that have interactive ranges as a multi-interaction combination.

[0050] It should be noted that the interactive range between the data acquisition ranges of the three edge nodes here refers to the three-dimensional space where the data acquisition ranges of the three edge nodes intersect with the corresponding spheres.

[0051] Obtain the three edge nodes corresponding to a multi-interaction combination, label the three edge nodes as O1, O2, and O3 respectively, and connect the three edge nodes O1, O2, and O3 in sequence to obtain the triangular plane J (e.g., Figure 4 );

[0052] Obtain the preset radii corresponding to the three edge nodes as projection distances E1, E2, and E3. Project the three edge nodes O1, O2, and O3 to positions with projection distances E1, E2, and E3 respectively from the normal vector direction of the triangle plane J. This yields the projection points O1″, O2″, and O3″ corresponding to the three edge nodes O1, O2, and O3. Connect the projection points O1″, O2″, and O3″ to obtain the drawing plane H (e.g., ...). Figure 3 In the drawing plane H, with projection points O1″, O2″ and O3″ as the centers respectively, according to the preset radii (i.e. projection distances E1, E2 and E3) corresponding to the three edge nodes O1, O2 and O3 respectively, draw two-dimensional plane circles C1, C2 and C3 with projection points O1″, O2″ and O3″ as the centers respectively.

[0053] The boundary points of the intersection region between two-dimensional plane circles C1, C2, and C3 are labeled A1, A2, and A3, respectively. These boundary points are considered the outer edges of the intersection region. Connecting these intersection points A1, A2, and A3 yields the top interactive shape. Connecting the intersection points A1, A2, and A3 of the triangle plane J with the corresponding edge nodes O1, O2, and O3 in the top interactive shape yields the first interactive range F1 (e.g., ...). Figure 4 Using the triangular plane J as a reference, the second interaction range F2 is obtained by symmetrically transforming the first interaction range F1. The first interaction range F1 and the second interaction range F2 are symmetrical about the triangular plane J. The range between the first interaction range F1 and the second interaction range F2 is taken 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 Dc corresponding to all combinations is finally obtained, where c represents different double interaction combinations, c = 1, 2, ..., a3, a3 represents the total number of double interaction combinations, a3 is a positive integer, and a2 satisfies a1-2≥a3.

[0054] Through geometric calculations, overlapping data collection areas between different edge nodes can be accurately identified. As the basis for optimizing data collection, the overlapping areas are divided into "dual areas" and "multi-areas". When calculating the interaction range of multiple areas, not only is the existence of intersection identified, but the internal structure is also revealed through geometric modeling. This provides richer spatial information for subsequent collaborative processing and data distribution. The subsequent implementation server judgment module provides key "map" information, enabling the system to make more intelligent server selections based on the specific area type where the data points are located.

[0055] In edge computing, multiple edge nodes are typically responsible for collecting data from different regions or devices. The data collection ranges of these edge nodes may overlap, and the overlapping area is their shared data collection space. The multi-region interaction range represents this shared space. It is derived by calculating the intersection area between the data collection ranges of multiple edge nodes. The multi-region interaction range represents the effective area for shared data collection among multiple edge nodes. By accurately calculating the intersection area and considering symmetry, the multi-region interaction range can optimize data distribution and sharing in the edge computing environment. The multi-region interaction range can help the system understand which edge nodes share data, thereby optimizing data collection allocation. Analyzing the multi-region interaction range can help determine task allocation strategies, especially for large-scale edge computing systems, effectively avoiding redundant calculations in overlapping areas.

[0056] The data collection range category setting module allows you to set independent ranges as Class I collection ranges, dual-area interactive ranges as Class II collection ranges, and multi-area interactive ranges as Class III collection ranges.

[0057] The implementation server determination module, when data collection is required, obtains the collection location coordinates of the data point to be collected. Based on the location of the data point, it determines the pre-collection range type. According to the pre-collection range type, it analyzes the distances between the data point and the corresponding edge nodes within the interaction range of that range type. Finally, it determines the implementation server corresponding to the data point and designates it as the edge server for data collection. Specifically, the implementation server is configured as follows:

[0058] Obtain the coordinates of the data point to be collected. Based on the coordinates of the data point, determine the various collection range types corresponding to the location of the data point. If there are three types of collection ranges, use the corresponding three types of collection ranges as the pre-collection range type for the data point to be collected. If there are no three types of collection ranges but there are two types of collection ranges, use the corresponding two types of collection ranges as the pre-collection range type for the data point to be collected. If there is only one type of collection range, use the one type of collection range as the pre-collection range type for the data point to be collected. If there are no collection range types, no processing is performed.

[0059] The specific method for determining the implementation server corresponding to the data points to be collected is as follows:

[0060] First, the coordinates of the collection locations corresponding to the 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).

[0061] If the data point to be collected does not have any collection range type, the distance between the data point and the edge nodes of each edge server will be obtained. 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.

[0062] The specific method for calculating the distance between the collected data points and the edge nodes of each edge server is as follows:

[0063] pass Calculate the distance between the collected data points and the edge nodes of each edge server;

[0064] The following calculations of the distances between the location coordinates of the data points to be collected and each edge node are all performed using the same method, which is an existing and mature technology, and therefore will not be repeated in the following description.

[0065] If the type of the pre-collection range corresponding to the data point to be collected is a type of collection range, it means that the location coordinates of the data point to be collected are within an independent range. Then, the edge node of the independent range is determined to be the implementation server of the data point to be collected.

[0066] If the type of the pre-collection range corresponding to the data point to be collected is a type II collection range, it means that the location coordinates of the data point to be collected are within the dual-region interaction range. Then, obtain the edge nodes corresponding to each dual-region interaction range, calculate the distance between the location coordinates of the data point to be collected and each edge node, 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, randomly select one of the edge nodes with equal distances as the implementation server corresponding to the data point to be collected.

[0067] 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 of the data point to be collected are within the multi-region interaction range. Then, obtain the corresponding edge nodes in the multi-interaction combination of each multi-region interaction range to which it belongs, calculate the distance between the location coordinates of the data point to be collected and each edge node, 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, then randomly select one of the edge nodes with equal distances as the implementation server corresponding to the data point to be collected.

[0068] During data collection, the system determines which edge node is responsible for collecting the data point based on the distance relationship 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-area interaction range or a multi-area interaction range, the system selects the edge node with the closest distance as the implementation server.

[0069] Based on the different types of regions where data points are located, the system can adopt differentiated and intelligent decision-making strategies, avoiding blind task allocation. In overlapping areas, the optimal server is selected by distance judgment, reducing latency and improving collection efficiency. Precise server determination avoids redundant collection, ensuring that each data point is handled by only the most suitable server, thereby maximizing the utilization of edge computing resources. Because it can quickly and accurately locate the edge server responsible for collection, decision-making time and invalid data transmission are reduced, thereby improving the overall response speed of the system. It avoids unnecessary resource contention and repetitive operations, which helps to improve the stability and reliability of the system. It effectively solves the problem of overlapping and redundancy in data collection in edge computing environments, greatly optimizes resource allocation, and improves the efficiency, robustness and scalability of the overall system, which has significant engineering practice value and innovation.

[0070] By analyzing the interaction range of multiple regions, the system can determine which edge nodes share the data acquisition space. This optimizes data acquisition allocation and avoids redundant data acquisition. Especially in large-scale edge data acquisition systems, it can improve the efficiency of data acquisition and save data acquisition resources. Through the optimization of data acquisition tasks, the edge computing system is ensured to be more efficient and accurate in the data acquisition process.

[0071] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0072] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A gateway data acquisition and optimization system based on edge computing, characterized in that, include: The data acquisition range acquisition module obtains the data acquisition range corresponding to each edge node based on the rated data acquisition distance of each edge server corresponding to the edge node. The interaction range acquisition module analyzes the interaction range between the data collection ranges corresponding to each edge node to obtain independent range, dual-region interaction range, and multi-region interaction range. The data collection range category setting module allows you to set independent ranges as Class I collection ranges, dual-area interactive ranges as Class II collection ranges, and multi-area interactive ranges as Class III collection ranges. The implementation server judgment module obtains the collection location coordinates corresponding to the data point to be collected when data needs to be collected. Based on the location of the collection location coordinates of the data point to be collected, it determines the type of the pre-collection range corresponding to the data point to be collected. Based on the type of the pre-collection range, it analyzes the distance between the data point to be collected and each corresponding edge node within the interaction range of the type of the collection range, and determines the implementation server corresponding to the data point to be collected, and uses the implementation server 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, characterized in that, The specific method for obtaining the data collection range corresponding to each edge node is as follows: Each edge server is taken as an edge node, and the rated data acquisition distance of each edge server is taken as the preset radius of the data acquisition range of each edge node. Taking the edge node of each edge server as the point, and according to the preset radius of the data acquisition range of each edge server, the corresponding sphere of each edge server is drawn. The sphere of each edge server is taken as the data acquisition range of each edge server.

3. The gateway data acquisition and optimization system based on edge computing according to claim 1, characterized in that, The specific method for obtaining an independent scope is as follows: The data collection range corresponding to each edge node is marked as an independent range Wi, where i represents the edge node corresponding to each edge server, i=1,2,...,a1, a1 represents 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, characterized in that, The specific method for obtaining the interaction range of the two regions is as follows: Each edge node is paired up to obtain a pair with an interactive range as a double-interaction combination. The interactive range between the data acquisition ranges of the two edge nodes in each double-interaction combination is taken as the double-region interactive range corresponding to each double-interaction combination. The double-region interactive range is the three-dimensional space of the intersection between the corresponding spheres between the data acquisition ranges of the two edge nodes.

5. The gateway data acquisition and optimization system based on edge computing according to claim 1, characterized in that, The specific method for obtaining the multi-region interaction range is as follows: Each edge node is combined in triplicate. The combination of three edge nodes with interactive ranges is taken as a multi-interaction combination. The three edge nodes corresponding to a multi-interaction combination are then labeled O1, O2, and O3. Connecting the three edge nodes O1, O2, and O3 sequentially yields a triangular plane J. Projecting the three edge nodes O1, O2, and O3 yields projection points O1″, O2″, and O3″. Connecting the projection points O1″, O2″, and O3″ yields a drawing plane H. A two-dimensional plane circle C1 is drawn on drawing plane H with the projection points O1″, O2″, and O3″ as centers. C2 and C3. Connect the boundary points A1, A2 and A3 of the intersection region between the two-dimensional plane circles C1, C2 and C3 to obtain the top interactive shape. Connect the intersection points A1, A2 and A3 of the triangle plane J and the top interactive shape, and the edge nodes O1, O2 and O3 respectively to obtain the first interactive range F1. Perform a symmetrical transformation on the first interactive range F1 to obtain the second interactive range F2. The range between the first interactive range F1 and the second interactive range F2 is used as the multi-region interactive range of the corresponding interactive combination. By traversing the interactive range of each multi-interactive combination, the multi-region interactive range corresponding to all combinations is finally obtained.

6. The gateway data acquisition and optimization system based on edge computing according to claim 5, characterized in that, The specific method for obtaining projection points O1″, O2″, and O3″ is as follows: The three edge nodes O1, O2 and O3 are projected to positions E1, E2 and E3 respectively, which are distances from the direction of the normal vector of the triangle plane J. The projection distances E1, E2 and E3 are 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, characterized in that, The specific method for drawing two-dimensional plane circles C1, C2, and C3 with projection points O1″, O2″, and O3″ as centers on the drawing plane H is as follows: Within the drawing plane H, with projection points O1″, O2″, and O3″ as centers, and using the projection distances E1, E2, and E3 corresponding to the three edge nodes O1, O2, and O3 as radii, draw two-dimensional plane circles C1, C2, and C3 with projection points O1″, O2″, and O3″ as centers.

8. The gateway data acquisition and optimization system based on edge computing according to claim 5, characterized in that, The specific method for obtaining the second interaction range F2 by performing a symmetrical transformation on the first interaction range F1 is as follows: Using the triangular plane J as a reference, the second interactive range F2 is obtained by performing a symmetrical transformation on the first interactive range F1. The first interactive range F1 and the second interactive range F2 are symmetrical about the triangular plane J.

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

10. The gateway data acquisition and optimization system based on edge computing according to claim 9, characterized in that, The specific method for determining the implementation server corresponding to the data points to be collected is as follows: If the type of the pre-collection range corresponding to the data point to be collected is a type of collection range, then the edge node of that 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 have any collection range type, the distance between the 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 type II 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 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 three types of collection range, calculate the distance between the data point to be collected and each edge node in 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 that are equidistant, then one of the edge nodes that are equidistant is randomly selected as the implementation server corresponding to the data point to be collected.

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