CN112738756A - Internet of things equipment data collection method and device - Google Patents
Internet of things equipment data collection method and device Download PDFInfo
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- CN112738756A CN112738756A CN202110042822.6A CN202110042822A CN112738756A CN 112738756 A CN112738756 A CN 112738756A CN 202110042822 A CN202110042822 A CN 202110042822A CN 112738756 A CN112738756 A CN 112738756A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W4/30—Services specially adapted for particular environments, situations or purposes
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- H04W40/00—Communication routing or communication path finding
- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
- H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
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Abstract
The application provides a method and a device for collecting data of Internet of things equipment, wherein the method comprises the following steps: acquiring a dependency relationship between the Internet of things equipment and each edge node in the current time slice, and determining the node level of each edge node according to the dependency relationship; dividing edge networks according to the node grade of each edge node and a preset spatial index algorithm to obtain a plurality of single edge networks; the single-edge network comprises a plurality of edge nodes; and obtaining the shortest data collection path corresponding to the current time slice according to the plurality of single edge networks and a preset data collection path algorithm, and collecting data in the current time slice according to the shortest data collection path. According to the data collection method for the equipment of the Internet of things, sensing data collection of the edge network of the Internet of things is carried out through dynamic calculation of the shortest data collection path, energy consumption of data collection of the Internet of things is reduced, and efficiency of data collection of the Internet of things is improved.
Description
Technical Field
The application relates to the field of Internet of things, in particular to a method and a device for collecting equipment data of the Internet of things.
Background
With the rapid development of software and hardware technologies in intelligent equipment, the intelligent equipment with sensing, computing and wireless communication capabilities constructs a self-organized network, namely an internet of things, through a wireless communication technology. The internet of things is an emerging and cross subject, and has been widely applied to various fields such as military and industry for environmental monitoring and anomaly detection. Under the traditional internet of things environment, the sensor senses environmental data and uploads the data to the cloud. With the development of the related technology, the large scale and the time-space correlation of the internet of things are gradually highlighted, and the environment of the internet of things needs node movement to realize real-time environment monitoring. The movement of the nodes of the internet of things causes the network topology for collecting data to be changed continuously, and frequent adjustment of the network topology in a large-scale environment of the internet of things causes unnecessary network energy consumption, and energy of the sensor nodes is exhausted prematurely, so that the network is unavailable.
Disclosure of Invention
In view of this, an object of the present application is to provide a method and an apparatus for collecting data of an internet of things device, so as to solve the problem of how to reduce energy consumption for collecting data of the internet of things in the prior art.
In a first aspect, an embodiment of the present application provides an internet of things device data collection method, where the method includes:
acquiring a dependency relationship between the Internet of things equipment and each edge node in the current time slice, and determining the node level of each edge node according to the dependency relationship;
dividing edge networks according to the node level of each edge node and a preset spatial index algorithm to obtain a plurality of single edge networks; the single-edge network comprises a plurality of edge nodes;
and obtaining the shortest data collection path corresponding to the current time slice according to the plurality of single edge networks and a preset data collection path algorithm, and collecting data in the current time slice according to the shortest data collection path.
In some embodiments, the obtaining a dependency relationship between the mobile device and each edge node in the current time slice, and determining a node level of each edge node according to the dependency relationship includes:
determining the dependency relationship between each edge node and the Internet of things equipment managed by the edge node according to the Internet of things equipment under each edge node in the current time slice;
and determining the node level of each edge node according to the dependency relationship and a preset level division rule.
In some embodiments, the edge networks are divided according to the node level of each edge node and a preset spatial index algorithm to obtain a plurality of single edge networks; the single edge network includes a plurality of edge nodes, including:
obtaining the number of the single-edge networks according to the node grade sums of all the edge nodes and the maximum node grade sum which can be accommodated by the single-edge networks in the preset spatial index algorithm;
and dividing the edge networks according to the number of the single-edge networks and a space index algorithm according to a space coordinate division region rule to obtain a plurality of single-edge networks.
In some embodiments, the obtaining, according to the multiple single edge networks and a preset data collection path algorithm, a shortest data collection path corresponding to a current time slice, and collecting data in the current time slice according to the shortest data collection path includes:
randomly selecting one edge node from each single-edge network to be set as a cluster head node; the cluster head node is used for converging the perception data of other edge nodes in the same single edge network;
calculating by taking any cluster head node as an initial collection point according to a preset data collection path algorithm to obtain a shortest data collection path corresponding to the current time slice;
and sequentially collecting the perception data of each single-edge network from each cluster head node according to the shortest data collection path corresponding to the current time slice.
In a second aspect, an embodiment of the present application provides an internet of things device data collection apparatus, including:
the level module is used for acquiring the dependency relationship between the Internet of things equipment and each edge node in the current time slice and determining the node level of each edge node according to the dependency relationship;
the dividing module is used for dividing the edge network according to the node grade of each edge node and a preset spatial index algorithm to obtain a plurality of single edge networks; the single-edge network comprises a plurality of edge nodes;
and the collection module is used for obtaining the shortest data collection path corresponding to the current time slice according to the plurality of single edge networks and a preset data collection path algorithm and collecting data in the current time slice according to the shortest data collection path.
In some embodiments, the ranking module comprises:
the analysis unit is used for determining the dependency relationship between each edge node and the Internet of things equipment managed by the edge node according to the Internet of things equipment under each edge node in the current time slice;
and the grade unit is used for determining the node grade of each edge node according to the dependency relationship and a preset grade division rule.
In some embodiments, the partitioning module comprises:
the computing unit is used for obtaining the number of the single-edge networks according to the node grade sums of all the edge nodes and the maximum node grade sum which can be accommodated by the single-edge networks in the preset spatial index algorithm;
and the dividing unit is used for dividing the edge network according to the number of the single-edge networks and the region division rule according to the space coordinates in the space index algorithm to obtain a plurality of single-edge networks.
In some embodiments, the collection module comprises:
the setting unit is used for randomly selecting one edge node from each single-edge network to be set as a cluster head node; the cluster head node is used for converging the perception data of other edge nodes in the same single edge network;
the path unit is used for calculating by taking any cluster head node as an initial collection point according to a preset data collection path algorithm to obtain a shortest data collection path corresponding to the current time slice;
and the collecting unit is used for sequentially collecting the perception data of each single-edge network from each cluster head node according to the shortest data collecting path corresponding to the current time slice.
In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method in any one of the above first aspects when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs the steps of the method in any one of the above first aspects.
According to the data collection method for the Internet of things equipment, the node level of each edge node is determined according to the dependency relationship between the Internet of things equipment and each edge node in the current time slice, then the edge networks are divided by combining a preset spatial index algorithm to obtain a plurality of single edge networks, finally the shortest data collection path corresponding to the current time slice is calculated according to a preset data collection path algorithm, and data collection of the edge networks is carried out according to the shortest data collection path. According to the method for collecting the data of the equipment in the Internet of things, the sensing data of the edge network of the Internet of things is collected by dynamically calculating the shortest data collecting path, so that the utilization efficiency of a mobile backhaul and a core network is improved, the energy consumption of the data collection of the Internet of things is reduced, and the efficiency of the data collection of the Internet of things is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flow chart of a data collection method for an internet of things device according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a method for calculating a shortest data collection path according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an internet of things device data collection apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a method for collecting data of equipment of the internet of things, which comprises the following steps as shown in fig. 1:
s101, acquiring a dependency relationship between the Internet of things equipment and each edge node in the current time slice, and determining the node level of each edge node according to the dependency relationship;
step S102, dividing edge networks according to the node grade of each edge node and a preset spatial index algorithm to obtain a plurality of single edge networks; the single edge network comprises a plurality of edge nodes;
step S103, obtaining the shortest data collection path corresponding to the current time slice according to the plurality of single edge networks and the preset data collection path algorithm, and collecting data in the current time slice according to the shortest data collection path.
Specifically, in the internet of things, the dependency relationship between the internet of things device and the edge node is dynamically changed, and the internet of things device managed by one edge node in the current time slice may move to the jurisdiction range of other edge nodes in the next time slice, so that the collection of the sensing data of the internet of things device should be dynamic.
The method comprises the steps of obtaining the dependency relationship between each edge node and the Internet of things equipment in the edge network in the current time slice, namely, each edge node and the Internet of things equipment managed by the edge node, and dividing the node grade of the edge node according to the number of the Internet of things equipment managed by each edge node and the number of the Internet of things equipment corresponding to each grade.
Then, the edge network is divided by a preset spatial index algorithm, the preset spatial index algorithm generally selects STR-Tree (Sort-Tile-Recursive Tree, Recursive grid sorting algorithm), the edge network is regarded as a root node, the edge node is regarded as an indexed node, and the division of leaf nodes is performed, and the divided leaf nodes are also single edge networks. The partitioning process is performed according to the node level of each edge node and the maximum node level that each single edge network can accommodate.
And taking each single-edge network as a data collection node, obtaining the shortest data collection path corresponding to the current time slice by a preset data collection path algorithm, collecting data in the current time slice along the path, finishing the current time slice when the node grade in the point edge network exists and exceeds the preset value, creating a new time slice, repeating the steps of node grade division, single-edge network division and shortest data collection path calculation of the edge nodes, and dynamically updating the data collection path.
In some embodiments, the step S101 of obtaining a dependency relationship between the mobile device and each edge node in the current time slice, and determining a node level of each edge node according to the dependency relationship includes:
step 1011, determining a dependency relationship between each edge node and the internet of things equipment managed by the edge node according to the internet of things equipment under each edge node in the current time slice;
and 1012, determining the node level of each edge node according to the dependency relationship and the preset level division rule.
Specifically, the node level of the edge node is related to the number of the internet of things devices managed by the edge node, and the application provides a preset level division rule, which is specifically as follows:
1) the node grade of the edge node is divided into 10 grades;
2) the threshold for each level may be adjusted, for example: the method comprises the following steps that 50 pieces of Internet of things equipment are in the first level, 100 pieces of Internet of things equipment are in the first level and the like;
3) the node level of the edge node corresponding to the maximum number of the managed Internet of things devices with the number exceeding 9 levels is 10 levels.
Frequent jitter of the network topology can be reduced by setting the threshold value of the node level.
The above ranking rule may also be implemented in the form of a dynamic model, for example, at a certain time node, the number of mobile internet of things nodes governed by the edge node ends [ i ] is S [ i ], the ranking threshold is set to T, and ends [ i ]. cl is represented as the ranking value of the edge node ends [ i ]. The calculation model is as follows:
in some embodiments, in step S102, the edge networks are divided according to the node level of each edge node and a preset spatial index algorithm, so as to obtain a plurality of single edge networks; the above-mentioned single edge network includes a plurality of edge nodes, including:
step 1021, obtaining the number of single-edge networks according to the node level sums of all the edge nodes and the maximum node level sum which can be accommodated by the single-edge network in the preset spatial index algorithm;
and 1022, dividing the edge networks according to the number of the single edge networks and the region division rule according to the space coordinates in the space index algorithm to obtain a plurality of single edge networks.
Specifically, in the embodiment of the present application, each edge node is regarded as an indexed node, an edge network is regarded as a root node, a single edge network is regarded as a leaf node, and a two-layer route STR-tree is constructed.
The node grade sums of all the edge nodes are firstly obtained, and then the number of the single-edge networks to be divided is calculated according to the preset maximum node grade sum which can be accommodated by the single-edge network, for example, the node grade sum of all the edge nodes is R, the preset maximum node grade sum which can be accommodated by the single-edge network is 10, and then the number S of the single-edge networks to be divided is equal to (R/10).
When the single-edge network is divided, the network is divided into the single-edge network according to the x-axis coordinate sequence
The regions are then divided into
And in each area, the two divisions are intersected to obtain a single-edge network.
Along with the change of time, due to the movement of the internet of things equipment, the grade of the edge node is changed, and the edge network which is divided in the last time slice may not be adapted to the current network condition, so that the edge network needs to be divided again to adjust the network topology. The STR-Tree query may be applied to determine whether the edge node level in the single edge network exceeds the maximum node level that can be accommodated, and further determine whether the single edge network needs to be re-divided.
In some embodiments, the step S103 of obtaining a shortest data collection path corresponding to the current time slice according to the plurality of single edge networks and a preset data collection path algorithm, and collecting data in the current time slice according to the shortest data collection path includes:
step S201, randomly selecting one edge node from each single edge network to be set as a cluster head node; the cluster head node is used for converging the sensing data of other edge nodes in the same single edge network;
step S202, according to a preset data collection path algorithm, calculating by taking any cluster head node as an initial collection point to obtain a shortest data collection path corresponding to the current time slice;
and step S203, sequentially collecting the sensing data of each single-edge network from each cluster head node according to the shortest data collection path corresponding to the current time slice.
Specifically, after the single-edge networks are divided, one edge node is randomly selected from each single-edge network to serve as a cluster head node, sensing data of other edge nodes in the same single-edge network are gathered to the cluster head node, and the sensing data in the single-edge network can be collected only through the cluster head node. Cluster head node data is collected by moving one mobile edge node among single edge networks, and network energy consumption is saved by planning a data collection path of the mobile edge node.
The mobile edge node must visit each single edge network once, which can be seen as a hamiltonian loop, i.e. the calculation of the shortest data collection path can be modeled as a traveler problem.
Let d (i, V ') represent the shortest path length from the cluster head node i to the cluster head node i, passing through each vertex in V ' once and only once, and finally returning to the cluster head node i, when starting, V ' ═ V- { i }, traverse the set V, and when V is an empty set, the shortest path can be calculated. Wherein, the dynamic programming function of the TSP problem is as follows:
d(i，V′)＝min{cik+d(k，V-{k})}(k∈V′)
d(k，{})＝cki(k≠i)
the embodiment of the application further provides an internet of things device data collection device, which includes:
the level module 30 is configured to obtain a dependency relationship between the internet of things device and each edge node in the current time slice, and determine a node level of each edge node according to the dependency relationship;
a dividing module 31, configured to divide the edge network according to the node level of each edge node and a preset spatial index algorithm, so as to obtain multiple single edge networks; the single edge network comprises a plurality of edge nodes;
and the collecting module 32 is configured to obtain a shortest data collecting path corresponding to the current time slice according to the multiple single edge networks and a preset data collecting path algorithm, and perform data collection according to the shortest data collecting path in the current time slice.
In some embodiments, the ranking module 30 includes:
an analysis unit 301, configured to determine, according to the internet of things device under each edge node in the current time slice, a dependency relationship between each edge node and the internet of things device managed by the edge node;
a level unit 302, configured to determine a node level of each edge node according to the dependency relationship and a preset level division rule.
In some embodiments, the dividing module 31 includes:
a calculating unit 311, configured to obtain the number of single edge networks according to the node level sums of all the edge nodes and the maximum node level sum that can be accommodated by a single edge network in a preset spatial index algorithm;
the dividing unit 312 is configured to divide the edge network according to the number of single edge networks and the region division rule according to the spatial coordinate in the spatial index algorithm, so as to obtain a plurality of single edge networks.
In some embodiments, the collecting module 32 includes:
a setting unit 321, configured to randomly select one edge node from each single edge network to set as a cluster head node; the cluster head node is used for converging the sensing data of other edge nodes in the same single edge network;
a path unit 322, configured to calculate, according to a preset data collection path algorithm, with any cluster head node as an initial collection point, to obtain a shortest data collection path corresponding to a current time slice;
and a collecting unit 323, configured to sequentially collect the sensing data of each single-edge network from each cluster head node according to the shortest data collection path corresponding to the current time slice.
Corresponding to the method for collecting data of the internet of things device in fig. 1, an embodiment of the present application further provides a computer device 400, as shown in fig. 4, the device includes a memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402, where the processor 402 implements the method for collecting data of the internet of things device when executing the computer program.
Specifically, the memory 401 and the processor 402 can be general memories and processors, which are not specifically limited herein, and when the processor 402 runs a computer program stored in the memory 401, the method for collecting data of an internet of things device can be executed, so that the problem of how to reduce energy consumption for collecting data of the internet of things in the prior art is solved.
Corresponding to the method for collecting data of the internet of things device in fig. 1, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for collecting data of the internet of things device are performed.
Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, and the like, and when a computer program on the storage medium is executed, the method for collecting data of the internet of things can be executed, so that a problem of how to reduce energy consumption for collecting data of the internet of things in the prior art is solved. According to the method for collecting the data of the equipment in the Internet of things, the sensing data of the edge network of the Internet of things is collected by dynamically calculating the shortest data collecting path, so that the utilization efficiency of a mobile backhaul and a core network is improved, the energy consumption of the data collection of the Internet of things is reduced, and the efficiency of the data collection of the Internet of things is improved.
In the embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An Internet of things equipment data collection method is characterized by comprising the following steps:
acquiring a dependency relationship between the Internet of things equipment and each edge node in the current time slice, and determining the node level of each edge node according to the dependency relationship;
dividing edge networks according to the node level of each edge node and a preset spatial index algorithm to obtain a plurality of single edge networks; the single-edge network comprises a plurality of edge nodes;
and obtaining the shortest data collection path corresponding to the current time slice according to the plurality of single edge networks and a preset data collection path algorithm, and collecting data in the current time slice according to the shortest data collection path.
2. The method of claim 1, wherein the obtaining a dependency relationship between the mobile device and each edge node within the current time slice and determining a node level of each edge node according to the dependency relationship comprises:
determining the dependency relationship between each edge node and the Internet of things equipment managed by the edge node according to the Internet of things equipment under each edge node in the current time slice;
and determining the node level of each edge node according to the dependency relationship and a preset level division rule.
3. The method according to claim 1, wherein the dividing of the edge network is performed according to the node level of each edge node and a preset spatial index algorithm to obtain a plurality of single edge networks; the single edge network includes a plurality of edge nodes, including:
obtaining the number of the single-edge networks according to the node grade sums of all the edge nodes and the maximum node grade sum which can be accommodated by the single-edge networks in the preset spatial index algorithm;
and dividing the edge networks according to the number of the single-edge networks and a space index algorithm according to a space coordinate division region rule to obtain a plurality of single-edge networks.
4. The method of claim 1, wherein the obtaining a shortest data collection path corresponding to a current time slice according to the plurality of single edge networks and a preset data collection path algorithm, and performing data collection according to the shortest data collection path in the current time slice comprises:
randomly selecting one edge node from each single-edge network to be set as a cluster head node; the cluster head node is used for converging the perception data of other edge nodes in the same single edge network;
calculating by taking any cluster head node as an initial collection point according to a preset data collection path algorithm to obtain a shortest data collection path corresponding to the current time slice;
and sequentially collecting the perception data of each single-edge network from each cluster head node according to the shortest data collection path corresponding to the current time slice.
5. An internet of things equipment data collection device, comprising:
the level module is used for acquiring the dependency relationship between the Internet of things equipment and each edge node in the current time slice and determining the node level of each edge node according to the dependency relationship;
the dividing module is used for dividing the edge network according to the node grade of each edge node and a preset spatial index algorithm to obtain a plurality of single edge networks; the single-edge network comprises a plurality of edge nodes;
and the collection module is used for obtaining the shortest data collection path corresponding to the current time slice according to the plurality of single edge networks and a preset data collection path algorithm and collecting data in the current time slice according to the shortest data collection path.
6. The apparatus of claim 5, wherein the ranking module comprises:
the analysis unit is used for determining the dependency relationship between each edge node and the Internet of things equipment managed by the edge node according to the Internet of things equipment under each edge node in the current time slice;
and the grade unit is used for determining the node grade of each edge node according to the dependency relationship and a preset grade division rule.
7. The apparatus of claim 5, wherein the partitioning module comprises:
the computing unit is used for obtaining the number of the single-edge networks according to the node grade sums of all the edge nodes and the maximum node grade sum which can be accommodated by the single-edge networks in the preset spatial index algorithm;
and the dividing unit is used for dividing the edge network according to the number of the single-edge networks and the region division rule according to the space coordinates in the space index algorithm to obtain a plurality of single-edge networks.
8. The apparatus of claim 5, wherein the collection module comprises:
the setting unit is used for randomly selecting one edge node from each single-edge network to be set as a cluster head node; the cluster head node is used for converging the perception data of other edge nodes in the same single edge network;
the path unit is used for calculating by taking any cluster head node as an initial collection point according to a preset data collection path algorithm to obtain a shortest data collection path corresponding to the current time slice;
and the collecting unit is used for sequentially collecting the perception data of each single-edge network from each cluster head node according to the shortest data collecting path corresponding to the current time slice.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of the preceding claims 1-4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method of any one of the preceding claims 1 to 4.
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