Perception resource adjustment method, communication system, device and storage medium

Abstract

The present application provides a perception resource adjustment method, communication system, device and storage medium. First, based on the first perception data from the first perception node and the second perception data from the second perception node, the perception range overlap area of ​​the first target perceived by the first perception node and the second perception node is determined. Subsequently, based on the perception range overlap area, the first perception coverage area of ​​the first perception node and the second perception coverage area of ​​the second perception node, the target perception node is determined in the first perception node and the second perception node. After determining the target perception node, the perception resources of the target perception node are adjusted. The embodiment of the present application can improve the resource utilization of the perception node by dynamically adjusting the perception resources of the perception node.

Description

Technical Field

[0001] The embodiments of the present application relate to, but are not limited to, the field of communication technology, and in particular to a perception resource adjustment method, communication system, device, and storage medium. Background Art

[0002] In the current integrated network architecture of telepresence and computing, the initial network planning process also faces the problems of site addition and deletion and changes in the deployment topology architecture. In actual deployment, the optional installation locations of a site are generally limited, resulting in site spacing and density varying with the specific deployment area. In addition, the coverage capability of a single perception node is generally preset during network planning. Therefore, when the actual network is running after planning is completed, the actual coverage and perception resource allocation are very likely to be suboptimal, which may also lead to waste of perception resources and increased interference. Summary of the Invention

[0003] The embodiments of the present application provide a perception resource adjustment method, device, and storage medium, which can dynamically adjust the perception resources of a perception node, thereby improving the resource utilization of the perception node.

[0004] On the one hand, an embodiment of the present application provides a perception resource adjustment method, the method comprising: determining, based on first perception data from a first perception node and second perception data from a second perception node, a perception range overlapping area where a first target commonly perceived by the first perception node and the second perception node is located; determining a target perception node among the first perception node and the second perception node based on the perception range overlapping area, the first perception coverage area of ​​the first perception node, and the second perception coverage area of ​​the second perception node; and adjusting the perception resources of the target perception node.

[0005] On the other hand, an embodiment of the present application also provides a communication system, including a first computing node, a first perception node, and a second perception node; the first computing node determines the perception range overlapping area of ​​the first target commonly perceived by the first perception node and the second perception node based on the first perception data from the first perception node and the second perception data from the second perception node; the first computing node determines the target perception node among the first perception node and the second perception node based on the perception range overlapping area, the first perception coverage area of ​​the first perception node, and the second perception coverage area of ​​the second perception node; the first computing node adjusts the perception resources of the target perception node.

[0006] On the other hand, an embodiment of the present application also provides a communication device, comprising: at least one processor; at least one memory for storing at least one program; at least one of the programs is run by at least one of the processors to implement the above-mentioned perception resource adjustment method when executed.

[0007] On the other hand, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the above-described perception resource adjustment method.

[0008] On the other hand, an embodiment of the present application also provides a computer program product, including a computer program or computer instructions, wherein the computer program or the computer instructions are stored in a computer-readable storage medium, the processor of the device reads the computer program or the computer instructions from the computer-readable storage medium, and the processor executes the computer program or the computer instructions, so that the device performs the perceptual resource adjustment method as described above.

[0009] In an embodiment of the present application, first, based on the first perception data from the first perception node and the second perception data from the second perception node, the perception range overlapping area where the first target perceived by the first perception node and the second perception node is located is determined. Subsequently, based on the perception range overlapping area, the first perception coverage area of ​​the first perception node and the second perception coverage area of ​​the second perception node, the target perception node is determined in the first perception node and the second perception node. After determining the target perception node, the perception resources of the target perception node are adjusted. The embodiment of the present application can determine which perception node (i.e., the target perception node) is more suitable for undertaking a specific perception task by identifying the perception range overlapping area where the target perceived by the first perception node and the second perception node is located, effectively solving the problems of poor resource allocation, waste of resources, and increased interference faced in the current integrated network architecture of tele-sensing and computing, and ensuring that the resources of each perception node are efficiently utilized. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a diagram of the synaesthesia integration network architecture provided by an embodiment of the present application;

[0011] Figure 2 This is a flowchart of a method for adjusting perception resources provided by an embodiment of the present application;

[0012] Figure 3 This is a schematic diagram of computing node distribution provided by an embodiment of the present application;

[0013] Figure 4 This is a schematic diagram of the overlapping area of ​​the perception range at a top-down angle provided by a specific example of this application;

[0014] Figure 5 This embodiment of the present application provides Figure 2 Specific flow chart of step S210;

[0015] Figure 6 This embodiment of the present application provides Figure 2 Specific flow chart of step S220;

[0016] Figure 7 This embodiment of the present application provides Figure 2 Specific flow chart of step S230;

[0017] Figure 8 Another embodiment of the present application provides Figure 2 Specific flow chart of step S230;

[0018] Figure 9 This is a schematic diagram of a single computing node scenario provided by a specific example of this application;

[0019] Figure 10 This is a schematic diagram of a single computing node scenario provided by another specific example of this application;

[0020] Figure 11 This is a schematic diagram of a two-computing node scenario provided by a specific example of this application;

[0021] Figure 12 This is a schematic diagram of a two-computing node scenario provided by another specific example of this application;

[0022] Figure 13 This is a schematic diagram of a communication system architecture provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical methods and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0024] It should be noted that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different from that in the flowchart. In the description of the specification, claims and the above-mentioned drawings, the meaning of multiple (or multiple) is more than two, greater than, less than, exceed, etc. are understood to exclude the number itself, and above, below, within, etc. are understood to include the number itself. If there is a description of "first", "second", etc., it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0025] In the current integrated network architecture of telepresence and computing, sensor nodes not only perform communication tasks but also need to perform environmental perception to support diverse application scenarios such as smart transportation, smart cities, and the Internet of Things. This dual functionality places higher demands on the allocation and management of network resources. However, network planning and deployment present numerous challenges. First, the addition and deletion of sites, as well as changes in the deployment topology, are inevitable. As the network evolves, new sensor nodes may need to be added, while existing nodes may be removed due to technology upgrades or changing business needs. This dynamic change requires the network to be highly flexible and scalable to accommodate the constant adjustment of the number and location of sites. Second, in actual deployments, the choice of sensor node installation locations is limited. Due to geographical, environmental, or infrastructure constraints, each site typically has a limited number of possible locations. This results in significant variations in site spacing and density across different deployment areas, which in turn affects network coverage and perception performance. Furthermore, the coverage capabilities of individual sensor nodes assumed during network planning often deviate from actual operational requirements. Due to the complexity and diversity of network environments, the actual coverage may not match the initial planning expectations. Furthermore, the allocation of sensing resources may require dynamic adjustment due to changes in factors such as network load and the type and number of sensing targets. To address these issues, existing network planning methods often struggle to achieve optimal sensing resource allocation. In actual network operation, sensing resources may be over-concentrated or wasted, leading to decreased network performance and increased interference. Therefore, dynamically adjusting the sensing resources of sensing nodes to achieve optimal overall network performance has become a key issue that needs to be addressed in current integrated sensing and computing network architectures.

[0026] In order to be able to effectively reduce the complexity of collaborative processing between multiple computing nodes, efficiently integrate and process data, embodiments of the present application provide a kind of perception resource adjustment method, communication system, communication equipment, computer-readable storage medium and computer program product, first, according to the first perception data from the first perception node and the second perception data from the second perception node, determine the perception range overlap area where the first target perceived by the first perception node and the second perception node are located. Subsequently, according to the perception range overlap area, the first perception coverage area of ​​the first perception node and the second perception coverage area of ​​the second perception node, determine the target perception node in the first perception node and the second perception node. After determining the target perception node, adjust the perception resource of the target perception node. Embodiments of the present application, by identifying the perception range overlap area where the target perceived by the first perception node and the second perception node are located, can determine which perception node (i.e., target perception node) is more suitable for taking on a specific perception task, effectively solve the problems such as poor resource allocation, waste of resources and increased interference faced in the current integrated network architecture of communication and computing, and ensure that the resources of each perception node are efficiently utilized.

[0027] Based on the above analysis, the embodiments of the present application will be further described below in conjunction with the accompanying drawings.

[0028] See also Figure 1 , Figure 1 This is a diagram of the synaesthesia integrated network architecture provided by the embodiment of the present application. In the synaesthesia and computing integrated network architecture, the core network domain is mainly composed of perception network elements and communication network elements, which jointly support core functions such as communication mobility management, perception management and control. Specifically, as a key component of the communication network, the communication network element undertakes core responsibilities such as data transmission, signaling processing and network management, ensuring efficient processing and forwarding of data traffic, while supporting various signaling processes and network management functions to ensure the smooth operation of the communication network. The perception network element plays an important role in the synaesthesia network. It is responsible for various functions closely related to perception, such as perception authorization, capability interaction, network element selection, control and data processing. On the other hand, on the access network side, the synaesthesia and computing network integrates the three major functions of communication, perception and computing. As Figure 1As shown, perception nodes refer to those devices, modules or components that have both communication and perception capabilities, which can transmit and receive data, communication signals, perception signals and control information, etc. The computing nodes are nodes that have the ability to process communication or perception information and can provide computing resources. A computing node can be connected to multiple perception nodes. It is worth noting that the embodiments of the present application do not limit the number of perception nodes connected to the computing nodes and their specific deployment methods. The specific number and their specific deployment methods can be set according to actual needs. In addition, it should be noted that the functional entities of the communication network elements and the perception network elements also have the ability to sink to the access network, and the embodiments of the present application do not impose any special restrictions on this.

[0029] Reference Figure 2 , Figure 2 This is a flow chart of a method for adjusting sensing resources provided by an embodiment of the present application. The method includes but is not limited to steps S210 to S230.

[0030] Step S210: determining, based on the first perception data from the first perception node and the second perception data from the second perception node, an overlapping area of ​​the perception ranges of the first target perceived by both the first perception node and the second perception node;

[0031] Step S220: determining a target sensing node from the first sensing node and the second sensing node according to the overlapping sensing range area, the first sensing coverage area of ​​the first sensing node, and the second sensing coverage area of ​​the second sensing node;

[0032] Step S230: Adjust the sensing resources of the target sensing node.

[0033] For example, a sensing node (such as a first sensing node) generally refers to a device or system in the Internet of Things (IoT) used to sense and collect information about the physical world. They are the fundamental units in the IoT architecture, responsible for interacting with the physical world, collecting data, and transmitting it to the network for further processing and analysis. Sensing nodes typically integrate a variety of sensors to detect environmental parameters such as temperature, humidity, light, sound, and pressure. These nodes can collect sensor data in real time and convert it into digital signals. Furthermore, sensing nodes typically have wireless communication capabilities, such as Wi-Fi and Bluetooth, for transmitting data to other nodes or the cloud. In a tele-sensing and computing integrated network, sensing nodes combine communication and sensing functions, integrating a wide range of devices, modules, and components. They not only efficiently transmit and receive data, but also process communication signals, capture sensing signals, and effectively convey control commands. Typically, these nodes are deployed on the access network side and include base stations, relay stations, and terminal devices with communication and sensing capabilities. Within a specific sensing range, sensing nodes can accurately monitor and identify various target objects or phenomena. In addition, the perception data (such as the first perception data and the second perception data) is a set of information obtained after the perception node performs a perception activity on the target within its perception range. The specific target information of the information content not only covers the basic serial number and unique identifier of the target, the precise latitude and longitude coordinates, the relative distance between the target, the speed, the acceleration and other dynamic parameters, but also includes spatial positioning information such as azimuth and pitch angle, as well as key features such as the type classification and signal strength of the target. On the other hand, the computing node is the core component in the network responsible for processing communication or perception information and providing computing resources. These nodes may appear in the form of servers, or dedicated computing devices such as computing boards, or even base stations, relay stations or terminal devices that integrate computing power and resources. In the deployment of the integrated network architecture of synaesthesia and computing, the perception nodes and computing nodes are usually configured at the access network level. It is worth noting that if the perception node has a certain computing power, it can also play the role of a computing node. Of course, the implementation of the computing node is not limited to the integration with the perception node. They can also exist by independently deploying computing power resources. The embodiments of the present application do not specifically limit this type of deployment method.

[0034] For example, each sensing node can be connected to a computing node, which can perform collaborative networking and fusion processing on the sensory data collected by one or more associated sensing nodes, and report the integrated data to the sensing network element. Network fusion refers to the overall process of creating a seamless target perception result within the network coverage area through target association and information fusion when building a synaesthesia network. Target association refers to the matching and association of targets captured by different sensing nodes. A target generally refers to any perceptible entity, such as a person or object captured by a camera, an object scanned by a radar, or any entity identified by other devices. Taking two sensing nodes (e.g., base station 1 and base station 2) as an example, if each perceives a target, base station 1 and base station 2 will compare and associate their respective perceived targets to determine whether they perceive the same target. Once confirmed, the target is associated. Information fusion refers to the merging and unification of information on the same target based on target association. For example, if the coordinates of a target relative to base station 1 are (x1, y1, z1) and the coordinates relative to base station 2 are (x2, y2, z2), after information fusion, the unified coordinates of the target relative to base station 1 and base station 2 will become (x3, y3, z3).

[0035] It should be noted that the connection architecture between computing nodes and sensing nodes is highly flexible and diverse. They can support multiple connection modes, including but not limited to Figure 1 The architecture of multiple sensing nodes and one computing node shown also supports a one-to-one connection architecture (i.e., one sensing node corresponds to one computing node). Furthermore, it can also adapt to more complex scenarios, such as an architecture where multiple sensing nodes are interconnected with multiple computing nodes, and these computing nodes can be further aggregated into a unified computing node (similar to the concept of a master node). In addition, the coverage areas of computing nodes can overlap, and the coverage areas of sensing nodes can also overlap.

[0036] For example, in an integrated network of synaesthesia and computing, specific computing nodes are selected as central nodes, while other computing nodes surrounding these central nodes are non-central nodes. Such a layout optimizes the network architecture, thereby ensuring efficient flow and processing of data. For example, the specific process of selecting a computing node as a central node is as follows: First, starting from the outermost edge of the entire network, select those nodes surrounded by other computing nodes and designate them as central nodes of a specific area. At the same time, other nodes around these central nodes are marked as non-central nodes accordingly. In order to ensure the consistency of network connections and smooth data flow, a stable interconnection link will be established between the central node and the non-central node. This marking and connection process will continue until all computing nodes in the network are clearly classified and connected. Furthermore, if the perception node to which a computing node is connected is remote (i.e., far away), then the computing node will also be specially identified as a non-central node corresponding to the remote area. In Figure 3 In the example, computing nodes 1, 2, 3, and 4 are selected and marked central nodes, while the remaining nodes are considered non-central nodes.

[0037] Exemplarily, a first computing node may be connected to at least one second computing node, wherein the first computing node is selected as a central node and the second computing node is selected as a non-central node. The first computing node may establish connections with multiple first sensing nodes and receive sensing data (e.g., first sensing data) from the first sensing nodes; each second computing node may establish connections with multiple second sensing nodes, receive sensing data (e.g., second sensing data) transmitted by these subordinate sensing nodes, and then send this data to the first computing node, so that the first computing node can execute the sensing resource adjustment process from steps S210 to S230.

[0038] For example, for each computing node marked as a central node (hereinafter referred to as the central computing node, such as the first computing node), at a certain periodic time point (for example, a period of 60 seconds), all computing nodes marked as non-central nodes (hereinafter referred to as non-central computing nodes, such as the second computing node) can send the latest non-network-fused perception data to the central node to which they are connected. The perception data contains specific target information, including but not limited to the target sequence number and identification, longitude and latitude information, distance, speed, acceleration, azimuth, pitch angle, target type, target signal strength, etc.

[0039] For example, after receiving the perception data from the non-central computing nodes, the central computing node can associate the perception targets that have not been networked and integrated, which it has recently received from each perception node, with the target set consisting of the perception targets reported by other non-central computing nodes in the period, to determine whether the perception targets belonging to different perception nodes can be determined as the same target, and further integrate the same targets to obtain the integrated perception target. For example, Figure 4 The figure shows the fusion status on a horizontal plane at the same height. Computational Node 1 is the central node, while Computational Nodes 2 and 3 are non-central nodes. Each circular area is divided into three equal sectors with an angle of 120 degrees, each corresponding to the sensing coverage area of ​​a sensing node. Black dots represent sensing targets that have been successfully associated and fused.

[0040] For example, the central computing node can maintain two storage spaces, one for storing the raw perception data received above, and the other for storing all the data that are periodically associated and fused. Once the storage space reaches the capacity limit, the data records with the longest storage time can be automatically removed to ensure the effective use of storage resources.

[0041] For example, after the central computing node receives the first perception data and the second perception data, since these perception data include the target's geographic location (latitude and longitude), distance measurement, speed value, acceleration value, azimuth angle, pitch angle and other physical parameters, the central computing node can further determine the overlapping area of ​​the perception range of the first target perceived by the first perception node and the second perception node. Figure 5 As shown, the specific process of step S210 includes but is not limited to step S510 and step S520.

[0042] Step S510: Based on the first perception data from the first perception node and the second perception data from the at least one second perception node, perform target fusion on the perception target corresponding to the first perception data and the perception target corresponding to the at least one second perception data to obtain a first target commonly perceived by the first perception node and the at least one second perception node;

[0043] Step S520: Determine the perception range overlapping area where the first target is located.

[0044] Exemplarily, target matching and fusion operations are performed based on the first perception data received from the first perception node and the second perception data received from the second perception node. This process aims to compare and integrate the corresponding perception targets in the two sets of data, and ultimately confirm the first target observed jointly by the first perception node and the second perception node. After successfully fusing the two sets of perception data and identifying the jointly perceived first target, by further analyzing the target location information (such as longitude and latitude, distance, azimuth, etc.) provided by the first perception data and the second perception data, the overlapping area where the first target is located, formed by the intersection of the perception ranges of the two perception nodes, can be determined.

[0045] For example, when executing step S510, a correlation judgment process can be performed on the perception targets corresponding to the two sets of data based on the first perception data provided by the first perception node and the second perception data provided by the second perception node, to obtain a preliminary judgment result, i.e., a first judgment result. Subsequently, based on this first judgment result, it is determined whether the first perception target and the second perception target need to be fused. If the first judgment result clearly indicates that the two perception targets actually point to the same perception target, the first perception target and the second perception target can be fused into a unified perception target, i.e., the first target.

[0046] Exemplarily, correlation judgment is a key step in comparing and analyzing the relationship between the first perception target corresponding to the first perception data and the second perception target corresponding to the second perception data. This step aims to determine whether the two perception targets point to the same entity. When performing correlation judgment processing, direct comparison method and feature matching method can be used. Specifically, the direct comparison method involves directly comparing the two sets of perception data. The implementation steps of this method include: first preprocessing the two sets of perception data to ensure that the format, unit and precision of the two sets of data are consistent so that direct comparison can be performed; then, the values ​​of the two sets of data are compared one by one to calculate the difference; then, based on the preset threshold and difference, the correlation between the two sets of data is judged. For example, if the difference is less than a certain threshold, the two perception targets are considered to be correlated. On the other hand, the feature matching method involves extracting features of the two sets of perception data and matching based on these features. Features can be time features of the data (such as periodicity), etc. By comparing the features of the two sets of data, it can be determined whether they point to the same perception target. The implementation steps of this method include: first extracting features from the two sets of perception data to extract representative features from the two sets of data; then using an appropriate matching algorithm (such as distance measurement, similarity calculation, classification algorithm, etc.) to compare the features of the two sets of data; and then judging the correlation between the two sets of data based on the matching results. If the feature matching degree is high, it is considered that the two perception targets are correlated. It should be noted that in actual applications, the choice of correlation judgment method depends on the nature, format, accuracy of the data, and the required judgment accuracy and efficiency. It is necessary to combine multiple methods for comprehensive judgment, and this application does not impose any restrictions on this.

[0047] Exemplarily, these perception targets that have undergone target fusion, the perception targets corresponding to the first perception data, and the perception targets corresponding to the second perception data can all be stored in the central computing node. For the data set composed of all perception targets that have been successfully fused in the storage space of the central computing node, the Alpha Shape algorithm (Alpha Shape) or the Beta Shape algorithm (Beta Shape) can be used to construct a polyhedron consisting of overlapping areas (i.e., repeated coverage areas) based on the latitude and longitude information and altitude information of the target, and recorded as a fused overlapping area. It is worth noting that in the process of constructing a polyhedron using the Alpha Shape algorithm, in order to obtain a suitable polyhedron, the size of the alpha value can be adjusted according to the density of the target points, such as setting it to a value close to the average distance between adjacent target points. Figure 4 As shown, the polygonal area A formed by multiple straight lines can be regarded as a cross section of a polyhedron.

[0048] For example, in the process of determining the overlapping area of ​​the perception range where the first target is located, the overlapping area of ​​the perception range of the first perception node and the second perception node can be determined first. It is worth noting that after step S510, the first target has been accurately positioned in this overlapping area. Subsequently, based on the specific location data of the first target, the perception range actually occupied by the first target, that is, the specific overlapping area of ​​the perception range, can be further determined in the identified overlapping area. For the preliminary steps of this process, the respective perception coverage areas of the first perception node and the second perception node can be clarified first, and then the common coverage parts between them can be identified on this basis. For example, for the original perception targets of all perception nodes (such as the perception targets corresponding to the first perception data and the perception targets corresponding to the second perception data), a polyhedron can be constructed according to the above-mentioned Alpha shape or Beta shape algorithm to determine the perception coverage area of ​​each perception node. For example, in Figure 4 In the figure, each different fan-shaped area represents the sensing coverage area of ​​a sensing node, and the angle of the sensing coverage area of ​​each sensing node is 120 degrees.

[0049] For example, after determining the overlapping area of ​​the perception range of the first target, the target perception node can be further determined. Figure 6 As shown, the specific process of step S220 includes but is not limited to steps S610 to S630.

[0050] Step S610: determining a spatial overlap coverage coefficient based on the overlapping area of ​​the sensing range and the first sensing coverage area of ​​the first sensing node to obtain a first spatial overlap coverage coefficient of the first sensing node;

[0051] Step S620: determining a spatial overlap coverage coefficient based on the overlapping area of ​​the sensing range and the second sensing coverage area of ​​the second sensing node to obtain a second spatial overlap coverage coefficient of the second sensing node;

[0052] Step S630: Determine a target sensing node from the first sensing node and the second sensing node according to the first spatial overlap coverage coefficient and the second spatial overlap coverage coefficient.

[0053] Exemplarily, the determination process of the spatial overlapping coverage coefficient includes the following steps: first determine the overlapping coverage area between the first area and the second area, wherein the first area is the perception range overlapping area, and the second area is the first perception coverage area of ​​the first perception node or the second perception coverage area of ​​the second perception node; then determine the target spatial overlapping coverage coefficient based on the overlapping coverage area and the second area, wherein when the second area is the first perception coverage area of ​​the first perception node, the target spatial overlapping coverage coefficient is the first spatial overlapping coverage coefficient; when the second area is the second perception coverage area of ​​the second perception node, the target spatial overlapping coverage coefficient is the second spatial overlapping coverage coefficient.

[0054] For example, in determining the first spatial overlap coverage coefficient of the first sensing node, the overlapping coverage area between the sensing range overlap area and the first sensing coverage area of ​​the first sensing node can be determined. Then, based on the determined overlapping coverage area and the first sensing coverage area, the first spatial overlap coverage coefficient of the first sensing node can be further calculated.

[0055] Exemplarily, the calculation process of the first spatial overlap coverage coefficient is as follows: after determining the overlapping area of ​​the perception range where the first target is located, the volume of the overlapping part of the overlapping area and the perception coverage area of ​​the first perception node (hereinafter referred to as the volume of the first overlapping coverage area) can be calculated first, and then the total volume of the coverage area of ​​each perception node can be calculated. On this basis, the volume of the first overlapping coverage area can be compared with the total volume of the coverage area of ​​the perception node to obtain the spatial overlap coverage coefficient. For example, assuming that the volume of the first overlapping coverage area is V_over l ap and the total volume of the coverage area of ​​the first perception node is V_total, the first spatial overlap coverage coefficient (denoted as C_over l ap) can be calculated as: C_over l ap = V_over l ap / V_total. It should be noted that the calculation process of the second spatial overlap coverage coefficient is essentially the same as the calculation process of the first spatial overlap coverage coefficient. To avoid repetition, it will not be elaborated on here.

[0056] Exemplarily, after determining the first spatial overlap coverage coefficient and the second spatial overlap coverage coefficient, the target perception node can be determined according to the following logic: if the first spatial overlap coverage coefficient is greater than a preset coefficient threshold, the first perception node is determined as the target perception node; if the second spatial overlap coverage coefficient is greater than the preset coefficient threshold, the second perception node is determined as the target perception node; if both the first spatial overlap coverage coefficient and the second spatial overlap coverage coefficient are greater than the preset coefficient threshold, then both the first perception node and the second perception node are regarded as target perception nodes.

[0057] For example, when the target sensing node is determined to be the first sensing node, the specific process of adjusting the sensing resources of the target sensing node in step S230 is as follows: Figure 7 As shown, including but not limited to steps S710 to S730.

[0058] Step S710: Determine a first angle range of a first overlapping coverage area of ​​a first sensing node;

[0059] Step S720: determining a target sensing resource adjustment strategy according to the sensing resources covered by the first sensing node within the first angle range;

[0060] Step S730: Adjust the sensing resources of the first sensing node according to the target sensing resource adjustment strategy.

[0061] For example, when adjusting their target sensing resources, sensing nodes can adopt various strategies to adapt to different environments and needs. These strategies include, but are not limited to: increasing the sensing beam width and decreasing the sensing beam gain; reducing the sensing beam transmit power; reducing the number of repeated symbols in the transmitted signal and reducing the sensing gain in the Doppler dimension; and reducing the sensing sequence length. Specifically, increasing the sensing beam width means that the node's sensing range becomes wider, covering more potential targets. Reducing the sensing beam gain generally means weakening the signal strength. By reducing the gain, excessively strong signals can be prevented from interfering with other devices or causing unnecessary energy waste. Reducing the sensing beam transmit power can reduce node energy consumption and extend its service life. Furthermore, reducing power can reduce interference with other communication devices and improve overall network stability. Reducing the number of repeated symbols in the transmitted signal can shorten signal transmission time and improve communication efficiency. The Doppler effect is a change in signal frequency caused by the relative motion between the target and the node. Reducing the sensing gain in the Doppler dimension can be beneficial in certain static or low-speed motion scenarios. A sensing sequence is a signal pattern used to detect targets and extract information. Reducing the length of the sensing sequence can shorten the node's response time and improve its real-time performance. It should be noted that when adjusting its target sensing resources, sensing nodes can select appropriate strategies based on specific application scenarios and requirements. These strategies can be used individually or in combination to achieve the best balance between sensing performance and energy consumption.

[0062] For example, when the spatial overlap coverage coefficient of a certain sensing node exceeds a preset threshold (such as 0.5), it indicates that the coverage range of the node overlaps too much with other nodes, which may lead to resource waste and a decrease in overall perception efficiency. In order to optimize resource utilization and improve perception performance, the following steps can be taken for adjustment: first, determine the overlapping area in the node coverage range, and then determine the angle range to which the area belongs based on the specific location of the overlapping area. For the determined overlapping angle range, a variety of adjustment strategies can be formulated, such as adjusting the beam width, beam pointing, transmit power, number of repeated symbols, perception sequence length, etc. The main purpose of the adjustment is to reduce the coverage of the node to the coverage range in order to make full use of resources and improve overall perception efficiency and performance. The adjustment principle is that after the resources are adjusted, the expected perception coverage range can be obtained through theoretical calculation according to the RCS (radar cross-section) of the weak target, and the new coverage range is required to have no perception holes.

[0063] For example, in step S720, after determining the angular range of the overlapping area, an appropriate adjustment strategy can be formulated based on the sensing resource availability in that area. This includes, but is not limited to: adjusting beam width: By changing the beam width, coverage of the overlapping area can be reduced or increased. Adjusting beam pointing: By fine-tuning the beam pointing, coverage focus can be shifted to other non-overlapping areas. Adjusting transmit power: Reducing transmit power in the overlapping area to reduce interference and energy consumption. Optimizing sensing sequences: By changing the length or content of sensing sequences, sensing performance can be optimized. It is worth noting that when formulating the adjustment strategy, multiple factors must be considered, such as the node's computing power, storage limitations, energy supply, and overall network requirements. At the same time, it is necessary to ensure that the adjusted coverage area covers important targets and avoids sensing holes. Furthermore, after determining the target sensing resource adjustment strategy, the sensing resources of the first sensing node can be adjusted. This includes modifying the node's configuration parameters, such as beam width and beam pointing, to implement the formulated strategy. Furthermore, during the adjustment process, changes in node performance and coverage can be monitored to ensure the effectiveness of the adjustment.

[0064] For example, after adjusting the sensing resources of a first sensing node, its sensing coverage area can be further re-determined, and the spatial overlap coverage coefficient can be recalculated based on the new coverage area to ensure the effectiveness of the adjustment. This specific process includes: first, re-determining the first sensing coverage area of ​​the first sensing node after the sensing resource adjustment; then, based on the re-determined first sensing coverage area, re-determining the first spatial overlap coverage coefficient of the first sensing node until the re-determined first spatial overlap coverage coefficient is less than or equal to a preset coefficient threshold. Specifically, according to a defined target sensing resource adjustment policy, the sensing resources of the first sensing node, such as beam width, beam pointing, and transmit power, are adjusted. After adjusting the sensing resources, the first sensing coverage area of ​​the first sensing node is re-determined. This step typically involves re-measuring and recalculating parameters such as node location, antenna pattern, and beam width. Within the re-determined sensing coverage area, sensing target points that are not within the new coverage range are removed. These target points may no longer be covered due to the sensing resource adjustment. The first spatial overlap coverage coefficient of the first sensing node is recalculated based on the removed sensing target points and the re-determined sensing coverage area. If the re-determined spatial overlap coverage coefficient is still greater than the preset threshold, the adjustment process continues iteratively. Based on the new spatial overlap coverage coefficient and the sensing coverage area, a new target sensing resource adjustment strategy is formulated and sensing resources are adjusted until the re-determined spatial overlap coverage coefficient is less than or equal to the preset threshold, or the number of iterations exceeds the specified number. If the re-determined spatial overlap coverage coefficient is less than or equal to the preset threshold, the adjustment is deemed valid and the iteration process for the next sensing node is continued.

[0065] Exemplarily, the specific process of determining the second spatial overlapping coverage coefficient of the second perception node in step S620 includes: first determining the overlapping coverage area between the overlapping area of ​​the perception range of the first target and the second perception coverage area of ​​the second perception node; then determining the second spatial overlapping coverage coefficient of the second perception node based on the overlapping coverage area and the second perception coverage area. It should be noted that although the process of determining the second spatial overlapping coverage coefficient of the second perception node is different from the process of determining the first spatial overlapping coverage coefficient of the first perception node for different nodes, the two are consistent in core logic. In view of the fact that similar processes have been explained in detail before, in order to avoid unnecessary repetition, the process will not be repeated here.

[0066] See also Figure 8 When the target perception node is a second perception node and the second perception node is connected to a second computing node (non-central computing node), in step S230, the specific process of the central computing node adjusting the perception resources of the target perception node may also include but is not limited to steps S810 to S830.

[0067] Step S810: Determine a second angle range of a second overlapping coverage area of ​​a second sensing node;

[0068] Step S820: determining a target sensing resource adjustment strategy according to the sensing resources of the second sensing node covering the second angle range;

[0069] Step S830: Send the target sensing resource adjustment policy to the second computing node, so that the second computing node adjusts the sensing resources of the second sensing node according to the target sensing resource adjustment policy.

[0070] For example, step S810 is to identify and define the angular range covered by the second overlapping coverage area of ​​the second sensing node. This range is generally determined based on parameters such as the physical location, antenna pattern, beamwidth, and overlap with the sensing range of other sensing nodes or targets of the second sensing node.

[0071] Exemplarily, after executing step S830, the second perception coverage area of ​​the second perception node after the perception resource adjustment can be further re-determined; then, based on the re-determined second perception coverage area, the second spatial overlap coverage coefficient of the second perception node is re-determined until the re-determined second spatial overlap coverage coefficient is less than or equal to the preset coefficient threshold.

[0072] It should be noted that although the specific process of steps S810 to S830 and the specific process of steps S710 to S730 target different nodes, the core logic of the two is consistent. Since similar processes have been described in detail before, the specific process of steps S810 to S830 will not be repeated here to avoid unnecessary repetition.

[0073] It should be noted that after completing the above sensor resource adjustment, the first computing node, marked as the central node, can perform corresponding adjustments to its connected sensor nodes according to the determined adjustment strategy and send the adjustment strategies of other sensor nodes to their respective connected computing nodes, which then execute the adjustment strategies. It is worth noting that all relevant sensor resource adjustment steps are completed within the agreed interaction cycle time, and the next round of dynamic sensor resource adjustment optimization is executed at the beginning of the next cycle.

[0074] The perception resource adjustment method provided in this application is described below using two specific application scenarios, where the first application scenario is a single computing node scenario and the second application scenario is a two computing node scenario.

[0075] Application scenario 1:

[0076] In this scenario, one computing node is connected to 12 sensing nodes. Each 120-degree fan-shaped area in the figure corresponds to a sensing node. The coverage overlap between these sensing nodes is as follows: Figure 9 As shown. For computing nodes, the interaction cycle of dynamic resource adjustment can be flexibly configured to 60 seconds. According to the established interaction cycle, for the computing node, the following overlapping area determination scheme is executed: (1) After receiving the latest perception data sent by each perception node, the computing node associates the perception targets corresponding to these perception data. When it is determined that the perception targets from different perception nodes are the same target, these perception targets are fused to obtain the fused perception targets. (2) The computing node maintains two storage spaces, one for storing the above original perception data from different perception nodes, and the other for storing all data that are periodically associated and fused (such as the fused perception targets). When the storage space is full, the stored data with the longest duration is deleted. (3) For the data set composed of all perception targets that have been successfully fused in the storage space, the Alpha shape algorithm is used to construct a polyhedron composed of overlapping areas based on the latitude, longitude and altitude information of the target to obtain the fused overlapping area. By setting the alpha value to a value close to the average distance between adjacent target points, a similar Figure 9 The polygonal area B formed by the combination of multiple black straight lines in the image can be regarded as a cross section of the polyhedron. (4) Construct a polyhedron for the sensing targets of all sensing nodes in the storage space according to the above-mentioned Alpha shape algorithm, and determine the coverage area of ​​each sensing node. The cross section of the polyhedron is expected to be similar to Figure 9 The fan-shaped areas of different color grayscales in the image are 120 degrees in horizontal angle. (5) Calculate the volume of the overlapping part of each sensing node and the fusion overlap area to obtain the overlapping area volume of the sensing node. (6) Calculate the spatial overlap coverage coefficient of each sensing node. Assume that the overlapping area volume of the sensing node is V o , the total volume of the area covered by the sensing node is V A , then the spatial overlap coverage coefficient can be calculated as: σ1=V0 / V A .

[0077] In addition, the following dynamic resource adjustment optimization scheme is executed for all the sensing nodes included in the computing node data in sequence according to the agreed interaction cycle: (1) Taking sensing node 1 as an example, assuming that its calculated spatial overlap coverage coefficient is 0.7, which exceeds the threshold coefficient 0.5, and the horizontal angle range of the overlapping area of ​​the sensing node is 120 degrees and the vertical angle range is 30 degrees, then the sensing beam width within the angle range can be increased and the number of repeated symbols of the transmitted signal can be reduced, which can be obtained as follows: Figure 10The coverage direction of area C shown in the figure. (2) After determining the adjusted coverage range of the perception node, remove the perception target points of the perception node that do not belong to the new coverage range and recalculate its spatial overlap coverage coefficient. When it is confirmed that the spatial overlap coverage coefficient of the perception node, 0.45, is less than the preset threshold (0.5), the resource adjustment optimization of the next perception node can be entered. (3) Until all perception nodes are adjusted. After the computing node marked as the central node completes the above perception resource adjustment, it will perform corresponding adjustments on all the perception nodes connected to it. It should be noted that the above steps are all completed within the agreed interaction cycle time; at the beginning of the next cycle, the next dynamic adjustment optimization of perception resources will be performed.

[0078] Application scenario 2:

[0079] In this scenario, each sensor node is connected to a computing node, with a total of two computing nodes, and each computing node is connected to 12 sensor nodes. Each 120-degree fan-shaped area in the figure corresponds to a sensor node. The coverage overlap between these nodes includes the overlap between computing nodes and between sensor nodes, such as Figure 11 As shown. In this example, computing node 1 is set as the central node, while computing node 2 is a non-central node connected to computing node 1. Assume that the interaction cycle for dynamic resource adjustment has been determined to be 10 seconds. Based on this agreed interaction cycle, the following overlapping area determination scheme will be executed for computing node 1 designated as the central node: (1) At a determined periodic time point, computing node 2 sends the latest perception data that has not been networked and integrated to computing node 1; the perception data contains specific target information, including but not limited to target sequence number and identification, latitude and longitude information, distance, speed, acceleration, azimuth, pitch angle, target type, target signal strength, etc. (2) After computing node 1 receives the perception data from computing node 2, it associates the perception targets received by this node from each of the included perception nodes with the target set composed of the perception targets reported by computing node 2 in this period, determines whether the perception targets belonging to different perception nodes can be determined as the same target, and fuses the same target to obtain the fused perception target. (3) Computing node 1 maintains two storage spaces, one for storing the above raw data and the other for storing all the data that have been periodically associated and fused. When the storage space is full, the stored data with the longest duration is deleted. (4) For the data set consisting of all the perceived targets that form target association pairs and are successfully fused in the storage space, the Alpha shape algorithm is used to construct a polyhedron consisting of overlapping areas based on the latitude, longitude and altitude information of the targets to obtain the fused overlapping areas. Figure 11The area D surrounded by the black line shows the overlapping area between the two computing nodes. (5) Construct a polyhedron for the sensing targets of all sensing nodes in the storage space according to the above-mentioned Alpha shape algorithm to determine the coverage area of ​​each sensing node; (6) Calculate the volume of the overlapping part of each sensing node and the fusion overlap area to obtain the overlapping area volume of the sensing node. (7) Calculate the spatial overlap coverage coefficient of each sensing node. Assume that the overlapping area volume of the sensing node is V1 and the total volume of the coverage area of ​​the sensing node is V B , then the spatial overlap coverage coefficient can be calculated as: σ2=V1 / V B .

[0080] In addition, for all the perception nodes included in the data of computing node 1, the following dynamic resource adjustment optimization scheme is executed according to the agreed interaction cycle. At the same time, the following judgment and adjustment scheme is executed in sequence for the perception nodes included in the data received by all nodes (taking computing node 1, computing node 2 and computing node 3 in the figure as an example), where the preset threshold is set to 0.5. (1) First iteration: Determine that the spatial overlap coverage coefficient of perception node 1 is greater than 0.5, determine the angle range to which the overlap area of ​​the perception node belongs, and determine the adjustment strategy for the perception resources of the perception node covering the angle range. The coverage area after adjustment is as follows: Figure 12 As shown in area E in . After determining the adjusted coverage range of the sensing node, remove the sensing target points that do not belong to the new coverage range of the sensing node and recalculate its spatial overlap coverage coefficient, and confirm that the spatial overlap coverage coefficient of node 1 (assuming it is 0.35) is less than the preset threshold, and then enter the resource adjustment optimization of the next sensing node. (2) Second iteration: Determine that the spatial overlap coverage coefficient of sensing node 2 is greater than 0.5, determine the angle range to which the overlapping area of ​​the sensing node belongs, and determine the adjustment strategy for the sensing resources of the sensing node covering the angle range. The adjusted coverage area is as follows Figure 12 As shown in the area F in . After determining the adjusted coverage range of the sensing node, remove the sensing target points that do not belong to the new coverage range of the sensing node and recalculate the spatial overlap coverage coefficient of node 1. Confirm that the spatial overlap coverage coefficient of node 1 (assuming it is 0.4) is less than the preset threshold, and then enter the resource adjustment optimization of the next sensing node. (3) The third iteration: determine that the spatial overlap coverage coefficient of sensing node 3 is greater than 0.5, determine the angle range to which the overlapping area of ​​the sensing node belongs, and determine the adjustment strategy for the sensing resources of the sensing node covering the angle range. The adjusted coverage area is as follows Figure 12As shown in area G in . After determining the adjusted coverage range of the sensing node, remove the sensing target points that do not belong to the new coverage range of the sensing node and recalculate the spatial overlap coverage coefficient of node 1. Confirm that the spatial overlap coverage coefficient of node 1 (assuming it is 0.45) is less than the preset threshold, and then enter the resource adjustment optimization of the next sensing node. (4) Until all sensing nodes are adjusted. After computing node 1 completes the above sensing resource adjustment, it adjusts the sensing node to which it is connected, and sends the adjustment strategy of sensing node 2 to the corresponding computing node, which executes the adjustment strategy. It should be noted that the above steps are all completed within the agreed interaction cycle time; at the beginning of the next cycle, the next dynamic adjustment optimization of sensing resources is performed.

[0081] See also Figure 13 , Figure 13 This is a schematic diagram of a communication system architecture provided by an embodiment of the present application, which consists of a first computing node, at least one second computing node, a first perception node and multiple second perception nodes. Among them, the first computing node is interconnected with multiple second computing nodes, the first perception node is connected to the first computing node, and the multiple second perception nodes realize data interaction with the entire system through the second computing node. Specifically, the first computing node can not only receive the first perception data from the first perception node, but also obtain the second perception data provided by the second perception node through the transit of the second computing node. Based on this data information, the first computing node can determine the overlapping area of ​​the perception range of the first target perceived by the first perception node and the second perception node. Furthermore, the first computing node can determine the target perception node among the two perception nodes based on the information of this overlapping area, combined with the first perception coverage area of ​​the first perception node and the second perception coverage area of ​​the second perception node. It should be noted that the first computing node can also be connected to multiple first perception nodes and receive the first perception data sent by multiple first perception nodes.

[0082] It should be noted that the operational logic between the nodes in this system architecture closely corresponds to the aforementioned perception resource adjustment method. For its specific implementation details, please refer to the detailed description of the method previously described. To avoid redundancy, we will not repeat it here.

[0083] In addition, an embodiment of the present application also discloses a communication device, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements the perception resource adjustment method as in any of the previous embodiments.

[0084] In addition, an embodiment of the present application further discloses a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to execute the perceptual resource adjustment method in any of the previous embodiments.

[0085] In addition, an embodiment of the present application also discloses a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. The processor of the device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the device executes the perception resource adjustment method as in any of the previous embodiments.

[0086] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0087] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above implementation mode. Technical personnel familiar with the field can also make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A method for adjusting sensing resources, the method comprising: Determine, based on first perception data from a first perception node and second perception data from a second perception node, an overlapping area of ​​perception ranges of a first target commonly perceived by the first perception node and the second perception node; Determine a spatial overlap coverage coefficient based on the overlapping area of ​​the sensing range and the first sensing coverage area of ​​the first sensing node to obtain a first spatial overlap coverage coefficient of the first sensing node; performing a determination process on the spatial overlap coverage coefficient according to the overlapping area of ​​the sensing range and the second sensing coverage area of ​​the second sensing node to obtain a second spatial overlap coverage coefficient of the second sensing node; Determining a target sensing node among the first sensing nodes and the second sensing nodes according to the first spatial overlap coverage coefficient and the second spatial overlap coverage coefficient; Adjust the sensing resources of the target sensing node.

2. The method according to claim 1, characterized in that The method is applied to a first computing node, the first computing node is connected to at least one second computing node, the number of the second perception nodes is multiple, each second computing node is connected to multiple second perception nodes, and the second perception data is sent to the first computing node by the second perception node to which it belongs through the connected second computing node.

3. The method according to claim 1, characterized in that The number of the second sensing node is at least one; The determining, based on first perception data from a first perception node and second perception data from a second perception node, an overlapping area of ​​perception ranges of a first target commonly perceived by the first perception node and the second perception node includes: performing target fusion on a sensed target corresponding to the first sensed data and a sensed target corresponding to the at least one second sensed data, based on first sensed data from the first sensed node and second sensed data from the at least one second sensed node, to obtain a first target commonly sensed by the first sensed node and the at least one second sensed node; Determine a perception range overlap area where the first target is located.

4. The method according to claim 3, characterized in that The step of performing target fusion on a perception target corresponding to the first perception data and a perception target corresponding to the at least one second perception data based on first perception data from the first perception node and second perception data from the at least one second perception node to obtain a first target commonly perceived by the first perception node and the at least one second perception node includes: performing, based on first perception data from the first perception node and second perception data from at least one of the second perception nodes, correlation determination processing on a perception target corresponding to the first perception data and a perception target corresponding to at least one of the second perception data, to obtain a first determination result; According to the first judgment result, target fusion is performed on the perception target corresponding to the first perception data and the perception target corresponding to at least one of the second perception data to obtain the first target commonly perceived by the first perception node and at least one of the second perception nodes.

5. The method according to claim 4, characterized in that The step of performing target fusion on the perception target corresponding to the first perception data and the perception target corresponding to at least one of the second perception data according to the first judgment result to obtain a first target commonly perceived by the first perception node and the at least one of the second perception nodes includes: If the first judgment result is that the perception target corresponding to the first perception data and the perception target corresponding to at least one of the second perception data belong to the same target, the perception target corresponding to the first perception data and the perception target corresponding to at least one of the second perception data are fused into one target to obtain the first target jointly perceived by the first perception node and at least one of the second perception nodes.

6. The method according to claim 3, characterized in that The determining the perception range overlapping area where the first target is located includes: determining an overlapping area between a sensing range of the first sensing node and a sensing range of at least one of the second sensing nodes, wherein the first target is located within the overlapping area; According to the position information of the first target, a perception range overlapping area where the first target is located is determined in the overlapping area.

7. The method according to claim 1, characterized in that The determination process of the spatial overlap coverage coefficient comprises the following steps: Determine an overlapping coverage area between a first area and a second area, wherein the first area is the sensing range overlapping area, and the second area is a first sensing coverage area of ​​the first sensing node or a second sensing coverage area of ​​the second sensing node; The target space overlapping coverage coefficient is determined based on the overlapping coverage area and the second area, wherein when the second area is the first perception coverage area of ​​the first perception node, the target space overlapping coverage coefficient is the first space overlapping coverage coefficient; when the second area is the second perception coverage area of ​​the second perception node, the target space overlapping coverage coefficient is the second space overlapping coverage coefficient.

8. The method according to claim 7, characterized in that The target sensing node is the first sensing node; and adjusting the sensing resources of the target sensing node includes: Determining a first angular range of the overlapping coverage area of ​​the first sensing node; Determining a target sensing resource adjustment strategy according to the sensing resources of the first sensing node covering the first angle range; Adjust the sensing resources of the first sensing node according to the target sensing resource adjustment strategy.

9. The method according to claim 8, characterized in that After adjusting the sensing resources of the first sensing node according to the target sensing resource adjustment strategy, the method further includes: Re-determining the first sensing coverage area of ​​the first sensing node after the sensing resource is adjusted; According to the re-determined first sensing coverage area, the first spatial overlap coverage coefficient of the first sensing node is re-determined until the re-determined first spatial overlap coverage coefficient is less than or equal to a preset coefficient threshold.

10. The method according to claim 7, characterized in that The target sensing node is the second sensing node, and the second sensing node is connected to the second computing node; and the adjusting of the sensing resources of the target sensing node includes: Determining a second angular range of the overlapping coverage area of ​​the second sensing node; Determining a target sensing resource adjustment strategy according to the sensing resources of the second sensing node covering the second angle range; The target sensing resource adjustment policy is sent to the second computing node, so that the second computing node adjusts the sensing resources of the second sensing node according to the target sensing resource adjustment policy.

11. The method according to claim 10, characterized in that After sending the target sensing resource adjustment policy to the second computing node so that the second computing node adjusts the sensing resources of the second sensing node according to the target sensing resource adjustment policy, the method further includes: Re-determining the second sensing coverage area of ​​the second sensing node after the sensing resource is adjusted; According to the re-determined second perception coverage area, the second spatial overlap coverage coefficient of the second perception node is re-determined until the re-determined second spatial overlap coverage coefficient is less than or equal to a preset coefficient threshold.

12. The method according to claim 8 or 10, characterized in that The target-aware resource adjustment strategy includes at least one of the following: Increase the sensing beam width and reduce the sensing beam gain; Reduce the transmit power of the sensing beam; Reduce the number of repeated symbols of the transmitted signal and reduce the perceived gain in the Doppler dimension; Reduce the length of the perception sequence.

13. The method according to claim 1, wherein The determining, according to the first spatial overlap coverage coefficient and the second spatial overlap coverage coefficient, a target sensing node from among the first sensing node and the second sensing node comprises one of the following: If the first spatial overlapping coverage coefficient is greater than a preset coefficient threshold, determining the first sensing node as a target sensing node; If the second spatial overlapping coverage coefficient is greater than a preset coefficient threshold, determining the second sensing node as a target sensing node; If both the first spatial overlapping coverage coefficient and the second spatial overlapping coverage coefficient are greater than a preset coefficient threshold, both the first sensing node and the second sensing node are determined as target sensing nodes.

14. A communication system, characterized in that: comprising a first computing node, a first sensing node, and a second sensing node; The first computing node determines, based on the first perception data from the first perception node and the second perception data from the second perception node, an overlapping area of ​​perception ranges of a first target commonly perceived by the first perception node and the second perception node; The first computing node determines a spatial overlap coverage coefficient based on the overlapping area of ​​the sensing range and the first sensing coverage area of ​​the first sensing node to obtain a first spatial overlap coverage coefficient of the first sensing node; performing a determination process on the spatial overlap coverage coefficient based on the overlapping area of ​​the perception ranges and the second perception coverage area of ​​the second perception node to obtain a second spatial overlap coverage coefficient of the second perception node; and determining a target perception node from the first perception node and the second perception node based on the first spatial overlap coverage coefficient and the second spatial overlap coverage coefficient; The first computing node adjusts the sensing resources of the target sensing node.

15. The communication system according to claim 14, wherein: The communication system also includes at least one second computing node, and the number of second perception nodes is multiple. Each second computing node is connected to the first computing node and multiple second perception nodes respectively. The second perception data is sent to the first computing node by the second perception node to which it belongs through the connected second computing node.

16. A communication device, characterized in that: include: at least one processor; at least one memory for storing at least one program; At least one of the programs is executed by at least one of the processors to perform the perceptual resource adjustment method according to any one of claims 1 to 13.

17. A computer-readable storage medium storing computer-executable instructions, characterized in that: The computer-executable instructions are used to execute the perceptual resource adjustment method according to any one of claims 1 to 13.

18. A computer program product comprising a computer program or computer instructions, characterized in that The computer program or the computer instruction is stored in a computer-readable storage medium, the processor of the communication device reads the computer program or the computer instruction from the computer-readable storage medium, and the processor executes the computer program or the computer instruction, so that the communication device executes the perception resource adjustment method described in any one of claims 1 to 13.

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