A Sensing Information Multicast Decision-Making Method and Device in a Sensor-Integrated Vehicle Network

Abstract

This invention discloses a method and apparatus for multicast decision-making of sensing information in a sensor-integrated vehicle-to-everything (V2X) network. The method includes: determining all groups within the service coverage area of ​​the V2X access point where the vehicle node to be assigned is located, and all vehicle nodes within each group; calculating the correlation coefficient of sensing information for each group based on the distance between the vehicle node to be assigned and all vehicle nodes within the group, and the set of sensing information uploaded to the V2X access point by the vehicle node to be assigned and all vehicle nodes within the group, thereby filtering candidate groups; calculating the service quality satisfaction coefficient for each candidate group based on the service quality status set of all vehicle nodes within the candidate groups; and selecting the candidate group with the largest product of the sensing information correlation coefficient and the service quality satisfaction coefficient as the optimal allocation decision group. This method can effectively reduce resource consumption and improve communication efficiency, providing high-precision and lightweight intelligent support for V2X networks.

Description

Technical Field

[0001] This invention relates to a method and apparatus for multicast decision-making of sensing information in a sensor-integrated vehicle network. Background Technology

[0002] Intelligent Transportation Systems (ITS) effectively integrate advanced communication, information, sensing, control, and computer technologies to form a comprehensive transportation system that ensures safety, improves efficiency, enhances the environment, and conserves energy. This leads to the establishment of a real-time, accurate, and efficient large-scale, all-round intelligent transportation system. Vehicle-to-everything (V2X) networks, supported by wireless communication network technology, serve as the link enabling information sharing and interactive decision-making between vehicles, roads, people, and the network itself, forming the cornerstone of intelligent transportation systems. However, current V2X networks face the dual challenges of scarce frequency band resources and high hardware redundancy. The scarcity of frequency band resources necessitates efficient transmission of massive amounts of data and real-time sensing of external information within limited network bandwidth. Simultaneously, the high hardware redundancy hinders the development of V2X networks towards lower cost and lighter weight.

[0003] Against this backdrop, sensor-communication integration has emerged as an exciting research field. Its goal is to organically combine communication and sensing technologies to achieve environmental perception and intelligent information processing, thereby building a more intelligent and efficient communication system. With the development of wireless communication network technology, a new framework for sensor-communication integration to empower the Internet of Vehicles (IoV) has emerged. Through the deep integration of sensing and communication functions, it improves the utilization of frequency band resources and achieves a qualitative leap in simplifying hardware design, thereby enhancing the real-time response capability and stability of the IoV, ultimately building an efficient, secure, and intelligent IoV.

[0004] In multi-vehicle collaborative mode, vehicles do not rely solely on their own sensor data but can access a wide range of data from other vehicles and traffic infrastructure, thereby expanding their environmental perception range. Through information sharing mechanisms, each vehicle can acquire information beyond its direct perception boundaries, allowing it to consider a more comprehensive traffic environment when making decisions. When communication between vehicles and vehicle-to-everything (V2X) access points is needed to exchange sensory information to further assist autonomous driving decisions, the complex road traffic environment of V2X means that broadcast methods suffer from severe channel contention, resulting in large message propagation delays and high message redundancy, which can affect the normal transmission and reception of emergency messages. Multicast, by sending data only to a specific set of receivers instead of to all nodes, effectively solves the problem of single-point transmission and multi-point reception, achieving efficient single-point to multi-point data propagation in the network, and significantly saving network bandwidth, reducing network load, and lowering message redundancy. Summary of the Invention

[0005] To address the decision-making problem of vehicle group affiliation during multicast downlink data transmission and obtain the optimal allocation decision group for vehicle nodes to be assigned, this invention provides a method and apparatus for multicast decision-making of sensing information in a sensor-integrated vehicle network.

[0006] In a first aspect, embodiments of the present invention provide a method for multicast decision-making of sensing information in a sensor-integrated vehicle network, which may include:

[0007] Identify all groups within the service coverage area of ​​the vehicle network access point where the vehicle node to be assigned is located, and all vehicle nodes within each group;

[0008] Based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and the set of perception information uploaded by the vehicle node to be assigned and each vehicle node in the group to the vehicle network access point, the correlation coefficient of perception information between the vehicle node to be assigned and the group is calculated, and multiple groups are selected as candidate groups in descending order of the correlation coefficient of perception information.

[0009] Based on the service quality status set of all vehicle nodes in the candidate group and the service quality requirements of the vehicle node to be assigned, the service quality satisfaction coefficient of the vehicle node to be assigned to the candidate group is calculated.

[0010] The candidate group with the largest product of the perceived information relevance coefficient and the service quality satisfaction coefficient is selected as the optimal allocation decision group for the vehicle nodes to be assigned.

[0011] In one or more optional embodiments of this application, the step of calculating the correlation coefficient between the vehicle node to be assigned and the group based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and the set of perception information uploaded by the vehicle node to be assigned and each vehicle node in the group to the vehicle network access point, and then selecting multiple groups as candidate groups according to the correlation coefficient from largest to smallest, includes:

[0012] Based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and a preset distance fuzzy membership function, the distance correlation coefficient between the vehicle node to be assigned and each vehicle node is calculated.

[0013] Based on the set of perception information uploaded to the vehicle network access point by the vehicle node to be assigned and each vehicle node in the group, the correlation coefficient between the perception information of the vehicle node to be assigned and each vehicle node in the overlapping time period is calculated.

[0014] Based on the distance correlation coefficient between the vehicle node to be assigned and each other vehicle node, and the correlation coefficient of the perceived information between the vehicle node to be assigned and each other vehicle node during the overlapping time period, the correlation coefficient of the perceived information between the vehicle node to be assigned and the group is calculated:

[0015] Based on the correlation coefficient of perceived information, a first preset number of groups are selected as candidate groups in descending order of their relative importance.

[0016] In one or more optional embodiments of this application, the step of calculating the correlation coefficient between the vehicle node to be assigned and the group based on the distance correlation coefficient between the vehicle node to be assigned and each other vehicle node, and the correlation coefficient of the perceived information between the vehicle node to be assigned and each other vehicle node during the overlapping time period, includes:

[0017] Based on the distance correlation coefficient between the vehicle node to be assigned and each other vehicle node, and the correlation coefficient of the perceived information between the vehicle node to be assigned and each other vehicle node during the overlapping time period, the correlation coefficient of the perceived information between the vehicle node to be assigned and the group is calculated using the following formula:

[0018]

[0019] In the formula, Cov k,i J represents the correlation coefficient of the perceived information between the vehicle node k to be assigned and the i-th group. i Let A(||d) represent the set of all vehicle nodes within the i-th group, where sum is a function to count the number of nodes in the set, and ∑ is the summation symbol. k -d j ||) represents the distance correlation coefficient between vehicle node k and vehicle node j to be assigned, and desk is the probability cutoff function. k and d j Xcorr(R) represents the geographic coordinates of vehicle node k and vehicle node j to be assigned, respectively. j,t ,R k,t [t1,t2]∩[t3,t4] represents the correlation coefficient of the perceived information between the vehicle node k to be assigned and the vehicle node j during the overlapping time period [t1,t2]∩[t3,t4], where R is the correlation coefficient. j,t R is the set of perception information uploaded by vehicle node j to the vehicle network access point. k,t Let t be the set of perception information uploaded by the vehicle node k to the vehicle network access point, where long is a function for calculating the time length, t1 and t2 are the start and end times of vehicle node j uploading perception information to the vehicle network access point, respectively, and t3 and t4 are the times when the vehicle node k to be assigned enters the service coverage area of ​​the vehicle network access point and the times when it uploads perception information to the vehicle network access point, respectively, where t is a time variable.

[0020] In one or more optional embodiments of this application, the step of calculating the distance correlation coefficient between the vehicle node to be assigned and each vehicle node based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and a preset distance fuzzy membership function, includes:

[0021] Based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and using the following preset formula for the fuzzy membership function, the distance correlation coefficient between the vehicle node to be assigned and each other vehicle node is calculated:

[0022]

[0023] In the formula, This represents a preset distance fuzzy membership function, where 'a' is an intermediate variable of the distance fuzzy membership function, and 'D' is the value of 'D'. max D is the upper limit of the preset distance-related reference value. min θ is the preset lower limit of the distance-related reference value, and θ is the preset exponential coefficient of the distance-related value.

[0024] In one or more optional embodiments of this application, the service quality requirements of the vehicle node to be assigned include the service quality requirement parameters of the vehicle node to be assigned for multiple service quality types respectively.

[0025] The step of calculating the service quality satisfaction coefficient of the vehicle node to be assigned to the candidate group based on the service quality status set of all vehicle nodes in the candidate group and the service quality requirements of the vehicle node to be assigned includes:

[0026] Based on the service quality requirements parameters of the vehicle nodes to be assigned for multiple service quality types, determine the fuzzy membership function corresponding to each service quality type;

[0027] Based on the fuzzy membership function corresponding to each service quality category, and the service quality status set of all vehicle nodes in the candidate group, the service quality satisfaction coefficient of the vehicle node to be assigned to the candidate group is calculated according to the following formula:

[0028]

[0029] In the formula, Satis k,i Θ represents the service quality satisfaction coefficient of the vehicle node k to be assigned among the i-th candidate groups. i Let be the set of service quality statuses of all vehicle nodes within the i-th candidate group, where ∑ is the summation symbol, ∏ is the product symbol, and desk is the probability cutoff function. Let D be the fuzzy membership function corresponding to the h-th quality of service category. j,t (h) represents the value of the multicast downlink transmission at vehicle node j with respect to the h-th quality of service type at time t in the quality of service status set, sum is a function of the number of sets, and N is the number of quality of service types.

[0030] In one or more optional embodiments of this application, the quality of service types include latency, packet loss rate, latency jitter, and bandwidth.

[0031] The step of determining the fuzzy membership function corresponding to each service quality type based on the service quality requirement parameters of the vehicle nodes to be assigned for multiple service quality types includes:

[0032] Based on the time delay requirements of the vehicle nodes to be assigned, the following fuzzy membership functions corresponding to the time delays are determined:

[0033]

[0034] In the formula, Let represent the fuzzy membership function corresponding to the time delay, and 'c' be the intermediate variable of the fuzzy membership function corresponding to the time delay. k For the delay requirement parameter of the vehicle node k to be assigned, delay max θ is the preset maximum latency, and θ is the preset exponential coefficient of the service quality fluctuation for the vehicle node k to be assigned.

[0035] Based on the packet loss rate requirements of the vehicle nodes to be assigned, the following fuzzy membership functions corresponding to the packet loss rates are determined:

[0036]

[0037] In the formula, Let represent the fuzzy membership function corresponding to the packet loss rate, where c is the intermediate variable of the fuzzy membership function corresponding to the packet loss rate, and loss is... k Let k be the required packet loss rate parameter for the vehicle node to be assigned, and let loss be the parameter for loss. max θ is the preset maximum packet loss rate, and θ is the preset exponential coefficient of the service quality fluctuation for the vehicle node k to be assigned.

[0038] Based on the delay jitter requirements of the vehicle nodes to be assigned, the following fuzzy membership functions corresponding to delay jitter are determined:

[0039]

[0040] In the formula, Let c represent the fuzzy membership function corresponding to the jitter, and c be the intermediate variable of the fuzzy membership function corresponding to the jitter. kLet jitter be the required parameter for the time delay jitter of the vehicle node k to be assigned. max θ is the preset maximum latency jitter, and θ is the preset exponential coefficient of the service quality fluctuation for the vehicle node k to be assigned.

[0041] Based on the bandwidth requirements of the vehicle nodes to be assigned, the following fuzzy membership functions corresponding to the bandwidth are determined:

[0042]

[0043] In the formula, Let represent the fuzzy membership function corresponding to the bandwidth, and 'c' be the intermediate variable of the fuzzy membership function corresponding to the bandwidth. k For the bandwidth requirement parameter of the vehicle node k to be assigned, bandwidth max The preset maximum bandwidth, bandwidth min θ is the preset minimum bandwidth, and θ is the preset exponential coefficient of service quality fluctuation for the vehicle node k to be allocated.

[0044] Secondly, embodiments of the present invention provide a sensing information multicast decision-making device for an integrated sensing vehicle network, which may include:

[0045] The data acquisition module is used to determine all groups within the service coverage area of ​​the vehicle network access point where the vehicle node to be assigned is located, as well as all vehicle nodes within each group;

[0046] The first calculation module is used to calculate the correlation coefficient of the perception information between the vehicle node to be assigned and the group based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and the set of perception information uploaded by the vehicle node to be assigned and each vehicle node in the group to the vehicle network access point, and to select multiple groups as candidate groups according to the perception information correlation coefficient from large to small.

[0047] The second calculation module is used to calculate the service quality satisfaction coefficient of the vehicle node to be assigned to the candidate group based on the service quality status set of all vehicle nodes in the candidate group and the service quality requirements of the vehicle node to be assigned.

[0048] The screening and decision-making module is used to select the candidate group with the largest product of the perceived information relevance coefficient and the service quality satisfaction coefficient as the optimal allocation decision group for the vehicle nodes to be allocated.

[0049] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the sensing information multicast decision-making method in the integrated sensory vehicle network as described above.

[0050] Fourthly, embodiments of the present invention provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the sensing information multicast decision-making method in the integrated sensory vehicle network as described above.

[0051] Fifthly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, it implements the multicast decision-making method for sensing information in a sensor-integrated vehicle network as described above.

[0052] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0053] This invention provides a multicast decision-making method for sensing information in a sensor-integrated vehicle network. The method first determines all groups and their vehicle nodes within the service range of the access point where the vehicle node to be assigned is located. Then, based on the distance between the vehicle node to be assigned and the vehicles in each group, and the sensing information uploaded by them, a correlation coefficient of sensing information is calculated. Multiple groups are then selected as candidate groups based on this correlation coefficient. Next, considering the service quality status of each candidate group and the needs of the vehicle node to be assigned, a service quality satisfaction coefficient is calculated. Finally, the group with the largest product of the sensing information correlation coefficient and the service quality satisfaction coefficient is selected as the optimal allocation decision group. This method offers significant technical advantages to the Internet of Vehicles (IoV) by dynamically optimizing the multicast decision-making mechanism for perceived information. Based on the correlation coefficient of perceived information, it accurately selects candidate groups, significantly reducing redundant data transmission and effectively alleviating the problem of bandwidth resource shortage. Secondly, by combining the historical service quality data of candidate groups with the real-time needs of the vehicle nodes to be allocated, it quantitatively evaluates the service quality satisfaction, ensuring high adaptability of multicast transmission across various service qualities, improving the efficiency of emergency information transmission, and achieving a globally optimal balance between resource allocation and transmission performance. This not only ensures real-time sharing of perceived information but also reduces network load and hardware redundancy costs. In complex and dynamic traffic scenarios, this method significantly enhances the intelligence level, network stability, and accuracy of autonomous driving decisions in the IoV, providing an efficient and lightweight solution for the efficient operation of intelligent transportation systems.

[0054] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0055] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0056] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0057] Figure 1 This is a flowchart illustrating the sensing information multicast decision-making method in the integrated sensing vehicle network provided in this embodiment of the invention.

[0058] Figure 2 This is an example diagram of a vehicle-to-everything (V2X) network provided in an embodiment of the present invention;

[0059] Figure 3 A schematic diagram illustrating the correlation coefficients of perceived information for each group provided in an embodiment of the present invention;

[0060] Figure 4 This is a schematic diagram illustrating the service quality satisfaction coefficients of each candidate group provided in an embodiment of the present invention.

[0061] Figure 5 This is a schematic diagram of the structure of the sensing information multicast decision-making device in the integrated sensing vehicle network provided in this application embodiment. Detailed Implementation

[0062] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0063] The inventors discovered that, due to the limitations of broadcast methods for propagating downlink sensing information in existing technologies, a dynamic topology map formed by analyzing and modeling vehicle location information in high-speed driving scenarios can be used to transmit downlink sensing information to a group of vehicles with similar sensing information needs using multicast. Therefore, it is necessary to consider how to determine the group to which a new vehicle node belongs after entering the service area of ​​the vehicle network access point. This allows the vehicle network access point to transmit the processed integrated sensing information to the group via multicast downlink, meeting the needs of rational allocation of communication sensing resources and efficient sharing of sensing data in autonomous driving scenarios. Based on this, the inventors further developed this invention, providing a method and device for multicast decision-making of sensing information in an integrated sensing vehicle network.

[0064] Example 1

[0065] Embodiment 1 of the present invention provides a method for multicast decision-making of sensing information in a sensor-integrated vehicle network, referring to... Figure 1As shown, the method may include the following steps S101-S104:

[0066] S101: Determine all groups within the service coverage area of ​​the vehicle network access point where the vehicle node to be assigned is located, and all vehicle nodes within each group.

[0067] S102: Based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and the set of perception information uploaded by the vehicle node to be assigned and each vehicle node in the group to the vehicle network access point, calculate the correlation coefficient of perception information between the vehicle node to be assigned and the group, and select multiple groups as candidate groups according to the correlation coefficient of perception information from large to small.

[0068] S103: Based on the service quality status set of all vehicle nodes in the candidate group and the service quality requirements of the vehicle nodes to be assigned, calculate the service quality satisfaction coefficient of the vehicle nodes to be assigned to the candidate group.

[0069] S104: The candidate group with the largest product of the perceived information relevance coefficient and the service quality satisfaction coefficient is selected as the optimal allocation decision group for the vehicle nodes to be assigned.

[0070] This invention provides a multicast decision-making method for sensing information in a sensor-integrated vehicle network. The method first determines all groups and their vehicle nodes within the service range of the access point where the vehicle node to be assigned is located. Then, based on the distance between the vehicle node to be assigned and the vehicles in each group, and the sensing information uploaded by them, a correlation coefficient of sensing information is calculated. Multiple groups are then selected as candidate groups based on this correlation coefficient. Next, considering the service quality status of each candidate group and the needs of the vehicle node to be assigned, a service quality satisfaction coefficient is calculated. Finally, the group with the largest product of the sensing information correlation coefficient and the service quality satisfaction coefficient is selected as the optimal allocation decision group. This method offers significant technical advantages to the Internet of Vehicles (IoV) by dynamically optimizing the multicast decision-making mechanism for perceived information. Based on the correlation coefficient of perceived information, it accurately selects candidate groups, significantly reducing redundant data transmission and effectively alleviating the problem of bandwidth resource shortage. Secondly, by combining the historical service quality data of candidate groups with the real-time needs of the vehicle nodes to be allocated, it quantitatively evaluates the service quality satisfaction, ensuring high adaptability of multicast transmission across various service qualities, improving the efficiency of emergency information transmission, and achieving a globally optimal balance between resource allocation and transmission performance. This not only ensures real-time sharing of perceived information but also reduces network load and hardware redundancy costs. In complex and dynamic traffic scenarios, this method significantly enhances the intelligence level, network stability, and accuracy of autonomous driving decisions in the IoV, providing an efficient and lightweight solution for the efficient operation of intelligent transportation systems.

[0071] In step S101 above, all groups within the service coverage area of ​​the vehicle network access point where the vehicle node to be assigned is located, and all vehicle nodes within each group are determined.

[0072] Specifically, in an integrated sensory vehicle network, each vehicle node can be grouped into different groups by the vehicle network access point. Each vehicle node can upload its surrounding environment perception information to the vehicle network access point, and the access point can transmit the processed integrated perception information to different groups via multicast downlink. When a vehicle node to be assigned enters the service coverage area of ​​the vehicle network access point, the access point needs to assign a suitable group to the node based on existing information. This existing information includes all groups within the service coverage area of ​​the vehicle network access point, and all vehicle nodes within each group.

[0073] For example, within the service coverage area of ​​a vehicle-to-everything (V2X) access point, the group and vehicle node numbers are i and j, respectively, where i∈I and j∈J. Here, J is the set of vehicle nodes within the service coverage area of ​​the V2X access point, and I is the set of groups within the service coverage area of ​​the V2X access point. The specific group and vehicle node numbers within the service coverage area of ​​the V2X access point are as follows: Figure 2 As shown, the outer circle represents the service coverage area of ​​the vehicle network access point, which includes 6 groups, i∈{1,2,3,4,5,6}. In this method, J is used. i Let i represent the set of vehicle nodes in the i-th group. Figure 2 In the given list, the vehicle node set J1 in the first group is {7, 12, 15, 27}, the vehicle node set J2 in the second group is {1, 3, 9, 19, 25, 33}, the vehicle node set J3 in the third group is {4, 5, 6}, the vehicle node set J4 in the fourth group is {8, 11, 23}, the vehicle node set J5 in the fifth group is {20, 30}, and the vehicle node set J6 in the sixth group is {37, 43, 47}. Figure 2 The vehicle node numbered 36 is a vehicle node that has just entered the service coverage area of ​​this vehicle network access point and is yet to be assigned.

[0074] In step S102 above, based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and the set of perception information uploaded by the vehicle node to be assigned and each vehicle node in the group to the vehicle network access point, the correlation coefficient of perception information between the vehicle node to be assigned and the group is calculated. Multiple groups are then selected as candidate groups based on the correlation coefficient, from largest to smallest. Specifically, this includes the following steps S1021-S1024:

[0075] S1021: Based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and the preset distance fuzzy membership function, calculate the distance correlation coefficient between the vehicle node to be assigned and each vehicle node.

[0076] Specifically, it can be that, based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and using a preset distance fuzzy membership function as shown in Formula 1 below, the distance correlation coefficient between the vehicle node to be assigned and each vehicle node can be calculated:

[0077]

[0078] In the formula, This represents the preset fuzzy membership function, where 'a' is an intermediate variable of the fuzzy membership function, and 'D' is the distance fuzzy membership function. max D is the upper limit of the preset distance-related reference value. min Let θ be the preset lower limit of the distance-related reference value, and let θ be the preset exponential coefficient of the distance-related relationship. Also, let the distance between vehicle nodes range from [0, D]. max This is to ensure that the calculations are based on a reasonable range.

[0079] Among them, the upper limit of the relevant reference value D max It can be set to 2, which is the distance from the lower limit of the relevant reference value D. min For example, it can be set to 0.5, with the unit being kilometers. The distance-related exponential coefficient θ can be set to 2. Substituting the preset value, Formula 1 above can be expressed as:

[0080]

[0081] In the formula, This represents the preset fuzzy membership function for distance, where 'a' is an intermediate variable of the fuzzy membership function for distance.

[0082] In this embodiment, in step S1021, due to the increasing complexity of decision-making problems and the growing uncertainty of decision-making situations, coupled with the inherent fuzziness of decision-makers' thinking, it becomes increasingly difficult for decision-makers to express their preferences for certain decision objects using precise numbers in certain uncertain decision-making situations. Therefore, this method introduces fuzzy mathematics theory into the sensor-integrated vehicle network, pre-setting a distance fuzzy membership function to quantify the distance correlation coefficient between vehicle nodes, serving as the data basis for subsequent perception information correlation coefficients. Through the nonlinear mapping of the fuzzy membership function, this method can effectively handle the distance uncertainty in the dynamic movement of vehicles, avoiding the rigidity of group boundaries caused by traditional hard threshold partitioning, thereby improving the decision-making flexibility in complex traffic scenarios. Furthermore, this method achieves dynamic adjustment of distance correlation through parameterized design, such as exponential coefficients and upper and lower limits of reference values. It can adaptively optimize the group partitioning accuracy according to changes in actual road network density or communication environment, further enhancing the robustness and universality of the system, providing theoretical support and practical feasibility for efficient resource scheduling in the vehicle network.

[0083] S1022: Based on the set of perception information uploaded to the vehicle network access point by the vehicle node to be assigned and each vehicle node in the group, calculate the correlation coefficient between the perception information of the vehicle node to be assigned and each vehicle node in the overlapping time period.

[0084] Specifically, for each vehicle node in the group, determine the set of perception information R corresponding to that vehicle node j. j,t ={r j (t)|t1≤t≤t2}, where t is the time variable and r j (t) represents the perception information of vehicle node j about the surrounding environment at time t, and t1 and t2 are the start and end times of vehicle node j uploading the perception information to the vehicle network access point, respectively.

[0085] Similarly, for a vehicle node to be assigned, determine the set of perception information R corresponding to the vehicle node k to be assigned. k,t ={r k (t)|t3≤t≤t4}, where t is the time variable and r k (t) represents the perception information of the vehicle node k to be assigned to the surrounding environment at time t. t3 and t4 are the times when the vehicle node k to be assigned enters the service coverage area of ​​the vehicle network access point and the times when it uploads the perception information to the vehicle network access point, respectively.

[0086] Determine the overlapping time period between vehicle node j and the vehicle node k to be assigned, i.e., the intersection of the start time t1 and end time t2 of vehicle node j uploading sensing information to the vehicle network access point, the time t3 when the vehicle node k to be assigned enters the service coverage area of ​​the vehicle network access point, and the time t4 when it uploads sensing information to the vehicle network access point, i.e., [t1,t2]∩[t3,t4]. Calculate the correlation coefficient of all sensing information of vehicle node j and vehicle node k to be assigned within this time period, i.e., Xcorr(R j,t ,R k,t ,[t1,t2]∩[t3,t4]), where Xcorr represents the correlation coefficient, which can be calculated using the Pearson correlation formula or other correlation coefficient formulas, without being specifically limited here.

[0087] In this embodiment, step S1022 calculates the correlation coefficient of sensing information between vehicle nodes to accurately quantify spatiotemporal data consistency, effectively reducing redundant transmission and improving the utilization rate of sensing information. Based on sensing data matching within the same time period, it ensures homogeneity of demands within the multicast group, significantly reducing network load and latency jitter, and enhancing the real-time performance of emergency event transmission.

[0088] S1023: Based on the distance correlation coefficient between the vehicle node to be assigned and each vehicle node, and the correlation coefficient of the perceived information between the vehicle node to be assigned and each vehicle node during the overlapping time period, the correlation coefficient of the perceived information between the vehicle node to be assigned and the group is calculated according to the following formula 3:

[0089]

[0090] In the formula, Cov k,i J represents the correlation coefficient of perceived information between the vehicle node k to be assigned and the i-th group. i Let A(||d) represent the set of all vehicle nodes within the i-th group, where sum is a function to count the number of nodes in the set, and ∑ is the summation symbol. k -d j ||) represents the distance correlation coefficient between vehicle node k and vehicle node j to be assigned, and desk is the probability cutoff function. k and d j Xcorr(R) represents the geographic coordinates of vehicle node k and vehicle node j to be assigned, respectively. j,t ,R k,t [t1,t2]∩[t3,t4] represents the correlation coefficient of the perceived information between vehicle node k and vehicle node j during the overlapping time period [t1,t2]∩[t3,t4]. R j,t R is the set of perception information uploaded by vehicle node j to the vehicle-to-everything (V2X) access point. k,tLet be the set of perception information uploaded by the vehicle node k to the vehicle network access point, where long is a function for calculating the time length, t1 and t2 are the start and end times of vehicle node j uploading perception information to the vehicle network access point, respectively, and t3 and t4 are the times when the vehicle node k enters the service coverage area of ​​the vehicle network access point and the times when it uploads perception information to the vehicle network access point, respectively, where t is a time variable.

[0091] The probability truncation function is a mathematical function used to limit the range of probability values ​​or truncate extreme probabilities. In this method, the probability truncation function `desk` is used to filter distance correlation coefficients, retaining only data with distance correlation coefficients greater than or equal to a preset critical probability, as shown in Formula 4 below:

[0092]

[0093] In the formula, desk is the probability truncation function, b is an intermediate variable of the probability truncation function used to substitute the distance correlation coefficient, and τ is the preset critical probability. The preset critical probability τ can be set to 0.8 for example.

[0094] S1024: Select a first preset number of groups as candidate groups based on the correlation coefficient of perceived information from largest to smallest.

[0095] Specifically, it can be done by determining a first preset number l and selecting the top v groups with higher correlation coefficients of perceived information as candidate groups.

[0096] For example, based on the determination in step S101 above... Figure 2 The example shown illustrates groups and vehicle nodes within the service coverage area of ​​the vehicle-to-everything (V2X) access point. For six of these groups, the correlation coefficient of the perceived information corresponding to each group can be calculated through step S102 described above. The specific values ​​are as follows: Figure 3 As shown, the correlation coefficients of the perceived information are ordered from largest to smallest as follows: Group 6, Group 4, Group 5, Group 3, Group 1, and Group 2. If the first preset quantity l = 3, then Group 4, Group 5, and Group 6 are selected as candidate groups. This can be represented as the set Γ of candidate groups for the vehicle node k to be assigned. k ={4,5,6}.

[0097] In this embodiment, step S102 significantly optimizes vehicle network resource utilization and transmission efficiency by calculating the correlation coefficient between the perceived information of the vehicle node to be assigned and each group, and selecting candidate groups. First, based on the correlation between the vehicle distance fuzzy membership function and the perceived information, the data homogeneity of nodes within each group is accurately quantified, prioritizing highly correlated groups as candidates, reducing redundant data transmission, and alleviating bandwidth resource pressure. Second, fuzzy mathematics theory is introduced to handle the uncertainty of vehicle dynamic topology, breaking through the limitations of traditional hard threshold division and improving the flexibility and decision-making accuracy of group division in complex scenarios. Finally, the perceived information correlation coefficient, as one of the key parameters, is combined with comprehensive indicators such as service quality to collaboratively optimize multicast decisions. While ensuring the real-time nature of environmental perception, this avoids the limitations of a single indicator, providing multi-dimensional technical support for the efficient utilization and intelligent evolution of vehicle network resources.

[0098] In step S103 above, based on the service quality status set of all vehicle nodes in the candidate group and the service quality requirements of the vehicle nodes to be assigned, the service quality satisfaction coefficient of the vehicle nodes to be assigned to the candidate group is calculated. The service quality requirements of the vehicle nodes to be assigned include the requirement parameters of the vehicle nodes for multiple service quality categories. Specifically, this includes the following steps S1031-S1032:

[0099] S1031: Based on the service quality requirements of the vehicle nodes to be assigned for multiple service quality categories, determine the fuzzy membership function corresponding to each service quality category. The service quality categories may include latency, packet loss rate, latency jitter, and bandwidth. Specifically, this includes the following steps S10311-S10314:

[0100] S10311: Based on the delay requirements of the vehicle nodes to be assigned, determine the fuzzy membership function corresponding to the delay shown in Formula 5 below:

[0101]

[0102] In the formula, Let represent the fuzzy membership function corresponding to the time delay, and 'c' be the intermediate variable of the fuzzy membership function corresponding to the time delay. k For the delay requirement parameter of the vehicle node k to be assigned, delay max Let θ be the preset maximum delay, and θ be the preset exponential coefficient of the service quality fluctuation for the vehicle node k to be allocated. Meanwhile, let the multicast downlink transmission delay range be [0, delay]. max This is to ensure that the calculations are based on a reasonable range.

[0103] Among them, the maximum delay maxFor example, the delay parameter can be set to 20. k For example, it can be set to 10, with the unit being milliseconds (ms). The preset exponential coefficient θ of the vehicle node k to be assigned to the service quality fluctuation can be set to 2 for example. Then, after substituting the preset value, the above formula 5 can be expressed as:

[0104]

[0105] In the formula, Let c represent the fuzzy membership function corresponding to the time delay, and c is the intermediate variable of the fuzzy membership function corresponding to the time delay.

[0106] S10312: Based on the packet loss rate requirements of the vehicle nodes to be assigned, determine the fuzzy membership function corresponding to the packet loss rate shown in Formula 7 below:

[0107]

[0108] In the formula, Let represent the fuzzy membership function corresponding to the packet loss rate, where c is the intermediate variable of the fuzzy membership function corresponding to the packet loss rate, and loss is... k Let k be the required packet loss rate parameter for the vehicle node to be assigned, and let loss be the parameter for loss. max Let θ be the preset maximum packet loss rate, and θ be the preset exponential coefficient of the service quality fluctuation for the vehicle node k to be assigned. Meanwhile, let the packet loss rate for multicast downlink transmission range from [0, loss] to [0, loss]. max This is to ensure that the calculations are based on a reasonable range.

[0109] Among them, the maximum packet loss rate loss max For example, the loss parameter can be set to 0.1%. k For example, it can be set to 0.05%, and the preset exponential coefficient θ of the vehicle node k to be assigned to the service quality fluctuation can be set to 2. Then, after substituting the preset value, the above formula 7 can be expressed as:

[0110]

[0111] In the formula, Let represent the fuzzy membership function corresponding to the packet loss rate, and c is the intermediate variable of the fuzzy membership function corresponding to the packet loss rate.

[0112] S10313: Based on the time delay jitter requirements of the vehicle nodes to be assigned, determine the fuzzy membership function corresponding to the time delay jitter shown in Formula 9 below:

[0113]

[0114] In the formula, Let c represent the fuzzy membership function corresponding to the jitter, and c be the intermediate variable of the fuzzy membership function corresponding to the jitter. k Let jitter be the required parameter for the time delay jitter of the vehicle node k to be assigned. max Let θ be the preset maximum latency jitter, and θ be the preset exponential coefficient of the service quality fluctuation for the vehicle node k to be allocated. Meanwhile, let the latency jitter of multicast downlink transmission range from [0, jitter] to [0, jitter]. max This is to ensure that the calculations are based on a reasonable range.

[0115] Among them, the maximum latency jitter max The jitter parameter can be set to 5, which is an example of the required value. k For example, it can be set to 1, with the unit being milliseconds (ms). The preset exponential coefficient θ of the vehicle node k to be assigned to the service quality fluctuation can be set to 2. Then, after substituting the preset value, the above formula 9 can be expressed as:

[0116]

[0117] In the formula, Let c represent the fuzzy membership function corresponding to the time delay jitter, and c is the intermediate variable of the fuzzy membership function corresponding to the time delay jitter.

[0118] S10314: Based on the bandwidth requirements of the vehicle nodes to be assigned, determine the fuzzy membership function corresponding to the bandwidth shown in Formula 11 below:

[0119]

[0120] In the formula, Let represent the fuzzy membership function corresponding to the bandwidth, and 'c' be the intermediate variable of the fuzzy membership function corresponding to the bandwidth. k For the bandwidth requirement parameter of the vehicle node k to be assigned, bandwidth max The preset maximum bandwidth, bandwidth min Let θ be the preset minimum bandwidth, and θ be the preset exponential coefficient of the service quality fluctuation for the vehicle node k to be allocated. Meanwhile, let the range of multicast downlink transmission delay jitter be [bandwidth]. min ,bandwidth max This is to ensure that the calculations are based on a reasonable range.

[0121] Among them, the maximum bandwidth max For example, it can be set to 20, the minimum bandwidth. max For example, the bandwidth requirement parameter can be set to 5. kFor example, it can be set to 15, in megabits per second (Mbps). The preset exponential coefficient θ of the service quality fluctuation of the vehicle node k to be allocated can be set to 2. Then, after substituting the preset value, the above formula 11 can be expressed as:

[0122]

[0123] In the formula, Let c represent the fuzzy membership function corresponding to the bandwidth, and c is the intermediate variable of the fuzzy membership function corresponding to the bandwidth.

[0124] S1032: Based on the fuzzy membership function corresponding to each service quality category and the service quality status set of all vehicle nodes in the candidate group, the service quality satisfaction coefficient of the vehicle node to be assigned to the candidate group is calculated according to the following formula 13:

[0125]

[0126] In the formula, Satis k,i This represents the service quality satisfaction coefficient of the vehicle node k to be assigned among the i-th candidate groups, where ∑ is the summation symbol, ∏ is the product symbol, and desk is the probability cutoff function. Let Θ be the fuzzy membership function corresponding to the h-th quality of service category. i Let Θ be the set of service quality statuses of all vehicle nodes within the i-th candidate group. i ={D j,t (h)|j∈J i ,t5≤t≤t6,1≤h≤N},D j,t (h) represents the value of the multicast downlink transmission at vehicle node j with respect to the h-th quality of service category at time t in the quality of service status set, where t5 and t6 are respectively Θ i The start and end times are given, sum is a function to count the number of sets, and N is the number of service quality categories.

[0127] For example, based on the determination in step S101 above... Figure 2 The example shown illustrates groups and vehicle nodes within the service coverage area of ​​the vehicle-to-everything (V2X) access point. For the six groups, through step S102, groups 4, 5, and 6 are identified as candidate groups. Through step S103, the service quality satisfaction coefficients of the vehicle nodes to be assigned to these three candidate groups are calculated, with specific values ​​as follows: Figure 4 As shown, the service quality satisfaction coefficients are sorted from largest to smallest as follows: Group 5, Group 6, and Group 4.

[0128] In this embodiment, step S104 provides a refined decision-making basis for vehicle network resource scheduling by dynamically calculating the service quality satisfaction coefficient of candidate groups. First, based on historical service quality data, the transmission performance of groups is quantified to accurately match the real-time needs of the vehicle nodes to be allocated, prioritizing low-latency, high-stability groups to directly improve the reliability and real-time performance of emergency information transmission. Second, by combining a parameterized model and dynamic threshold design, this method can adapt to network load fluctuations, such as automatically optimizing group allocation strategies when bandwidth is tight, avoiding communication interruptions caused by network congestion. Furthermore, the service quality coefficient and the perceived correlation coefficient work synergistically to avoid bias in a single indicator, balance resource efficiency and transmission quality, and reduce reliance on redundant hardware.

[0129] In step S104 above, the candidate group with the largest product of the perceived information relevance coefficient and the service quality satisfaction coefficient is taken as the optimal allocation decision group for the vehicle nodes to be assigned.

[0130] For example, based on the determination in step S101 above... Figure 2 The example shown is of groups and vehicle nodes within the service coverage area of ​​the vehicle network access point. Through the calculations in S102-S104 above, it is determined that the product of the perception information relevance coefficient and the service quality satisfaction coefficient corresponding to group 5 is the largest. Therefore, the optimal allocation decision group for the vehicle nodes to be allocated is group 5.

[0131] In this embodiment of the application, step S104 above filters the optimal allocation decision group through the product maximization strategy, realizes multi-dimensional evaluation, completes the global optimization of vehicle network resource allocation and transmission performance, can realize the efficient utilization of network resources in complex dynamic scenarios, provide stable communication guarantee for autonomous driving, and promote the evolution of vehicle network towards lightweight and intelligent directions.

[0132] Example 2

[0133] Based on the same inventive concept, embodiments of the present invention also provide a sensing information multicast decision-making device for a sensor-integrated vehicle network, referring to... Figure 5 As shown, the device includes:

[0134] The data acquisition module 101 is used to determine all groups within the service coverage area of ​​the vehicle network access point where the vehicle node to be assigned is located, as well as all vehicle nodes within each group.

[0135] The first calculation module 102 is used to calculate the correlation coefficient of the perception information between the vehicle node to be assigned and the group based on the distance between the vehicle node to be assigned and all vehicle nodes in the group, and the set of perception information uploaded by the vehicle node to be assigned and each vehicle node in the group to the vehicle network access point, and to select multiple groups as candidate groups according to the perception information correlation coefficient from large to small.

[0136] The second calculation module 103 is used to calculate the service quality satisfaction coefficient of the vehicle node to be assigned to the candidate group based on the service quality status set of all vehicle nodes in the candidate group and the service quality requirements of the vehicle node to be assigned.

[0137] The screening decision module 104 is used to select the candidate group with the largest product of the perceived information relevance coefficient and the service quality satisfaction coefficient as the optimal allocation decision group for the vehicle nodes to be allocated.

[0138] Example 3

[0139] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the sensing information multicast decision-making method in the integrated sensory vehicle network as described in Embodiment 1 above.

[0140] Example 4

[0141] Based on the same inventive concept, embodiments of the present invention also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the sensing information multicast decision-making method in the integrated sensory vehicle network as described in Embodiment 1 above.

[0142] Example 5

[0143] Based on the same inventive concept, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, it implements the sensing information multicast decision method in the integrated sensory vehicle network as described in Embodiment 1 above.

[0144] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0145] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0146] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0148] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for sensing information groupcast decision in integrated sensing and communication vehicular networking, the method comprising: The method comprises the following steps: determining all groups in which vehicle nodes in a service coverage range of an Internet of Vehicles access point are located, and all vehicle nodes in each group; calculating a distance correlation coefficient of the to-be-assigned vehicle node and each vehicle node according to distances between the to-be-assigned vehicle node and all vehicle nodes in each group and a preset distance fuzzy membership function; calculating a correlation coefficient of perception information of the to-be-assigned vehicle node and each vehicle node in an overlapping period according to a set of perception information uploaded by the to-be-assigned vehicle node and each vehicle node to the Internet of Vehicles access point; calculating a perception information correlation degree coefficient between the to-be-assigned vehicle node and each group according to the distance correlation coefficient of the to-be-assigned vehicle node and each vehicle node and the correlation coefficient of perception information of the to-be-assigned vehicle node and each vehicle node in the overlapping period; selecting a plurality of groups as candidate groups according to the perception information correlation degree coefficient from large to small; the overlapping period is an intersection time of a time at which each vehicle node uploads perception information to the Internet of Vehicles access point and a time at which the to-be-assigned vehicle node enters the service coverage range of the Internet of Vehicles access point; determining a fuzzy membership function corresponding to each quality of service type according to requirement parameters of the to-be-assigned vehicle node for each quality of service type; calculating a quality of service satisfaction degree coefficient of the to-be-assigned vehicle node for the candidate groups based on the fuzzy membership function corresponding to each quality of service type and a set of quality of service conditions of all vehicle nodes in the candidate groups according to the following formula: where Satis k,i denotes the satisfaction degree coefficient of the service quality between the i-th candidate group for the vehicle node k to be allocated, Θ i is the set of service quality conditions of all vehicle nodes in the i-th candidate group, ∑ is the summation symbol, ∏ is the product symbol, desk is the probability truncation function, is the fuzzy membership function corresponding to the h-th service quality category, D j,t (h) is the value of the multicast downlink transmission measured by the vehicle node j at time t about the h-th service quality category at the vehicle node j in the set of service quality conditions, sum is a function of the number of statistical sets, and N is the number of service quality categories. selecting a candidate group with a maximum product of the perception information correlation degree coefficient and the quality of service satisfaction degree coefficient as an optimal allocation decision group of the to-be-assigned vehicle node.

2. The method of claim 1, wherein, The step of selecting a plurality of groups as candidate groups according to the perception information correlation degree coefficient from large to small comprises the following step: selecting a first preset number of groups as candidate groups according to the perception information correlation degree coefficient from large to small.

3. The method according to claim 1 or 2, characterized in that, The step of calculating the perception information correlation degree coefficient between the to-be-assigned vehicle node and each group according to the distance correlation coefficient of the to-be-assigned vehicle node and each vehicle node and the correlation coefficient of perception information of the to-be-assigned vehicle node and each vehicle node in the overlapping period comprises the following step: calculating the perception information correlation degree coefficient between the to-be-assigned vehicle node and each group according to the distance correlation coefficient of the to-be-assigned vehicle node and each vehicle node and the correlation coefficient of perception information of the to-be-assigned vehicle node and each vehicle node in the overlapping period based on the following formula: where Cov k,i denotes the degree of correlation coefficient of the perception information between the to-be-assigned vehicle node k and the ith group, J i denotes the set of all vehicle nodes in the ith group, sum is a function of counting the number of sets, ∑ is a summation symbol, A(||d k -d j ||) is the distance correlation coefficient between the to-be-assigned vehicle node k and the vehicle node j, desk is a probability truncation function, d k and d j are the geographic coordinates of the to-be-assigned vehicle node k and the vehicle node j, respectively, Xcorr(R j,t ,R k,t , [t1, t2]∩[t3, t4] is the correlation coefficient of the perception information between the to-be-assigned vehicle node k and the vehicle node j in the overlapping time period [t1, t2]∩[t3, t4], R j,t is the set of perception information uploaded by the vehicle node j to the vehicle Internet access point, R k,t is the set of perception information uploaded by the to-be-assigned vehicle node k to the vehicle Internet access point, long is a function of calculating the length of time, t1 and t2 are the start time and end time, respectively, of the vehicle node j uploading perception information to the vehicle Internet access point, t3 and t4 are the time when the to-be-assigned vehicle node k enters the service coverage of the vehicle Internet access point, and the time when the perception information is uploaded to the vehicle Internet access point, respectively, and t is a time variable.

4. The method according to claim 1 or 2, characterized in that, The step of calculating the distance correlation coefficient of the to-be-assigned vehicle node and each vehicle node according to distances between the to-be-assigned vehicle node and all vehicle nodes in the group and a preset distance fuzzy membership function comprises the following step: calculating the distance correlation coefficient of the to-be-assigned vehicle node and each vehicle node according to distances between the to-be-assigned vehicle node and all vehicle nodes in the group based on a formula of the preset distance fuzzy membership function: In the formula, represents a preset distance fuzzy membership function, a is a middle variable of the distance fuzzy membership function, D max is a preset upper limit of a distance-related reference value, D min is a preset lower limit of a distance-related reference value, and θ is a preset distance-related exponential coefficient.

5. The method of claim 1, wherein, The quality of service types include delay, packet loss rate, delay jitter and bandwidth. The method comprises the following steps: According to the requirement parameter of the latency of the to-be-assigned vehicle node, a fuzzy membership function corresponding to the following latency is determined: In the formula, represents a fuzzy membership function corresponding to the delay, c is a middle variable of the fuzzy membership function corresponding to the delay, delay k is a requirement parameter of the delay of the vehicle node k to be allocated, delay max is a preset maximum delay, and θ is a preset index coefficient of the quality of service fluctuation of the vehicle node k to be allocated. According to the requirement parameter of the packet loss rate of the to-be-assigned vehicle node, a fuzzy membership function corresponding to the following packet loss rate is determined: In the formula, loss loss represents a fuzzy membership function corresponding to the packet loss rate, c is a middle variable of the fuzzy membership function corresponding to the packet loss rate, loss k loss is a required parameter of the packet loss rate of the vehicle node k to be allocated, loss max loss is a preset maximum packet loss rate, and θ is a preset index coefficient of the quality of service fluctuation of the vehicle node k to be allocated. According to the requirement parameter of the latency jitter of the to-be-assigned vehicle node, a fuzzy membership function corresponding to the following latency jitter is determined: In the formula, represents a fuzzy membership function corresponding to the delay jitter, c is a middle variable of the fuzzy membership function corresponding to the delay jitter, jitter k is a requirement parameter of the vehicle node k to the delay jitter to be allocated, jitter max is a preset maximum delay jitter, and θ is a preset exponential coefficient of the quality of service fluctuation of the vehicle node k to be allocated. According to the requirement parameter of the bandwidth of the to-be-assigned vehicle node, a fuzzy membership function corresponding to the following bandwidth is determined: In the formula, represents a bandwidth corresponding fuzzy membership function, c is a middle variable of the bandwidth corresponding fuzzy membership function, bandwidth k is a requirement parameter of the bandwidth of the vehicle node k to be allocated, bandwidth max is a preset maximum bandwidth, bandwidth min is a preset minimum bandwidth, and θ is a preset index coefficient of the quality of service fluctuation of the vehicle node k to be allocated.

6. An integrated sensing and communication vehicle networking perception information groupcast decision apparatus, characterized in that, The method comprises the following steps: The data acquisition module is configured to determine all groups in the service coverage range of the Internet of Vehicles access point where the to-be-assigned vehicle node is located and all vehicle nodes in each group. The first calculation module is configured to calculate the distance correlation coefficient of the to-be-assigned vehicle node and each vehicle node according to the distance between the to-be-assigned vehicle node and all vehicle nodes in each group and a preset distance fuzzy membership function. The first calculation module is configured to calculate the perception information correlation coefficient of the to-be-assigned vehicle node and each vehicle node in the overlapping period according to the set of perception information uploaded by the to-be-assigned vehicle node and each vehicle node in the group to the Internet of Vehicles access point. The first calculation module is configured to calculate the perception information correlation degree coefficient of the to-be-assigned vehicle node and the group according to the distance correlation coefficient of the to-be-assigned vehicle node and each vehicle node and the perception information correlation coefficient of the to-be-assigned vehicle node and each vehicle node in the overlapping period. The first calculation module is configured to filter a plurality of groups as candidate groups according to the perception information correlation degree coefficient from large to small. The second calculation module is configured to determine the fuzzy membership function corresponding to each service quality category according to the requirement parameter of each service quality category of the to-be-assigned vehicle node. The second calculation module is configured to calculate the service quality satisfaction degree coefficient of the to-be-assigned vehicle node to the candidate group based on the following formula according to the fuzzy membership function corresponding to each service quality category and the set of service quality conditions of all vehicle nodes in the candidate group: where Satis k,i denotes the satisfaction degree coefficient of the service quality of the i-th candidate group for the vehicle node k to be allocated, Θ i is the set of service quality conditions of all vehicle nodes in the i-th candidate group, ∑ is the summation symbol, ∏ is the product symbol, desk is the probability truncation function, is the fuzzy membership function corresponding to the h-th service quality category, D j,t (h) is the value of the multicast downlink transmission measured by the vehicle node j at time t at the vehicle node j about the h-th service quality category in the set of service quality conditions, sum is a function of the number of statistical sets, and N is the number of service quality categories. The filtering decision module is configured to take the candidate group with the maximum product of the perception information correlation degree coefficient and the service quality satisfaction degree coefficient as the optimal allocation decision group of the to-be-assigned vehicle node.

7. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to realize the perception information multicasting decision method in the integrated sensing and communication Internet of Vehicles.

8. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to realize the perception information multicasting decision method in the integrated sensing and communication Internet of Vehicles.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-8. The processor executes the computer program to realize the perception information multicasting decision method in the integrated sensing and communication Internet of Vehicles.

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