Sensing information multicast decision-making method and device in communication and sensing integrated Internet of Vehicles

Abstract

The invention discloses a sensing information multicast decision-making method and device in a communication and sensing integrated Internet of Vehicles. The method comprises the steps of determining all groups in an Internet of Vehicles access point service coverage range where to-be-allocated vehicle nodes are located and all vehicle nodes in each group; according to the distance between the to-be-distributed vehicle node and all the vehicle nodes in the group and a sensing information set which is uploaded to the vehicle networking access point by the to-be-distributed vehicle node and all the vehicle nodes in the group, calculating a sensing information correlation degree coefficient of each group so as to screen an alternative group; calculating a service quality satisfaction degree coefficient of each alternative group according to the service quality condition set of all the vehicle nodes in the alternative groups; and taking the alternative group with the maximum product of the perceptual information correlation degree coefficient and the service quality satisfaction degree coefficient as an optimal allocation decision group. According to the method, resource consumption can be effectively reduced, communication efficiency can be improved, and high-precision and light-weight intelligent support is provided for the Internet of Vehicles.

Description

Technical Field

[0001] The present invention relates to a method and apparatus for multicast decision-making of sensing information in an integrated communication and sensing vehicle network. Background Art

[0002] Intelligent transportation systems can effectively integrate advanced communication technologies, information technologies, sensing technologies, control technologies, computer technologies, etc. to form a comprehensive transportation system that ensures safety, improves efficiency, improves the environment, and saves energy, thereby establishing a real-time, accurate, and efficient large-scale and all-round intelligent integrated transportation system. As a link enabling information sharing and interactive decision-making between vehicles, vehicle-road, vehicle-people, and vehicle-network, the vehicle network supported by wireless communication network technology is the cornerstone of building an intelligent transportation system. However, the current vehicle network is facing the dual challenges of scarce frequency band resources and high hardware redundancy. Due to the scarcity of frequency band resources, it is necessary to complete the efficient transmission of massive data and realize the real-time sensing of external information under limited network bandwidth. At the same time, considering the high hardware redundancy will hinder the development of the vehicle network towards low-cost and lightweight directions.

[0003] In this context, integrated communication and sensing emerges as an exciting research field, aiming to organically combine communication technology and sensing technology to realize environmental perception and intelligent processing of information, thereby constructing a more intelligent and efficient communication system. With the development of wireless communication network technology, a new framework for integrating communication and sensing to empower the vehicle network has emerged. By deeply integrating sensing and communication functions, it improves the utilization rate of frequency band resources and also achieves a qualitative leap in simplifying hardware design, thereby enhancing the real-time response ability of the vehicle network and improving the stability of the network to construct an efficient, safe, and intelligent vehicle network.

[0004] In the multi-vehicle cooperation mode, vehicles can access a wide range of data from other vehicles and traffic infrastructure instead of relying solely on their own sensing data, thereby expanding the scope of their environmental perception. Through the information sharing mechanism, each vehicle can obtain information beyond its direct sensing boundary, enabling it to consider a more comprehensive traffic environment when making decisions. When vehicles need to communicate with the vehicle network access point to exchange sensing information to further assist in autonomous driving decision-making, due to the complex road traffic environment of the vehicle network, the broadcast method will have problems such as large message propagation delay and high message redundancy rate caused by serious channel contention, which will affect the normal propagation and reception of emergency event messages. The multicast method, by sending data only to a specific group of receivers instead of all nodes, can effectively solve the problem of single-point sending and multi-point receiving, realize efficient data propagation from a single point to multiple points in the network, and can greatly save network bandwidth, reduce network load, and reduce message redundancy rate. Summary of the Invention

[0005] To solve the decision-making problem of vehicle belonging groups during multicast downlink data transmission and obtain the optimal allocation decision group for the vehicle nodes to be allocated, an embodiment of the present invention provides a method and device for sensing information multicast decision-making in a communication-sensing integrated vehicle network.

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

[0007] Determine all groups within the service coverage of the vehicle network access point where the vehicle nodes to be allocated are located, and all vehicle nodes within each group;

[0008] According to the distances between the vehicle nodes to be allocated and all vehicle nodes within the group, and the sets of sensing information respectively uploaded by the vehicle nodes to be allocated and each vehicle node within the group to the vehicle network access point, calculate the correlation coefficient of sensing information between the vehicle nodes to be allocated and the group, and screen multiple groups as alternative groups according to the descending order of the correlation coefficient of sensing information;

[0009] According to the set of service quality statuses of all vehicle nodes within the alternative groups and the requirements of the vehicle nodes to be allocated for service quality, calculate the service quality satisfaction coefficient of the vehicle nodes to be allocated for the alternative groups;

[0010] Take the alternative group with the largest product of the correlation coefficient of sensing information and the service quality satisfaction coefficient as the optimal allocation decision group for the vehicle nodes to be allocated.

[0011] In one or some optional implementation manners of the embodiments of the present application, the step of calculating the correlation coefficient of sensing information between the vehicle nodes to be allocated and the group according to the distances between the vehicle nodes to be allocated and all vehicle nodes within the group, and the sets of sensing information respectively uploaded by the vehicle nodes to be allocated and each vehicle node within the group to the vehicle network access point, and screening multiple groups as alternative groups according to the descending order of the correlation coefficient of sensing information includes:

[0012] According to the distances between the vehicle nodes to be allocated and all vehicle nodes within the group, and a preset distance fuzzy membership function, calculate the distance correlation coefficient between the vehicle nodes to be allocated and each vehicle node;

[0013] According to the sets of sensing information uploaded by the vehicle nodes to be allocated and each vehicle node within the group to the vehicle network access point, calculate the correlation coefficient of sensing information between the vehicle nodes to be allocated and each vehicle node during the overlapping period;

[0014] Calculate the correlation coefficient of the perception information between the vehicle node to be allocated and the group according to the distance correlation coefficient between the vehicle node to be allocated and each vehicle node, and the correlation coefficient of the perception information between the vehicle node to be allocated and each vehicle node during the overlapping period:

[0015] Select the first preset number of groups as alternative groups according to the correlation coefficient of the perception information from large to small.

[0016] In one or some optional embodiments of the present application, the calculating the correlation coefficient of the perception information between the vehicle node to be allocated and the group according to the distance correlation coefficient between the vehicle node to be allocated and each vehicle node, and the correlation coefficient of the perception information between the vehicle node to be allocated and each vehicle node during the overlapping period includes:

[0017] Calculate the correlation coefficient of the perception information between the vehicle node to be allocated and the group based on the following formula according to the distance correlation coefficient between the vehicle node to be allocated and each vehicle node, and the correlation coefficient of the perception information between the vehicle node to be allocated and each vehicle node during the overlapping period:

[0018]

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

[0020] In one or some alternative embodiments of the embodiments of the present application, calculating the distance correlation coefficient between the vehicle node to be allocated and each vehicle node according to the distances between the vehicle node to be allocated and all vehicle nodes in the group and a preset distance fuzzy membership function includes:

[0021] Calculating the distance correlation coefficient between the vehicle node to be allocated and each vehicle node according to the distances between the vehicle node to be allocated and all vehicle nodes in the group, based on the formula of the following preset distance fuzzy membership function:

[0022]

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

[0024] In one or some alternative embodiments of the embodiments of the present application, the requirements of the vehicle node to be allocated for the service quality include the requirement parameters of the vehicle node to be allocated for multiple service quality types respectively;

[0025] Calculating the service quality satisfaction degree coefficient of the vehicle node to be allocated for the alternative group according to the service quality status set of all vehicle nodes in the alternative group and the requirements of the vehicle node to be allocated for the service quality includes:

[0026] Determining the fuzzy membership function corresponding to each service quality type according to the requirement parameters of the vehicle node to be allocated for multiple service quality types respectively;

[0027] Calculating the service quality satisfaction degree coefficient of the vehicle node to be allocated for the alternative group based on the fuzzy membership function corresponding to each service quality type and the service quality status set of all vehicle nodes in the alternative group according to the following formula:

[0028]

[0029] In the formula, Satis k,i represents the service quality satisfaction degree coefficient of the vehicle node k to be allocated for the i-th alternative group, Θ i is the service quality status set of all vehicle nodes in the i-th alternative group, ∑ is the summation symbol, ∏ is the product symbol, and desk is the probability truncation function, is the fuzzy membership function corresponding to the h-th quality of service category, D j,t (h) is the value of the multicast downlink transmission measured at vehicle node j at time t in the set of quality of service conditions for the h-th quality of service category at vehicle node j, sum is a function for counting the number of sets in the statistical set, and N is the number of quality of service categories.

[0030] In one or some alternative embodiments of the present application, the quality of service categories include delay, packet loss rate, delay jitter, and bandwidth;

[0031] Determining the fuzzy membership function corresponding to each quality of service category according to the requirement parameters of the to-be-allocated vehicle nodes for multiple quality of service categories includes:

[0032] According to the requirement parameter of the to-be-allocated vehicle node for delay, determine the following fuzzy membership function corresponding to delay:

[0033]

[0034] In the formula, represents the fuzzy membership function corresponding to delay, c is the intermediate variable of the fuzzy membership function corresponding to delay, delay k is the requirement parameter of the to-be-allocated vehicle node k for delay, delay max is the preset maximum delay, and θ is the exponential coefficient of the to-be-allocated vehicle node k for the quality of service fluctuation;

[0035] According to the requirement parameter of the to-be-allocated vehicle node for the packet loss rate, determine the following fuzzy membership function corresponding to the packet loss rate:

[0036]

[0037] In the formula, represents the fuzzy membership function corresponding to the packet loss rate, c is the intermediate variable of the fuzzy membership function corresponding to the packet loss rate, loss k is the requirement parameter of the to-be-allocated vehicle node k for the packet loss rate, loss max is the preset maximum packet loss rate, and θ is the exponential coefficient of the to-be-allocated vehicle node k for the quality of service fluctuation;

[0038] According to the requirement parameter of the to-be-allocated vehicle node for delay jitter, determine the following fuzzy membership function corresponding to delay jitter:

[0039]

[0040] In the formula, represents the fuzzy membership function corresponding to delay jitter, c is the intermediate variable of the fuzzy membership function corresponding to delay jitter, jitter kjitter is the parameter requirement of the vehicle node k to be allocated for jitter max is the preset maximum jitter, and θ is the exponential coefficient of the vehicle node k to be allocated for the service quality fluctuation preset;

[0041] According to the parameter requirement of the vehicle node to be allocated for bandwidth, determine the fuzzy membership function corresponding to the following bandwidth:

[0042]

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

[0044] In a second aspect, an embodiment of the present invention provides a perception information multicast decision-making device in an integrated communication and sensing vehicle network, which may include:

[0045] A data acquisition module, configured to determine all groups within the service coverage range of the vehicle network access point where the vehicle node to be allocated is located, and all vehicle nodes in each group;

[0046] A first calculation module, configured to calculate the correlation degree coefficient of the perception information between the vehicle node to be allocated and the group according to the distances between the vehicle node to be allocated and all vehicle nodes in the group, and the set of perception information uploaded by the vehicle node to be allocated and each vehicle node in the group to the vehicle network access point, and screen multiple groups as alternative groups from large to small according to the correlation degree coefficient of the perception information;

[0047] A second calculation module, configured to calculate the service quality satisfaction degree coefficient of the vehicle node to be allocated for the alternative group according to the set of service quality conditions of all vehicle nodes in the alternative group and the service quality requirements of the vehicle node to be allocated;

[0048] A screening decision module, configured to use the alternative group with the largest product of the correlation degree coefficient of the perception information and the service quality satisfaction degree coefficient as the optimal allocation decision group of the vehicle node to be allocated.

[0049] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program / instructions are stored, and when the computer program / instructions are executed by a processor, the perception information multicast decision-making method in the integrated communication and sensing vehicle network as described above is implemented.

[0050] In a fourth aspect, an embodiment of the present invention provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the method for multicast decision-making of sensing information in the integrated communication and sensing vehicle network as described above.

[0051] In a fifth aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory, which, when executed by the processor, implement the method for multicast decision-making of sensing information in the integrated communication and sensing vehicle network as described above.

[0052] The beneficial effects of the above technical solutions provided by the embodiments of the present invention at least include:

[0053] An embodiment of the present invention provides a method for multicast decision-making of sensing information in an integrated communication and sensing vehicle network. In this method, in the vehicle network, first, all groups and their vehicle nodes within the service range of the access point where the vehicle node to be allocated is located are determined. Then, according to the distances between the vehicle node to be allocated and the vehicle nodes in each group and the sensing information uploaded by them, the correlation coefficient of the sensing information is calculated, and multiple groups are selected as alternative groups according to the correlation coefficient of the sensing information. Next, in combination with the service quality status of each alternative group and the requirements of the vehicle node to be allocated, the service quality satisfaction coefficient is calculated. Finally, the group with the largest product of the correlation coefficient of the sensing information and the service quality satisfaction coefficient is used as the optimal allocation decision group. This method provides significant technical advantages for the vehicle network by dynamically optimizing the multicast decision-making mechanism of sensing information. Based on the correlation coefficient of the sensing information, alternative groups are accurately selected, significantly reducing redundant data transmission and effectively alleviating the problem of tight bandwidth resources. Secondly, by combining the historical service quality data of the alternative groups with the real-time requirements of the vehicle node to be allocated, the service quality satisfaction is quantitatively evaluated to ensure high adaptability in various service qualities of the multicast transmission, improving the emergency information transmission efficiency, and achieving the global optimal balance of resource allocation and transmission performance. It not only ensures the real-time sharing of sensing information but also reduces the network load and hardware redundancy cost, significantly enhancing the intelligent level, network stability, and accuracy of autonomous driving decision-making of the vehicle network in complex dynamic traffic scenarios, and providing an efficient and lightweight solution for the efficient operation of the intelligent transportation system.

[0054] Other features and advantages of the present invention will be described in the following specification, and, in part, will become apparent from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written specification and the drawings.

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

[0056] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the accompanying drawings:

[0057] Figure 1 It is a schematic flowchart of a method for making a perception information multicast decision in a sense-communication integrated vehicle network provided by an embodiment of the present invention;

[0058] Figure 2 It is an example diagram of a vehicle network provided by an embodiment of the present invention;

[0059] Figure 3 It is a schematic diagram of the correlation degree coefficient of perception information of each group provided by an embodiment of the present invention;

[0060] Figure 4 It is a schematic diagram of the service quality satisfaction degree coefficient of each alternative group provided by an embodiment of the present invention;

[0061] Figure 5 It is a schematic structural diagram of a perception information multicast decision device in a sense-communication integrated vehicle network provided by an embodiment of the present application. Detailed implementation manners

[0062] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that a more thorough understanding of the present disclosure can be obtained and the scope of the present disclosure can be fully communicated to those skilled in the art.

[0063] The inventors found that in the prior art, due to the deficiency of the broadcast method for downlink perception information dissemination, a dynamic topology map formed by analyzing and modeling vehicle position information in a vehicle high-speed driving scenario can be used to perform downlink perception information transmission for a group of vehicles with similar perception information requirements using the multicast method. Therefore, it is necessary to consider how to decide the group to which a vehicle belongs after a new vehicle node enters the service area of a vehicle network access point, so that the vehicle network access point can multicast and downlink the processed comprehensive perception information to this group to meet the requirements of reasonable allocation of communication perception resources and efficient sharing of perception data in an autonomous driving scenario. Based on this, after further research and development, the inventors made the present invention and provided a method and device for making a perception information multicast decision in a sense-communication integrated vehicle network.

[0064] Embodiment 1

[0065] In Embodiment 1 of the present invention, a method for making a perception information multicast decision in a sense-communication integrated vehicle network is provided. Referring to Figure 1As shown in the figure, the method may include the following steps S101 - S104:

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

[0067] S102: Calculate the correlation coefficient of the perception information between the vehicle node to be assigned and the group according to the distances between the vehicle node to be assigned and all vehicle nodes within the group, and the set of perception information uploaded by the vehicle node to be assigned and each vehicle node within the group respectively. Then, screen multiple groups as alternative groups according to the correlation coefficient of the perception information from large to small.

[0068] S103: Calculate the satisfaction coefficient of the service quality of the vehicle node to be assigned for the alternative groups according to the set of service quality statuses of all vehicle nodes within the alternative groups and the requirements of the vehicle node to be assigned for the service quality.

[0069] S104: Take the alternative group with the largest product of the correlation coefficient of the perception information and the satisfaction coefficient of the service quality as the optimal allocation decision group for the vehicle node to be assigned.

[0070] The embodiment of the present invention provides a method for multicast decision-making of perception information in an integrated communication and sensing vehicle networking. This 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 in the vehicle networking. Then, according to the distances between the vehicle node to be assigned and the vehicle nodes within each group and the perception information they upload, calculate the correlation coefficient of the perception information, and screen multiple groups as alternative groups according to the correlation coefficient of the perception information. Next, combine the service quality statuses of each alternative group and the requirements of the vehicle node to be assigned to calculate the satisfaction coefficient of the service quality. Finally, take the group with the largest product of the correlation coefficient of the perception information and the satisfaction coefficient of the service quality as the optimal allocation decision group. This method provides significant technical advantages for the vehicle networking by dynamically optimizing the multicast decision-making mechanism of the perception information. Based on the correlation coefficient of the perception information, it accurately screens alternative groups, greatly reduces redundant data transmission, and effectively alleviates the problem of tight frequency band resources. Secondly, by combining the historical service quality data of the alternative groups with the real-time requirements of the vehicle node to be assigned, it quantitatively evaluates the satisfaction of the service quality, ensures high adaptability in various service qualities of the multicast transmission, improves the efficiency of emergency information transmission, and realizes the global optimal balance of resource allocation and transmission performance. It not only guarantees the real-time sharing of perception information but also reduces the network load and hardware redundancy cost. In complex dynamic traffic scenarios, this method significantly enhances the intelligent level, network stability, and accuracy of autonomous driving decisions of the vehicle networking, providing an efficient and lightweight solution for the efficient operation of the intelligent transportation system.

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

[0072] Specifically, in the integrated communication and sensing vehicle networking, each vehicle node can be grouped differently by the vehicle networking access point. At the same time, each vehicle node can upload the perception information of the surrounding environment to the vehicle networking access point, and the vehicle networking access point can multicast the processed comprehensive perception information to different groups. When the vehicle node to be assigned enters the service coverage of the vehicle networking access point, the vehicle networking access point needs to assign a suitable group for the vehicle node to be assigned according to the existing information. Among them, the existing information includes all groups within the service coverage of the vehicle networking access point, and all vehicle nodes within each group.

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

[0074] In the above step S102, according to the distances between the vehicle node to be assigned and all vehicle nodes within the group, and the set of perception information respectively uploaded by the vehicle node to be assigned and each vehicle node within the group to the vehicle networking access point, the correlation coefficient of the perception information between the vehicle node to be assigned and the group is calculated, and multiple groups are selected as alternative groups according to the descending order of the correlation coefficient of the perception information. Specifically, it includes the following steps S1021 - S1024:

[0075] S1021: Calculate the distance correlation coefficient between the vehicle node to be assigned and each vehicle node according to the distances between the vehicle node to be assigned and all vehicle nodes in the group, and a preset distance fuzzy membership function.

[0076] Specifically, it can be that, according to the distances between the vehicle node to be assigned and all vehicle nodes in the group, based on the preset distance fuzzy membership function shown in the following formula 1, calculate the distance correlation coefficient between the vehicle node to be assigned and each vehicle node:

[0077]

[0078] In the formula, represents the preset distance fuzzy membership function, a is the intermediate variable of the distance fuzzy membership function, D max is the upper limit of the preset distance correlation reference value, D min is the lower limit of the preset distance correlation reference value, θ is the exponential coefficient related to the preset distance. At the same time, let the value range of the distance between vehicle nodes be [0, D max , to ensure that the calculation is based on a reasonable range.

[0079] Among them, the upper limit D max of the distance correlation reference value can be exemplarily set to 2, the lower limit D min of the distance correlation reference value can be exemplarily set to 0.5, the unit is kilometers, and the exponential coefficient θ related to the distance can be exemplarily set to 2. Then, after substituting the preset values, the above formula 1 can be expressed as:

[0080]

[0081] In the formula, represents the preset distance fuzzy membership function, a is the intermediate variable of the distance fuzzy membership function.

[0082] In the embodiment of the present application, in the above step S1021, due to the increasing complexity of decision-making problems and the uncertainty of decision-making scenarios, coupled with the inherent ambiguity of decision-makers' thinking, in some uncertain decision-making scenarios, it is increasingly difficult for decision-makers to express their preferences for decision-making objects in certain decisions using precise numbers. Therefore, this method introduces fuzzy mathematics theory into the synaesthetic integrated vehicle network, and presets a distance fuzzy membership function to quantify the distance correlation coefficient between vehicle nodes, serving as the data basis for the subsequent correlation degree coefficient of perception information. Through the non-linear mapping of the fuzzy membership function, this method can effectively handle the distance uncertainty in the dynamic movement of vehicles, avoid the problem of rigid group boundaries caused by traditional hard threshold division, and thus improve the decision-making flexibility in complex traffic scenarios. In addition, through parametric design, such as exponential coefficients and upper and lower limits of reference values, this method realizes the dynamic adjustment of distance correlation, can adaptively optimize the group division accuracy according to the actual road network density or communication environment changes, further enhance the robustness and universality of the system, and provide theoretical support and practical feasibility for the efficient resource scheduling of the vehicle network.

[0083] S1022: Calculate the correlation coefficient of the perception information of the vehicle node to be allocated and each vehicle node in the group within the overlapping period according to the set of perception information uploaded by the vehicle node to be allocated and each vehicle node in the group to the vehicle network access point.

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

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

[0086] Determine the overlapping period of vehicle node j and the vehicle node k to be assigned, that is, the intersection of the start time t1 and the end time t2 when vehicle node j uploads sensing information to the vehicle network access point, and the time t3 when the vehicle node k to be assigned enters the service coverage range of the vehicle network access point and the time t4 when it uploads sensing information to the vehicle network access point, that is, [t1, t2] ∩ [t3, t4], and calculate the correlation coefficient of all sensing information of vehicle node j and the vehicle node k to be assigned during this period, that is, Xcorr(R j,t ,R k,t ,[t1,t2]∩[t3,t4]), where Xcorr represents the correlation coefficient, which can be calculated by the Pearson correlation coefficient formula or other correlation coefficient formulas, and is not specifically limited here.

[0087] In the embodiment of this application, by calculating the correlation coefficient of sensing information between vehicle nodes in the above step S1022, the spatio-temporal data consistency is accurately quantified, redundant transmission is effectively reduced, and the utilization rate of sensing information is improved. Based on the matching of sensing data in the common period, the homogeneity of requirements within the multicast group is ensured, the network load and delay jitter are significantly reduced, and the real-time performance of emergency event transmission is enhanced.

[0088] S1023: According to the distance correlation coefficient between the vehicle node to be assigned and each vehicle node, and the correlation coefficient of the sensing information between the vehicle node to be assigned and each vehicle node during the overlapping period, calculate the correlation degree coefficient of the sensing information between the vehicle node to be assigned and the group based on the following formula 3:

[0089]

[0090] In the formula, Cov k,i represents the correlation degree coefficient of the sensing information between the vehicle node k to be assigned and the i-th group, J i represents the set of all vehicle nodes in the i-th group, sum is a function for counting the number of elements in the set, ∑ is the summation symbol, A(||d k -d j ||) is the distance correlation coefficient between the vehicle node k to be assigned and the vehicle node j, desk is the probability truncation function, d k and d j are the geographical coordinates of the vehicle node k to be assigned and the vehicle node j respectively, Xcorr(R j,t ,R k,t ,[t1,t2]∩[t3,t4] is the correlation coefficient of the sensing information between the vehicle node k to be assigned and the vehicle node j during the overlapping period [t1, t2] ∩ [t3, t4], R j,t is the set of sensing information uploaded by the vehicle node j to the vehicle network access point, R k,t$\Phi$ is the set of perception information uploaded by the vehicle node $k$ to be assigned to the vehicle network access point, long is a function of the calculation time length, $t_1$ and $t_2$ are respectively the start time and end time when the vehicle node $j$ uploads perception information to the vehicle network access point, $t_3$ and $t_4$ are respectively the time when the vehicle node $k$ to be assigned enters the service coverage area of the vehicle network access point, and the time when it uploads perception information to the vehicle network access point, and $t$ is a time variable.

[0091] Among them, 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 screen the distance correlation coefficients, and only the data with distance correlation coefficients greater than or equal to the preset critical probability are retained, 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, which is used to substitute the distance correlation coefficient, and $\tau$ is the preset critical probability. Among them, the preset critical probability $\tau$ can be exemplarily set to 0.8.

[0094] S1024: Screen the first preset number of groups as alternative groups according to the perception information correlation degree coefficients from large to small.

[0095] Specifically, it can be to determine the first preset number $l$, and take the first $v$ groups with larger perception information correlation degree coefficients as alternative groups.

[0096] Illustrate with an example. According to the Figure 2 shown example of groups and vehicle nodes within the service coverage area of the vehicle network access point determined in step S101 above, for the 6 groups among them, through the above step S102, the perception information correlation degree coefficients corresponding to the 6 groups can be calculated, and the specific values are as Figure 3 shown. Among them, the perception information correlation degree coefficients are sorted from large to small as follows: group 6, group 4, group 5, group 3, group 1, group 2. If the first preset number $l = 3$, then take group 4, group 5, and group 6 as alternative groups. It can be expressed as the alternative group set $\Gamma$ of the vehicle node $k$ to be assigned k $=\{4,5,6\}$.

[0097] In the embodiment of the present application, in the above step S102, by calculating the correlation coefficient of the perception information between the vehicle node to be allocated and each group, and screening to obtain alternative groups, the utilization and transmission efficiency of the vehicle networking resources are significantly optimized. First, based on the correlation between the vehicle distance fuzzy membership function and the perception information, the data homogenization degree of the nodes in each group is accurately quantified, and the groups with high correlation are preferentially screened as alternatives, reducing redundant data transmission and alleviating the pressure on the frequency band resources. Second, the fuzzy mathematics theory is introduced to process the uncertainty of the vehicle dynamic topology, breaking through the limitations of the traditional hard threshold division, and improving the flexibility and decision-making accuracy of group division in complex scenarios. Finally, as one of the key parameters, the correlation coefficient of the perception information, combined with comprehensive indicators such as the quality of service, collaboratively optimizes the multicast decision. While ensuring the real-time nature of environmental perception, it avoids the limitations of a single indicator, providing multi-dimensional technical support for the efficient utilization and intelligent evolution of the vehicle networking resources.

[0098] In the above step S103, according to the set of quality of service conditions of all vehicle nodes in the alternative group and the requirements of the vehicle node to be allocated for the quality of service, the satisfaction coefficient of the vehicle node to be allocated for the alternative group is calculated. Among them, the requirements of the vehicle node to be allocated for the quality of service include the requirement parameters of the vehicle node to be allocated for multiple quality of service types respectively. Specifically, it includes the following steps S1031 - S1032:

[0099] S1031: According to the requirement parameters of the vehicle node to be allocated for multiple quality of service types respectively, determine the fuzzy membership function corresponding to each quality of service type. Among them, the quality of service types may include delay, packet loss rate, delay jitter, and bandwidth. Specifically, it includes the following steps S10311 - S10314:

[0100] S10311: According to the requirement parameter of the vehicle node to be allocated for delay, determine the fuzzy membership function of delay as shown in formula 5 below:

[0101]

[0102] In the formula, represents the fuzzy membership function of delay, c is the intermediate variable of the fuzzy membership function of delay, delay k is the requirement parameter of the vehicle node k to be allocated for delay, delay max is the preset maximum delay, and θ is the exponential coefficient of the vehicle node k to be allocated for the quality of service fluctuation. At the same time, it is assumed that the delay value range of the multicast downlink transmission is [0, delay max , to ensure that the calculation is based on a reasonable range.

[0103] Among them, the maximum delay delay maxIt can be exemplarily set to 20, and the required parameter delay of the time delay k It can be exemplarily set to 10, with the unit of millisecond (ms). The exponential coefficient θ of the preset vehicle node k to be allocated for the service quality fluctuation can be exemplarily set to 2. After substituting the preset values, the above formula 5 can be expressed as:

[0104]

[0105] In the formula, represents 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: According to the required parameter of the vehicle node to be allocated for the packet loss rate, determine the fuzzy membership function corresponding to the packet loss rate as shown in the following formula 7:

[0107]

[0108] In the formula, represents the fuzzy membership function corresponding to the packet loss rate, c is the intermediate variable of the fuzzy membership function corresponding to the packet loss rate, and loss k is the required parameter of the vehicle node k to be allocated for the packet loss rate, and loss max is the preset maximum packet loss rate, and θ is the exponential coefficient of the preset vehicle node k to be allocated for the service quality fluctuation. At the same time, it is assumed that the value range of the packet loss rate of the multicast downlink transmission is [0, loss max to ensure that the calculation is based on a reasonable range.

[0109] Among them, the maximum packet loss rate loss max can be exemplarily set to 0.1%, the required parameter loss of the packet loss rate k can be exemplarily set to 0.05%, and the exponential coefficient θ of the preset vehicle node k to be allocated for the service quality fluctuation can be exemplarily set to 2. After substituting the preset values, the above formula 7 can be expressed as:

[0110]

[0111] In the formula, represents 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: According to the required parameter of the vehicle node to be allocated for the delay jitter, determine the fuzzy membership function corresponding to the delay jitter as shown in the following formula 9:

[0113]

[0114] In the formula, Represents the fuzzy membership function corresponding to the delay jitter. c is the intermediate variable of the fuzzy membership function corresponding to the delay jitter, and jitter k Is the required parameter of the delay jitter for the vehicle node k to be allocated, jitter max Is the preset maximum delay jitter, and θ is the exponential coefficient of the service quality fluctuation for the vehicle node k to be allocated. At the same time, it is assumed that the value range of the delay jitter in the multicast downlink transmission is [0, jitter max , to ensure that the calculation is based on a reasonable range.

[0115] Among them, the maximum delay jitter jitter max Can be exemplarily set to 5, and the required parameter of the delay jitter jitter k Can be exemplarily set to 1, with the unit of millisecond (ms). The exponential coefficient θ of the service quality fluctuation for the vehicle node k to be allocated can be exemplarily set to 2. Then, after substituting the preset values, the above formula 9 can be expressed as:

[0116]

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

[0118] S10314: According to the required parameter of the bandwidth for the vehicle node to be allocated, determine the fuzzy membership function corresponding to the bandwidth shown in the following formula 11:

[0119]

[0120] In the formula, Represents the fuzzy membership function corresponding to the bandwidth, c is the intermediate variable of the fuzzy membership function corresponding to the bandwidth, and bandwidth k Is the required parameter of the bandwidth for the vehicle node k to be allocated, bandwidth max Is the preset maximum bandwidth, bandwidth min Is the preset minimum bandwidth, and θ is the exponential coefficient of the service quality fluctuation for the vehicle node k to be allocated. At the same time, it is assumed that the value range of the delay jitter in the multicast downlink transmission is [bandwidth min , bandwidth max , to ensure that the calculation is based on a reasonable range.

[0121] Among them, the maximum bandwidth bandwidth max Can be exemplarily set to 20, the minimum bandwidth bandwidth max Can be exemplarily set to 5, and the required parameter of the bandwidth bandwidth kIt can be exemplarily set to 15, with the unit of megabits per second (Mbps). The exponential coefficient θ of the preset service quality fluctuation for the to-be-allocated vehicle node k can be exemplarily set to 2. Then, after substituting the preset values, the above formula 11 can be expressed as:

[0122]

[0123] In the formula, represents 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: According to the fuzzy membership function corresponding to each service quality type and the service quality status set of all vehicle nodes in the alternative group, calculate the service quality satisfaction degree coefficient of the to-be-allocated vehicle node for the alternative group based on the following formula 13:

[0125]

[0126] In the formula, Satis k,i represents the service quality satisfaction degree coefficient of the to-be-allocated vehicle node k for the i-th alternative 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 type, Θ i is the service quality status set of all vehicle nodes in the i-th alternative group, which can be expressed as Θ i ={D j,t (h)|j∈J i ,t5≤t≤t6,1≤h≤N}, D j,t (h) is the value of the multicast downlink transmission measured at vehicle node j at time t for the h-th service quality type at vehicle node j in the service quality status set. t5 and t6 are the start time and end time of Θ i respectively, sum is the function for counting the number of sets, and N is the number of service quality types.

[0127] Illustrate with an example. According to the group and vehicle node examples within the service coverage range of the vehicle networking access point determined in the above step S101 Figure 2 shown, for the 6 groups among them, through the above step S102, groups 4, 5, and 6 can be determined as alternative groups. Through the above step S103, calculate the service quality satisfaction degree coefficients of the to-be-allocated vehicle node for these 3 alternative groups. The specific values are as Figure 4 shown, and the service quality satisfaction degree coefficients are sorted from largest to smallest as follows: group 5, group 6, group 4.

[0128] In the embodiment of the present application, step S104 provides a refined decision-making basis for vehicle networking resource scheduling by dynamically calculating the service quality satisfaction coefficient of alternative groups. First, the group transmission performance is quantified based on historical service quality data, and the real-time requirements of the vehicle nodes to be allocated are accurately matched. Groups with low latency and high stability are preferentially selected to directly improve the reliability and real-time performance of emergency information transmission. Second, combined with the design of a parametric model and a dynamic threshold, this method can adapt to network load fluctuations. For example, when the bandwidth is tight, it can automatically optimize the group allocation strategy to avoid communication interruptions caused by network congestion. In addition, the service quality coefficient and the perception correlation coefficient work together to avoid deviations in a single indicator, balance resource efficiency and transmission quality, and reduce the dependence on redundant hardware.

[0129] In step S104 above, the alternative group with the largest product of the perception information correlation coefficient and the service quality satisfaction coefficient is used as the optimal allocation decision group for the vehicle nodes to be allocated.

[0130] For example, according to the Figure 2 shown example of groups and vehicle nodes within the service coverage of the vehicle networking access point determined in step S101 above, through the calculations of S102 - S104 above, it is determined that the product of the perception information correlation coefficient and the service quality satisfaction coefficient corresponding to group 5 is the largest. Then, the optimal allocation decision group for the vehicle nodes to be allocated is group 5.

[0131] In the embodiment of the present application, step S104 screens the optimal allocation decision group through a product maximization strategy to achieve multi-dimensional evaluation, complete the global optimization of vehicle networking resource allocation and transmission performance, and can achieve the efficient utilization of network resources in complex dynamic scenarios, provide stable communication guarantee for autonomous driving, and at the same time promote the evolution of vehicle networking towards lightweight and intelligent directions.

[0132] Embodiment 2

[0133] Based on the same inventive concept, the embodiment of the present invention also provides a perception information multicast decision-making device in a communication and sensing integrated vehicle networking. Referring to Figure 5 shown, this device includes:

[0134] A data acquisition module 101, configured to determine all groups within the service coverage of the vehicle networking access point where the vehicle nodes to be allocated are located, and all vehicle nodes within each group;

[0135] The first calculation module 102 is configured to calculate a correlation coefficient of the perception information between the vehicle node to be allocated and the group according to the distances between the vehicle node to be allocated and all vehicle nodes in the group, and the sets of perception information respectively uploaded by the vehicle node to be allocated and each vehicle node in the group, and screen multiple groups as alternative groups according to the correlation coefficient of the perception information from large to small;

[0136] The second calculation module 103 is configured to calculate a satisfaction coefficient of the service quality of the vehicle node to be allocated for the alternative group according to the set of service quality conditions of all vehicle nodes in the alternative group and the requirements of the vehicle node to be allocated for the service quality;

[0137] The screening decision module 104 is configured to use the alternative group with the largest product of the correlation coefficient of the perception information and the satisfaction coefficient of the service quality as the optimal allocation decision group of the vehicle node to be allocated.

[0138] Embodiment III

[0139] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program / instructions are stored, and when the computer program / instructions are executed by a processor, the method for multicast decision-making of perception information in the integrated communication and sensing vehicle network described in Embodiment I above is implemented.

[0140] Embodiment IV

[0141] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the method for multicast decision-making of perception information in the integrated communication and sensing vehicle network described in Embodiment I above is implemented.

[0142] Embodiment V

[0143] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory, and when the processor executes the computer program, the method for multicast decision-making of perception information in the integrated communication and sensing vehicle network described in Embodiment I above is implemented.

[0144] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) containing computer-usable program codes.

[0145] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0146] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0148] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for multicast decision of perception information in a synaesthesia integrated vehicle networking, characterized in that, Including: Determine all groups within the service coverage of the vehicle networking access point where the vehicle node to be allocated is located, and all vehicle nodes within each group; According to the distances between the vehicle node to be allocated and all vehicle nodes within the group, and the set of perception information respectively uploaded by the vehicle node to be allocated and each vehicle node within the group to the vehicle networking access point, calculate the perception information correlation degree coefficient between the vehicle node to be allocated and the group, and screen multiple groups as alternative groups from largest to smallest according to the perception information correlation degree coefficient; According to the set of service quality statuses of all vehicle nodes within the alternative group, and the requirements of the vehicle node to be allocated for service quality, calculate the service quality satisfaction degree coefficient of the vehicle node to be allocated for the alternative group; Take the alternative group with the largest product of the perception information correlation degree coefficient and the service quality satisfaction degree coefficient as the optimal allocation decision group of the vehicle node to be allocated.

2. The method according to claim 1, wherein The step of calculating the perception information correlation degree coefficient between the vehicle node to be allocated and the group according to the distances between the vehicle node to be allocated and all vehicle nodes within the group, and the set of perception information respectively uploaded by the vehicle node to be allocated and each vehicle node within the group to the vehicle networking access point, and screening multiple groups as alternative groups from largest to smallest according to the perception information correlation degree coefficient includes: According to the distances between the vehicle node to be allocated and all vehicle nodes within the group, and a preset distance fuzzy membership function, calculate the distance correlation coefficient between the vehicle node to be allocated and each vehicle node; According to the set of perception information respectively uploaded by the vehicle node to be allocated and each vehicle node within the group to the vehicle networking access point, calculate the correlation coefficient of the perception information between the vehicle node to be allocated and each vehicle node during the overlapping period; According to the distance correlation coefficient between the vehicle node to be allocated and each vehicle node, and the correlation coefficient of the perception information between the vehicle node to be allocated and each vehicle node during the overlapping period, calculate the perception information correlation degree coefficient between the vehicle node to be allocated and the group: Screen the first preset number of groups as alternative groups from largest to smallest according to the perception information correlation degree coefficient.

3. The method according to claim 2, wherein The step of calculating the perception information correlation degree coefficient between the vehicle node to be allocated and the group according to the distance correlation coefficient between the vehicle node to be allocated and each vehicle node, and the correlation coefficient of the perception information between the vehicle node to be allocated and each vehicle node during the overlapping period includes: Based on the following formula, calculate the perception information correlation degree coefficient between the vehicle node to be allocated and the group according to the distance correlation coefficient between the vehicle node to be allocated and each vehicle node, and the correlation coefficient of the perception information between the vehicle node to be allocated and each vehicle node during the overlapping period: where Cov k,i represents the correlation coefficient of the sensing information between the vehicle node k to be allocated and the i-th group, J i represents the set of all vehicle nodes within the i-th group, sum is a function for counting the number of elements in the set, ∑ is the summation symbol, A(||d k -d j ||) is the distance correlation coefficient between the vehicle node k to be allocated and the vehicle node j, desk is the probability truncation function, d k and d j are the geographical coordinates of the vehicle node k to be allocated and the vehicle node j respectively, Xcorr(R j,t ,R k,t ,[t1,t2]∩[t3,t4] is the correlation coefficient of the sensing information between the vehicle node k to be allocated and the vehicle node j within the overlapping time period [t1,t2]∩[t3,t4], R j,t is the set of sensing information uploaded by the vehicle node j to the vehicle network access point, R k,t is the set of sensing information uploaded by the vehicle node k to be allocated to the vehicle network access point, long is a function for calculating the time length, t1 and t2 are the start time and end time respectively when the vehicle node j uploads the sensing information to the vehicle network access point, t3 and t4 are the time when the vehicle node k to be allocated enters the service coverage area of the vehicle network access point and the time when it uploads the sensing information to the vehicle network access point respectively, and t is the time variable.

4. The method according to claim 2, wherein The step of calculating the distance correlation coefficient between the vehicle node to be allocated and each vehicle node according to the distances between the vehicle node to be allocated and all vehicle nodes within the group, and a preset distance fuzzy membership function includes: Based on the distances between the vehicle node to be allocated and all vehicle nodes in the group, and according to the following formula of the preset distance fuzzy membership function, calculate the distance correlation coefficient between the vehicle node to be allocated and each vehicle node: In the formula, represents a preset distance fuzzy membership function, a is the intermediate variable of the distance fuzzy membership function, and D max is the upper limit of the preset distance-related reference value, D min is the lower limit of the preset distance-related reference value, and θ is the exponential coefficient related to the preset distance.

5. The method according to claim 1, wherein The requirements of the vehicle node to be allocated for the quality of service include the requirement parameters of the vehicle node to be allocated for multiple quality of service types respectively; The calculation of the satisfaction degree coefficient of the vehicle node to be allocated for the alternative group according to the quality of service status set of all vehicle nodes in the alternative group and the requirements of the vehicle node to be allocated for the quality of service includes: According to the requirement parameters of the vehicle node to be allocated for multiple quality of service types respectively, determine the fuzzy membership function corresponding to each quality of service type; According to the fuzzy membership function corresponding to each quality of service type and the quality of service status set of all vehicle nodes in the alternative group, calculate the satisfaction degree coefficient of the vehicle node to be allocated for the alternative group based on the following formula: where Satis k,i represents the service quality satisfaction coefficient of the vehicle node k to be allocated for the i-th alternative group, Θ i is the set of service quality conditions of all vehicle nodes within the i-th alternative 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 at the vehicle node j at time t for the h-th service quality category at the vehicle node j in the service quality condition set, sum is the function for counting the number of elements in the set, and N is the number of service quality categories.

6. The method according to claim 1, characterized in that, The quality of service types include delay, packet loss rate, delay jitter and bandwidth; The determination of the fuzzy membership function corresponding to each quality of service type according to the requirement parameters of the vehicle node to be allocated for multiple quality of service types respectively includes: According to the requirement parameter of the vehicle node to be allocated for delay, determine the following fuzzy membership function corresponding to delay: In the formula, represents the fuzzy membership function corresponding to the time delay, c is the intermediate variable of the fuzzy membership function corresponding to the time delay, and delay k is the parameter requirement of vehicle node k to be allocated for the time delay, and delay max is the preset maximum time delay, is the exponential coefficient of the service quality fluctuation preset for vehicle node k to be allocated; According to the requirement parameter of the vehicle node to be allocated for packet loss rate, determine the following fuzzy membership function corresponding to packet loss rate: In the formula, represents the fuzzy membership function corresponding to the packet loss rate, c is the intermediate variable of the fuzzy membership function corresponding to the packet loss rate, and loss k is the required parameter of the vehicle node k to be allocated for the packet loss rate, and loss max is the preset maximum packet loss rate, is the exponential coefficient of the service quality fluctuation preset for the vehicle node k to be allocated; According to the requirement parameter of the vehicle node to be allocated for delay jitter, determine the following fuzzy membership function corresponding to delay jitter: In the formula, represents the fuzzy membership function corresponding to the delay jitter, c is the intermediate variable of the fuzzy membership function corresponding to the delay jitter, and jitter k is the requirement parameter of the vehicle node k to be allocated for the delay jitter, and jitter max is the preset maximum delay jitter, is the exponential coefficient of the vehicle node k to be allocated for the service quality fluctuation preset; According to the requirement parameter of the vehicle node to be allocated for bandwidth, determine the following fuzzy membership function corresponding to bandwidth: In the formula, represents the fuzzy membership function corresponding to the bandwidth, c is the intermediate variable of the fuzzy membership function corresponding to the bandwidth, and bandwidth k is the parameter requirement of the vehicle node k to be allocated for the bandwidth, and bandwidth max is the preset maximum bandwidth, and bandwidth min is the preset minimum bandwidth, is the exponential coefficient of the service quality fluctuation preset for the vehicle node k to be allocated.

7. A perception information multicast decision-making device in a synesthesia integrated vehicle networking, characterized in that including: A data acquisition module, configured to determine all groups within the service coverage of the vehicle network access point where the vehicle node to be allocated is located, and all vehicle nodes in each group; A first calculation module, configured to calculate the correlation coefficient of the perception information between the vehicle node to be allocated and the group according to the distances between the vehicle node to be allocated and all vehicle nodes in the group, and the perception information sets respectively uploaded by the vehicle node to be allocated and each vehicle node in the group to the vehicle network access point, and screen multiple groups as alternative groups according to the descending order of the correlation coefficient of the perception information; A second calculation module, configured to calculate the satisfaction degree coefficient of the vehicle node to be allocated for the alternative group according to the quality of service status set of all vehicle nodes in the alternative group and the requirements of the vehicle node to be allocated for the quality of service; A screening decision module, configured to use the alternative group with the largest product of the correlation coefficient of the perception information and the satisfaction degree coefficient of the quality of service as the optimal allocation decision group of the vehicle node to be allocated.

8. A computer-readable storage medium having computer programs / instructions stored thereon, characterized in that, When the computer program / instructions are executed by a processor, the method for multicast decision of perception information in the integrated communication and sensing vehicle network according to any one of claims 1-6 is implemented.

9. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by a processor, the method for making a multicast decision on sensing information in the integrated communication and sensing vehicle network according to any one of claims 1-6 is implemented.

10. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the method for making a multicast decision on sensing information in the integrated communication and sensing vehicle network according to any one of claims 1-6.

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