CN116456303A - Cloud evaluation method for failure state of vehicle-to-network side unit by instantaneous disturbance separation - Google Patents
Cloud evaluation method for failure state of vehicle-to-network side unit by instantaneous disturbance separation Download PDFInfo
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Abstract
The invention discloses a cloud evaluation method for failure states of vehicle-to-network side units separated by instantaneous disturbance, which is characterized in that the instantaneous disturbance is separated by the importance of random disturbance in an urban vehicle networking environment, and a random disturbance instantaneous separation model is established and utilized in a vehicle networking cloud to evaluate and early warn the failure states of the road side units. Comprising the following steps: 1. establishing a spatial position model of the road side unit and the vehicle-mounted unit; 2. constructing a road side unit health index according to the signal intensity change state; 3. establishing a random disturbance instantaneous separation model and correcting a road side unit health index calculation method; 4. and setting an invalidation state early warning threshold value in the cloud server of the Internet of vehicles, and giving out a judging method. The method and the system can monitor and calculate the failure state of the road side unit in real time, thereby providing powerful guarantee for the communication stability of the Internet of vehicles and the automatic driving safety of the intelligent network vehicle.
Description
Technical Field
The invention belongs to the field of vehicle networking and intelligent traffic control systems, and particularly relates to a cloud evaluation method for a failure state of a vehicle networking network side unit by means of instantaneous disturbance separation.
Background
With the development of artificial intelligence technology and the popularization of novel network communication technologies such as 5G, the automobile industry also starts to be deeply transformed, and intelligent network-connected automobiles gradually become a new trend of the development of the automobile industry and a new direction of research. The intelligent network-connected automobile integrates the modern communication technology and the network technology by means of advanced devices such as sensors, controllers, communication equipment and cloud servers through the internet technology, achieves the purposes of intelligent perception, intelligent decision, man-machine co-driving, identity recognition and the like by means of information exchange and information sharing of 'man-car-road-cloud', and has wide application in urban traffic, logistics freight, military and other scenes.
Under the intelligent network connection road environment, reliable data communication guarantee is a premise and a foundation for realizing all functions. The communication among the vehicles, the vehicle and the cloud computing center needs reliable data communication guarantee. The Road Side Units (RSU) are fixed on two sides of a Road, directly or indirectly participate in internet of vehicles (Vehicle to Everything, V2X) communication, play roles of route forwarding and data preliminary processing, and are important infrastructure in the internet of vehicles. The RSU can age naturally along with the increase of the service time, and random disturbance generated under severe service environment can influence the RSU to different degrees: the instantaneous disturbance is short-time influence on the RSU under the action of factors such as environment, such as power failure, bad weather and the like, and has short duration of influence on the failure state of the RSU and strong restorability; long term disturbances typically cause direct damage to the RSU, accelerating the RSU into a fault condition. After the RSU fails, the system distributes the load of the RSU to adjacent RSUs, so that the aging and failure of other RSUs are accelerated, the failure state in the system is propagated, and the maintenance cost of the Internet of vehicles equipment is greatly increased. Failure of the RSU can cause communication blockage among vehicles, and untimely acquisition of road condition information can seriously affect safety of intelligent driving and life and property safety of passengers of the vehicles.
Therefore, in the environment of the internet of vehicles, how to scientifically distinguish the disturbance types and further evaluate the failure state of the RSU device has become an urgent problem to be solved.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a cloud evaluation method for the failure state of the vehicle networking network side unit with instantaneous disturbance separation, so that the failure state of the road side unit can be mastered in time in the vehicle networking environment, and the timely evaluation and early warning of the failure state of RSU equipment are realized, thereby providing important guarantee for supporting information exchange and sharing of 'people-vehicle-road-cloud', guaranteeing the communication stability of a vehicle networking system and the safety and reliability of intelligent driving of an intelligent network-connected vehicle.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the cloud evaluation method for the failure state of the vehicle-to-vehicle network side unit with instantaneous disturbance separation is characterized by being applied to a network environment formed by a road side unit RSU, a vehicle-mounted unit OBU and a central cloud server, and comprises the following steps:
step 1, constructing an instantaneous space position model of an RSU and a vehicle OBU, and calculating signal intensity;
step 1.1, in urban road scene, monitoring the communication state of the jth RSU in the period T j The time sequence of the communication between the OBU and the RSU on the nth vehicle is marked as T j ＝{t 1,j ,t 2,j ,...,t i,j ,...,t k,j And t is }, where i,j Monitoring period T for jth RSU communication status j The ith communication time in the RSU communication state monitoring period is the total communication time in the RSU communication state monitoring period;
step 1.2, establishing the ith communication time t by using the method (1) i,j Space position model of OBU and RSU on the nth vehicle:
in the formula (1), L n (t i,j ) For the ith communication time t i,j The straight line distance between the OBU on the nth vehicle and the RSU is h, and h is the height between the RSU and the road surface, l h,n (t i,j ) For the ith communication time t i,j Lateral horizontal distance of OBU from RSU on nth vehicle v,n (t i,j ) For the ith communication time t i,j Longitudinal horizontal distance of the OBU on the nth vehicle from the RSU; θ 1,n For the tilt angle θ of the OBU relative to the horizontal on the nth vehicle 2 Is the declination angle of the RSU relative to horizontal; phi (phi) 1,n (t i,j ) For the ith communication time t i,j Lateral inclination angle phi of OBU relative to RSU on nth vehicle 2,n (t i,j ) For the ith communication time t i,j Lateral inclination of the lower RSU relative to the on-board OBU of the nth vehicle; η and μ are the angular scaling coefficients;
step 1.3, obtaining the ith communication time t by using the formula (2) i,j Signal strength si between OBU and RSU on the nth vehicle i,n ：
In the formula (2), P OBU For the power of the vehicle OBU signal transmitting module, A e For the effective receiving area of the RSU signal receiving module, U 1 And U 2 Directed graphs of OBU signaling and RSU signaling, respectively;
step 2, constructing an RSU health index;
step 2.1, defining that the residual life RUL of the RSU is reduced to 0% to be in a complete failure state, and the process from 100% to 0% of RUL is the life cycle of the RSU;
let the jth RSU communicate with the monitoring period T j The communication signal intensity matrix of the internal RSU and the vehicle OBU isThereby obtaining the communication signal intensity matrix in the life cycle of the RSU as SI ∑ ＝{SI 1 ,SI 2 ,...,SI j ,...,SI m -a }; wherein si i,n,j Representing a jth RSU communication status monitoring period T j Inner ith communication time t i,j The communication signal intensity of the OBU and the RSU on the nth vehicle; m is the total number of communication state monitoring periods in the life cycle of the RSU;
step 2.2, defining the health status of the RSU life cycle includes: health, sub-health and failure;
communication signal strength matrix SI over RSU lifecycle Σ Randomly selecting three signal intensities and respectively taking the three signal intensities as an e-th feature center point for iterative computation to obtain a signal intensity feature set corresponding to a final healthy feature center point as a reference signal intensity matrix SI under healthy features h ；
Step 2.3, in the urban road scene, monitoring the period T in the communication state of the jth RSU j Monitoring the real-time signal intensity to obtain a signal intensity matrix SI ral,j Thereby obtaining SI ral,j And SI (information and information) h Is a difference matrix S of (2) j ；
Calculating a jth RSU communication status monitoring period T using (3) j Lower RSU health index H j ：
In the formula (3), S j T Is S j Is transposed of Sigma j -1 Is S j Is the inverse of the covariance matrix of (2), gamma is the normalization parameter;
step 3, constructing an RSU random disturbance instantaneous separation model for correcting an RSU health index;
step 3.1, establishing a j-th RSU communication state monitoring period T j Within a discrete random disturbance intensity sequenceWherein, xi p,j Representing a jth RSU communication status monitoring period T j Disturbance intensity, w, of the p-th random disturbance within j Monitoring period T for jth RSU communication status j The total number of random disturbances within;
step 3.2, establishing a jth RSU communication status monitoring period T by utilizing the method (4) j RSU random disturbance model inside:
in the formula (4), I rd,j Monitoring period T for jth RSU communication status j Average intensity of random disturbance in t p For random disturbance xi p,j Duration of gamma p Is the random disturbance intensity xi p,j Rate of change over time;
step 3.3, constructing a j-th RSU communication state monitoring period T j Random perturbation set withinWherein e p,j (t p ,ξ p,j ) Monitoring the disturbance intensity in the period for the jth RSU communication state as xi p,j Duration t p P-th random perturbation of (a);
deleting set E in turn j After each element in the list, a new random disturbance set is obtained respectivelyWherein E 'is' p,j To delete E j P-th random disturbance e in (b) p,j (t p ,ξ p,j ) A new set of the post;
step 3.4, calculating random disturbance e by using the method (5) p,j (t p ,ξ p,j ) Disturbance importance at SD (E j ,E′ p,j )；
SD(E j ,E′ p,j )＝max{sd(E j ,E′ p,j ),sd(E′ p,j ,E j )} (5)
In the formula (5), sd (E) j ,E′ p,j ) Representation E j To E' p,j Directed inter-set distance, sd (E' p,j ,E j ) Representing E' p,j To E j Directed inter-set distance, and has:
in formula (6), e p,j For E j P-th random disturbance in (a); e' p,j For set E' p,j P-th random disturbance in (a); the distance norm is I;
judging whether the formula (7) is satisfied, if so, gathering the random disturbance E j P-th random disturbance e in (b) p,j Added to the transient random disturbance set E β The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, random disturbance set E j P-th random disturbance e in (b) p,j Added to the long-term random perturbation set E α In (a) and (b); thereby gathering random disturbances E j Random perturbations in greater than the perturbation separation threshold β are added to the long-term random perturbation set E α In which a random disturbance less than a disturbance separation threshold betarandom disturbance is added to a set E of transient random disturbances β ；
SD(E j ,E p ′ ,j )＜β (7)
In the formula (7), beta is a disturbance separation threshold value;
step 3.5, establishing a jth RSU communication status monitoring period T by utilizing the step (8) j Random disturbance transient separation model of RSU in:
in the formula (8), I rd-α,j Monitoring period T for jth RSU communication status j Average intensity of internal long-term random disturbance, ζ p′,j Representing a jth RSU communication status monitoring period T j Inner long term random disturbance set E α The disturbance intensity, w ', of the p ' th random disturbance in (1) ' j Monitoring period T for jth RSU communication status j Long-term random perturbation set E in α The total number of random disturbances in t p′ For random disturbance xi p′,j Duration of gamma p′ Is the random disturbance intensity xi p′,j Rate of change over time;
step 3.6, constructing a jth RSU communication state monitoring period T by utilizing the step (9) j RSU health index H after internal correction T,j ：
Step 4, setting a failure state early warning threshold value to be H in the cloud server e ；
In the j-th RSU communication state monitoring period T j In case of H T,j ＜H e And the cloud server generates and records the RSU failure state early warning information, otherwise, the RSU does not reach the early warning state and does not need early warning.
The cloud evaluation method for the failure state of the vehicle-to-network side unit with instantaneous disturbance separation is also characterized in that the step 2.2 is performed according to the following steps:
step 2.2.1, defining the current iteration number as f, and initializing f=1;
communication signal strength matrix SI over RSU lifecycle Σ Three signal intensities si 'are randomly selected' i,n,j ，si″ i,n,j And si' i,n,j And respectively used as the e-th characteristic central point Z e E {1,2,3}, where when e=1, Z e Represents a health feature center point, when e=2, Z e Represents a sub-health feature center point, when e=3, Z e Representing fault characteristicsCenter point, Z 1 ＝si′ i,n,j ，Z 2 ＝si″ i,n,j ，Z 3 ＝si″ i,n,j ；
With the e-th characteristic central point Z e As the e-th feature center point at the f-th iteration
Step 2.2.2, calculating a communication signal intensity matrix SI in the life cycle of the RSU Σ Signal strength si of middle xy To the e feature center point at the f-th iterationEuclidean distance>Wherein si xy Is SI Σ Signal intensity of the x-th row and y-th column;
the signal intensity si is calculated at the f-th iteration xy Assigning the feature center point corresponding to the minimum Euclidean distance; up to SI Σ After all the residual signal intensities are distributed to the corresponding characteristic center points, the method can obtainAn e-th signal strength feature set under the f-th iteration of the feature center point;
step 2.2.3, calculating the center point of the e-th signal strength feature set under the f-th iteration, and taking the center point as the e-th feature center point under the f+1th iteration
Step 2.2.4, after f+1 is assigned to f, return to step 2.2.2 for execution untilThus obtaining the final e-th signal intensity feature set and the signal intensity taking the final healthy feature center point as the feature center pointThe degree feature set is used as a reference signal intensity matrix SI under health features h 。
The electronic device of the invention comprises a memory and a processor, wherein the memory is used for storing a program for supporting the processor to execute the cloud evaluation method, and the processor is configured to execute the program stored in the memory.
The invention relates to a computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, performs the steps of the cloud evaluation method.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention designs a cloud evaluation method for the failure state of an on-vehicle network side unit with instantaneous disturbance separation, defines an RSU health index through the deviation state of the communication signal intensity of an RSU and an OBU, gives out an instantaneous disturbance strategy which does not affect the RSU failure state for a long time according to the importance separation of random disturbance, and finally forms the RSU failure state evaluation method and an early warning strategy in the on-vehicle cloud, thereby improving the communication stability of an on-vehicle system and having wide application prospect;
2. the invention considers the comprehensive influence of natural use aging and long-term random disturbance on the failure degree of the RSU, makes up the defect of the existing method on the consideration of the influence factor of random disturbance, enhances the fault sensing capability of the road side unit system, can reduce the workload of maintenance personnel and reduces the traffic safety hidden trouble;
3. the invention is suitable for various RSUs and road conditions, has strong portability, can be expanded to the field of failure state research of the whole vehicle and other equipment, and has strong universality.
Drawings
FIG. 1 is a flow chart of the invention for establishing a spatial location model of RSU and OBU and establishing an RSU health index;
FIG. 2 is a flow chart of the invention for constructing a random disturbance transient separation model and correcting the RSU health index.
Detailed Description
In this embodiment, a cloud evaluation method for a failure state of an on-vehicle network side unit with instantaneous disturbance separation is characterized in that the cloud evaluation method is applied to a network environment formed by a road side unit RSU, an on-vehicle unit OBU and a central cloud server, and is implemented by separating the instantaneous disturbance and the long-term disturbance through the importance of random disturbance, and establishing and utilizing a random disturbance instantaneous separation model at an on-vehicle cloud. Firstly, establishing a space position model of an RSU and an OBU, and using a deviation state of the signal intensity of the RSU relative to the signal intensity under the health condition as an evaluation index to give a calculation method of the RSU health index, as shown in figure 1; secondly, constructing an RSU random disturbance instantaneous separation model in a monitoring period, separating low-importance instantaneous disturbance, giving a calculation method of long-term random disturbance average intensity, and correcting an RSU health index calculation method, as shown in figure 2; and finally, setting an early warning threshold value of the failure state in the cloud server, and giving out a judging method. Specifically, the method comprises the following steps:
step 1, constructing an instantaneous space position model of an RSU and a vehicle OBU, and calculating signal intensity;
step 1.1, in urban road scene, monitoring the communication state of the jth RSU in the period T j The time sequence of the communication between the OBU and the RSU on the nth vehicle is marked as T j ＝{t 1,j ,t 2,j ,...,t i,j ,...,t k,j And t is }, where i,j Monitoring period T for jth RSU communication status j The ith communication time in the RSU communication state monitoring period is the total communication time in the RSU communication state monitoring period;
step 1.2, establishing the ith communication time t by using the method (1) i,j Space position model of OBU and RSU on the nth vehicle:
in the formula (1), L n (t i,j ) For the ith communication time t i,j The straight line distance between the OBU on the nth vehicle and the RSU is h, and h is the height between the RSU and the road surface, l h,n (t i,j ) For the ith communication time t i,j Lateral horizontal distance of OBU from RSU on nth vehicle v,n (t i,j ) For the ith communication time t i,j Longitudinal horizontal distance of the OBU on the nth vehicle from the RSU; θ 1,n For the tilt angle θ of the OBU relative to the horizontal on the nth vehicle 2 Is the declination angle of the RSU relative to horizontal; phi (phi) 1,n (t i,j ) For the ith communication time t i,j Lateral inclination angle phi of OBU relative to RSU on nth vehicle 2,n (t i,j ) For the ith communication time t i,j Lateral inclination of the lower RSU relative to the on-board OBU of the nth vehicle; η and μ are the angular scaling coefficients;
step 1.3, obtaining the ith communication time t by using the formula (2) i,j Signal strength si between OBU and RSU on the nth vehicle i,n ：
In the formula (2), P OBU For the power of the vehicle OBU signal transmitting module, A e For the effective receiving area of the RSU signal receiving module, U 1 And U 2 Directed graphs of OBU signaling and RSU signaling, respectively;
step 2, constructing an RSU health index;
step 2.1, defining that the residual life RUL of the RSU is reduced to 0% to be in a complete failure state, and the process from 100% to 0% of RUL is the life cycle of the RSU;
let the jth RSU communicate with the monitoring period T j The communication signal intensity matrix of the internal RSU and the vehicle OBU isThereby obtaining the communication signal intensity matrix in the life cycle of the RSU as SI ∑ ＝{SI 1 ,SI 2 ,...,SI j ,...,SI m -a }; wherein si i,n,j Representing a jth RSU communication status monitoring period T j Inner ith communication time t i,j The communication signal intensity of the OBU and the RSU on the nth vehicle; m is in the life cycle of RSUThe total number of communication state monitoring cycles;
step 2.2, defining the health status of the RSU life cycle includes: health, sub-health and failure;
step 2.3, defining the current iteration number as f, and initializing f=1;
communication signal strength matrix SI over RSU lifecycle Σ Three signal intensities si' are randomly selected i,n,j ，si″ i,n,j And si' i,n,j And respectively used as the e-th characteristic central point Z e E {1,2,3}, where when e=1, Z e Represents a health feature center point, when e=2, Z e Represents a sub-health feature center point, when e=3, Z e Represents the fault characteristic central point, Z 1 ＝si′ i,n,j ，Z 2 ＝si″ i, n,j，Z 3 ＝si″ i,n,j ；
With the e-th characteristic central point Z e As the e-th feature center point at the f-th iteration
Step 2.4, calculating a communication signal intensity matrix SI in the life cycle of the RSU Σ Signal strength si of middle xy To the e feature center point at the f-th iterationEuclidean distance>Wherein si xy Is SI ∑ Signal intensity of the x-th row and y-th column;
the signal intensity si is calculated at the f-th iteration xy Assigning the feature center point corresponding to the minimum Euclidean distance; up to SI ∑ After all the residual signal intensities are distributed to the corresponding characteristic center points, the method can obtainThe e-th iteration at the f-th iteration of the feature center pointA signal strength feature set;
step 2.5, calculating the center point of the e-th signal strength characteristic set under the f-th iteration, and taking the center point as the e-th characteristic center point under the f+1th iteration
Step 2.6, after f+1 is assigned to f, returning to step 2.4 for execution untilThus obtaining the final e-th signal intensity feature set, and taking the signal intensity feature set with the final healthy feature center point as the reference signal intensity matrix SI under the healthy feature h ；
Step 2.7, in the urban road scene, monitoring the period T in the communication state of the jth RSU j Monitoring the real-time signal intensity to obtain a signal intensity matrix SI ral,j Thereby obtaining SI ral,j And SI (information and information) h Is a difference matrix S of (2) j ；
Calculating a jth RSU communication status monitoring period T using (3) j Lower RSU health index H j ：
In the formula (3), S j T Is S j Is transposed of Sigma j -1 Is S j Is the inverse of the covariance matrix of (2), gamma is the normalization parameter;
step 3, constructing an RSU random disturbance instantaneous separation model for correcting an RSU health index;
step 3.1, establishing a j-th RSU communication state monitoring period T j Within a discrete random disturbance intensity sequenceWherein, xi p,j Representing a jth RSU communication status monitoring period T j Inner firstDisturbance intensity of p random disturbance, w j Monitoring period T for jth RSU communication status j The total number of random disturbances within;
step 3.2, establishing a jth RSU communication status monitoring period T by utilizing the method (4) j RSU random disturbance model inside:
in the formula (4), I rd,j Monitoring period T for jth RSU communication status j Average intensity of random disturbance in t p For random disturbance xi p,j Duration of gamma p Is the random disturbance intensity xi p,j Rate of change over time;
step 3.3, constructing a j-th RSU communication state monitoring period T j Random perturbation set withinWherein e p,j (t p ,ξ p,j ) Monitoring the disturbance intensity in the period for the jth RSU communication state as xi p,j Duration t p P-th random perturbation of (a);
deleting set E in turn j After each element in the list, a new random disturbance set is obtained respectivelyWherein E 'is' p,j To delete E j P-th random disturbance e in (b) p,j (t p ,ξ p,j ) A new set of the post;
step 3.4, calculating random disturbance e by using the method (5) p,j (t p ,ξ p,j ) Disturbance importance at SD (E j ,E′ p,j )；
SD(E j ,E′ p,j )＝max{sd(E j ,E′ p,j ),sd(E′ p,j ,E j )} (5)
In the formula (5), sd (E) j ,E′ p,j ) Representation E j To E' p,j Directed inter-set distance, sd (E' p,j ,E j ) Representing E' p,j To E j Directed inter-set distance, and has:
in formula (6), e p,j For E j P-th random disturbance in (a); e' p,j For set E' p,j P-th random disturbance in (a); the distance norm is I;
judging whether the formula (7) is satisfied, if so, gathering the random disturbance E j P-th random disturbance e in (b) p,j Added to the transient random disturbance set E β The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, random disturbance set E j P-th random disturbance e in (b) p,j Added to the long-term random perturbation set E α In (a) and (b); thereby gathering random disturbances E j Random perturbations in greater than the perturbation separation threshold β are added to the long-term random perturbation set E α In which a random disturbance less than a disturbance separation threshold betarandom disturbance is added to a set E of transient random disturbances β ；
SD(E j ,E′ p,j )＜β (7)
In the formula (7), beta is a disturbance separation threshold value;
step 3.5, establishing a jth RSU communication status monitoring period T by utilizing the step (8) j Random disturbance transient separation model of RSU in:
in the formula (8), I rd-α,j Monitoring period T for jth RSU communication status j Average intensity of internal long-term random disturbance, ζ p′,j Representing a jth RSU communication status monitoring period T j Inner long term random disturbance set E α The disturbance intensity, w ', of the p ' th random disturbance in (1) ' j Monitoring period T for jth RSU communication status j Long-term random perturbation set E in α The total number of random disturbances in t p′ For random disturbance xi p′,j Duration of gamma p′ Is the random disturbance intensity xi p′,j Rate of change over time;
step 3.6, constructing a jth RSU communication state monitoring period T by utilizing the step (9) j RSU health index H after internal correction T,j ：
Step 4, setting a failure state early warning threshold value to be H in the cloud server e ；
In the j-th RSU communication state monitoring period T j In case of H T,j ＜H e And the cloud server generates and records the RSU failure state early warning information, otherwise, the RSU does not reach the early warning state and does not need early warning.
In this embodiment, an electronic device includes a memory for storing a program supporting the processor to execute the above method, and a processor configured to execute the program stored in the memory.
In this embodiment, a computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method described above.
In summary, the cloud evaluation method for the failure state of the vehicle-to-network side unit with instantaneous disturbance separation is provided, the influence of the natural aging of the RSU and the long-term random disturbance on the failure state is comprehensively considered after the instantaneous random disturbance is separated, the defects of the existing method on the RSU failure sensing capability and the early warning capability are overcome, the communication stability of the vehicle-to-network system is improved, the maintenance cost is reduced, and the cloud evaluation method has a wide application prospect.
Claims (4)
1. The cloud evaluation method for the failure state of the vehicle-mounted network side unit with the instantaneous disturbance separation is characterized by being applied to a network environment formed by a road side unit RSU, a vehicle-mounted unit OBU and a central cloud server, and comprises the following steps of:
step 1, constructing an instantaneous space position model of an RSU and a vehicle OBU, and calculating signal intensity;
step 1.1, in urban road scene, monitoring the communication state of the jth RSU in the period T j The time sequence of the communication between the OBU and the RSU on the nth vehicle is marked as T j ＝{t 1,j ,t 2,j ,...,t i,j ,...,t k,j And t is }, where i,j Monitoring period T for jth RSU communication status j The ith communication time in the RSU communication state monitoring period is the total communication time in the RSU communication state monitoring period;
step 1.2, establishing the ith communication time t by using the method (1) i,j Space position model of OBU and RSU on the nth vehicle:
in the formula (1), L n (t i,j ) For the ith communication time t i,j The straight line distance between the OBU on the nth vehicle and the RSU is h, and h is the height between the RSU and the road surface, l h,n (t i,j ) For the ith communication time t i,j Lateral horizontal distance of OBU from RSU on nth vehicle v,n (t i,j ) For the ith communication time t i,j Longitudinal horizontal distance of the OBU on the nth vehicle from the RSU; θ 1,n For the tilt angle θ of the OBU relative to the horizontal on the nth vehicle 2 Is the declination angle of the RSU relative to horizontal; phi (phi) 1,n (t i,j ) For the ith communication time t i,j Lateral inclination angle phi of OBU relative to RSU on nth vehicle 2,n (t i,j ) For the ith communication time t i,j Lateral inclination of the lower RSU relative to the on-board OBU of the nth vehicle; η and μ are the angular scaling coefficients;
step 1.3, obtaining the ith communication time t by using the formula (2) i,j Signal strength si between OBU and RSU on the nth vehicle i,n ：
In the formula (2), P OBU For the power of the vehicle OBU signal transmitting module, A e For the effective receiving area of the RSU signal receiving module, U 1 And U 2 Directed graphs of OBU signaling and RSU signaling, respectively;
step 2, constructing an RSU health index;
step 2.1, defining that the residual life RUL of the RSU is reduced to 0% to be in a complete failure state, and the process from 100% to 0% of RUL is the life cycle of the RSU;
let the jth RSU communicate with the monitoring period T j The communication signal intensity matrix of the internal RSU and the vehicle OBU isThereby obtaining the communication signal intensity matrix in the life cycle of the RSU as SI Σ ＝{SI 1 ,SI 2 ,...,SI j ,...,SI m -a }; wherein si i,n,j Representing a jth RSU communication status monitoring period T j Inner ith communication time t i,j The communication signal intensity of the OBU and the RSU on the nth vehicle; m is the total number of communication state monitoring periods in the life cycle of the RSU;
step 2.2, defining the health status of the RSU life cycle includes: health, sub-health and failure;
communication signal strength matrix SI over RSU lifecycle Σ Randomly selecting three signal intensities and respectively taking the three signal intensities as an e-th feature center point for iterative computation to obtain a signal intensity feature set corresponding to a final healthy feature center point as a reference signal intensity matrix SI under healthy features h ；
Step 2.3, in the urban road scene, monitoring the period T in the communication state of the jth RSU j Monitoring the real-time signal intensity to obtain a signal intensity matrix SI ral,j Thereby obtaining SI ral,j And SI (information and information) h Is a difference matrix S of (2) j ；
Calculating a jth RSU communication status monitoring period T using (3) j Lower RSU health index H j ：
In the formula (3), S j T Is S j Is transposed of Sigma j -1 Is S j Is the inverse of the covariance matrix of (2), gamma is the normalization parameter;
step 3, constructing an RSU random disturbance instantaneous separation model for correcting an RSU health index;
step 3.1, establishing a j-th RSU communication state monitoring period T j Within a discrete random disturbance intensity sequenceWherein, xi p,j Representing a jth RSU communication status monitoring period T j Disturbance intensity, w, of the p-th random disturbance within j Monitoring period T for jth RSU communication status j The total number of random disturbances within;
step 3.2, establishing a jth RSU communication status monitoring period T by utilizing the method (4) j RSU random disturbance model inside:
in the formula (4), I rd,j Monitoring period T for jth RSU communication status j Average intensity of random disturbance in t p For random disturbance xi p,j Duration of gamma p Is the random disturbance intensity xi p,j Rate of change over time;
step 3.3, constructing a j-th RSU communication state monitoring period T j Random perturbation set withinWherein e p,j (t p ,ξ p,j ) Monitoring a period for a jth RSU communication stateThe disturbance intensity in the interior is xi p,j Duration t p P-th random perturbation of (a);
deleting set E in turn j After each element in the list, a new random disturbance set is obtained respectivelyWherein E 'is' p,j To delete E j P-th random disturbance e in (b) p,j (t p ,ξ p,j ) A new set of the post;
step 3.4, calculating random disturbance e by using the method (5) p,j (t p ,ξ p,j ) Disturbance importance at SD (E j ,E′ p,j )；
SD(E j ,E′ p,j )＝max{sd(E j ,E′ p,j ),sd(E′ p,j ,E j )} (5)
In the formula (5), sd (E) j ,E′ p,j ) Representation E j To E' p,j Directed inter-set distance, sd (E' p,j ,E j ) Representing E' p,j To E j Directed inter-set distance, and has:
in formula (6), e p,j For E j P-th random disturbance in (a); e' p,j For set E' p,j P-th random disturbance in (a); the distance norm is I;
judging whether the formula (7) is satisfied, if so, gathering the random disturbance E j P-th random disturbance e in (b) p,j Added to the transient random disturbance set E β The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, random disturbance set E j P-th random disturbance e in (b) p,j Added to the long-term random perturbation set E α In (a) and (b); thereby gathering random disturbances E j Random perturbations in greater than the perturbation separation threshold β are added to the long-term random perturbation set E α In which a random perturbation less than a perturbation separation threshold betarandom perturbation is added to a set of transient random perturbationsSynthesis E β ；
SD(E j ,E′ p,j )＜β (7)
In the formula (7), beta is a disturbance separation threshold value;
step 3.5, establishing a jth RSU communication status monitoring period T by utilizing the step (8) j Random disturbance transient separation model of RSU in:
in the formula (8), I rd-α,j Monitoring period T for jth RSU communication status j Average intensity of internal long-term random disturbance, ζ p′,j Representing a jth RSU communication status monitoring period T j Inner long term random disturbance set E α The disturbance intensity, w ', of the p ' th random disturbance in (1) ' j Monitoring period T for jth RSU communication status j Long-term random perturbation set E in α The total number of random disturbances in t p′ For random disturbance xi p′,j Duration of gamma p′ Is the random disturbance intensity xi p′,j Rate of change over time;
step 3.6, constructing a jth RSU communication state monitoring period T by utilizing the step (9) j RSU health index H after internal correction T,j ：
Step 4, setting a failure state early warning threshold value to be H in the cloud server e ；
In the j-th RSU communication state monitoring period T j In case of H T,j ＜H e And the cloud server generates and records the RSU failure state early warning information, otherwise, the RSU does not reach the early warning state and does not need early warning.
2. The cloud evaluation method for the failure state of the vehicle-to-network side unit based on the instantaneous disturbance separation according to claim 1, wherein the step 2.2 is performed as follows:
step 2.2.1, defining the current iteration number as f, and initializing f=1;
communication signal strength matrix SI over RSU lifecycle Σ Three signal intensities si 'are randomly selected' i,n,j ，si″ i,n,j And si' i,n,j And respectively used as the e-th characteristic central point Z e E {1,2,3}, where when e=1, Z e Represents a health feature center point, when e=2, Z e Represents a sub-health feature center point, when e=3, Z e Represents the fault characteristic central point, Z 1 ＝si′ i,n,j ，Z 2 ＝si″ i,n,j ，Z 3 ＝si″ i,n,j ；
With the e-th characteristic central point Z e As the e-th feature center point at the f-th iteration
Step 2.2.2, calculating a communication signal intensity matrix SI in the life cycle of the RSU Σ Signal strength si of middle xy To the e feature center point at the f-th iterationEuclidean distance>Wherein si xy Is SI Σ Signal intensity of the x-th row and y-th column;
the signal intensity si is calculated at the f-th iteration xy Assigning the feature center point corresponding to the minimum Euclidean distance; up to SI Σ After all the residual signal intensities are distributed to the corresponding characteristic center points, the method can obtainAn e-th signal strength feature set under the f-th iteration of the feature center point;
step 2.2.3, calculating the center point of the e-th signal strength feature set under the f-th iteration, and taking the center point as the e-th feature center point under the f+1th iteration
Step 2.2.4, after f+1 is assigned to f, return to step 2.2.2 for execution untilThus obtaining the final e-th signal intensity feature set, and taking the signal intensity feature set with the final healthy feature center point as the reference signal intensity matrix SI under the healthy feature h 。
3. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program that supports the processor to perform the cloud evaluation method of claim 1 or 2, the processor being configured to execute the program stored in the memory.
4. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when run by a processor performs the steps of the cloud evaluation method of claim 1 or 2.
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