CN111464979A

CN111464979A - Electric drive vehicle control method based on Internet of vehicles, cloud server and storage medium

Info

Publication number: CN111464979A
Application number: CN202010247941.0A
Authority: CN
Inventors: 孙凯旋
Original assignee: Individual
Current assignee: Individual
Priority date: 2019-10-30
Filing date: 2019-10-30
Publication date: 2020-07-28
Also published as: CN110784848A; CN111432018A; CN110784848B

Abstract

The embodiment of the invention relates to the technical field of vehicle networking, in particular to a vehicle-mounted control method based on the vehicle networking, a cloud server and a storage medium, wherein the method can classify vehicle-mounted control devices with the same or similar signal attenuation response under global signal intensity disturbance, then uniformly distribute encrypted signal frequency bands, further improve the timeliness of information and data interaction between the vehicle-mounted control devices in the same classification, and avoid delay caused by the information and data interaction between all the vehicle-mounted control devices. In addition, the optimal driving strategy in the set road segment area can be determined based on the electronic map in the cloud server, and congestion in the set road segment area is avoided.

Description

Electric drive vehicle control method based on Internet of vehicles, cloud server and storage medium

Technical Field

The invention relates to the technical field of vehicle networking, in particular to an electric drive vehicle control method based on the vehicle networking, a cloud server and a storage medium.

Background

The development of the 5G technology is adhered to, the current internet of vehicles technology is rapidly developed, and automatic driving is gradually applied to electric vehicles. The vehicle-mounted control equipment is important equipment for controlling the electric drive vehicles, and the vehicle-mounted control equipment among different electric drive vehicles can realize the interaction of information and data, so that the safe and smooth running of the electric drive vehicles is ensured. However, in the prior art, timely and reliable control over the electric drive vehicle is difficult to carry out through vehicle-mounted control equipment.

Disclosure of Invention

In order to overcome at least the above disadvantages of the prior art, an object of the present invention is to provide a method for controlling an electric vehicle based on a vehicle networking, a cloud server and a storage medium.

The embodiment of the invention provides an electric drive vehicle control method based on Internet of vehicles, which is applied to a cloud server, wherein the cloud server is communicated with vehicle-mounted control equipment corresponding to each electric drive vehicle in a plurality of electric drive vehicles running in a set road section area, and the method at least comprises the following steps:

when the network state prompt information aiming at the set road section area is detected, monitoring the network state of the set road section area, and acquiring a signal attenuation coefficient correspondingly generated by each vehicle-mounted control device when the overall signal intensity of the network state is disturbed;

sequencing all vehicle-mounted control equipment in the set road section area based on the magnitude of all generated signal attenuation coefficients; selecting the vehicle-mounted control equipment with the highest power distribution weight from all the vehicle-mounted control equipment as target vehicle-mounted control equipment, and taking a signal attenuation coefficient of the target vehicle-mounted control equipment as a reference coefficient;

determining a difference between the reference coefficient and each of the all signal attenuation coefficients except the reference coefficient; counting to obtain a target coefficient of which the difference value is smaller than a set value in all the signal attenuation coefficients; distributing corresponding encrypted signal frequency bands for the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient;

filtering the vehicle-mounted control devices which are distributed with the encrypted signal frequency band in all the vehicle-mounted control devices, returning to the step of sequencing all the vehicle-mounted control devices in the set road segment area based on the generated signal attenuation coefficients;

the monitoring of the network state of the set road section area specifically includes:

acquiring a plurality of information interaction instructions in the set road section area;

grouping the plurality of information interaction instructions according to the characteristic vector corresponding to each information interaction instruction, and setting a label for each group of information interaction instructions;

determining the response rate of each group of information interaction instructions;

according to the labels corresponding to each group of information interaction instructions, carrying out weighted summation on each response rate to obtain a global response rate;

judging whether the global response rate reaches a preset response rate or not;

when the global response rate reaches the preset response rate, determining that the global signal intensity of the network state is disturbed;

the selecting, from all the vehicle-mounted control devices, a vehicle-mounted control device with the highest power distribution weight as a target vehicle-mounted control device specifically includes:

acquiring vehicle running parameters sent by all the vehicle-mounted control equipment aiming at each vehicle-mounted control equipment in all the vehicle-mounted control equipment; the vehicle driving parameters comprise a vibration parameter, a speed parameter, an acceleration parameter, a light parameter and an aerodynamic parameter;

determining the characteristic weight of each parameter in the vehicle driving parameters, wherein the characteristic weight comprises an influence factor, a correlation factor and a similarity factor;

respectively evaluating the characteristic weight of each parameter to obtain a first evaluation result; respectively evaluating the feature weights of every two parameters to obtain a second evaluation result; fusing the first evaluation result and the second evaluation result to obtain a global evaluation result; predicting the percentage consumption rate of the remaining capacity of the power battery of the electric drive vehicle corresponding to the vehicle-mounted control equipment based on the global evaluation result;

when the percentage consumption rate of the remaining electric quantity is predicted to be higher than a preset threshold value, determining the transmission delay of the vehicle-mounted electric equipment of the electric drive vehicle corresponding to the vehicle-mounted control equipment; sending an adjusting instruction to the vehicle-mounted control device in response to the determined transmission delay, wherein the adjusting instruction is used for instructing the vehicle-mounted control device to adjust the use state of the vehicle-mounted electric equipment of the electric drive vehicle corresponding to the vehicle-mounted control device;

when response information fed back by the vehicle-mounted control equipment based on the adjusting instruction is received, the vehicle-mounted control equipment is instructed to execute the adjusting strategy included in the adjusting instruction, and the corresponding execution success rate when the vehicle-mounted control equipment executes the adjusting strategy in a preset time period is determined;

searching a plurality of electric quantity load indexes of the vehicle-mounted control equipment on the level of the vehicle-mounted electric equipment, wherein the electric quantity load indexes comprise stability coefficients of safety performance dimensionality; acquiring load stability rates corresponding to the electric quantity load indexes of the vehicle-mounted control equipment, and weighting the load stability rates based on the stability coefficients to obtain target load stability rates; taking the geometric mean value of the execution success rate and the target load stability rate as a corresponding power distribution factor when the vehicle-mounted control equipment executes the regulation strategy; fusing the power distribution factor with the load balancing factor in the set road section area to obtain a power distribution index of the vehicle-mounted control equipment in the set road section area;

determining a power distribution disturbance rejection coefficient of the vehicle-mounted control equipment according to the power distribution divergence provided by the power distribution index; inputting the power distribution disturbance rejection coefficient into a power distribution simulator, and acquiring a power distribution simulation result generated by the power distribution simulator based on the power distribution disturbance rejection coefficient from the power distribution simulator;

determining vehicle condition information corresponding to each node of the power distribution simulation result, and determining a vehicle safety level according to the vehicle condition information; judging whether the vehicle safety level is greater than or equal to a preset level, if so, continuing to determine the vehicle condition information of the next node, and if not, removing the vehicle safety level corresponding to the node and continuing to determine the vehicle condition information of the next node;

obtaining power distribution weights corresponding to the vehicle-mounted control equipment according to the determined safety levels of all vehicles; selecting a highest value from all the obtained power distribution weights, and taking the vehicle-mounted control equipment corresponding to the highest value as the target vehicle-mounted control equipment;

the allocating corresponding encrypted signal frequency bands to the vehicle-mounted control device corresponding to the target coefficient and the vehicle-mounted control device corresponding to the reference coefficient specifically includes:

determining a shared channel address between the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient as an initial distribution address in the network state;

determining a first vehicle-mounted control device with a historical connection identifier corresponding to the vehicle-mounted control device corresponding to the reference coefficient in the vehicle-mounted control devices corresponding to the target coefficient based on the initial distribution address;

distributing a first signal frequency band for the vehicle-mounted control equipment corresponding to the reference coefficient and the first vehicle-mounted control equipment;

determining a signal interference coefficient of a second vehicle-mounted control device except the first vehicle-mounted control device in the vehicle-mounted control devices corresponding to the target coefficient;

adjusting the first signal frequency band according to the normalized increment corresponding to each signal interference coefficient to obtain a second signal frequency band; distributing the second signal interference frequency band to a second vehicle-mounted control device corresponding to the second signal interference frequency band;

integrating the first signal frequency band and the second signal frequency band into a group of Internet of vehicles networks based on a pre-coding mode, and setting a shielding mechanism for the Internet of vehicles networks;

the method further comprises the following steps:

determining real-time coordinate values of each vehicle-mounted control device in the set road section area in an electronic map;

counting real-time coordinate value increase and decrease information at a set intersection in the electronic map according to a set time interval;

determining the longitude and latitude of an increasing and decreasing area corresponding to each real-time coordinate value increasing and decreasing information;

dividing the real-time coordinate value increase and decrease information into a plurality of groups according to the longitude and latitude;

determining a road congestion coefficient corresponding to each real-time coordinate value increase and decrease information in each group according to each group in the groups, and screening the determined road congestion coefficients to obtain at least one expected coefficient;

generating a plurality of driving strategies according to the at least one expectation coefficient;

screening out an optimal driving strategy from the plurality of driving strategies;

and sending the optimal driving strategy to each vehicle-mounted control device in the set road section area.

Optionally, the obtaining of the signal attenuation coefficient correspondingly generated by each vehicle-mounted control device includes:

determining a three-dimensional coordinate value of each vehicle-mounted control device in a world coordinate system;

zooming each three-dimensional coordinate value in the electronic map to obtain a mapping coordinate value;

for each mapping coordinate value, judging whether the distance between the mapping coordinate value and the boundary of the set road section area is smaller than a set distance;

when the distance is greater than or equal to the set distance, acquiring a signal attenuation coefficient correspondingly generated by the vehicle-mounted control equipment corresponding to the mapping coordinate value corresponding to the distance;

when the distance is smaller than the set distance, determining the number of set vehicle-mounted control devices which are communicated with the vehicle-mounted control device corresponding to the distance, wherein the set vehicle-mounted control devices are located in the set road section area; and when the number exceeds the set proportion of the total number of the vehicle-mounted control devices in the set road segment area, acquiring a signal attenuation coefficient correspondingly generated by the vehicle-mounted control device corresponding to the mapping coordinate value corresponding to the distance.

Optionally, before allocating corresponding encrypted signal frequency bands to the vehicle-mounted control device corresponding to the target coefficient and the vehicle-mounted control device corresponding to the reference coefficient, the method further includes:

respectively acquiring a first vehicle signature of the vehicle-mounted control equipment corresponding to the target coefficient and a second vehicle signature of the vehicle-mounted control equipment corresponding to the reference coefficient;

evaluating a first signature attribute corresponding to the first vehicle signature and each contained first signature time to obtain a first evaluation result; evaluating second signature attributes corresponding to the second vehicle signature and each contained second signature moment to obtain a second evaluation result;

determining the state duration and the state switching frequency between the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient according to the first evaluation result and the second evaluation result;

determining the current node trust degree between the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient according to the state duration and the state switching frequency;

determining the next node trust level between the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient according to the state duration, the state switching frequency and the current trust level;

judging whether the node where the next node trust level is located is a system node or not;

if the node where the next node trust level is located is the system node, determining that the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient pass vehicle signature verification;

if the node where the next node trust level is located is not the system node, acquiring a communication strategy between the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient, and a communication language between the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient;

judging whether the communication strategy is matched with the communication language or not, and if so, determining that the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient pass vehicle signature verification; and if not, initializing the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient, and returning to the step of respectively acquiring a first vehicle signature of the vehicle-mounted control equipment corresponding to the target coefficient and a second vehicle signature of the vehicle-mounted control equipment corresponding to the reference coefficient.

Optionally, the method further comprises:

when the disturbance of the global signal intensity of the network state is eliminated, receiving a signal transceiving state feature vector corresponding to each vehicle-mounted control device in the set road segment area;

converting each signal transceiving state feature vector into a state switching vector according to the corresponding reduction coefficient of each vehicle-mounted control device;

acquiring a communication state prediction result of at least one vehicle-mounted control device in the set road segment area and the occurrence rate of the communication state prediction result according to each state switching vector;

generating a signal reduction matrix according to each signal transceiving state feature vector and each state switching vector, and acquiring a reduction feature value of the signal reduction matrix;

adjusting the occurrence rate according to the reduction characteristic value to obtain a target occurrence rate;

sequentially removing the distributed encrypted signal frequency band from each vehicle-mounted control device in the set road segment area according to the sequence of the target occurrence rate from low to high; and the target occurrence rate is used for representing signal impact and impact attenuation duration generated when the vehicle-mounted control equipment releases the distributed encrypted signal frequency band.

The embodiment of the invention also provides an electric drive vehicle control device based on the internet of vehicles, which is applied to a cloud server, wherein the cloud server is communicated with vehicle-mounted control equipment corresponding to each electric drive vehicle in a plurality of electric drive vehicles running in a set road section area, and the device at least comprises:

the signal attenuation coefficient acquisition module is used for monitoring the network state of the set road section area when the network state prompt information aiming at the set road section area is detected to exist, and acquiring the signal attenuation coefficient correspondingly generated by each vehicle-mounted control device when the overall signal intensity of the network state is disturbed;

the signal attenuation coefficient obtaining module is specifically configured to:

acquiring a plurality of information interaction instructions in the set road section area; grouping the plurality of information interaction instructions according to the characteristic vector corresponding to each information interaction instruction, and setting a label for each group of information interaction instructions; determining the response rate of each group of information interaction instructions; according to the labels corresponding to each group of information interaction instructions, carrying out weighted summation on each response rate to obtain a global response rate; judging whether the global response rate reaches a preset response rate or not; when the global response rate reaches the preset response rate, determining that the global signal intensity of the network state is disturbed;

the sequencing selection module is used for sequencing all vehicle-mounted control equipment in the set road section area based on the magnitude of all generated signal attenuation coefficients; selecting the vehicle-mounted control equipment with the highest power distribution weight from all the vehicle-mounted control equipment as target vehicle-mounted control equipment, and taking a signal attenuation coefficient of the target vehicle-mounted control equipment as a reference coefficient;

the sorting selection module is specifically configured to:

the encrypted signal frequency band distribution module is used for determining the difference between the reference coefficient and each signal attenuation coefficient except the reference coefficient in all the signal attenuation coefficients; counting to obtain a target coefficient of which the difference value is smaller than a set value in all the signal attenuation coefficients; distributing corresponding encrypted signal frequency bands for the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient;

the encrypted signal frequency band allocation module is specifically configured to:

a filtering module, configured to filter the vehicle-mounted control devices to which the encrypted signal frequency band is allocated from among all the vehicle-mounted control devices, and return to the step of sorting all the vehicle-mounted control devices in the set road segment area based on the magnitude of all the generated signal attenuation coefficients;

wherein, the encrypted signal frequency band allocation module is further configured to:

determining real-time coordinate values of each vehicle-mounted control device in the set road section area in an electronic map; counting real-time coordinate value increase and decrease information at a set intersection in the electronic map according to a set time interval; determining the longitude and latitude of an increasing and decreasing area corresponding to each real-time coordinate value increasing and decreasing information; dividing the real-time coordinate value increase and decrease information into a plurality of groups according to the longitude and latitude; determining a road congestion coefficient corresponding to each real-time coordinate value increase and decrease information in each group according to each group in the groups, and screening the determined road congestion coefficients to obtain at least one expected coefficient; generating a plurality of driving strategies according to the at least one expectation coefficient; screening out an optimal driving strategy from the plurality of driving strategies; and sending the optimal driving strategy to each vehicle-mounted control device in the set road section area.

The embodiment of the invention provides a cloud server, which comprises a processor, a memory and a bus, wherein the memory and the bus are connected with the processor; wherein, the processor and the memory complete mutual communication through the bus; the processor is used for calling the program instructions in the memory so as to execute the Internet of vehicles-based electric drive vehicle control method.

An embodiment of the present invention provides a storage medium having a program stored thereon, where the program is executed by a processor to implement the above-mentioned internet-of-vehicles-based electric drive vehicle control method.

The electric drive vehicle control method based on the internet of vehicles, the cloud server and the storage medium provided by the embodiment of the invention can sort the vehicle-mounted control devices according to the signal attenuation coefficients of the vehicle-mounted control devices when the global signal intensity is disturbed, then classify the vehicle-mounted control devices based on the power distribution weights and the signal attenuation coefficients of the vehicle-mounted control devices, thus classifying the vehicle-mounted control devices with the same or similar signal attenuation reactions under the disturbance of the global signal intensity, then uniformly distributing the encrypted signal frequency bands, further improving the timeliness of information and data interaction among the vehicle-mounted control devices in the same classification, avoiding the delay caused by the information and data interaction among all the vehicle-mounted control devices, and because the vehicle-mounted control devices carry out the information and data interaction based on different encrypted signal frequency bands, the transmission efficiency of the communication channel can be further improved, timely communication among different vehicle-mounted control devices can be ensured, and timely and reliable control over the electric drive vehicle is further ensured. In addition, the optimal driving strategy in the set road segment area can be determined based on the electronic map in the cloud server, and congestion in the set road segment area is avoided.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a block diagram of a car networking system 100 according to an embodiment of the present invention.

Fig. 2 is a flowchart of an electric drive vehicle control method based on the internet of vehicles according to an embodiment of the present invention.

Fig. 3 is a functional block diagram of an electric drive vehicle control device based on an internet of vehicles according to an embodiment of the present invention.

Fig. 4 is a schematic block diagram of a cloud server according to an embodiment of the present invention.

Icon:

100-a car networking system; 101-a cloud server; 1011-a processor; 1012-memory; 1013-a bus; 102-an in-vehicle control device;

200-electric drive vehicle control device based on vehicle networking; 201-signal attenuation coefficient obtaining module; 202-a sorting selection module; 203-encrypted signal frequency band distribution module; 204-a filtering module; 205-release module.

Detailed Description

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

The embodiment of the invention provides an electric drive vehicle control method based on an internet of vehicles, a cloud server and a storage medium, which are used for solving the technical problem that in the prior art, the electric drive vehicle is difficult to be controlled timely and reliably through vehicle-mounted control equipment.

In order to solve the technical problems, an electric drive vehicle control method based on the internet of vehicles, a cloud server and a storage medium provided by the embodiment of the invention have the following general ideas:

and when detecting that the prompt information aiming at the network state in the set road section area exists, monitoring the network state of the set road section area, and acquiring a signal attenuation coefficient correspondingly generated by each vehicle-mounted control device when the overall signal intensity of the network state is disturbed. Sequencing all vehicle-mounted control equipment in the set road section area based on the magnitude of all generated signal attenuation coefficients; and selecting the vehicle-mounted control equipment with the highest power distribution weight from all the vehicle-mounted control equipment as target vehicle-mounted control equipment, and taking the signal attenuation coefficient of the target vehicle-mounted control equipment as a reference coefficient. Determining a difference between the reference coefficient and each of the all signal attenuation coefficients except the reference coefficient; counting to obtain a target coefficient of which the difference value is smaller than a set value in all the signal attenuation coefficients; and distributing corresponding encrypted signal frequency bands for the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient. And filtering the vehicle-mounted control equipment which is distributed with the encrypted signal frequency band in all the vehicle-mounted control equipment, and returning to the step of sequencing all the vehicle-mounted control equipment in the set road segment area based on the generated signal attenuation coefficients.

In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.

Referring to fig. 1, fig. 1 shows a block diagram of a car networking system 100 according to an embodiment of the present invention, where the car networking system 100 includes a cloud server 101 and a plurality of vehicle-mounted control devices 102. Further, each vehicle-mounted control device 102 is provided to an electric drive vehicle, and the electric drive vehicle provided with the vehicle-mounted control device 102 travels in a set road section area, which can be divided according to actual conditions in the embodiment, and therefore is not limited herein.

Referring to fig. 2, a flowchart of a method for controlling an electric vehicle based on internet of vehicles according to an embodiment of the present invention is shown, where the method is applied to the cloud server 101 in fig. 1, and the method may include the following steps:

and S21, monitoring the network state of the set road section area when the network state prompt information aiming at the set road section area is detected, and acquiring the signal attenuation coefficient correspondingly generated by each vehicle-mounted control device when the global signal strength of the network state is disturbed.

In practical applications, with the expansion of the 5G application layer, there may be other communication interaction scenarios in the set road segment area, and in this case, the network state in the set road segment area may be affected, so as to affect the communication between the onboard controllers, and therefore, in order to accurately monitor the network state in the set road segment area, so as to provide a reliable judgment basis for the allocation of the encrypted signal frequency band of the onboard controllers, in S21, the monitoring of the network state in the set road segment area specifically includes the following:

s2111, acquiring a plurality of information interaction instructions in the set road segment area.

S2112, grouping the plurality of information interaction instructions according to the feature vector corresponding to each information interaction instruction, and setting a label for each group of information interaction instructions.

S2113, determining the response rate of each group of information interaction instructions.

S2114, according to the labels corresponding to each group of information interaction instructions, weighting and summing are carried out on each response rate, and the global response rate is obtained.

S2115, judging whether the global response rate reaches a preset response rate.

S2116, when the global response rate reaches the preset response rate, determining that the global signal strength of the network state is disturbed.

S22, sorting all vehicle-mounted control equipment in the set road section area based on the magnitude of all generated signal attenuation coefficients; and selecting the vehicle-mounted control equipment with the highest power distribution weight from all the vehicle-mounted control equipment as target vehicle-mounted control equipment, and taking the signal attenuation coefficient of the target vehicle-mounted control equipment as a reference coefficient.

When the encrypted signal frequency band is allocated to the onboard controllers, in order to ensure the driving safety of the electric vehicle corresponding to the onboard controllers, the encrypted signal frequency band needs to be allocated according to the power allocation weight, so that the power allocation weight of the onboard controllers needs to be accurately determined, for this reason, in S22, the onboard controller with the highest power allocation weight is selected from all the onboard controllers as a target onboard controller, which may specifically include the following:

s221, aiming at each vehicle-mounted control device in all the vehicle-mounted control devices, obtaining vehicle running parameters sent by the vehicle-mounted control device; wherein the vehicle driving parameters include a vibration parameter, a speed parameter, an acceleration parameter, a light parameter, and an aerodynamic parameter.

S222, determining the characteristic weight of each parameter in the vehicle driving parameters, wherein the characteristic weight comprises an influence factor, a correlation factor and a similarity factor.

S223, evaluating the characteristic weight of each parameter respectively to obtain a first evaluation result; respectively evaluating the feature weights of every two parameters to obtain a second evaluation result; fusing the first evaluation result and the second evaluation result to obtain a global evaluation result; and predicting the percentage consumption rate of the remaining capacity of the power battery of the electric drive vehicle corresponding to the vehicle-mounted control equipment based on the global evaluation result.

S224, when the percentage consumption rate of the residual electric quantity is predicted to be higher than a preset threshold value, determining the transmission delay of the vehicle-mounted electric equipment of the electric drive vehicle corresponding to the vehicle-mounted control equipment; and sending an adjusting instruction to the vehicle-mounted control device in response to the determined transmission delay, wherein the adjusting instruction is used for instructing the vehicle-mounted control device to adjust the use state of the vehicle-mounted electric equipment of the electric drive vehicle corresponding to the vehicle-mounted control device.

And S225, when response information fed back by the vehicle-mounted control equipment based on the adjusting instruction is received, the vehicle-mounted control equipment is instructed to execute the adjusting strategy included in the adjusting instruction, and the corresponding execution success rate when the vehicle-mounted control equipment executes the adjusting strategy in a preset time period is determined.

S226, searching a plurality of electric quantity load indexes of the vehicle-mounted control equipment on the level of the vehicle-mounted electric equipment, wherein the electric quantity load indexes comprise stability coefficients of safety performance dimensionality; acquiring load stability rates corresponding to the electric quantity load indexes of the vehicle-mounted control equipment, and weighting the load stability rates based on the stability coefficients to obtain target load stability rates; taking the geometric mean value of the execution success rate and the target load stability rate as a corresponding power distribution factor when the vehicle-mounted control equipment executes the regulation strategy; and fusing the power distribution factor and the load balance factor in the set road section area to obtain a power distribution index of the vehicle-mounted control equipment in the set road section area.

S227, determining a power distribution disturbance rejection coefficient of the vehicle-mounted control equipment according to the power distribution divergence provided by the power distribution index; and inputting the power distribution disturbance rejection coefficient into a power distribution simulator, and acquiring a power distribution simulation result generated by the power distribution simulator based on the power distribution disturbance rejection coefficient from the power distribution simulator.

S228, aiming at each node of the power distribution simulation result, determining vehicle condition information corresponding to the node, and determining a vehicle safety level according to the vehicle condition information; and judging whether the vehicle safety level is greater than or equal to a preset level, if so, continuously determining the vehicle condition information of the next node, and if not, removing the vehicle safety level corresponding to the node and continuously determining the vehicle condition information of the next node.

S229, obtaining the power distribution weight corresponding to the vehicle-mounted control equipment according to the determined safety levels of all vehicles; and selecting a highest value from all the obtained power distribution weights, and taking the vehicle-mounted control equipment corresponding to the highest value as the target vehicle-mounted control equipment.

It can be understood that based on S221-S223, the driving state of the electric vehicle can be taken into consideration, and then the percentage consumption rate of the remaining capacity of the power battery can be accurately determined, so as to provide a data base for subsequently determining the power distribution weight, and improve the comprehensiveness of determining the power distribution weight.

In S224, the transmission delay of the vehicle-mounted electric device can be taken into consideration, and the condition that the signal transmission and the response of the vehicle-mounted electric device are not synchronous is avoided, so that the dynamic characteristic of the electric drive vehicle when the adjustment command is processed can be accurately reflected, and the safety of the electric drive vehicle is ensured.

Through S225, the corresponding execution success rate of the vehicle-mounted controller in executing the adjustment strategy within the preset time period can be determined, so that a data basis is provided for the subsequent analysis of relevant indexes of power distribution.

Through S226-S227, power distribution simulation can be performed based on the power distribution simulator, and then a power distribution simulation result is obtained.

Through S228-S229, the power distribution weight can be determined based on the vehicle safety level of each vehicle-mounted simulator, so that the vehicle-mounted simulators can be ensured to be safely distributed when global signal intensity disturbance occurs in the network state, and the cloud server can conveniently distribute the encrypted signal frequency band.

S23, determining the difference value between the reference coefficient and each signal attenuation coefficient except the reference coefficient in all the signal attenuation coefficients; counting to obtain a target coefficient of which the difference value is smaller than a set value in all the signal attenuation coefficients; and distributing corresponding encrypted signal frequency bands for the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient.

When allocating a signal frequency band, different onboard controllers occupy different time slice resources of the signal frequency band, and even if the signal frequency band is in the same encrypted signal frequency band, a delay of signal transmission may occur, for this reason, in S23, the allocating corresponding encrypted signal frequency bands to the onboard controllers corresponding to the target coefficient and the onboard controllers corresponding to the reference coefficient specifically includes the following contents:

and S231, determining the shared channel address between the vehicle-mounted control device corresponding to the target coefficient and the vehicle-mounted control device corresponding to the reference coefficient as a starting distribution address in the network state.

And S232, determining the first vehicle-mounted control device which has the historical connection identifier corresponding to the vehicle-mounted control device corresponding to the reference coefficient in the vehicle-mounted control devices corresponding to the target coefficient based on the initial distribution address.

And S233, allocating a first signal frequency band for the vehicle-mounted control device corresponding to the reference coefficient and the first vehicle-mounted control device.

And S234, determining the signal interference coefficient of a second vehicle-mounted control device except the first vehicle-mounted control device in the vehicle-mounted control devices corresponding to the target coefficient.

S235, adjusting the first signal frequency band according to the normalized increment corresponding to each signal interference coefficient to obtain a second signal frequency band; and distributing the second signal interference frequency band to a second vehicle-mounted control device corresponding to the second signal interference frequency band.

S236, the first signal frequency band and the second signal frequency band are integrated into a group of Internet of vehicles network based on a pre-coding mode, and a shielding mechanism is set for the Internet of vehicles network.

Through S231-S236, can confirm first on-vehicle controller and second on-vehicle controller in the on-vehicle controller that the target coefficient corresponds, and then respectively for first on-vehicle controller and second on-vehicle controller distribution first signal frequency channel and second signal frequency channel, because the second signal frequency channel is based on first signal frequency channel adjustment obtains, so, can improve the signal frequency channel availability factor under the same encryption signal frequency channel, for more accurate signal frequency channel of every on-vehicle controller distribution. Furthermore, the first signal frequency band and the second signal frequency band are integrated into a set of internet of vehicles based on a pre-coding mode, and a shielding mechanism is arranged on the internet of vehicles, compared with common protocol encryption or key encryption, the encryption method in the S236 is generated on the cloud server side, so that the reliability of signal frequency band encryption can be improved, and the shielding mechanism is prevented from being cracked.

And S24, filtering the vehicle-mounted control devices which are distributed with the encrypted signal frequency band in all the vehicle-mounted control devices, and returning to the step of sequencing all the vehicle-mounted control devices in the set road segment area based on the generated signal attenuation coefficients.

Through S21-S24, vehicle-mounted control devices can be sequenced according to signal attenuation coefficients of the vehicle-mounted control devices when global signal strength is disturbed, then the vehicle-mounted control devices are classified based on power distribution weights and the signal attenuation coefficients of the vehicle-mounted control devices, so that the vehicle-mounted control devices with the same or similar signal attenuation reactions under the disturbance of the global signal strength can be classified, then encrypted signal frequency bands are uniformly distributed, the timeliness of information and data interaction among the vehicle-mounted control devices in the same classification is improved, delay caused by the information and data interaction among all the vehicle-mounted control devices is avoided, and as the vehicle-mounted control devices carry out the information and data interaction based on different encrypted signal frequency bands, the transmission efficiency of communication channels can be further improved, and timely communication among different vehicle-mounted control devices is ensured, thereby ensuring timely and reliable control of the electrically driven vehicle.

After the encrypted signal frequency band is allocated to the vehicle-mounted control devices in the set road segment area, in specific implementation, if the number of electric vehicles in the set road segment area is large, and because the vehicle-mounted control devices are in an encrypted signal frequency band interaction state, each vehicle-mounted control device in the set road segment area may not be able to determine an optimal driving strategy according to the driving conditions of other electric vehicles, so as to determine the optimal driving strategy and avoid congestion in the set road segment area, in this embodiment, the following may be further included:

and S31, determining the real-time coordinate value of each vehicle-mounted control device in the set road segment area in the electronic map.

And S32, counting the real-time coordinate value increase and decrease information of the set intersection in the electronic map according to the set time interval.

And S33, determining the longitude and latitude of the increasing and decreasing area corresponding to each real-time coordinate value increasing and decreasing information.

And S34, dividing the real-time coordinate value increase and decrease information into a plurality of groups according to the longitude and latitude.

And S35, determining the road congestion coefficient corresponding to each real-time coordinate value increase and decrease information in each group according to each group in the plurality of groups, and screening the determined road congestion coefficients to obtain at least one expected coefficient.

S36, generating a plurality of driving strategies according to the at least one expectation coefficient.

And S37, screening out the optimal driving strategy from the plurality of driving strategies.

And S38, sending the optimal driving strategy to each vehicle-mounted control device in the set road segment area.

It can be understood that through S31-S38, the optimal driving strategy in the set road segment area can be determined based on the electronic map in the cloud server, and thus congestion in the set road segment area is avoided.

Because there are multiple types of communication interaction scenes in the set road segment area, and the influence degree of each type of communication interaction scene on the network state of the set road segment area is different, in order to accurately determine the influence on the network state, in S2112, the multiple information interaction instructions can be grouped according to the feature vector corresponding to each information interaction instruction, and a label is set for each group of information interaction instructions, so that the information interaction instructions can be accurately classified, and the classification of the communication interaction scenes is realized.

In specific implementation, whether the communication interaction scene is established or not is an important factor influencing the network state, and if the communication interaction scene is not established, even if the number of the information interaction instructions is huge, the network state cannot be influenced, so that in order to accurately determine the influence on the network state, in S2113-S2114, the global response rate can be obtained based on the response rate and the tags, so that the influence on the network state can be accurately analyzed by taking the establishment of the communication interaction scene and the category of the communication interaction scene into consideration.

In a specific implementation, since there may be a plurality of onboard controllers, and the positioning information of the onboard controllers in the area adjacent to the set road section area may drift, in the above case, there may be a deviation in the signal attenuation coefficient received by the cloud server (which may receive the signal attenuation coefficient sent by the onboard controllers in the adjacent area), for this reason, in S21, the obtaining of the signal attenuation coefficient correspondingly generated by each onboard controller may specifically include the following:

and S2121, determining three-dimensional coordinate values of each vehicle-mounted controller in a world coordinate system.

And S2122, zooming each three-dimensional coordinate value in the electronic map to obtain a mapping coordinate value.

S2123, for each mapping coordinate value, determining whether a distance between the mapping coordinate value and the boundary of the set link region is smaller than a set distance.

And S2124, when the distance is greater than or equal to the set distance, obtaining a signal attenuation coefficient correspondingly generated by the vehicle-mounted controller corresponding to the mapping coordinate value corresponding to the distance.

S2125, when the distance is smaller than the set distance, determining the number of set vehicle-mounted controllers communicated with the vehicle-mounted controllers corresponding to the distance, wherein the set vehicle-mounted controllers are located in the set road section area; and when the number exceeds the set proportion of the total number of the vehicle-mounted controllers in the set road section area, acquiring a signal attenuation coefficient correspondingly generated by the vehicle-mounted controller corresponding to the mapping coordinate value corresponding to the distance.

In S2122, when the three-dimensional coordinate values are zoomed, the three-dimensional coordinate values can be corrected and denoised, so as to ensure the calibration accuracy in the electronic map.

Through the S2123-S2125, the situation that the vehicle-mounted controllers in the area adjacent to the set road section area are subjected to positioning drift can be taken into consideration, and the actual area of the vehicle-mounted controllers is further determined according to the number of the target vehicle-mounted controllers communicated with the vehicle-mounted controllers corresponding to the distance, so that the cloud server is prevented from receiving the signal attenuation coefficient sent by the vehicle-mounted controllers in the adjacent area, and the accuracy and the reliability of the cloud server for receiving the signal attenuation coefficient are further ensured.

It can be understood that in a large environment of the car networking, a trojan horse program or a hacker may intrude into the car-mounted controller, thereby stealing the private information in the car-mounted controller, and may also infect the car-mounted controllers communicating with each other, and for this reason, before allocating the corresponding encrypted signal frequency bands to the car-mounted controller corresponding to the target coefficient and the car-mounted controller corresponding to the reference coefficient, the embodiment may further include the following:

and S251, respectively acquiring a first vehicle signature of the vehicle-mounted control device corresponding to the target coefficient and a second vehicle signature of the vehicle-mounted control device corresponding to the reference coefficient.

S252, evaluating a first signature attribute corresponding to the first vehicle signature and each included first signature time to obtain a first evaluation result; and evaluating second signature attributes corresponding to the second vehicle signature and each contained second signature moment to obtain a second evaluation result.

And S253, determining the state duration and the state switching frequency between the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient according to the first evaluation result and the second evaluation result.

And S254, determining the current node trust level between the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient according to the state duration and the state switching frequency.

And S255, determining the next node trust level between the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient according to the state duration, the state switching frequency and the current trust level.

And S256, judging whether the node where the next node trust level is located is a system node.

And S257, if the node where the next node trust level is located is the system node, determining that the vehicle-mounted control device corresponding to the target coefficient and the vehicle-mounted control device corresponding to the reference coefficient pass vehicle signature verification.

And S258, if the node where the next node trust level is located is not the system node, acquiring a communication strategy between the vehicle-mounted control device corresponding to the target coefficient and the vehicle-mounted control device corresponding to the reference coefficient, and acquiring a communication language between the vehicle-mounted control device corresponding to the target coefficient and the vehicle-mounted control device corresponding to the reference coefficient.

S259, judging whether the communication strategy is matched with the communication language, and if so, determining that the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient pass vehicle signature verification; and if not, initializing the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient, and returning to the step of respectively acquiring a first vehicle signature of the vehicle-mounted control equipment corresponding to the target coefficient and a second vehicle signature of the vehicle-mounted control equipment corresponding to the reference coefficient.

Through S251-S253, the state duration and the state switching frequency between the vehicle-mounted controller corresponding to the target coefficient and the vehicle-mounted controller corresponding to the reference coefficient can be determined, so that the dynamic performance of the vehicle-mounted controller corresponding to the target coefficient and the dynamic performance of the vehicle-mounted controller corresponding to the reference coefficient can be taken into consideration, and the accuracy of safety verification of the vehicle-mounted controller is improved.

Through S254-S257, follow-up behaviors of the vehicle-mounted controller can be predicted in advance based on the node trust degree, so that abnormal risks are prevented, and the real-time performance of safety verification of the vehicle-mounted controller is improved.

Through S258-S259, the safety verification can be performed on the vehicle-mounted controller corresponding to the target coefficient and the vehicle-mounted controller corresponding to the reference coefficient based on the communication strategy and the communication language, so that the dimensionality of the safety verification is improved, and the reliability of the safety verification is ensured.

Furthermore, by initializing the vehicle-mounted controller corresponding to the target coefficient and the vehicle-mounted controller corresponding to the reference coefficient, the integrity of the distribution of the encrypted signal frequency band can be ensured, the phenomenon that the encrypted signal frequency band is restarted due to breakpoint occurrence in the distribution of the encrypted signal frequency band is avoided, and the utilization rate of cloud server resources is improved.

In specific implementation, if the disturbance of the global signal strength of the network state is eliminated, the encrypted signal frequency band allocated to each vehicle-mounted controller in the set road segment area needs to be timely contacted, so as to ensure that each vehicle-mounted controller in the set road segment area communicates in a normal network state, and reduce the equipment loss of each vehicle-mounted controller (the equipment generates heat seriously when each vehicle-mounted controller communicates in the allocated encrypted signal frequency band), for this reason, on the basis of the foregoing, the embodiment may further include the following contents:

and S261, when the disturbance of the global signal strength of the network state is eliminated, receiving the signal transceiving state feature vector corresponding to each vehicle-mounted control device in the set road segment area.

And S262, converting each signal transceiving state feature vector into a state switching vector according to the reduction coefficient corresponding to each vehicle-mounted control device.

And S263, acquiring a communication state prediction result of at least one vehicle-mounted control device in the set road segment area and the occurrence rate of the communication state prediction result according to each state switching vector.

And S264, generating a signal reduction matrix according to each signal transceiving state feature vector and each state switching vector, and acquiring a reduction feature value of the signal reduction matrix.

And S265, adjusting the occurrence rate according to the reduction characteristic value to obtain a target occurrence rate.

S266, sequentially removing the allocated encrypted signal frequency band from each vehicle-mounted control device in the set road segment area according to the sequence of the target occurrence rate from low to high; and the target occurrence rate is used for representing signal impact and impact attenuation duration generated when the vehicle-mounted control equipment releases the distributed encrypted signal frequency band.

It can be understood that through S261-S266, the signal impact and the impact attenuation time of each vehicle-mounted controller when the allocated encrypted signal frequency band is released can be taken into consideration, and the signal impact interference of each vehicle-mounted controller on the surrounding vehicle-mounted controllers when the allocated encrypted signal frequency band is released can be avoided.

Specifically, in S266, the allocated encrypted signal frequency band is released for each vehicle-mounted controller in the set road segment area in sequence from low to high according to the target occurrence rate, so that the impact on the signal when the vehicle-mounted controller releases the allocated encrypted signal frequency band can be minimized, and stable and reliable communication interaction among the plurality of vehicle-mounted controllers can be ensured under the condition that the network state is normal.

Further, in S37, the screening out an optimal driving strategy from the plurality of driving strategies specifically includes:

s371, determining a driving strategy with the shortest sum of the intersection waiting time periods from the plurality of driving strategies as an optimal driving strategy.

And S372, if the running strategies with the shortest intersection waiting time lengths exist in the running strategies, finding out the running strategy with the highest average vehicle speed from the running strategies with the shortest intersection waiting time lengths as the optimal running strategy.

And S373, if a plurality of driving strategies with the highest vehicle average speed exist in the plurality of driving strategies with the shortest waiting time, finding the driving strategy with the lowest average stopping frequency from the plurality of driving strategies with the highest vehicle average speed as the optimal driving strategy.

And S374, if the running strategies with the minimum average stopping times exist in the running strategies with the maximum vehicle average speed, finding the running strategy with the minimum average braking times from the running strategies with the minimum average stopping times as the optimal running strategy.

And S375, if the running strategies with the minimum average braking times exist in the running strategies with the minimum average parking times, finding the running strategy with the weakest average communication interference coefficient from the running strategies with the minimum average braking times as the optimal running strategy.

It can be understood that, through S371-S375, the optimal driving strategy can be determined from five dimensions of the intersection waiting time, the average speed of the vehicle, the average number of times of parking, the average number of times of braking, and the average communication interference coefficient, so that the reliability and applicability of determining the optimal driving strategy can be improved.

Optionally, in S32, the counting, according to a set time interval, real-time coordinate value increase and decrease information at a set intersection in the electronic map specifically includes:

and S321, respectively determining real-time coordinate value increasing and decreasing peak values from each set intersection in the set road segment area according to the set time intervals, and fitting the determined real-time coordinate value increasing and decreasing peak values to obtain a first real-time coordinate value increasing and decreasing curve.

And S322, performing iterative evolution on each first coordinate point in the first real-time coordinate value increasing and decreasing curve to obtain a second coordinate point, and obtaining a second real-time coordinate value increasing and decreasing curve according to the second coordinate point.

And S323, mapping the first real-time coordinate value increasing and decreasing curve and the second real-time coordinate value increasing and decreasing curve to the same coordinate plane, and determining the first real-time coordinate value increasing and decreasing curve, the second real-time coordinate value increasing and decreasing curve and an area value surrounded by the edge of the coordinate plane.

And S324, determining real-time coordinate value increase and decrease information of each set intersection according to the area value.

In this embodiment, based on S321-S324, the real-time coordinate value increasing and decreasing trend at each set intersection can be predicted based on the first real-time coordinate value increasing and decreasing curve and the second real-time coordinate value increasing and decreasing curve, so as to improve the accuracy and reliability of the real-time coordinate value increasing and decreasing information, further provide a reliable basis for determining the optimal driving strategy, ensure the accuracy and reliability of the optimal driving strategy, and simultaneously reduce the verification and correction of the real-time coordinate value increasing and decreasing information by the cloud server, further save the overhead of the cloud server, and thus improve the timeliness of determining the optimal driving strategy.

On the basis of the above, the embodiment of the invention provides an electrically driven vehicle control device 200 based on an internet of vehicles. Fig. 3 is a functional block diagram of an electric drive vehicle control device 200 based on a vehicle networking according to an embodiment of the present invention, where the electric drive vehicle control device 200 based on a vehicle networking includes:

the signal attenuation coefficient obtaining module 201 is configured to monitor the network state of the set road segment area when the presence of the network state prompt information for the set road segment area is detected, and obtain a signal attenuation coefficient correspondingly generated by each vehicle-mounted control device when the global signal strength of the network state is disturbed.

The signal attenuation coefficient obtaining module 201 is specifically configured to:

acquiring a plurality of information interaction instructions in the set road section area; grouping the plurality of information interaction instructions according to the characteristic vector corresponding to each information interaction instruction, and setting a label for each group of information interaction instructions; determining the response rate of each group of information interaction instructions; according to the labels corresponding to each group of information interaction instructions, carrying out weighted summation on each response rate to obtain a global response rate; judging whether the global response rate reaches a preset response rate or not; and when the global response rate reaches the preset response rate, determining that the global signal intensity of the network state is disturbed.

The sorting selection module 202 is configured to sort all vehicle-mounted control devices in the set road segment area based on the magnitude of all generated signal attenuation coefficients; and selecting the vehicle-mounted control equipment with the highest power distribution weight from all the vehicle-mounted control equipment as target vehicle-mounted control equipment, and taking the signal attenuation coefficient of the target vehicle-mounted control equipment as a reference coefficient.

The sorting and selecting module 202 is specifically configured to:

obtaining power distribution weights corresponding to the vehicle-mounted control equipment according to the determined safety levels of all vehicles; and selecting a highest value from all the obtained power distribution weights, and taking the vehicle-mounted control equipment corresponding to the highest value as the target vehicle-mounted control equipment.

An encrypted signal frequency band allocation module 203, configured to determine a difference between the reference coefficient and each signal attenuation coefficient of the all signal attenuation coefficients except the reference coefficient; counting to obtain a target coefficient of which the difference value is smaller than a set value in all the signal attenuation coefficients; and distributing corresponding encrypted signal frequency bands for the vehicle-mounted control equipment corresponding to the target coefficient and the vehicle-mounted control equipment corresponding to the reference coefficient.

The encrypted signal frequency band allocation module 203 is specifically configured to:

and integrating the first signal frequency band and the second signal frequency band into a group of Internet of vehicles network based on a pre-coding mode, and setting a shielding mechanism for the Internet of vehicles network.

The encrypted signal frequency band allocation module 203 is further configured to:

And a filtering module 204, configured to filter the vehicle-mounted control devices to which the encrypted signal frequency band is allocated from among all the vehicle-mounted control devices, and return to the step of sorting all the vehicle-mounted control devices in the set road segment area based on the magnitudes of all the generated signal attenuation coefficients.

In an optional manner, the signal attenuation coefficient obtaining module 201 is specifically configured to:

judging whether the global response rate reaches a preset response rate or not;

and when the global response rate reaches the preset response rate, determining that the global signal intensity of the network state is disturbed.

In an optional manner, the sorting and selecting module 202 is specifically configured to:

In an optional manner, the encrypted signal frequency band allocation module 203 is specifically configured to:

In an alternative approach, the internet-of-vehicles based electrically-driven vehicle control apparatus 200 further includes a release module 205 for:

The electric drive vehicle control device 20 based on the internet of vehicles includes a processor and a memory, the signal attenuation coefficient obtaining module 201, the sorting selecting module 202, the encrypted signal frequency band allocating module 203, the filtering module 204, the removing module 205, and the like are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can set up one or more, can in time communicate between the different on-vehicle control equipment through adjusting the kernel parameter, and then ensure to carry out timely, reliable control and ensure to confirm the best strategy of traveling in the road segment region of settlement based on the electronic map in the high in the clouds server to the electric drive car, avoid setting up the jam in the road segment region.

An embodiment of the present invention provides a storage medium having a program stored thereon, which when executed by a processor implements the internet-of-vehicles based electric drive vehicle control method.

The embodiment of the invention provides a processor, wherein the processor is used for running a program, and the program executes the electric drive vehicle control method based on the internet of vehicles during running.

An embodiment of the present invention provides a cloud server, as shown in fig. 4, a cloud server 101 includes at least one processor 1011, and at least one memory 1012 and a bus connected to the processor 1011; the processor 1011 and the memory 1012 complete communication with each other via the bus 1013; the processor 1011 is configured to call program instructions in the memory 1012 to perform the internet-of-vehicles based electric drive vehicle control method described above.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. An electric drive vehicle control method based on the Internet of vehicles is applied to a cloud server, the cloud server is communicated with an on-vehicle control device corresponding to each electric drive vehicle in a plurality of electric drive vehicles running in a set road section area, and the method at least comprises the following steps:

judging whether the global response rate reaches a preset response rate or not;

the method further comprises the following steps:

2. The method according to claim 1, wherein the obtaining of the signal attenuation coefficient generated by each vehicle-mounted control device comprises:

3. The method according to claim 1, wherein before allocating the respective encrypted signal frequency bands to the vehicle-mounted control device corresponding to the target coefficient and the vehicle-mounted control device corresponding to the reference coefficient, the method further comprises:

4. The method of claim 3, further comprising:

5. The cloud server is characterized by comprising a processor, a memory and a bus, wherein the memory and the bus are connected with the processor; wherein, the processor and the memory complete mutual communication through the bus; the processor is configured to invoke the program instructions in the memory to execute the internet-of-vehicles based electric drive vehicle control method of any of the above claims 1-4.

6. A storage medium, characterized in that it stores thereon a program which, when executed by a processor, implements the internet-of-vehicles based electric drive vehicle control method of any one of claims 1 to 4.