CN114995164B - New energy automobile safety early warning method and device based on Internet of things - Google Patents
New energy automobile safety early warning method and device based on Internet of things Download PDFInfo
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- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
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- G—PHYSICS
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- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Abstract
The invention provides a new energy automobile safety early warning method and device based on the Internet of things, and relates to the technical field of the Internet of things.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to a new energy automobile safety early warning method and device based on the Internet of things.
Background
The new energy automobile is often used in the ordinary life of people, and the safety accident of the existing new energy automobile mainly comprises the automobile fault caused by overhigh battery temperature, insufficient brake sensitivity and the safety accident caused by overhigh automobile speed, so that whether the battery temperature, the brake sensitivity and the automobile speed are in a safety range or not can be monitored constantly, and the method for early warning when the operation parameters of the new energy automobile exceed the safety range is needed, thereby ensuring the safety of drivers.
Disclosure of Invention
The invention aims to provide a new energy automobile safety early warning method and device based on the Internet of things, so as to solve the problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
on the one hand, the application provides a new energy automobile safety early warning method based on the internet of things, and the method comprises the following steps: acquiring operation parameter information of the new energy automobile and historical accident data of the new energy automobile, wherein the operation parameter information comprises battery operation temperature information, brake sensitivity information and automobile running speed information;
performing grey correlation analysis on preset historical operation parameter information of the new energy automobile and historical accident data of the new energy automobile to obtain a correlation value between each historical operation parameter and the historical accident data;
sending the correlation value, the historical operation parameter information of the new energy automobile and the historical accident data of the new energy automobile to an optimized prediction model for processing to obtain predicted maximum safety parameter information, wherein the prediction model is a model for predicting the maximum parameter of the safe operation of the new energy automobile, and the maximum safety parameter information is the maximum parameter of the safe operation of the new energy automobile;
sending the maximum safety parameter information to a preset three-dimensional space model for processing, and establishing a safety region based on the processed maximum safety parameter to obtain a three-dimensional space model containing the safety region;
and sending the operation parameter information of the new energy automobile to a three-dimensional space model containing a safe region for judgment, and displaying early warning information based on the judgment result.
On the other hand, this application still provides a new energy automobile safety precaution device based on thing networking, includes:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring operation parameter information of the new energy automobile and historical accident data of the new energy automobile, and the operation parameter information comprises battery operation temperature information, brake sensitivity information and automobile running speed information;
the system comprises a first processing unit, a second processing unit and a third processing unit, wherein the first processing unit is used for carrying out grey correlation analysis on preset historical operation parameter information of the new energy automobile and historical accident data of the new energy automobile to obtain a correlation value between each historical operation parameter and the historical accident data;
the second processing unit is used for sending the correlation value, the historical operation parameter information of the new energy automobile and the historical accident data of the new energy automobile to an optimized prediction model for processing to obtain predicted maximum safety parameter information, wherein the prediction model is a model for predicting the maximum parameters of the safe operation of the new energy automobile, and the maximum safety parameter information is the maximum parameters of the safe operation of the new energy automobile;
the third processing unit is used for sending the maximum safety parameter information to a preset three-dimensional space model for processing, and establishing a safety region based on the processed maximum safety parameter to obtain the three-dimensional space model containing the safety region;
and the judging unit is used for sending the operating parameter information of the new energy automobile to a three-dimensional space model containing a safe region for judgment and displaying early warning information based on the judgment result.
The beneficial effects of the invention are as follows:
according to the method, the historical accident data and the historical operating parameters of the new energy automobile are subjected to correlation analysis, the correlation degree of each historical operating parameter and the historical accident data is obtained through analysis, the maximum safe operating parameter is predicted according to the correlation degree, the historical accident data and the historical operating parameters, the safe range of the operating parameters in the automobile operating process is obtained, the automobile can be guaranteed to operate stably, and early warning can be carried out when the operating parameters of the automobile exceed the safe range.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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 schematic flow chart of a new energy automobile safety early warning method based on the internet of things in the embodiment of the invention;
fig. 2 is a schematic structural diagram of the new energy automobile safety early warning device based on the internet of things in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1:
the embodiment provides a new energy automobile safety early warning method based on the Internet of things.
Referring to fig. 1, it is shown that the method comprises step S1, step S2, step S3, step S4 and step S5.
The method comprises the following steps of S1, obtaining operation parameter information of the new energy automobile and historical accident data of the new energy automobile, wherein the operation parameter information comprises battery operation temperature information, brake sensitivity information and automobile running speed information;
it can be understood that the temperature information of the electromagnetic operation is detected by the temperature detection device and is uploaded through the internet of things at intervals of a predetermined time, the preferred predetermined time is 1S, and the brake sensitivity in the invention is the time when the pressure sensor acquires the pressure on the brake and the time interval when the automobile decelerates, the shorter the time interval, the higher the sensitivity is, wherein the sensitivity of the time interval is 0.01S-0.1S is 1, the sensitivity of the time interval is 0.1S-0.2S is 0.8, the sensitivity of the time interval is 0.2S-0.5S is 0.6, and the sensitivity of the time interval is more than 0.5S is 0.
S2, performing grey correlation analysis on preset historical operation parameter information of the new energy automobile and historical accident data of the new energy automobile to obtain a correlation value between each historical operation parameter and the historical accident data;
it can be understood that the invention embodies the relation between each historical operating parameter and each corresponding historical accident data by performing the correlation analysis on the historical operating parameters and the historical accident data, and further prepares for the following maximum parameter for predicting the safe operation of the automobile, wherein in the step S2 comprises a step S21, a step S22, a step S23 and a step S24.
Step S21, performing sequence analysis on historical accident data of the new energy automobile and historical operation parameter information of the new energy automobile, wherein the historical accident data of the new energy automobile is used as a parent sequence reflecting safety characteristics of the new energy automobile, the historical operation parameter information of the new energy automobile is used as a subsequence reflecting safety factors of the new energy automobile, and classified sequence data are obtained;
it can be understood that in this step, by performing sequence analysis on historical accident data and historical operation parameter information, a data sequence reflecting the overall behavior characteristics or development of the system is taken as a parent sequence, and a data sequence reflecting the composition of factors affecting the development of the system is taken as a subsequence.
S22, performing dimensionless quantization processing on the classified sequence data, and performing mean value calculation on the dimensionless quantized data to obtain mean value data of each sequence;
it can be understood that the dimensionless quantization in the invention is to arrange the historical accident data and the historical operation parameter information, establish an excel table, then perform forward, standardization and normalization processing on the data in the table, and perform mean value calculation on the processed data, wherein the formula of the mean value calculation is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the ith row is the jth data, and n is a total of n data.
Step S23, calculating a correlation coefficient between each piece of sub-sequence data and parent sequence data based on the mean data of each sequence and the classified sequence data;
it is understood that the calculation formula of the correlation coefficient in the above steps is:
wherein:
the correlation coefficient of the historical accident data and the historical operation parameter information after the non-dimensionalization processing is obtained; f is historical accident data after non-dimensionalization processing; k historical operating parameter information after dimensionless processing;
is a time sequence before the occurrence of a historical accident;
is a time sequence after the occurrence of a historical accident;
for the resolution factor, take 0-1.
And S24, calculating a correlation value of the correlation coefficient of each piece of sub-sequence data and the parent sequence data to obtain a correlation value of historical operation parameter information of each new energy automobile and historical accident data of the new energy automobile.
It is understood that the correlation value in the above steps is calculated as follows:
wherein:
the correlation degree corresponding to the independent variable t is obtained; t is the data type of the mother sequence; h is the data type of the subsequence; m is the total number of samples of the sub-sequence data;
is a relation coefficient of the sub-sequence data f relative to the dependent variable h.
S3, sending the correlation value, the historical operation parameter information of the new energy automobile and the historical accident data of the new energy automobile to an optimized prediction model for processing to obtain predicted maximum safety parameter information, wherein the prediction model is a model for predicting the maximum parameter of the safe operation of the new energy automobile, and the maximum safety parameter information is the maximum parameter of the safe operation of the new energy automobile;
it can be understood that in this step, the maximum parameter of safe operation of the new energy vehicle is predicted through the correlation value, the historical operation parameter information and the historical accident data of the new energy vehicle, the operation parameter of each accident is determined, the maximum value of each accident is adjusted through the correlation degree, the maximum parameter of safe operation of the vehicle is further determined, the situation that the operation parameter of the vehicle exceeds the maximum parameter value in normal operation is prevented, and the safety of drivers is guaranteed, and the maximum parameter of safe operation of the new energy vehicle in the invention means that if the operation parameter of the new energy vehicle exceeds the maximum parameter in the operation process, the probability of the accident is greatly increased, and if the operation parameter is smaller than the maximum parameter, the probability of the accident is close to 0, in this step, step S3 includes step S31, step S32 and step S33.
Step S31, carrying out normalization processing on the correlation value, the historical operation parameter information of the new energy automobile and the historical accident data of the new energy automobile, and dividing the data after the normalization processing into a training set and a verification set;
it can be understood that in this step, the calculation amount of the data is reduced by normalizing all the data, and the normalized data is classified, and all the data are classified before and after the occurrence time, and the data occurring before is used as training set data, and the data occurring after is used as verification set data.
Step S32, training the prediction network by adopting a training set, taking the correlation value as an input weight between the prediction network hidden layer and the network input layer, and optimizing the input weight between the prediction network input layer and the hidden layer and a preset threshold value by adopting a particle swarm optimization algorithm to obtain an optimized prediction model;
it can be understood that the prediction network is optimized through the particle swarm optimization algorithm, and the operation parameters in the new energy automobile are maximized, wherein the prediction network is preferably an RNN neural network, and in this step, step S32 includes step S321, step S322, step S323, and step S324.
Step S321, obtaining input parameters of a prediction network, and combining all input weights and thresholds to obtain particle swarm parameters by taking the input weights between an input layer and a hidden layer of the prediction network and a preset threshold as particles;
it can be understood that in this step, input parameters of the prediction network are input, where the input parameters include an input number of layers, hidden layer nodes, and an input number of layers, the input number of layers is determined by a history operation parameter type, in this embodiment, the number of layers is 3, the hidden layer nodes are input randomly, the output number of layers is 1, the particle swarm parameters include a maximum iteration number of the swarm, a swarm size, a particle update parameter, a speed range of each particle is-0.5 to 0.5, and a position range of each particle is-0.5 to 0.5.
Step S322, initializing input parameters of the prediction network, wherein an input weight and a threshold number between a prediction network input layer and a hidden layer are determined, and randomly initializing a particle vector dimension and a range according to the input weight and the threshold number between the prediction network input layer and the hidden layer to obtain initialized parameters;
it can be understood that this step initializes the dimension and range of the particle vector by inputting the weight and the threshold number, and the initialization range of the particle is-0.5 to 0.5.
Step S323, inputting the training set as input data to the prediction network, and calculating the particle fitness according to a fitness function in a particle swarm optimization algorithm to obtain the fitness value of each particle of the population;
it can be understood that the fitness function in this step is calculated as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the actual value of the a-th historical operating parameter,
and N is the predicted value of the a-th historical operating parameter, and is the group number of data in the training set.
Step S324, obtaining the individual optimal position and the global optimal position of the particle according to the fitness of the particle in the particle swarm, and continuously updating the speed and the position of all the particles by dynamically tracking the individual optimal position and the global optimal position based on the particle swarm optimization algorithm until the particle swarm optimization algorithm reaches the maximum iteration number, so as to obtain an optimized prediction model.
It can be understood that the updating criterion of the individual optimal position in this step is to select the individual position with a larger fitness value as the individual optimal position according to the size of the fitness value, and the updating criterion of the global optimal position is to select the global position with a larger fitness value as the global optimal position according to the size of the fitness value.
It can be understood that this step updates its location by the following two formulas:
wherein, the first and the second end of the pipe are connected with each other,
in order to achieve the updated speed, the speed,
as is the current speed of the vehicle,
and
to learn the factors, one would typically take 2,
is the current position of the particle, a is the total number of particles,
to take a random number between 0 and 1,
for the best position found by the present particles so far,
for the best position found by all particles to the current position,
is the inertia factor.
Wherein, the first and the second end of the pipe are connected with each other,
for the updated position of the particles,
is the position of the particle before the update,
the position before particle update.
It can be understood that in the step, the optimal position is determined and then serves as an optimized prediction model, wherein the maximum safety value is a boundary operation parameter of the automobile in the operation process, if the boundary operation parameter exceeds the operation parameter, the probability of accidents is increased to 90%, and then the maximum parameter of the safe operation of the new energy automobile serves as a threshold value, so that the safety early warning can be performed on the new energy automobile.
And S33, sending the verification set to the optimized prediction model to obtain a prediction result, judging whether the prediction result is consistent with the data in the verification set, and if so, sending all the normalized data to the optimized prediction model to obtain the maximum parameter information of the safe operation of the new energy automobile.
The method can be understood that the data after training of the training set are compared by adopting the verification set, and whether the optimized prediction model is accurate or not is judged by judging whether the data are consistent or not, so that the maximum parameter information of safe operation of the new energy automobile is obtained.
S4, sending the maximum safety parameter information into a preset three-dimensional space model for processing, and establishing a safety region based on the processed maximum safety parameter to obtain a three-dimensional space model containing the safety region;
it can be understood that in this step, the maximum safety parameter of the battery operating temperature information, the maximum safety parameter of the brake sensitivity information and the maximum safety parameter of the vehicle driving speed information are marked in the three-dimensional space model to construct a safety region by presetting the three-dimensional space model, and in this step, the step S4 includes step S41, step S42, step S43 and step S44.
Step S41, performing dimensionless quantization processing on all the maximum safety parameter information to obtain dimensionless quantized maximum safety parameter information;
it can be understood that in this step, all the maximum security parameters are subjected to unified dimension through dimensionless quantization processing, and then all the maximum security parameters are marked in the three-dimensional space model to construct a security region.
Step S42, sending the dimensionless quantized maximum safety parameter information to a three-dimensional space model for marking, wherein the dimensionless quantized maximum safety parameter information is subjected to coordinate transformation, and the transformed coordinate is marked in the three-dimensional space model to obtain the three-dimensional space model marked with the maximum safety parameter coordinate;
s43, connecting the three-dimensional space models marked with the maximum safety parameter coordinates, wherein the coordinate origin and the maximum safety parameter coordinates are connected in pairs to obtain an area formed by connecting the coordinate origin and the maximum safety parameter coordinates in pairs;
and S44, taking the area formed by connecting the coordinate origin and the maximum safety parameter coordinate pairwise as a safety area to obtain a three-dimensional space model containing the safety area.
The method comprises the following steps of converting coordinates of dimensionless quantized maximum safety parameter information, determining coordinates of each maximum safety parameter information, and connecting the coordinates pairwise to obtain a triangular conical region, wherein the triangular conical region is in the field of operating parameters of safe operation of the new energy automobile.
And S5, transmitting the operation parameter information of the new energy automobile to a three-dimensional space model containing a safe region for judgment, and displaying early warning information based on the judgment result.
The operation parameter information of the new energy automobile is sent to a three-dimensional space model containing a safe region to be subjected to dimensionless quantization processing, the operation parameter information of the new energy automobile subjected to dimensionless quantization processing is subjected to coordinate transformation, the operation parameter coordinates of the new energy automobile subjected to dimensionless quantization processing are determined, then the operation parameter coordinates of the new energy automobile subjected to dimensionless quantization processing are sent to the three-dimensional space model containing the safe region to be marked, whether the operation parameter coordinates of the new energy automobile subjected to dimensionless quantization processing are in the safe region or not is judged, if the operation parameter coordinates of the new energy automobile subjected to dimensionless quantization processing are not in the safe region, preset early warning information is sent to a display panel of the new energy automobile to be displayed, and the purpose of carrying out safety early warning on the new energy automobile is achieved.
The method can predict the operation parameters of the new energy automobile when an accident occurs each time through the historical operation parameter information corresponding to each historical accident data, then determines the maximum value of the operation parameters of the new energy automobile when the accident occurs each time through the particle swarm optimization algorithm, further determines the operation parameter range of the safe operation of the new energy automobile, further sets an early warning interval, and can perform early warning on the new energy automobile when the operation parameters of the new energy automobile reach the early warning interval.
Example 2:
as shown in fig. 2, the embodiment provides a new energy vehicle safety warning device based on the internet of things, and the device includes an obtaining unit 701, a first processing unit 702, a second processing unit 703, a third processing unit 704, and a determining unit 705.
The acquiring unit 701 is used for acquiring operation parameter information of the new energy automobile and historical accident data of the new energy automobile, wherein the operation parameter information comprises battery operation temperature information, brake sensitivity information and automobile running speed information;
the first processing unit 702 is configured to perform grey correlation analysis on preset historical operation parameter information of the new energy automobile and historical accident data of the new energy automobile to obtain a correlation value between each historical operation parameter and the historical accident data;
the second processing unit 703 is configured to send the correlation value, the historical operating parameter information of the new energy vehicle, and the historical accident data of the new energy vehicle to the optimized prediction model for processing, so as to obtain predicted maximum safety parameter information, where the prediction model is a model for predicting a maximum parameter of safe operation of the new energy vehicle, and the maximum safety parameter information is a maximum parameter of safe operation of the new energy vehicle;
a third processing unit 704, configured to send the maximum safety parameter information to a preset three-dimensional space model for processing, and establish a safety region based on the processed maximum safety parameter, so as to obtain a three-dimensional space model including the safety region;
the judging unit 705 is configured to send the operation parameter information of the new energy vehicle to a three-dimensional space model including a safe region for judgment, and display warning information based on the judgment result.
In a specific embodiment of the present disclosure, the first processing unit 702 includes an analyzing subunit 7021, a first processing subunit 7022, a first calculating subunit 7023, and a second calculating subunit 7024.
An analysis subunit 7021, configured to perform sequence analysis on the historical accident data of the new energy vehicle and the historical operating parameter information of the new energy vehicle, where the historical accident data of the new energy vehicle is used as a parent sequence reflecting safety characteristics of the new energy vehicle, and the historical operating parameter information of the new energy vehicle is used as a subsequence reflecting safety factors of the new energy vehicle, so as to obtain classified sequence data;
a first processing subunit 7022, configured to perform dimensionless quantization on the classified sequence data, and perform mean value calculation on the dimensionless quantized data to obtain mean value data of each sequence;
a first calculating subunit 7023, configured to calculate a correlation coefficient between each piece of sub-sequence data and parent sequence data based on the mean data of each sequence and the classified sequence data;
a second calculating subunit 7024, configured to perform relevance value calculation on the relevance coefficient between each piece of sub-sequence data and the corresponding piece of parent sequence data to obtain a relevance value between historical operating parameter information of each new energy vehicle and historical accident data of the new energy vehicle.
In a specific embodiment of the present disclosure, the first processing unit 702 includes a second processing subunit 7031, a third processing subunit 7032, and a determining subunit 7033.
The second processing subunit 7031 is configured to perform normalization processing on the correlation value, the historical operating parameter information of the new energy vehicle, and the historical accident data of the new energy vehicle, and divide the data after the normalization processing into a training set and a verification set;
a third processing subunit 7032, configured to train the prediction network by using a training set, use the correlation value as an input weight between the prediction network hidden layer and the network input layer, and optimize the input weight between the prediction network input layer and the hidden layer and a preset threshold by using a particle swarm optimization algorithm, so as to obtain an optimized prediction model;
and the judging subunit 7033 is configured to send the verification set to the optimized prediction model to obtain a prediction result, judge whether the prediction result is consistent with the data in the verification set, and send all the data after the normalization processing to the optimized prediction model if the prediction result is consistent with the data in the verification set, so as to obtain the maximum parameter information of the safe operation of the new energy automobile.
In a specific embodiment of the present disclosure, the third processing subunit 7032 includes a fourth processing subunit 70321, a fifth processing subunit 70322, a third computing subunit 70323, and a sixth processing subunit 70324.
A fourth processing subunit 70321, configured to obtain input parameters of a prediction network, and combine all input weights and thresholds to obtain particle swarm parameters by using the input weights between the prediction network input layer and the hidden layer and a preset threshold as particles;
a fifth processing subunit 70322, configured to initialize an input parameter of the prediction network, where an input weight and a threshold number between the prediction network input layer and the hidden layer are determined, and a particle vector dimension and a range are initialized randomly according to the input weight and the threshold number between the prediction network input layer and the hidden layer, so as to obtain an initialized parameter;
a third computing subunit 70323, configured to input the training set as input data to the prediction network, and compute a particle fitness according to a fitness function in the particle swarm optimization algorithm, so as to obtain a fitness value of each particle of the population;
a sixth processing subunit 70324, configured to obtain an individual optimal position and a global optimal position of the particle according to the fitness of the particles in the particle swarm, and continuously update the speeds and positions of all the particles by dynamically tracking the individual optimal position and the global optimal position based on the particle swarm optimization algorithm until the particle swarm optimization algorithm reaches the maximum iteration number, so as to obtain an optimized prediction model.
In a specific embodiment of the present disclosure, the third processing unit 704 includes a seventh processing subunit 7041, a marking subunit 7042, an eighth processing subunit 7043, and a ninth processing subunit 7044.
A seventh processing subunit 7041, configured to perform dimensionless quantization on all the maximum security parameter information to obtain dimensionless quantized maximum security parameter information;
a marking subunit 7042, configured to send the dimensionless quantized maximum safety parameter information to a three-dimensional space model for marking, where coordinate transformation is performed on the dimensionless quantized maximum safety parameter information, and the transformed coordinate is marked in the three-dimensional space model, so as to obtain a three-dimensional space model marked with a maximum safety parameter coordinate;
an eighth processing subunit 7043, configured to connect the three-dimensional space models marked with the maximum safety parameter coordinates, where the origin of coordinates and the maximum safety parameter coordinates are connected in pairs, and an area formed by connecting the origin of coordinates and the maximum safety parameter coordinates in pairs is obtained;
and a ninth processing subunit 7044, configured to use an area formed by connecting the coordinate origin and the maximum safety parameter coordinate two by two as a safety area, to obtain a three-dimensional space model including the safety area.
It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, optimization and the like within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. The new energy automobile safety early warning method based on the Internet of things is characterized by comprising the following steps:
acquiring operation parameter information of the new energy automobile and historical accident data of the new energy automobile, wherein the operation parameter information comprises battery operation temperature information, brake sensitivity information and automobile running speed information;
performing grey correlation analysis on preset historical operation parameter information of the new energy automobile and historical accident data of the new energy automobile to obtain a correlation value between each historical operation parameter and the historical accident data;
sending the correlation value, the historical operation parameter information of the new energy automobile and the historical accident data of the new energy automobile to an optimized prediction model for processing to obtain predicted maximum safety parameter information, wherein the prediction model is a model for predicting the maximum parameter of the safe operation of the new energy automobile, and the maximum safety parameter information is the maximum parameter of the safe operation of the new energy automobile;
sending the maximum safety parameter information to a preset three-dimensional space model for processing, and establishing a safety region based on the processed maximum safety parameter to obtain a three-dimensional space model containing the safety region;
the operation parameter information of the new energy automobile is sent to a three-dimensional space model containing a safe region for judgment, and early warning information is displayed based on a judgment result;
determining a safe region of the new energy automobile operation parameter information based on the maximum safe parameter information to obtain a three-dimensional space model containing the safe region, wherein the method comprises the following steps:
performing dimensionless quantization processing on all the maximum safety parameter information to obtain dimensionless quantized maximum safety parameter information;
sending the dimensionless quantized maximum safety parameter information to a three-dimensional space model for marking, wherein the dimensionless quantized maximum safety parameter information is subjected to coordinate transformation, and the transformed coordinates are marked in the three-dimensional space model to obtain the three-dimensional space model marked with the maximum safety parameter coordinates;
connecting the three-dimensional space models marked with the maximum safety parameter coordinates, wherein the origin of coordinates and the maximum safety parameter coordinates are connected in pairs to obtain an area formed by connecting the origin of coordinates and the maximum safety parameter coordinates in pairs;
and taking an area formed by connecting the coordinate origin and the maximum safety parameter coordinate in pairs as a safety area to obtain a three-dimensional space model containing the safety area.
2. The safety early warning method for the new energy automobile based on the internet of things according to claim 1, wherein the gray correlation analysis of the preset historical operation parameter information of the new energy automobile and the historical accident data of the new energy automobile comprises:
performing sequence analysis on historical accident data of the new energy automobile and historical operation parameter information of the new energy automobile, wherein the historical accident data of the new energy automobile is used as a parent sequence reflecting safety characteristics of the new energy automobile, and the historical operation parameter information of the new energy automobile is used as a subsequence reflecting safety factors of the new energy automobile, so as to obtain classified sequence data;
performing dimensionless quantization processing on the classified sequence data, and performing mean value calculation on the dimensionless quantized data to obtain mean value data of each sequence;
calculating a correlation coefficient of each subsequence data and parent sequence data based on the mean data of each sequence and the classified sequence data;
and calculating a correlation value of the correlation coefficient of each piece of sub-sequence data and the parent sequence data to obtain a correlation value of historical operation parameter information of each new energy automobile and historical accident data of the new energy automobile.
3. The safety early warning method for the new energy automobile based on the internet of things as claimed in claim 1, wherein the sending of the correlation value, the historical operation parameter information of the new energy automobile and the historical accident data of the new energy automobile to the optimized prediction model for processing comprises:
normalizing the correlation value, the historical operating parameter information of the new energy automobile and the historical accident data of the new energy automobile, and dividing the normalized data into a training set and a verification set;
training a prediction network by adopting a training set, taking the correlation value as an input weight between a prediction network hidden layer and a network input layer, and optimizing the input weight between the prediction network input layer and the hidden layer and a preset threshold value by adopting a particle swarm optimization algorithm to obtain an optimized prediction model;
and sending the verification set to an optimized prediction model to obtain a prediction result, judging whether the prediction result is consistent with the data in the verification set, and if so, sending all the data subjected to normalization processing to the optimized prediction model to obtain the maximum parameter information of safe operation of the new energy automobile.
4. The new energy automobile safety early warning method based on the internet of things as claimed in claim 3, wherein the optimizing and predicting the input weight and the preset threshold value between the network input layer and the hidden layer by adopting the particle swarm optimization algorithm comprises the following steps:
acquiring input parameters of a prediction network, and combining all input weights and thresholds to obtain particle swarm parameters by taking the input weights between an input layer and a hidden layer of the prediction network and a preset threshold as particles;
initializing input parameters of a prediction network, wherein input weight values and threshold values between a prediction network input layer and a hidden layer are determined, and randomly initializing the dimension and range of a particle vector according to the input weight values and the threshold values between the prediction network input layer and the hidden layer to obtain initialized parameters;
inputting the training set serving as input data into the prediction network, and calculating the particle fitness according to a fitness function in a particle swarm optimization algorithm to obtain the fitness value of each particle of the population;
obtaining the individual optimal position and the global optimal position of the particles according to the fitness of the particles in the particle swarm, and dynamically tracking the individual optimal position and the global optimal position based on the particle swarm optimization algorithm to continuously update the speed and the position of all the particles until the particle swarm optimization algorithm reaches the maximum iteration times to obtain an optimized prediction model.
5. The utility model provides a new energy automobile safety precaution device based on thing networking which characterized in that includes:
the system comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring operation parameter information of the new energy automobile and historical accident data of the new energy automobile, and the operation parameter information comprises battery operation temperature information, brake sensitivity information and automobile running speed information;
the system comprises a first processing unit, a second processing unit and a third processing unit, wherein the first processing unit is used for carrying out grey correlation analysis on preset historical operation parameter information of the new energy automobile and historical accident data of the new energy automobile to obtain a correlation value between each historical operation parameter and the historical accident data;
the second processing unit is used for sending the correlation value, the historical operation parameter information of the new energy automobile and the historical accident data of the new energy automobile to an optimized prediction model for processing to obtain predicted maximum safety parameter information, wherein the prediction model is a model for predicting the maximum parameters of the safe operation of the new energy automobile, and the maximum safety parameter information is the maximum parameters of the safe operation of the new energy automobile;
the third processing unit is used for sending the maximum safety parameter information to a preset three-dimensional space model for processing, and establishing a safety region based on the processed maximum safety parameter to obtain the three-dimensional space model containing the safety region; the obtaining of the three-dimensional space model including the safety region includes:
performing dimensionless quantization processing on all the maximum safety parameter information to obtain dimensionless quantized maximum safety parameter information;
sending the dimensionless quantized maximum safety parameter information to a three-dimensional space model for marking, wherein the dimensionless quantized maximum safety parameter information is subjected to coordinate conversion, and the converted coordinate is marked in the three-dimensional space model to obtain the three-dimensional space model marked with the maximum safety parameter coordinate;
connecting the three-dimensional space models marked with the maximum safety parameter coordinates, wherein the origin of coordinates and the maximum safety parameter coordinates are connected in pairs to obtain an area formed by connecting the origin of coordinates and the maximum safety parameter coordinates in pairs;
taking an area formed by connecting the coordinate origin and the maximum safety parameter coordinate pairwise as a safety area to obtain a three-dimensional space model containing the safety area;
and the judging unit is used for sending the operating parameter information of the new energy automobile to a three-dimensional space model containing a safe region for judgment and displaying early warning information based on a judgment result.
6. The new energy automobile safety early warning device based on the internet of things of claim 5, wherein the device comprises:
the analysis sub-unit is used for performing sequence analysis on historical accident data of the new energy automobile and historical operation parameter information of the new energy automobile, wherein the historical accident data of the new energy automobile is used as a parent sequence reflecting safety characteristics of the new energy automobile, and the historical operation parameter information of the new energy automobile is used as a subsequence reflecting safety factors of the new energy automobile to obtain classified sequence data;
the first processing subunit is used for performing dimensionless quantization processing on the classified sequence data and performing mean value calculation on the dimensionless quantized data to obtain mean value data of each sequence;
a first calculating sub-unit configured to calculate a correlation coefficient of each of the sub-sequence data and the parent sequence data based on the mean data of each of the sequences and the classified sequence data;
and the second calculating subunit is used for calculating a correlation value of the correlation coefficient between each piece of the sub-sequence data and the corresponding piece of the parent-sequence data to obtain a correlation value between historical operation parameter information of each new energy automobile and historical accident data of the new energy automobile.
7. The new energy automobile safety early warning device based on the internet of things of claim 5, wherein the device comprises:
the second processing subunit is used for carrying out normalization processing on the correlation value, the historical operation parameter information of the new energy automobile and the historical accident data of the new energy automobile, and dividing the data after the normalization processing into a training set and a verification set;
the third processing subunit is used for training the prediction network by adopting a training set, taking the relevance value as an input weight between the prediction network hidden layer and the network input layer, and optimizing the input weight between the prediction network input layer and the hidden layer and a preset threshold value by adopting a particle swarm optimization algorithm to obtain an optimized prediction model;
and the judging subunit is used for sending the verification set to the optimized prediction model to obtain a prediction result, judging whether the prediction result is consistent with the data in the verification set, and if so, sending all the data after normalization processing to the optimized prediction model to obtain the maximum parameter information of safe operation of the new energy automobile.
8. The new energy automobile safety early warning device based on thing networking of claim 7, characterized in that, the device includes:
the fourth processing subunit is used for acquiring input parameters of the prediction network, and combining all the input weights and the threshold values to obtain particle swarm parameters by taking the input weights between the input layer and the hidden layer of the prediction network and a preset threshold value as particles;
the fifth processing subunit is used for initializing the input parameters of the prediction network, determining the input weight and the threshold number between the input layer and the hidden layer of the prediction network, and randomly initializing the dimension and the range of the particle vector according to the input weight and the threshold number between the input layer and the hidden layer of the prediction network to obtain initialized parameters;
the third calculation subunit is used for inputting the training set serving as input data to the prediction network and calculating the particle fitness according to a fitness function in the particle swarm optimization algorithm to obtain the fitness value of each particle of the particle swarm;
and the sixth processing subunit is used for obtaining the individual optimal position and the global optimal position of the particle according to the fitness of the particle in the particle swarm, and continuously updating the speed and the position of all the particles by dynamically tracking the individual optimal position and the global optimal position based on the particle swarm optimization algorithm until the particle swarm optimization algorithm reaches the maximum iteration number, so as to obtain the optimized prediction model.
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