CN111785012B - Energy consumption road spectrum analysis method for vehicle cloud cooperative computing - Google Patents

Energy consumption road spectrum analysis method for vehicle cloud cooperative computing Download PDF

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CN111785012B
CN111785012B CN202010342525.9A CN202010342525A CN111785012B CN 111785012 B CN111785012 B CN 111785012B CN 202010342525 A CN202010342525 A CN 202010342525A CN 111785012 B CN111785012 B CN 111785012B
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vehicle
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energy consumption
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CN111785012A (en
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纪湘湘
陆林
蔡文
李晓聪
郭全祥
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South Sagittarius Integration Co Ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/14—Receivers specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/33—Multimode operation in different systems which transmit time stamped messages, e.g. GPS/GLONASS
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing

Abstract

A vehicle cloud cooperative computing energy consumption road spectrum analysis method comprises the steps of collecting vehicle position coordinates and voltage and current values of a vehicle in a certain period, conducting secondary filtering smoothing on collected track point coordinates, obtaining road network information in the vehicle running process, conducting road network matching on longitude and latitude information and road network information which are subjected to smoothing processing by using a hidden Markov algorithm, and obtaining matched road information of vehicle running; and calculating the energy consumption road spectrum value of the vehicle by time integration according to the voltage and the current corresponding to the matched vehicle road network coordinate point. According to the invention, the automobile position coordinate point mark is closely combined with a real map road network, and the existing collected position point energy consumption road spectrum information is really reflected on the automobile driving road network, so that the obtained energy consumption road spectrum information is more accurate, the road traffic condition can be monitored through the analysis of the road energy consumption map, the urban road energy consumption condition is monitored, a basis is provided for urban road planning construction and public traffic development, and the energy conservation and emission reduction of cities are realized.

Description

Energy consumption road spectrum analysis method for vehicle cloud cooperative computing
Technical Field
The invention belongs to the field of traffic, and particularly relates to an energy consumption road spectrum analysis method for vehicle cloud collaborative computing.
Background
With the growing concern about environmental issues, electric vehicles have become an important component of future green traffic. In recent years, various countries have come out of a series of policies to promote the development of electric vehicles. The main purpose of popularizing the electric automobile is to realize energy conservation and emission reduction in the traffic field, but the evaluation method for the running energy consumption of the electric automobile still has obvious defects. At present, the test and evaluation conclusion of the energy consumption of the electric automobile cannot be used for accurately predicting the energy consumption of the electric automobile in actual running. The running energy consumption of the electric automobile directly determines the benefits of energy conservation and emission reduction and determines the magnitude of the driving range, so that the method has an important effect on reasonable evaluation of the running energy consumption of the electric automobile.
The existing research mode mainly marks an automobile energy consumption value through a GPS coordinate point, then displays the automobile energy consumption value on a map, and combines the collected GPS coordinate point with automobile power consumption, but due to the limitation of GPS sampling precision and period, the collected automobile position information may be inaccurate, and the distance between the sampling point and the sampling point is too large, so that the obtained automobile energy consumption road spectrum information is inaccurate, and under the condition that some GPS samples are lost, the problem that the automobile energy consumption road spectrum information cannot be obtained also exists.
Disclosure of Invention
In view of the above problems, the present invention provides an energy consumption road spectrum analysis method for vehicle cloud collaborative computing, which overcomes or at least partially solves the above problems.
The invention discloses an energy consumption road spectrum analysis method for vehicle cloud cooperative computing, which comprises the following steps:
s100, acquiring vehicle real-time information by a vehicle-mounted terminal TBOX in a preset acquisition period, wherein the vehicle real-time information comprises vehicle longitude and latitude, vehicle voltage and current values;
s200, smoothing the longitude and latitude information by the vehicle-mounted terminal TBOX through a quadratic fitting filtering algorithm, and uploading the smoothed longitude and latitude, the vehicle voltage and the vehicle current value to a cloud platform;
s300, the Internet of vehicles platform obtains road network information in the vehicle running process, and the smoothed longitude and latitude information and the road network information are matched by using a hidden Markov road network matching algorithm to obtain vehicle voltage and current values corresponding to road network coordinate points of the running vehicle after matching, and further obtain power values corresponding to the coordinate points;
s400, calculating energy consumption values of all road sections according to the power values corresponding to all the coordinate points and the passing time to obtain energy consumption road spectrum values of all the road vehicles.
Further, in S100, a preset sampling period is adjusted according to a vehicle motion state, and the vehicle motion states are divided into 3 types: urban road driving state, highway driving state and stopping state.
Further, the method for smoothing the longitude and latitude information by the quadratic fit filtering algorithm comprises the following steps:
respectively averaging the longitude and the latitude of adjacent sampling moments, assuming that the current moment is t, the last sampling moment is t-1, and the corresponding longitude and latitude coordinate point pair is
Figure RE-GDA0002662688910000021
And
Figure RE-GDA0002662688910000022
then the average coordinate point pair is calculated as follows:
Figure RE-GDA0002662688910000023
Figure RE-GDA0002662688910000024
storing k (k is n-1) average coordinate point pairs obtained by n recently collected longitude and latitude coordinate points, and then respectively carrying out quadratic curve fitting on the longitude and the latitude by using an alternating least square algorithm; the quadratic formula to be fitted is as follows:
y＝β2x2+β1x+β0
first by longitude
Figure RE-GDA0002662688910000025
As a dependent variable y, latitude
Figure RE-GDA0002662688910000026
As an argument x, a t-time longitude regression equation can be obtained, expressed as:
Figure RE-GDA0002662688910000031
similarly, a regression equation of the latitude at the time t can be obtained, and is expressed as:
Figure RE-GDA0002662688910000032
the longitude and latitude at time t are respectively substituted into the right part of the corresponding regression equation to obtain the predicted latitude and longitude, which is expressed as
Figure RE-GDA0002662688910000033
And finally, taking the point pair as a smoothed result.
Further, in S300, the hidden markov road network matching algorithm searches a set of candidate road segments and candidate points for each GPS track point according to the road segment projection process, and then constructs a candidate graph, where a node in the graph is a candidate point set of each GPS track, and an edge is a shortest path set between any two adjacent candidate points, and combines an observation state probability on the node and a state transition probability on the edge, and the matching algorithm searches for a path with the highest probability in the candidate graph, thereby maximally improving the global matching probability; the specific method comprises the following steps:
s301, preparing a candidate set according to the trace point ptNumber of roads fromThe link in the last fifty kilometers range is searched as a candidate link set, and is represented as R ═ R { (R)1,r2,...,rjAnd projecting the candidate road segments to obtain a candidate point set, which is expressed as C ═ C1,c2,...,cjJ represents the number of candidate points;
s302, initial state probability representing the probability that the vehicle is initially positioned on a certain road section is represented by using the observation state probability of the corresponding GPS track point, and the observation track point is defined
Figure RE-GDA0002662688910000034
Probability of observation state bj(pt) The value of state j is calculated by modeling the localization noise as a zero mean gaussian distribution:
Figure RE-GDA0002662688910000035
where σ is the standard deviation of the positioning measurement,
Figure RE-GDA0002662688910000036
representing points of track ptAnd Ct j(Observation Point ptThe jth candidate point) indicating a GPS track point ptWhether to match a candidate point C on the real roadt jAnd the distance between the track point at the moment t and the candidate point is smaller without considering the adjacent points, and the probability that the candidate point is a real actual point is higher;
s303, calculating a state transition probability, wherein the transition probability is the probability formed by the distance between every two vehicles in the candidate road section set and is used for evaluating the possibility of the vehicles from one road section to another road section; according to the track point p between the front time point t-1 and the rear time point tt-1，ptAnd its candidate point
Figure RE-GDA0002662688910000041
Is estimated from pt-1To ptIsThe real path is
Figure RE-GDA0002662688910000047
To
Figure RE-GDA0002662688910000048
The shortest path likelihood, transition probability, is calculated as follows:
Figure RE-GDA0002662688910000042
where d isg＝dist(pt-1，pt) Representing two points of track pt-1To ptLarge circular distance of, and drRepresenting a passing candidate node
Figure RE-GDA0002662688910000043
And
Figure RE-GDA0002662688910000044
the length of the shortest path (consisting of candidate links) of (a), the value of parameter k is empirically set to 0.07;
s304. according to p0To ptEach point finds a road segment with the highest probability of matching t track points as a final matched actual road by using the viterbi algorithm according to the observed state probability and the state body probability calculated in the above S302 and S303.
Further, the specific method of S304 is as follows: the probability of the observed state (row vector) calculated for the candidate segment projected at the previous time t-1 is multiplied by pt-1To ptProjecting the obtained state transition probability (matrix) of the candidate road section to obtain a new likelihood vector, and taking the state (road section) corresponding to the maximum value as the most possible road section; the process is executed iteratively, and the most possible road sections at each moment are collected to form a maximum likelihood path, so that the finally matched running road route is formed.
Further, the specific method of S400 is as follows: suppose that a vehicle enters a road segment at time tin and exits the road segment at time tout, during which there are n sample points, the tableShown as { otin,otin+1,...,on}; according to otThe track point p contained intAfter the sequence is subjected to the road network matching, the road network matching is correctly matched to the road section; then based on otVoltage value u contained intSum current value vtMultiplying the power value by the current value to obtain a power consumption value on the road section r by integrating the time
Figure RE-GDA0002662688910000045
The calculation formula of (a) is as follows:
Figure RE-GDA0002662688910000046
in addition, for the same road section, different vehicles may pass through the road section at different time periods, so that the road section can be represented as an accumulated vehicle electrical value set obtained by calculating according to the formula for each vehicle
Figure RE-GDA0002662688910000051
Wherein m represents the number of vehicles, and the average power consumption value of each kilometer of the vehicles in the road section is used as an Energy Spectrum value (ES) of the vehicles, and the calculation formula is as follows:
Figure RE-GDA0002662688910000052
where es (r) represents the energy consumption road spectrum value for the road segment r, dist (r) represents the length of the road segment in km.
Further, the method of acquiring the vehicle position information is GPS sampling.
Further, the preset minimum period for acquiring the vehicle position information is 1 s.
Further, when a vehicle position information sampling point is missing, the missing point can be estimated by placing interpolation points of sampling period intervals along a certain path between two matching points.
The invention has the beneficial effects that: the invention closely combines the automobile position coordinate point mark with the real map road network, matches the position coordinate point on the map road network in the calculation process, and truly reflects the energy consumption road spectrum information of the existing collected position point on the automobile driving road network, so that the obtained energy consumption road spectrum information is more accurate, and under the condition that the position information collection point is lost, the automobile energy consumption road spectrum information can be collected through the road network information. The road traffic condition can be monitored through the analysis of the road energy consumption map, the urban road energy consumption condition can be monitored, a basis is provided for urban road planning and construction and public traffic development, and the urban road energy consumption map is helpful for realizing energy conservation and emission reduction.
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 practice 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.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of an energy consumption road spectrum analysis method for vehicle cloud collaborative computing in embodiment 1 of the present invention;
FIG. 2 shows a diagram of the present invention according to the trace point p in embodiment 1tAcquiring a candidate road section and projecting the candidate road section to a candidate point process;
FIG. 3 is a process of finding the best path network by the Viterbi dynamic programming algorithm in embodiment 1 of the invention;
fig. 4 is a schematic diagram of estimating a missing point by interpolation after a GPS positioning point is missing in embodiment 1 of the present invention.
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 method aims to solve the problems that in the prior art, the automobile energy consumption road spectrum information is inaccurate, and under the condition that some GPS samples are lost, the automobile energy consumption road spectrum information cannot be obtained. The embodiment of the invention provides an energy consumption road spectrum analysis method for vehicle cloud cooperative computing.
Example 1
As shown in fig. 1, the embodiment discloses an energy consumption road spectrum analysis method for vehicle cloud collaborative computing, which includes:
s100, acquiring vehicle real-time information by a vehicle-mounted terminal TBOX in a preset acquisition period, wherein the vehicle real-time information comprises vehicle longitude and latitude, vehicle voltage and current values;
specifically, the method for acquiring the vehicle position information is a method for acquiring the vehicle position information by using GPS sampling or/and beidou sampling, and the method for acquiring the vehicle position information is not limited in this embodiment. And uploading the longitude and latitude information of the vehicle to a cloud platform by the T-box for calculation, wherein the cloud platform stores global road network information.
In S100, a preset sampling period is adjusted according to the motion state of the vehicle, and the vehicle operation states are divided into 3 types: urban road driving state, highway driving state and stopping state.
The driving state of the urban road is as follows: this state is the shortest GPS sampling period T1 between the 3 different states, with a T1 period of 1s in some preferred aspects, i.e., the vehicle's location information is sampled at the most frequent frequency to ensure track accuracy data. The motion state of the device cannot be determined when the vehicle starts to take off. Therefore, initially setting it to the "urban road driving state" avoids erroneous determination at the cost of energy sacrifice.
The driving state of the expressway: for a device in this state, the sampling period of the GPS receiver is 2 × T1. This means that the vehicle is traveling a long road, and it is not necessary to sample the location information frequently for a sample of the vehicle. According to experience, the GPS sampling period is set to be twice that of urban driving, unnecessary energy consumption is avoided, and map matching errors are avoided.
A stop state: this state corresponds to a certain GPS sampling period T2 and can only be activated when the vehicle is stopped or travelling very slowly. For example, the vehicle is waiting at a red light intersection or is jammed in a traffic jam. Due to uncertainty in traffic conditions, it is not possible to determine how long the vehicle will stop. Therefore, we use the GPS sample period state T2 for this purpose, which is independent of T1. Of course, T2 should be no less than T1.
S200, smoothing the longitude and latitude information by the vehicle-mounted terminal TBOX through a quadratic fitting filtering algorithm, and uploading the smoothed longitude and latitude, the vehicle voltage and the vehicle current value to a cloud platform;
the method for smoothing the longitude and latitude information by the quadratic fit filtering algorithm comprises the following steps:
respectively averaging the longitude and the latitude of adjacent sampling moments, assuming that the current moment is t, the last sampling moment is t-1, and the corresponding longitude and latitude coordinate point pair is
Figure RE-GDA0002662688910000071
And
Figure RE-GDA0002662688910000072
then the average coordinate point pair is calculated as follows
Figure RE-GDA0002662688910000081
Figure RE-GDA0002662688910000082
Storing k (k is n-1) average coordinate point pairs obtained by n recently collected longitude and latitude coordinate points, and then performing quadratic curve fitting on the longitude and the latitude by using alternate least square smoothing; the quadratic formula to be fitted is as follows:
y＝β2x2+β1x+β0
first by longitude
Figure RE-GDA0002662688910000083
As a dependent variable y, latitude
Figure RE-GDA0002662688910000084
As an argument x, a t-time longitude regression equation can be obtained, expressed as:
Figure RE-GDA0002662688910000085
similarly, a regression equation of the latitude at the time t can be obtained, and is expressed as:
Figure RE-GDA0002662688910000086
the longitude and latitude at time t are respectively substituted into the right part of the corresponding regression equation to obtain the predicted latitude and longitude, which is expressed as
Figure RE-GDA0002662688910000087
And finally, taking the point pair as a smoothed result.
S300, the vehicle networking platform acquires road network information in the vehicle running process, and the smoothed longitude and latitude information and the road network information are matched by using a hidden Markov road network matching algorithm to obtain vehicle voltage and current values corresponding to the coordinate points of the road network on which the vehicle runs after matching, so as to further obtain power values corresponding to the coordinate points, wherein in S300, the hidden Markov road network matching algorithm specifically comprises the following steps:
the hidden Markov road network matching algorithm searches a group of candidate road sections and candidate points for each GPS track point according to the road section projection process, then constructs a candidate graph, wherein the nodes in the graph are a candidate point set of each GPS track, and the edges are a shortest path set between any two adjacent candidate points; the specific method comprises the following steps:
s301, preparing a candidate set. According to the locus p, see FIG. 2tThe road network data is searched for the link within the last fifty kilometers as a candidate link set, and the candidate link set is expressed as R ═ { R ═ R1,r2,...,rjAnd projecting the candidate road segments to obtain a candidate point set, which is expressed as C ═ C1,c2,...,cjJ represents the number of candidate points;
s302, initial state probability representing the probability that the vehicle is initially positioned on a certain road section is represented by using the observation state probability of the corresponding GPS track point, and the observation track point is defined
Figure RE-GDA0002662688910000091
Probability of observation state bj(pt) The value of state j is calculated by modeling the localization noise as a zero mean gaussian distribution:
Figure RE-GDA0002662688910000092
where σ is the standard deviation of the positioning measurement,
Figure RE-GDA0002662688910000093
representing points of track ptAnd Ct j(Observation Point ptThe jth candidate point) indicating a GPS track point ptWhether to match a candidate point C on the real roadt jAnd regardless of its neighbors, the smaller the distance between the trajectory point at time t and the candidate point, the greater the probability that this candidate point is a true real point.
S303, calculating the state transition probability, wherein the transition probability is the probability formed by the distance between every two vehicles in the candidate road section set and is used for evaluating the probability of the vehicles from one road section to another road sectionAnd (4) performance. According to the track point p between the front and back time points t-1 and tt-1，ptAnd its candidate point
Figure RE-GDA0002662688910000094
Is estimated from pt-1To ptIs that
Figure RE-GDA0002662688910000095
To
Figure RE-GDA0002662688910000096
The shortest path likelihood, transition probability, is calculated as follows:
Figure RE-GDA0002662688910000097
where d isg＝dist(pt-1，pt) Representing two points of track pt-1To ptLarge circular distance of, and drRepresenting a passing candidate node
Figure RE-GDA0002662688910000098
And
Figure RE-GDA0002662688910000099
the length of the shortest path (consisting of candidate links) of (a), the value of parameter k is empirically set to 0.07;
and S304, finding the road section with the highest probability of matching t track points as the final matched actual road by using a Viterbi algorithm according to the observed state probability and the state body probability of each point p0 to pt calculated in the S302 and the S303.
In the process of the viterbi algorithm, as shown in fig. 3, the probability of the observed state calculated for the candidate link projected at the previous time t-1 is multiplied by the state transition probability matrix of the candidate link projected from pt-1 to pt, and the state (link) corresponding to the value with the largest value is taken as the most likely position. The specific method of S304 is as follows: the probability of the observed state (row vector) calculated for the candidate segment projected at the previous time t-1 is multiplied by pt-1To ptAnd projecting the obtained state transition probability (matrix) of the candidate road section to obtain a new likelihood vector, and taking the state (road section) corresponding to the maximum value as the most possible road section. The process is executed iteratively, and the most possible road sections at each moment are collected to form a maximum likelihood path, so that the finally matched running road route is formed.
In some preferred embodiments, when a vehicle position information sampling point is missing, the missing point may be estimated by placing an interpolation point of a sampling period interval along a certain path between two matching points.
In particular, the matched vehicle trajectory output should actually be a series of time-stamped one second-apart geographic coordinates. However, the GPS receiver does not work properly during GPS outage and therefore there is a possibility that the route generated directly from map matching is incomplete and we must supplement its missing GPS points by estimating the location points. Since this is likely a route consisting of a sequence of road segments to which a series of multiple matching points are mapped. Thus, if the missing location point is between two consecutive matching points, we can estimate the point by placing one second interval interpolation points along the determined path between the two matching points. As shown in fig. 4, we can determine waypoints by evenly placing these three points that are missed by the GPS between two consecutive matching points (t-1 and t-5). After the estimation is completed, we can get a trace as the final result, which is composed of the consecutive time-stamped geographic coordinates.
S400, calculating energy consumption values of all road sections according to the power values corresponding to all the coordinate points and the passing time to obtain energy consumption road spectrum values of all the road vehicles.
Further, the specific method of S400 is as follows: suppose a vehicle enters a road segment at time tin and leaves the road segment at time tout, during which there are n sample points, denoted as { o }tin,otin+1,...,on}; according to otThe track point p contained intAfter the sequence is subjected to the road network matching, the road network matching is accurately matched to the road section. Then based on otThe vehicle voltage value u contained intSum current value vtMultiplying the power value by the current value to obtain a power consumption value on the road section r by integrating the time
Figure RE-GDA0002662688910000111
The calculation formula of (a) is as follows:
Figure RE-GDA0002662688910000112
in addition, for the same road section, different vehicles may pass through the road section at different time periods, so that the vehicle power consumption value set which can be calculated according to the formula for each vehicle in the road section is expressed as
Figure RE-GDA0002662688910000113
Wherein m represents the number of vehicles, and the average vehicle power consumption per kilometer of the road section is used as an Energy Spectrum value (ES) of the road section, and the calculation formula is as follows:
Figure RE-GDA0002662688910000114
where es (r) represents the energy consumption road spectrum value for the road segment r, dist (r) represents the length of the road segment in km.
In the embodiment, the automobile position coordinate point mark is closely combined with the real map road network, the position coordinate point is matched to the map road network in the calculation process, and the energy consumption road spectrum information of the existing acquired position point is really reflected on the automobile driving road network, so that the acquired energy consumption road spectrum information is more accurate, and the automobile energy consumption road spectrum information can be acquired through the road network information under the condition that the position information acquisition point is lost. The road traffic condition can be monitored through the analysis of the road energy consumption map, the urban road energy consumption condition can be monitored, a basis is provided for urban road planning and construction and public traffic development, and the urban road energy consumption map is helpful for realizing energy conservation and emission reduction.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".

Claims (6)

1. The energy consumption road spectrum analysis method for vehicle cloud collaborative computing is characterized by comprising the following steps:
s100, acquiring vehicle real-time information by a vehicle-mounted terminal TBOX in a preset acquisition period, wherein the vehicle real-time information comprises vehicle longitude and latitude, vehicle voltage and current values;
s200, the vehicle-mounted terminal TBOX adopts a quadratic fitting filtering algorithm to smooth the longitude and latitude information, and uploads the longitude and latitude information after smoothing, the vehicle voltage and the vehicle current value to a cloud platform;
s300, the Internet of vehicles platform obtains road network information in the vehicle running process, and the smoothed longitude and latitude information and the road network information are matched by using a hidden Markov road network matching algorithm to obtain vehicle voltage and current values corresponding to road network coordinate points of the running vehicle after matching, and further obtain power values corresponding to the coordinate points; the hidden Markov road network matching algorithm searches a group of candidate road sections and candidate points for each track point according to the road section projection process, then constructs a candidate graph, wherein nodes in the graph are candidate point sets of each track, edges are shortest path sets between any two adjacent candidate points and are combined with observation state probabilities on the nodes and state transition probabilities on the edges, and the matching algorithm searches a path with the highest probability in the candidate graph to improve the global matching probability to the maximum extent; the specific method comprises the following steps:
s301, preparing a candidate set according to the trace point ptThe road network data is searched for the link within the last fifty kilometers as a candidate link set, and the candidate link set is expressed as R ═ { R ═ R1,r2,...,rAA, where a denotes the number of candidate links; and point p of the tracktProjected onto these candidate links results in a set of candidate points, denoted C ═ C1,c2,...,cBB represents the number of candidate points;
s302, initial state probability representing the probability that the vehicle is initially positioned on a certain road section is represented by using the observation state probability of corresponding track points, and the track point p is definedtProbability of observation state bj(pt) Computing observation state probability b by modeling localization noise as zero mean gaussian distributionj(pt) The value of (c):
Figure FDA0003381381400000021
where σ is the standard deviation of the positioning measurement,
Figure FDA0003381381400000022
representing points of track ptAnd
Figure FDA0003381381400000023
the distance between the first and second electrodes, wherein,
Figure FDA0003381381400000024
representing observation point ptThe jth candidate point of (a), which indicates a trace point ptWhether to match a candidate point on the real road
Figure FDA0003381381400000025
The distance between the track point at the sampling moment t and the candidate point is smaller, and the probability that the candidate point is a real actual point is higher;
s303, calculating a state transition probability, wherein the state transition probability is the probability formed by the distance between every two vehicles in the candidate road section set and is used for evaluating the possibility of the vehicles from one road section to another road section; according to the track point p between the front and the back sampling time t-1 and tt-1，ptAnd its candidate point
Figure FDA0003381381400000026
Is estimated from pt-1To ptIs that
Figure FDA0003381381400000027
To
Figure FDA0003381381400000028
The shortest path probability, the state transition probability, is calculated as follows:
Figure FDA0003381381400000029
where d isg＝dist(pt-1，pt) Representing two points of track pt-1To ptLarge circular distance of, and drRepresenting passing candidate points
Figure FDA00033813814000000210
And
Figure FDA00033813814000000211
the length of the shortest path of (1), wherein the shortest path is composed of candidate links, and the value of the parameter k is empirically set to 0.07;
s304. according to p1To pTEach trace point is the observed state probability and the state transition probability calculated as described above in S302 and S303Finding a road section with the highest probability of matching the T track points by using a Viterbi algorithm as a finally matched actual road; the specific method of S304 is as follows: multiplying the probability of the observation state calculated by the candidate road section projected at the last sampling moment t-1 by pt-1To ptProjecting the obtained state transition probability of the candidate road section to obtain a new likelihood vector, and taking the road section corresponding to the maximum value as the most probable road section, wherein the observation state probability is a row vector and the state transition probability is a matrix; the process is executed in an iterative way, and the most probable road sections at all times are collected to form a maximum likelihood path, so that a finally matched running road route is formed;
s400, calculating energy consumption values of all road sections according to the power values corresponding to all coordinate points and the passing time to obtain energy consumption road spectrum values of all road vehicles, wherein the specific method of S400 comprises the following steps: suppose a vehicle enters a road segment at time tin and exits the road segment at time tout, during which there are n coordinate points; according to the track point p contained in the coordinate pointtAfter the sequence is subjected to road network matching, the sequence is correctly matched to the road section; then, based on the vehicle voltage value u contained in the coordinate pointtSum current value vtMultiplying the two to obtain a power value, and integrating the power value with time to obtain an energy consumption value on the road section r
Figure FDA0003381381400000031
The calculation formula of (a) is as follows:
Figure FDA0003381381400000032
in addition, for the same road section, different vehicles may pass through the same road section at different time periods, so that the energy consumption value set which can be calculated according to the formula for each vehicle in the road section is expressed as
Figure FDA0003381381400000033
Wherein m represents the number of vehicles, and the average power consumption value of each kilometer of the vehicles on the road section is used as the Energy consumption road spectrum value (Energy Spect)rum, ES), the calculation formula is as follows:
Figure FDA0003381381400000034
where es (r) represents the energy consumption road spectrum value for the road segment r, dist (r) represents the length of the road segment in km.
2. The energy consumption road spectrum analysis method of vehicle cloud cooperative computing according to claim 1, wherein a preset sampling period in S100 is adjusted according to a vehicle motion state, and the vehicle motion states are divided into 3 types: urban road driving state, highway driving state and stopping state.
3. The energy consumption road spectrum analysis method of vehicle cloud cooperative computing as claimed in claim 1, wherein the method for smoothing the longitude and latitude information by the quadratic fit filtering algorithm is as follows:
respectively averaging the longitude and the latitude of adjacent sampling moments, assuming that the current moment is t, the last sampling moment is t-1, and the corresponding longitude and latitude coordinate point pair is
Figure FDA0003381381400000041
And
Figure FDA0003381381400000042
Figure FDA0003381381400000043
then the average coordinate point pair is calculated as follows:
Figure FDA0003381381400000044
Figure FDA0003381381400000045
storing recently acquired n1K obtained from longitude and latitude coordinate points1A pair of average coordinate points, wherein k1＝n1-1, then using an alternating least squares algorithm, performing a quadratic curve fit to the longitude and latitude, respectively; the quadratic formula to be fitted is as follows:
y＝β2x2+β1x+β0
first by longitude
Figure FDA0003381381400000046
As a dependent variable y, latitude
Figure FDA0003381381400000047
As an argument x, a t-time longitude regression equation can be obtained, expressed as:
Figure FDA0003381381400000048
similarly, a regression equation of the latitude at the time t can be obtained, and is expressed as:
Figure FDA0003381381400000049
the longitude and latitude at time t are respectively substituted into the right part of the corresponding regression equation to obtain the predicted latitude and longitude, which is expressed as
Figure FDA0003381381400000051
And finally, taking the point pair as a smoothed result.
4. The vehicle cloud computing-based energy consumption road spectrum analysis method as claimed in claim 1, wherein the method for acquiring the vehicle position information is GPS sampling.
5. The vehicle cloud cooperative computing energy consumption road spectrum analysis method according to claim 1, wherein a minimum period for acquiring the vehicle position information in a preset acquisition period is 1 s.
6. The vehicle cloud computing-assisted energy consumption road spectrum analysis method as claimed in claim 1, wherein when a vehicle position information sampling point is missing, the missing point can be estimated by placing an interpolation point of a sampling period interval along a determined path between two matching points.
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