CN111783034A - Emission road spectrum analysis method for vehicle cloud cooperative computing - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000010183 spectrum analysis Methods 0.000 title claims abstract description 17
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- 229910002091 carbon monoxide Inorganic materials 0.000 description 3
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Abstract
A vehicle cloud cooperative computing emission road spectrum analysis method comprises the steps of collecting position coordinates of a vehicle and concentration of nitrogen oxides PPM (PPM) emitted by an engine at a certain period, conducting secondary filtering smoothing on collected track point coordinates, obtaining road network information in the running process of the vehicle, and conducting road network matching on the smoothed longitude and latitude information and the road network information by using a hidden Markov algorithm to obtain matched road information for running of the vehicle; and calculating to obtain a vehicle emission road spectrum value according to the matched vehicle road network coordinate points and the corresponding PPM concentration values. The invention closely combines the automobile position coordinate point mark with the real map road network, and truly reflects the existing collected position point emission road spectrum information on the automobile driving road network, so that the obtained emission road spectrum information is more accurate, the urban road emission condition is monitored through the road emission spectrum, the improvement of the road traffic mode represented by the traditional high-carbon energy is promoted, the development to clean energy is promoted, and the urban environment quality is improved.
Description
Technical Field
The invention belongs to the field of traffic, and particularly relates to an emission road spectrum analysis method based on vehicle cloud collaborative computing.
Background
With the acceleration of the urbanization process, the traffic problem of the super-huge cities is increasingly prominent, the traffic transportation becomes one of the largest industries of energy consumption in China, and the following problems of energy consumption and environmental pollution seriously threaten the sustainable development of human health and society. According to the annual newspaper of environmental management of Chinese motor vehicles published by the department of ecological environment in the recent years, in 2019, the total emission of four pollutants of motor vehicles in China is primarily calculated to be 4359.7 ten thousand tons, wherein 3327.3 ten thousand tons of carbon monoxide (CO), 407.1 ten thousand tons of Hydrocarbon (HC), 574.3 thousand tons of nitrogen oxide (NOx) and 50.9 ten thousand tons of Particulate Matters (PM). Automobiles are a major contributor to air pollution emissions from motor vehicles, with emissions of over 80% CO and HC, and over 90% NOx and PM. The problem of solving the emission of harmful gases of automobiles is urgent. And (4) a road spectrum is discharged, and the aim of calculating the road spectrum value on each road section is to find a road section with high discharge, so that key treatment is performed.
The existing research mode mainly marks the emission of harmful gas of an automobile through a GPS coordinate point, then displays the emission on a map, and combines the collected GPS coordinate point with the emission concentration of the harmful gas of the automobile, 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 emission road spectrum information is inaccurate, and under the condition that some GPS samples are lost, the problem that the automobile emission road spectrum information cannot be obtained can also exist.
Disclosure of Invention
In view of the above, the present invention provides an emission road spectrum analysis method of vehicle cloud collaborative computing, which overcomes or at least partially solves the above problems.
The invention discloses an emission road spectrum analysis method for vehicle cloud collaborative computing, which comprises the following steps:
s100, a vehicle-mounted terminal TBOX acquires vehicle real-time information in a preset acquisition period, wherein the vehicle real-time information comprises vehicle longitude and latitude and nitrogen oxide emission concentration PPM data;
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 and the nitrogen oxide concentration to a cloud platform;
s300, the vehicle networking platform acquires road network information in the vehicle running process, and the longitude and latitude information and the road network information after smoothing are matched by using a hidden Markov road network matching algorithm to obtain road network coordinate points and nitric oxide PPM concentration values after matching and obtain emission values corresponding to all coordinate points;
s400, calculating the emission value of each road section according to the emission value corresponding to each coordinate point to obtain the emission road spectrum value of each road vehicle.
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
And
then the average coordinate point pair is calculated as follows:
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
As a dependent variable y, latitude
As an argument x, a t-time longitude regression equation can be obtained, expressed as:
similarly, a regression equation of the latitude at the time t can be obtained, and is expressed as:
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
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: s301, preparing a candidate set. According to the locus 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,...,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
Probability of observation state bj(pt) The value of state j is calculated by modeling the localization noise as a zero mean gaussian distribution:
where σ is the standard deviation of the positioning measurement,
representing points of trajectory pt and 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 road sections in the candidate road section set and is used for evaluating the possibility of the vehicle from one road section to another road section. According to the track point p between the front and back time points t-1 and tt-1，ptAnd its candidate point
Is presumed frompt-1To ptIs that
To
The shortest path likelihood, transition probability, is calculated as follows:
where d isg＝dist(pt-1，pt) Representing two points of track pt-1To ptAnd dr represents passing through the candidate node
And
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.
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 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 otNitrogen oxide concentration information f contained intObtaining the accumulated PPM concentration on the road section r
The calculation formula of (a) is as follows:
in addition, for the same road section, different vehicles may pass through the road section at different time periods, so that the accumulated PPM concentration value set calculated according to the formula for each vehicle of the road section is represented as
Wherein m represents the number of vehicles, and the average PPM concentration value per kilometer of the road section is used as an Emission Spectrum (ES) value of the road section, and the calculation formula is as follows:
where es (r) represents the emission spectra value for the stretch r and dist (r) represents the length of the stretch 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 existing collected position point emission road spectrum information on the automobile driving road network, so that the obtained emission road spectrum information is more accurate, and under the condition that the position information collection point is lost, the automobile emission road spectrum information can also be collected through the road network information. The urban road emission condition is monitored through the road emission map, the development of the traditional high-carbon energy serving as a representative road traffic mode to clean energy such as new energy is promoted to be improved, and the urban environment quality is improved.
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 emission road spectrum analysis method of 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, automobile emission road spectrum information is inaccurate, and under the condition that some GPS samples are lost, automobile emission road spectrum information cannot be obtained. The embodiment of the invention provides an emission road spectrum analysis method for vehicle cloud cooperative computing.
Example 1
As shown in fig. 1, the embodiment discloses an emission road spectrum analysis method of vehicle cloud collaborative computing, which includes: s100, the vehicle-mounted terminal TBOX acquires vehicle real-time information in a preset acquisition period, wherein the vehicle real-time information comprises vehicle longitude and latitude and nitrogen oxide emission concentration PPM data.
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 and the nitrogen oxide concentration 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
And
then the average coordinate point pair is calculated as follows
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
As a dependent variable y, latitude
As an argument x, a t-time longitude regression equation can be obtained, expressed as:
similarly, a regression equation of the latitude at the time t can be obtained, and is expressed as:
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
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 longitude and latitude information and the road network information after the smoothing processing are matched by using a hidden Markov road network matching algorithm to obtain a road network coordinate point and a nitrogen oxide PPM concentration value of the running vehicle after matching and obtain an emission value corresponding to each coordinate point, 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. As in FIG. 2, according toTrace 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,...,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
Probability of observation state bj(pt) The value of state j is calculated by modeling the localization noise as a zero mean gaussian distribution:
where σ is the standard deviation of the positioning measurement,
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 road sections in the candidate road section set and is used for evaluating the possibility of the vehicle from one road section to another road section. According to the track point p between the front and back time points t-1 and tt-1，ptAnd its candidate point
Is estimated from pt-1To ptIs that
To
The shortest path likelihood, transition probability, is calculated as follows:
where d isg＝dist(pt-1，pt) Representing two points of track pt-1To ptAnd dr represents passing through the candidate node
And
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 in an iteration mode, and the most possible road sections at all times are collected to form a maximum likelihood path, so that the finally matched running road is formedA wire.
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 the emission value of each road section according to the emission value corresponding to each coordinate point to obtain the emission road spectrum value of each road vehicle.
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 otNitrogen oxide concentration information f contained intObtaining the accumulated PPM concentration on the road section r
The calculation formula of (a) is as follows:
in addition, for the same road section, different vehicles may pass through the road section at different time periods, so that the accumulated PPM concentration value set calculated according to the formula for each vehicle of the road section is represented as
Wherein m represents the number of vehicles, and the average PPM concentration value per kilometer of the road section is used as an Emission Spectrum (ES) value of the road section, and the calculation formula is as follows:
where es (r) represents the emission spectra value for the stretch r and dist (r) represents the length of the stretch 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, the existing collected position point emission road spectrum information is really reflected on the automobile driving road network, so that the obtained emission road spectrum information is more accurate, and the automobile emission road spectrum information can be collected through the road network information under the condition that the position information collection point is lost. The urban road emission condition is monitored through the road emission map, the development of the traditional high-carbon energy serving as a representative road traffic mode to clean energy such as new energy is promoted to be improved, and the urban environment quality is improved.
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 (9)
1. The emission road spectrum analysis method of vehicle cloud cooperative computing is characterized by comprising the following steps:
s100, a vehicle-mounted terminal TBOX acquires vehicle real-time information in a preset acquisition period, wherein the vehicle real-time information comprises vehicle longitude and latitude and nitrogen oxide emission concentration PPM data;
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 and the nitrogen oxide concentration to a cloud platform;
s300, the vehicle networking platform acquires road network information in the vehicle running process, and the longitude and latitude information and the road network information after smoothing are matched by using a hidden Markov road network matching algorithm to obtain road network coordinate points and nitric oxide PPM concentration values after matching and obtain emission values corresponding to all coordinate points;
s400, calculating the emission value of each road section according to the emission value corresponding to each coordinate point to obtain the emission road spectrum value of each road vehicle.
2. The emission road spectrum analysis method based on vehicle cloud cooperative computing as claimed in claim 1, wherein the 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 emission 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
And
then the average coordinate point pair is calculated as follows:
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
As a dependent variable y, latitude
As an argument x, a t-time longitude regression equation can be obtained, expressed as:
similarly, a regression equation of the latitude at the time t can be obtained, and is expressed as:
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
And finally, taking the point pair as a smoothed result.
4. The method as claimed in claim 1, wherein 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 a road segment projection process, and then constructs a candidate graph, where nodes in the graph are a candidate point set of each GPS track, and edges are a shortest path set between any two adjacent candidate points, and combine an observation state probability on the nodes and a state transition probability on the edges, and the matching algorithm searches for a path with the highest probability in the candidate graph, so as to maximize the global matching probability; 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,...,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
Probability of observation state bj(pt) The value of state j is calculated by modeling the localization noise as a zero mean gaussian distribution:
where σ is the standard deviation of the positioning measurement,
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
Is estimated from pt-1To ptIs that
To
The shortest path likelihood, transition probability, is calculated as follows:
where d isg＝dist(pt-1，pt) Representing two points of track pt-1To ptAnd dr represents passing through the candidate node
And
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.
5. The emission road spectrum analysis method of vehicle cloud cooperative computing according to claim 4, wherein 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.
6. The emission road spectrum analysis method of vehicle cloud cooperative computing according to claim 1, wherein 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 correctly matched to the road section; then based on otNitrogen oxide concentration information f contained intObtaining the accumulated PPM concentration on the road section r
The calculation formula of (a) is as follows:
in addition, for the same road section, different vehicles may pass through the road section at different time periods, so that the accumulated PPM concentration value set calculated according to the formula for each vehicle of the road section is represented as
Wherein m represents the number of vehicles, and the average PPM concentration value per kilometer of the road section is used as an Emission Spectrum (ES) value of the road section, and the calculation formula is as follows:
where es (r) represents the emission spectra value for the stretch r and dist (r) represents the length of the stretch in km.
7. The vehicle cloud computing-assisted emission road spectrum analysis method according to claim 1, wherein the method for acquiring the vehicle position information is GPS sampling.
8. The vehicle cloud cooperative computing emission road spectrum analysis method according to claim 1, wherein a minimum period for acquiring the vehicle position information in a preset period is 1 s.
9. The vehicle cloud-coordinated emission 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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