CN115456485A - Typical industry logistics analysis method and system based on truck driving track - Google Patents
Typical industry logistics analysis method and system based on truck driving track Download PDFInfo
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Abstract
The invention provides a typical industry logistics analysis method and a system based on a truck driving track, which comprises the following steps: acquiring track data sets of all freight trucks; acquiring a complete trip chain of each cargo truck; acquiring enterprise spatial position information and an enterprise preset logistics radiation range value of a head enterprise corresponding to a typical industry in a target area, and determining a freight space associated position range of each head enterprise according to the enterprise spatial position information and the enterprise preset logistics radiation range value; determining a heavy goods transportation starting point and a heavy goods transportation terminal point; and fusing the heavy goods transportation starting point, the heavy goods transportation end point and the freight space associated position range, and determining the actual logistics intensity of the typical industry in the target area according to the frequency of the heavy goods transportation starting point and the heavy goods transportation end point in the freight space associated position range. The typical industry logistics analysis method based on the truck driving track realizes the determination of the actual logistics intensity of the typical industry in the target area.
Description
Technical Field
The invention relates to the technical field of logistics intensity analysis based on computer processing, in particular to a typical industry logistics analysis method and system based on a truck traveling track.
Background
At present, the methods for exploring the flow direction of goods in a typical industry mainly comprise two methods: the first is survey inquiry method, that is, the method is to issue inquiry tables to typical enterprises to survey the freight scale, the main transportation direction and the transportation mode of the enterprises one by one; and the other is a highway networking charging data analysis method, namely analyzing highway networking charging data, identifying the starting and ending points of the truck, restoring the running track of the truck, calculating the freight traffic flow and analyzing the flow direction.
The existing method for finding out the flow direction of goods has the following problems: the survey inquiry method has the defects of large workload, low efficiency, long time consumption and the like, the accuracy rate of survey results is low, accurate data support cannot be provided for planning management departments, and effective analysis conclusions are difficult to form; the highway networking charging data analysis method has the technical problems that the total flow of all goods transportation can be analyzed only, and goods cannot be distinguished for freight characteristic analysis.
In view of the above, there is a need to provide a method for analyzing the logistics of a typical industry based on the driving track of a truck, so as to solve or at least alleviate the above-mentioned drawbacks.
Disclosure of Invention
The invention mainly aims to provide a typical industry logistics analysis method based on a truck traveling track, and aims to solve the technical problems of large workload and low accuracy of survey results in the existing method for finding out the flow direction of goods under survey and inquiry.
In order to achieve the purpose, the invention provides a typical industry logistics analysis method based on a truck traveling track, which comprises the following steps of: s10, acquiring a track data set of all freight trucks with preset loads in a target area and in a historical target time period; s20, carrying out data cleaning and data processing on the track data set to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point; s30, acquiring enterprise spatial position information and an enterprise preset logistics radiation range value of the head enterprise corresponding to the typical industry in the target area, determining a freight space associated position range of each head enterprise according to the enterprise spatial position information and the enterprise preset logistics radiation range value, and forming a freight space associated position range data set of the head enterprise corresponding to the typical industry by data sets of the freight space associated position ranges of all the head enterprises; s40, determining a trip chain starting point of the spatial position in the freight space associated position range data set as a heavy goods transportation starting point, and determining a trip chain end point of the spatial position in the freight space associated position range data set as a heavy goods transportation end point; and S50, fusing the heavy cargo transportation starting point, the heavy cargo transportation end point and the freight space associated position range, and determining the actual logistics intensity of the typical industry in the target area according to the frequency of the heavy cargo transportation starting point and the heavy cargo transportation end point in the freight space associated position range.
Further, the method also comprises the following steps: s60, acquiring a research area in the target area, and acquiring node space position standard information of each traffic facility node in the research area, wherein a data set of all the node space position standard information forms a node space position standard information data set;
s70, adopting a formula
Calculating the node logistics intensity corresponding to any traffic facility node i
Wherein, the point i is the position point of the traffic facility node, the point o is the starting point of heavy goods transportation, the point d is the end point of heavy goods transportation, the starting point and the heavy goods transportationThe freight-carrying trucks with the movement track of (o, d) formed by the freight transportation terminal are recorded
Freight wagons with (o, d) running paths
Is written as
，
For trucks having a trajectory of (i, d), the set of trucks having a trajectory of (i, d) is recorded
For trucks with trajectory (o, i), the set of trucks with trajectory (o, i) is recorded
The maximum load of a truck having a track of (o, d) is recorded
And r is the average load factor of the truck.
Further, step S60 specifically includes: acquiring a research area in a target area, and acquiring node space position initial information of each traffic facility node in the research area; and performing data cleaning and data processing on the initial information of the node spatial position to determine standard information of the node spatial position from the initial information of the node spatial position, wherein the standard information of the node spatial position comprises a node unique identifier, a node type, a node grade and a node longitude and latitude corresponding to each traffic facility node, and a data set of all the standard information of the node spatial position forms a standard information data set of the node spatial position.
Further, the method also comprises the following steps: s80, obtaining partDetermining a main outflow region set of freight transportation corresponding to a typical industry from the frequency of occurrence of heavy freight transportation starting points in each target administrative area in the target area: (
) (ii) a S90, acquiring the occurrence frequency of the heavy goods transportation end points of each target administrative area in the target area, and determining a main inflow area set (a) of goods transportation corresponding to a typical industry
) (ii) a Traverse the set of primary outflow regions (
) And a set of primary inflow regions (
) Acquiring all combinations of inflow and outflow of cargo transportation at the level of the target administrative area: (
) Wherein, in the process,
for the outgoing collection of the cargo shipment,
for the incoming set of the transport of the goods,
，
。
further, the method also comprises the following steps: using a formula
Calculating and obtaining daily average freight behavior frequency of head enterprises corresponding to typical industries, wherein,
to be driven from
To is that
The collection of trucks of the freight transportation act,
is composed of
The k-th truck in the set,
is composed of
The k-th truck in the set completes the slave within the historical target study period
To
The frequency of freight transport behaviors, t is the number of days crossed by the running track of the freight truck.
Further, the method also comprises the following steps: using a formula
Calculating and obtaining the daily average cargo transportation quantity M corresponding to the running track of one cargo truck, wherein,
is derived from
To
The collection of trucks of the freight transportation act,
is composed of
The k-th truck in the set,
is composed of
The k-th truck in the set completes the slave within the historical target study period
To
The frequency of goods transportation behaviors, t is the number of days crossed by the running track of the freight truck,
the maximum load capacity of a truck, and r is the average load rate of the truck.
Further, step S30 specifically includes: acquiring enterprise data information in a target area, wherein the enterprise data information comprises an enterprise name, an enterprise type, enterprise registered funds, enterprise address information and an enterprise management range; determining the enterprises related to the typical industry as proposed enterprises according to the enterprise operation range; classifying the proposed enterprises according to scale by adopting a one-dimensional K-means algorithm based on enterprise registered funds, and determining head enterprises associated with typical industries from the proposed enterprises; the enterprise data information of the head enterprise is fused to the geographic map interest data, plant space area data of the head enterprise is obtained, and enterprise space position information of the head enterprise is determined through the plant space area data; and determining the freight space associated position range of each head enterprise according to the enterprise space position information and the enterprise preset logistics radiation range value, wherein the data sets of the freight space associated position ranges of all the head enterprises form a freight space associated position range data set of the head enterprise corresponding to the typical industry.
Further, if the actual logistics intensity in the target area exceeds the preset intensity, prompt information of adding a truck driver service station, a gas station, a parking facility and a service facility at the corresponding target area is sent out.
The invention also provides a logistics analysis system based on the truck driving track, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the typical industry logistics analysis method based on the truck driving track are realized.
Compared with the prior art, the invention has the following advantages:
the invention provides a typical industry logistics analysis method based on truck traveling tracks, which comprises the steps of carrying out data processing and data cleaning on track data sets after obtaining the track data sets of all trucks with preset loads in a target area and in a historical target time period, and further obtaining a trip chain starting point and a trip chain end point of each truck, wherein the trip chain starting point and the trip chain end point are starting and ending points of a complete trip chain of the trucks; then, determining a freight space associated position range of each head enterprise by acquiring enterprise space position information of the head enterprise corresponding to the typical industry in the target area and an enterprise preset logistics radiation range value, and acquiring a freight space associated position range data set of the head enterprise corresponding to the typical industry; determining the trip chain starting point of a spatial position in the freight space associated position range data set as a heavy goods transportation starting point, and determining the trip chain end point of the spatial position in the freight space associated position range data set as a heavy goods transportation end point; and finally, the heavy goods transportation starting point, the heavy goods transportation end point and the freight space associated position range are fused, the actual logistics intensity of the typical industry in the target area is determined according to the frequency of the heavy goods transportation starting point and the heavy goods transportation end point in the freight space associated position range, the actual logistics intensity of the typical industry in the target area is determined after data processing is carried out according to the acquired track data set of the freight truck provided by the national freight transportation platform and the acquired data set of the freight space associated position range, accurate data support can be provided for a planning management department, and the technical problems that the workload is large and the accuracy of survey results is low due to the fact that the flow direction of goods is found under query of the existing survey are solved.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the embodiments or technical solutions of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a typical industry logistics analysis method based on a truck traveling track according to an embodiment of the invention;
FIG. 2 is a schematic flow chart of an exemplary method for analyzing industry logistics based on a truck travel track according to another embodiment of the present invention;
FIG. 3 is a schematic flow chart of an exemplary method for analyzing industry logistics based on a truck travel track according to yet another embodiment of the present invention;
fig. 4 is a schematic diagram of freight relations of each administrative center according to still another embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Moreover, the technical solutions in the embodiments of the present invention may be combined with each other, but it is necessary to be able to be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent, and is not within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a typical industry logistics analysis method based on a truck driving track, including the following steps: s10, acquiring track data sets of all goods trucks with preset loads in a target area and in a historical target time period; s20, performing data cleaning and data processing on the track data set to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point; s30, acquiring enterprise spatial position information and an enterprise preset logistics radiation range value of a head enterprise corresponding to a typical industry in the target area, determining a freight space associated position range of each head enterprise according to the enterprise spatial position information and the enterprise preset logistics radiation range value, and forming a freight space associated position range data set of the head enterprise corresponding to the typical industry by data sets of the freight space associated position ranges of all the head enterprises; s40, determining the trip chain starting point of the spatial position in the freight space associated position range data set as a heavy cargo transportation starting point, and determining the trip chain end point of the spatial position in the freight space associated position range data set as a heavy cargo transportation end point; s50, fusing the heavy cargo transportation starting point, the heavy cargo transportation end point and the freight space associated position range, and determining the actual logistics intensity of the typical industry in the target area according to the frequency of occurrence of the heavy cargo transportation starting point and the heavy cargo transportation end point in the freight space associated position range; then, determining a freight space associated position range of each head enterprise by acquiring enterprise space position information of the head enterprise corresponding to the typical industry in the target area and an enterprise preset logistics radiation range value, and acquiring a freight space associated position range data set of the head enterprise corresponding to the typical industry; determining the trip chain starting point of a spatial position in the freight space associated position range data set as a heavy goods transportation starting point, and determining the trip chain end point of the spatial position in the freight space associated position range data set as a heavy goods transportation end point; and finally, the heavy goods transportation starting point, the heavy goods transportation end point and the freight space associated position range are fused, the actual logistics intensity of the typical industry in the target area is determined according to the frequency of the heavy goods transportation starting point and the heavy goods transportation end point in the freight space associated position range, the actual logistics intensity of the typical industry in the target area is determined after data processing is carried out according to the acquired track data set of the freight truck provided by the national freight transportation platform and the acquired data set of the freight space associated position range, accurate data support can be provided for a planning management department, and the technical problems that the workload is large and the accuracy of survey results is low due to the fact that the flow direction of goods is found under query of the existing survey are solved.
It can be understood that, in the present invention, the target region may be a certain country (e.g. china), or may be a certain province of a certain country or a prefecture city (e.g. the province of hunnan or Chongqing, etc.); the historical target time period can be one year, one month or other time, and the historical target time period can be set according to actual requirements; the preset load can be 12 tons or more than 12 tons or 20 tons or more, and the preset load can be set according to actual requirements.
It will be appreciated that from the trajectory data for each truck, a complete trip chain for a single truck may be determined, the complete trip chain having a trip chain start point and a trip chain end point. In the invention, a track data coordinate system can be associated based on longitude and latitude of a trip chain starting point and a trip chain terminal point; the freight space associated position range is associated with a geographic information data coordinate system based on a geographic information system, finally, after a track data coordinate system and the geographic information data coordinate system are associated, the trip chain starting point of a spatial position in the freight space associated position range data set is determined to be a heavy goods transportation starting point, the trip chain end point of the spatial position in the freight space associated position range data set is determined to be a heavy goods transportation end point, the heavy goods transportation starting point, the heavy goods transportation end point and the freight space associated position range are fused, and the actual logistics intensity of the typical industry in the target area is determined according to the occurrence frequency of the heavy goods transportation starting point and the heavy goods transportation end point in the freight space associated position range. Wherein the higher the occurrence frequency of the heavy cargo transportation starting point and the heavy cargo transportation ending point in the freight space associated position range, the greater the actual logistics intensity of a typical industry in the target area.
It is understood that a typical industry may be a particular industry, such as the cold chain industry, the engineering machinery industry, the steel industry, and the agricultural product industry; the head enterprise is the front enterprise in the typical industry.
Further, referring to fig. 2, in another embodiment of the present invention, the method further includes step S60, obtaining a research area in the target area, obtaining node spatial position standard information of each transportation facility node in the research area, and forming a node spatial position standard information data set by a data set of all the node spatial position standard information; s70, adopting a formula
Calculating the logistics intensity of any node corresponding to the traffic facility node i
The point i is the position point of the transportation facility node, the point o is the heavy goods transportation starting point, the point d is the heavy goods transportation terminal point, and the heavy goods transportation starting point and the heavy goods transportation terminal point form the freight wagon with the running track of (o, d)
The freight wagon of which the running track is (o, d)
Is recorded as
，
The freight trucks with the running tracks of (i, d) are recorded as
The freight trucks with the running tracks of (o, i) are recorded as
The maximum load capacity of the truck with the running track of (o, d) is recorded
And r is the average load rate of the truck.
It is understood that the transportation facility nodes include port transit nodes, train station transit nodes, and airport station transit nodes. The traffic facility node data records the basic information of traffic facility nodes in different industries of public, water, iron and air, the key node of a waterway record is a port, the key node of a railway record is a railway station, and an aviation record isThe key node of (a) is the airport. Analysis shows that if the starting point or the end point of the truck track of the truck falls within the radiation range of the transportation facility nodes of different transportation modes, the transportation facility nodes do not have the capacity or the capacity for producing or digesting important goods is low, and if the starting point or the end point falls within the radiation range of the port transfer node, the track is regarded as a freight track component of the public water combined transportation/molten iron combined transportation; and the track with the starting point or the end point falling in the radiation range of the transfer node of the railway station is regarded as the road-rail combined transportation/rail-air combined transportation, and the track with the starting point or the end point falling in the radiation range of the transfer node of the airport station is regarded as the road-air combined transportation/rail-air combined transportation. Through the analysis, the node logistics intensity corresponding to any traffic facility node i can be accurately acquired
And then accurately judging the specific function of each traffic facility node in the target area on typical industry transportation.
Alternatively, the research area may be a country, may be a province of the country, and may be one city (e.g., changsha city of Hunan province) or a plurality of cities of a certain province. It is understood that through the above steps, the node physical distribution strength as shown in table 1 can be analyzed.
TABLE 1
| Serial number | Node name | Node classes | Node longitude | Node latitude | Average physical strength (ten thousand tons/day) |
Further, a research area in the target area is obtained, and node space position initial information of each traffic facility node in the research area is obtained; and performing data cleaning and data processing on the initial node spatial position information to determine standard node spatial position information from the initial node spatial position information, wherein the standard node spatial position information comprises a node unique identifier, a node type, a node grade and a node longitude and latitude corresponding to each traffic facility node, and a data set of all the standard node spatial position information forms a standard node spatial position information data set.
Further, please refer toFig. 3 shows, in a further embodiment of the present invention, in order to facilitate obtaining a freight space contact characteristic of a typical industry, the method further includes the steps of: s80, acquiring the frequency of occurrence of the heavy goods transportation starting point in each target administrative area in the target area, and determining a main outflow area set (in) (80) of the goods transportation corresponding to the typical industry
) (ii) a S90, acquiring the occurrence frequency of the heavy goods transportation terminal of each target administrative area in the target area, and determining a main inflow area set (S) of goods transportation corresponding to the typical industry
) (ii) a Traverse the set of primary outflow regions (
) And the set of primary inflow regions (
) Acquiring all combinations of inflow and outflow of cargo transportation for the hierarchy of the target administrative area: (
) Wherein, in the process,
for the outgoing collection of the transport of goods,
for the incoming collection of the cargo shipment,
，
。
optionally, for the freight space relationship characteristics of the typical industry, the freight start and end point frequency of the typical industry can be respectively counted at three levels of province (SH), city (SI), and county (XI) (inflow area and outflow area), the hierarchical main freight contact area is identified, a freight traffic flow direction table of the typical industry is obtained, and the main transport indexes under each flow direction condition are counted by combining with the truck track data. Specifically, referring to fig. 4, the freight relationship of each administrative center is determined by acquiring the administrative centers of each state in the province of Hunan province.
Further, the method also comprises the following steps: using a formula
Calculating and obtaining daily average freight behavior frequency of head enterprises corresponding to the typical industry, wherein,
is derived from
To
A collection of trucks for the act of transporting goods,
is composed of
The k-th truck in the set,
is composed of
The kth truck in the set completes the slave within the historical target study period
To
The frequency of freight transport behaviors, t is the number of days crossed by the running track of the freight truck.
Further, the method also comprises the following steps: using a formula
Calculating and obtaining the daily average cargo transportation quantity M corresponding to the running track of one cargo truck, wherein,
to be driven from
To is that
The collection of trucks of the freight transportation act,
is composed of
The k-th truck in the set,
is composed of
The k-th truck in the set completes the slave within the historical target study period
To is that
The frequency of goods transportation behaviors, t is the number of days crossed by the running track of the freight truck,
the maximum load capacity of a truck, and r is the average load rate of the truck.
It is understood that through the above steps, a cargo transportation flow direction table as shown in table 2 can be analyzed.
TABLE 2
Further, the step S30 specifically includes: acquiring enterprise data information in the target area, wherein the enterprise data information comprises an enterprise name, an enterprise type, enterprise registered funds, enterprise address information and an enterprise operation range; determining the enterprises related to the typical industry as proposed enterprises according to the enterprise operation range; classifying the proposed enterprises according to scale by adopting a one-dimensional K-means algorithm based on the enterprise registered funds, and determining head enterprises associated with the typical industry from the proposed enterprises; fusing the enterprise data information of the head enterprise to geographic map interest data to obtain plant space area data of the head enterprise, and determining enterprise space position information of the head enterprise through the plant space area data; and determining a freight space associated position range of each head enterprise according to the enterprise space position information and the enterprise preset logistics radiation range value, wherein the data sets of the freight space associated position ranges of all the head enterprises form a freight space associated position range data set of the head enterprise corresponding to the typical industry.
Optionally, the initial enterprise data is first subjected to data pre-processing. Because the initial enterprise data has more recorded fields and the correlation between partial fields and the flow direction of goods is not strong, the initial data needs to be cleaned, and the accuracy of mining analysis can be improved after the data is cleaned, and meanwhile, the method is favorable for reducing the operation difficulty and saving the computing resources. In the specific implementation: the method comprises the following steps that firstly, irrelevant fields are deleted, initial enterprise data comprise 14 fields, and not all the fields are required to be used in goods flow direction analysis, so that only 5 fields including company names, registered capital, company types, address information and operation ranges are extracted from the fields for analysis; deleting missing data, wherein null values are invalid data in data mining and analysis, which is objectively not beneficial to improving data analysis efficiency and even possibly leads to wrong analysis results, so that the null values in the data are positioned, and the whole data containing the null values are deleted; and the third part is data standardization, wherein the standardization mainly aims at two fields of registered capital and address information. In the registered capital, the original data has the problems of different units such as ' yuan ' and ten thousand yuan ' and unit loss, and the units need to be unified into ten thousand yuan; in the address information, the original data format is not uniform, for example, "zilian town lute 5 group of zili county, wuyangzhou" lacks province-level and city-level addresses, "hounan province sang plant county, liu jia village, xinyangchun" lacks city-level addresses, and the like. In order to facilitate subsequent AOI and POI matching, the address format needs to be unified into 'province-city (state) -district (county) -street (village and town) -house number'. Firstly, original data is broken into five-level addresses of province, city (state), district (county), street (village and town) and house number according to a target format; then judging whether the address needs to be standardized according to whether the five-level address is complete or not, and screening out row data needing to be standardized; and finally, extracting the name of the company from the screened line data, extracting the specific address of the company from the Baidu map webpage in batches by using modules such as selenium, beautiful Soup and the like in the Python language, and storing the specific address according to a target format. The processed enterprise data field case is the enterprise information data shown in table 3.
TABLE 3
Specifically, the screening and spatialization operations of the business head enterprise implementing typical line association are as follows: and screening enterprise data corresponding to the typical industry. In the steps, the field standardization is carried out on the enterprise data preliminarily, and the basic information of the enterprise such as the operation category, the enterprise type, the registered fund, the address information, the operation range and the like is determined. Carrying out keyword retrieval and semantic classification on the field of the operation range, and extracting enterprise data of the industry to be researched; and classifying the enterprise data in the industry and screening head enterprises. The method applies a one-dimensional K-means algorithm to classify the enterprise data, and aims to classify the enterprises according to scale so as to extract main production enterprises in various industries. The enterprise registered fund information is used as a classification basis field, and the method has the advantages that the classification can be performed according to the distribution characteristics of data, the data objects with higher similarity are more likely to be classified into one class, the key point is to continuously adjust the classification number (K), the classification effect is judged by using the difference goodness of fit (GVF) index, and the optimal classification structure is obtained by comparison.
Where SDAM is the variance of the original data and SDCM is the sum of each class of variance. SDAM is a constant, SDCM and GVF are related to classification number, SDCM value is reduced and GVF value is increased along with the increase of value, the larger GVF is in a certain range, the better classification effect is, when the GVF is larger, the classification effect is better
Is equal to
When the utility model is used, the water is discharged,
，
take into account
The value must not be too large, and the method takes
When it is used
The value is the number of classifications; the data spatialization of the head enterprise aims to obtain the spatial area data of the actual production space of the head enterprise, and is based on the headAnd carrying out spatial indexing and fuzzy positioning on the enterprise address information. The enterprise data lack accurate space positioning information, so address information is selected as a space positioning basis, a relatively accurate enterprise space positioning point is obtained through matching, and then the head enterprise positioning point is matched to the AOI and POI data. The POI is generally called an interest point, generally refers to point data in an Internet electronic map, and basically comprises four attributes of name, address, coordinate and category; the AOI is called an interest plane, and compared with the POI, the AOI has an additional boundary coordinate list except the information such as name, type and the like. And superposing the enterprise spatial points obtained in the last step with AOI and POI data, and matching information such as enterprise names, business categories, POI categories, AOI categories and the like, so that the accuracy of the enterprise spatial data is further improved. Considering that the expected output is enterprise-oriented data, and the spatial positioning precision of POI and AOI data is higher than that of address information positioning data, the priority of data selection in the matching process is as follows: AOI > POI > enterprise points obtained based on address information spatial index; and finally, performing image superposition and area extraction on the enterprise point data obtained by arrangement. And (3) superposing the remote sensing images with higher instantaneity, and interpreting and extracting the spatial area data of the factory buildings of the enterprise by taking the enterprise point data as a positioning center. The POI data main fields are shown in table 4.
TABLE 4
Further, if the actual logistics intensity in the target area exceeds the preset intensity, prompt information of adding a truck driver service station, a gas station, a parking facility and a service facility at the corresponding target area is sent out. In specific implementation, if the actual logistics intensity in the target area exceeds the preset intensity, a traffic passage, a freight hub and a freight service facility are additionally or alternatively improved.
The invention also provides a logistics analysis system based on the truck driving track, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the typical industry logistics analysis method based on the truck driving track are realized.
In the above technical solutions, the above are only preferred embodiments of the present invention, and the technical scope of the present invention is not limited thereby, and all the technical concepts of the present invention include the claims of the present invention, which are directly or indirectly applied to other related technical fields by using the equivalent structural changes made in the content of the description and the drawings of the present invention.
Claims (9)
1. A typical industry logistics analysis method based on a truck traveling track is characterized by comprising the following steps:
s10, acquiring track data sets of all goods trucks with preset loads in a target area and in a historical target time period;
s20, performing data cleaning and data processing on the track data set to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point;
s30, acquiring enterprise spatial position information and an enterprise preset logistics radiation range value of a head enterprise corresponding to a typical industry in the target area, determining a freight space associated position range of each head enterprise according to the enterprise spatial position information and the enterprise preset logistics radiation range value, and forming a freight space associated position range data set of the head enterprise corresponding to the typical industry by data sets of the freight space associated position ranges of all the head enterprises;
s40, determining the trip chain starting point of the spatial position in the freight space associated position range data set as a heavy cargo transportation starting point, and determining the trip chain end point of the spatial position in the freight space associated position range data set as a heavy cargo transportation end point;
and S50, fusing the heavy goods transportation starting point, the heavy goods transportation end point and the freight space associated position range, and determining the actual logistics intensity of the typical industry in the target area according to the frequency of the heavy goods transportation starting point and the heavy goods transportation end point in the freight space associated position range.
2. The method for analyzing the typical industry logistics based on the traveling track of the truck as recited in claim 1, further comprising the steps of:
s60, acquiring a research area in the target area, acquiring node space position standard information of each traffic facility node in the research area, and forming a node space position standard information data set by data sets of all the node space position standard information;
s70, adopting a formula
Calculating the logistics intensity of any node corresponding to the traffic facility node i
Wherein, the point i is the position point of the transportation facility node, the point o is the heavy goods transportation starting point, the point d is the heavy goods transportation terminal point, and the running track formed by the heavy goods transportation starting point and the heavy goods transportation terminal point is
Said freight wagon being marked
The running track is
Said goods wagon
Is recorded as
，
For the trucks with the running track of (i, d), the set of trucks with the running track of (i, d) is recorded
，
The freight trucks with the running tracks of (o, i) are recorded as
The running track is
The maximum load capacity of the truck is recorded
And r is the average load rate of the cargo truck.
3. The method for analyzing the typical industry logistics analysis based on the traveling track of the truck as claimed in claim 2, wherein the step S60 specifically comprises:
acquiring a research area in the target area, and acquiring node space position initial information of each traffic facility node in the research area;
and performing data cleaning and data processing on the initial node spatial position information to determine standard node spatial position information from the initial node spatial position information, wherein the standard node spatial position information comprises a unique node identifier, a node type, a node grade and a node longitude and latitude corresponding to each traffic facility node, and a data set of all the standard node spatial position information forms a standard node spatial position information data set.
4. The method for analyzing the typical industry logistics based on the traveling track of the truck as claimed in claim 1, further comprising the steps of:
s80, acquiring the frequency of appearance of the heavy goods transportation starting points in each target administrative area in the target area, and determining a main outflow area set (A) of goods transportation corresponding to the typical industry
）；
S90, acquiring the occurrence frequency of the heavy goods transportation terminal of each target administrative area in the target area, and determining a main inflow area set (S) of goods transportation corresponding to the typical industry
）；
Traverse the set of primary outflow regions (
) And the set of primary inflow regions (a)
) Acquiring all combinations of inflow and outflow of cargo transportation for the hierarchy of the target administrative area: (
) Wherein, in the process,
for the outgoing collection of the cargo shipment,
for the incoming set of the transport of the goods,
，
。
5. the method for analyzing the typical industry logistics based on the traveling track of the truck as claimed in claim 4, further comprising the steps of:
using a formula
Calculating and obtaining daily average freight behavior frequency of the head enterprise corresponding to the typical industry, wherein,
to be driven from
To is that
A collection of trucks for the act of transporting goods,
is composed of
The k-th truck in the set,
is composed of
The kth truck in the set completes within the historical target study period
To
The frequency of the goods transportation behaviors and t are the number of days crossed by the running track of the goods van.
6. The method for analyzing the typical industry logistics based on the traveling track of the truck as claimed in claim 4, further comprising the steps of:
using the formula
Calculating and obtaining the average daily cargo transportation amount M corresponding to the running track of one cargo truck, wherein,
to be driven from
To is that
A collection of trucks for the act of transporting goods,
is composed of
The k-th truck in the set,
is composed of
The kth truck in the set completes within the historical target study period
To
Frequency of cargo transport behavior, t being the number of days spanned by the trajectory of the truck,
the maximum load capacity of the truck, r is the average load rate of the truck.
7. The method for analyzing the typical industry logistics based on the traveling track of the truck as claimed in claim 1, wherein the step S30 specifically comprises:
acquiring enterprise data information in the target area, wherein the enterprise data information comprises an enterprise name, an enterprise type, enterprise registered funds, enterprise address information and an enterprise operation range;
determining the enterprises related to the typical industry as proposed enterprises according to the enterprise operation range;
classifying the proposed enterprises according to scale by adopting a one-dimensional K-means algorithm based on the enterprise registered funds, and determining head enterprises associated with the typical industry from the proposed enterprises;
fusing the enterprise data information of the head enterprise to geographic map interest data to obtain factory building space area data of the head enterprise, and determining enterprise space position information of the head enterprise according to the factory building space area data;
and determining a freight space associated position range of each head enterprise according to the enterprise space position information and the enterprise preset logistics radiation range value, wherein a data set of the freight space associated position ranges of all the head enterprises forms a freight space associated position range data set of the head enterprises corresponding to the typical industry.
8. The method for analyzing the typical industry logistics based on the traveling track of the truck as claimed in claim 1,
and if the actual logistics intensity in the target area exceeds the preset intensity, sending prompt information for adding a truck driver service station, a gas station, a parking facility and a service facility at the corresponding target area.
9. A logistics analysis system based on a truck traveling track, characterized by comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the typical industry logistics analysis method based on the truck traveling track according to any one of claims 1-8 are realized.
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