CN114493188B - Freight order receiving recommendation method and device, electronic equipment and storage medium - Google Patents
Freight order receiving recommendation method and device, electronic equipment and storage medium Download PDFInfo
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- CN114493188B CN114493188B CN202210013441.XA CN202210013441A CN114493188B CN 114493188 B CN114493188 B CN 114493188B CN 202210013441 A CN202210013441 A CN 202210013441A CN 114493188 B CN114493188 B CN 114493188B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention provides a freight order taking recommendation method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring track data of freight transport personnel and trucks in transportation; determining the credibility of the freight personnel based on the track data; and determining the order taking sequence of the freight personnel based on the credibility of the freight personnel, and recommending the order taking sequence to the user. According to the objective track data, the credibility of the freight transport personnel is determined, so that the determined credibility is strongly related to the track data; order receiving recommendation is carried out based on the credibility strongly related to the track data, so that a better recommendation effect can be achieved, and possible risks of untrustworthy freight transport personnel are avoided.
Description
Technical Field
The invention relates to the technical field of logistics, in particular to a freight receipt recommendation method and device, electronic equipment and a storage medium.
Background
The logistics transportation is an industrial base stone of a medium-current column in the modern society.
The current logistics transportation platform order receiving mode is to determine order receiving personnel based on the distance between a user and a truck; the random order receiving recommendation method can recommend many untrusted freight transport personnel to the user, so that certain risks exist in the process of transporting goods.
Disclosure of Invention
The invention solves the problem that the random order receiving recommendation method of the current logistics transportation platform has certain risks.
In order to solve the above problems, the present invention first provides a freight order recommendation method, which includes:
acquiring track data of freight transport personnel and trucks in transportation;
determining a confidence level of the freight person based on the trajectory data;
and determining the order taking sequence of the freight personnel based on the credibility of the freight personnel, and recommending the order taking sequence to the user.
Preferably, the determining the credibility of the freight person based on the trajectory data includes:
acquiring road network data;
determining road network scores according to the road network data and the track data of the trucks;
determining a distance score according to the trajectory data of the freight personnel and the trajectory data of the truck;
determining the credibility of the freight carrier.
Preferably, the determining a road network score according to the road network data and the trajectory data of the truck comprises:
carrying out deviation rectification processing on the track data of the truck;
calculating the fitting degree of the corrected track data and the road network data;
determining the road network score based on the fitness.
Preferably, the determining a distance score according to trajectory data of a freight carrier and the trajectory data of a truck comprises:
acquiring track data of a truck and track data of a freight carrier when the freight is loaded;
and determining the distance score according to the track data of the truck and the track data of the freight carrier.
Preferably, the determining the credibility of the freight person based on the trajectory data further includes:
acquiring a first watermark photo and a second watermark photo of different channels, and determining the photo score;
preferably, the acquiring the first watermark photo and the second watermark photo of different channels and determining the photo score includes:
acquiring the first watermark photo, and generating a first counterfeit identification character string based on a preset mode;
acquiring the second watermark photo, and generating a second counterfeit identification character string based on the preset mode;
and determining the photo score according to the first and second anti-counterfeiting character strings.
Preferably, the determining the credibility of the freight person based on the trajectory data further includes:
and acquiring a big data score in the transportation.
Secondly, a freight order receiving recommending device is provided, which comprises:
the track acquisition module is used for acquiring track data of freight transport personnel and trucks in transportation;
a credibility determination module for determining the credibility of the freight personnel based on the trajectory data;
and the credible recommendation module is used for determining the order taking sequence of the freight personnel based on the credibility of the freight personnel and recommending the order taking sequence to the user.
Again, an electronic device is provided, comprising a processor and a computer readable storage medium storing a computer program, which when read and executed by the processor, implements the method as described in the foregoing.
Finally, a computer-readable storage medium is provided, in which a computer program is stored, which, when read and executed by a processor, implements the method as described above.
According to the objective track data, the credibility of the freight transport personnel is determined, so that the determined credibility is strongly related to the track data; order receiving recommendation is carried out based on the credibility strongly related to the track data, so that a better recommendation effect can be achieved, and possible risks of untrustworthy freight transport personnel are avoided.
Drawings
FIG. 1 is a flow chart of a method for freight pick-up recommendation according to an embodiment of the present invention;
FIG. 2 is a flowchart of a shipping order pickup recommendation method S200 according to an embodiment of the invention;
FIG. 3 is a flowchart of a shipping order taking recommendation method S220 according to an embodiment of the invention;
FIG. 4 is a flowchart of a shipping order recommendation method S230 according to an embodiment of the invention;
fig. 5 is a flowchart of a shipping order recommendation method S200 according to another embodiment of the invention;
FIG. 6 is a flowchart of a freight pick-up recommendation method S240 according to an embodiment of the invention;
FIG. 7 is a flowchart of a shipping order taking recommendation method S200 according to yet another embodiment of the invention;
FIG. 8 is a table of weight score assignments based on weather and road influence;
FIG. 9 is a block diagram of a shipping order taking recommendation device according to an embodiment of the present invention;
fig. 10 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
The embodiment of the application provides a freight order taking recommendation method which can be executed by a freight order taking recommendation device, and the freight order taking recommendation device can be integrated in electronic equipment such as a computer, a server and a computer. Fig. 1 is a flowchart illustrating a shipping order recommendation method according to an embodiment of the present invention; the freight order receiving recommendation method comprises the following steps:
s100, acquiring track data of freight personnel and trucks in transportation;
s200, determining the credibility of the freight personnel based on the track data;
and S300, determining order receiving sequence of the freight personnel based on the credibility of the freight personnel, and recommending the order receiving sequence to the user.
In this implementation, the recommendation is made to the user, specifically, the user may be arranged from high to low according to the credibility of the freight transportation personnel, traversal is started from the first place, whether the first freight transportation personnel meets the mandatory terms (for example, the distance between the freight transportation vehicle and the transportation starting point of the user is short) is determined, if yes, the first freight transportation personnel is recommended to the user, and if not, the recommendation is not made; until the recommended number of people reaches a preset threshold value or the credibility of the freight personnel is reduced to a preset threshold value or other preset conditions are reached
According to the objective track data, the credibility of the freight transport personnel is determined, so that the determined credibility is strongly related to the track data; order receiving recommendation is carried out based on the credibility strongly related to the track data, so that a better recommendation effect can be achieved, and possible risks of untrustworthy freight transport personnel are avoided.
Another freight receipt recommendation method is provided in the embodiment of the present application, and is similar to the aforementioned freight receipt recommendation method, except that, as shown in fig. 2, the step S200 of determining the credibility of the freight crew based on the trajectory data includes:
s210, obtaining road network data;
s220, determining road network scores according to the road network data and the track data of the trucks;
s230, determining a distance score according to the trajectory data of the freight personnel and the trajectory data of the truck;
and S260, determining the credibility of the freight personnel.
In this way, the road network score is determined according to the road network data with high association degree and the track data of the truck, and the association relation between the track data and the reliability can be established from multiple dimensions by combining the distance score.
Preferably, as shown in fig. 3, the step S220 of determining a road network score according to the road network data and the trajectory data of the truck includes:
s221, performing deviation rectification processing on the track data of the truck;
positioning of the truck and the freight transportation personnel by the positioning device during driving may be cheap, and the deviation causes great errors in data analysis, statistics and the like.
Preferably, the trajectory data is subjected to deviation rectification processing through a deviation rectification algorithm.
Wherein, the concrete process of rectifying processing includes:
because the calculation process lags the motion process, after the track points are checked, the effective points are added into the distance calculation process; judging the effective points according to the distance between the track points and the weight points; the weight point is obtained by the previous weight point and the new track point according to a certain weight (the initial weight point is selected as the first track point); in the running process of the vehicle track, only one effective weight point represents a stable point of a section of effective track, if a point of a suspected offset point appears, one effective weight point is generated again to represent a new suspected offset point as the weight of the starting point, if 5 points after the new suspected offset point do not have offset, the section of track is effective, and the effective weight point is added into the effective track point and updated; the weight point represents the final stable point of a section of track, and when the number of the track points represented by the weight point is less than 5, the weight point indicates that the section of point is possibly an offset point, and the weight point is removed.
Preferably, the specific process of the deviation rectifying process further includes: valid trajectory data is generated.
Preferably, the generating effective track data specifically includes:
first we simplify the problem of how to determine if a point falls within the tolerance of the line formed by the two points, the distance being described as: a point a and a point b are two planned points, wherein a point c is an actual point, and whether the point c is within the tolerance range of a straight line formed by connecting the point a and the point b is judged; three steps to solve the problem of simplification. Respectively as follows: roughly judging; judging whether the cable is out of line; and (6) judging the vertical line. The detailed process is as follows:
the rough judgment: whether the connecting line of the point c, the point a and the point b is within the tolerance range, namely whether ac or bc is within the tolerance range; if yes, returning to true; otherwise, further judgment is carried out.
It is determined whether the point c is outside the line ab, i.e. the c-point to ab depends on the extension of ab (if this is the case, it is difficult to determine whether the criterion is met by only giving a tolerance range, and a plurality of tolerance-related parameters such as horizontal tolerance and vertical tolerance are needed, and for simplicity, in this case, false is returned directly). If the drop is on ab, proceed to the next step.
The perpendicular distance d from point c to ab is calculated. Judging whether d is within the tolerance range, and if d is within the tolerance range, returning to true; otherwise, false is returned.
S222, calculating the fitting degree of the track data and the road network data after deviation rectification;
preferably, the degree of fitting between the trajectory data and the road network data is calculated by an LCSS algorithm (longest common subsequence trajectory similarity measure algorithm).
Preferably, the fitting degree calculation formula is:
in the formula, tr1 is an effective route, and n is an effective route length; tr2 is road network data, and m is the length of the road network data; head (tr) is the first point of the route, rest (tr) is a subsequence of all points except the first point; a is a minimum distance threshold, and when the distance between two points is smaller than A, the two points are regarded as the same point.
And S223, determining the road network score based on the fitting degree.
Preferably, when calculating the road network score of the locus position, when the road network fitting degree is greater than 95%, marking 10 points, when the road network fitting degree is greater than 85% and less than 95%, marking 9 points, and sequentially decreasing until reaching 0.
Specifically, empirical route data is obtained; determining the road network score based on the fitness and empirical route data.
Preferably, when the fitting degree X does not exceed the specified threshold value A, the fitting degree of the effective route and the road network data is high, and a weight w21 is obtained; when the fitting degree X exceeds a specified threshold value A, the fitting degree of the effective route and road network data is low, and the effective route and the empirical route are continuously compared to calculate the coincidence rate Y; when the coincidence rate Y does not exceed the designated threshold value B, the coincidence degree of the effective route and the empirical route is high, and the weight w21' is obtained; and when the coincidence rate Y exceeds the specified threshold value B, the coincidence degree of the effective route and the empirical route is low, and the situation that the freight transport personnel has the circuitous behavior is judged.
Preferably, the calculation formula of the coincidence ratio Y is:
coincidence rate of bar locus Y = mileage of coincident locus/mileage of reference locus
= mileage of effective route after deviation correction/mileage of empirical route
Preferably, as shown in fig. 4, the step S230 of determining a distance score according to the trajectory data of the freight carrier and the trajectory data of the truck includes:
s231, acquiring track data of a truck and track data of a freight carrier when the cargo is loaded;
and S232, determining the distance score according to the track data of the truck and the track data of the freight carrier.
Specifically, whether the loading point of a freight worker is accurate or not is judged according to the information of the distance between the mobile phone and the truck, so that a distance score is obtained, wherein the vehicle positioning is obtained by uploading through a vehicle machine, and the user positioning can be obtained by uploading the gps positioning of the mobile terminal app. When the freight vehicle is shut down, the vehicle-mounted data is suspended for uploading, but the position data of people always exists, the on-the-way state of the freight personnel is judged through the last vehicle positioning (track data) and the current positioning (track data) of people, and the distance score is calculated according to the information of the distance between the mobile phone and the vehicle, namely the real-time positioning between the vehicle-mounted data and the freight personnel; if the position deviation is too large, it is an abnormal situation, and the score is lower.
Preferably, when the distance score of the vehicle owner from the vehicle is calculated, if the distance score exceeds 1km for <5 times, the vehicle owner is judged to reasonably mark 10 points, if the distance score exceeds 5 times, the vehicle owner is less than 10 times, 8 points are counted, and the like.
The present embodiment provides another freight receipt recommendation method, which is similar to the aforementioned freight receipt recommendation method, except that, as shown in fig. 5,
the S200, determining the credibility of the freight carrier based on the trajectory data, further includes:
s240, acquiring a first watermark photo and a second watermark photo of different channels, and determining the photo score;
therefore, the grading of the watermark photo and the grading of the track point position information are comprehensively considered, and the freight transport personnel can be comprehensively graded in the freight transport process accurately and efficiently.
This and high, solved the communication pain point between freight transportation personnel and the user through the watermark photo, increased the degree of trust to the freight transportation personnel, provided more words rights for the freight transportation personnel simultaneously.
Preferably, as shown in fig. 6, the step S240 of acquiring the first watermark photo and the second watermark photo of different channels and determining the photo score includes:
s241, acquiring the first watermark photo, and generating a first authentication character string based on a preset mode;
preferably, the preset generation manner of the first counterfeit identification character string is as follows:
converting the first watermark picture into a first character string, dividing the first character string into a plurality of sections, and encrypting after intercepting the first N characters in each section to obtain each section of encrypted character string; and splicing the encrypted character strings to obtain a first authentication character string.
Preferably, the first watermark picture is converted into the first character string by means of base 64.
Base64 is an encoding method for transmitting 8-Bit byte codes, and is a method for representing binary data based on 64 printable characters.
Preferably, the first watermark photo is generated for the background server and transmitted to the freight transportation personnel.
Preferably, the second watermark photo is acquired through a channel of a user.
S242, acquiring the second watermark photo, and generating a second authentication character string based on the preset mode;
and S243, determining the photo score according to the first authentication character string and the second authentication character string.
Preferably, when the photo score of the watermark photo is calculated, the photo is judged to be not modified by 10 points and is modified by 0 points. Thus, the score occupation ratio caused by photo modification is greatly increased, and the difference of credibility among the freight transport personnel is increased.
Thus, through the first watermark photo and the second watermark photo, it can be judged whether the cargo transportation personnel performs concealment or modification actions when the user performs interaction, so as to determine the corresponding photo score.
Preferably, the information degree score is obtained according to whether the information such as the "position", "the map", "the remark" and the like is displayed in the first watermark photo and the second watermark photo.
Preferably, when the watermark photo provides information evaluation, such as filling in the information of 'position', 'map' and 'remark', the mark is counted for 0-10 points according to filling items.
Preferably, the labeling information in the first watermark photo and the second watermark photo is labeled on the first watermark photo and the second watermark photo in a watermark mode.
The present embodiment provides another freight order taking recommendation method, which is similar to the aforementioned freight order taking recommendation method, except that, as shown in fig. 7,
the S200, determining the credibility of the freight carrier based on the trajectory data, further includes:
and S250, acquiring the score of the big data in the transportation.
Specifically, the grade of the big data in transportation is a grade obtained by comprehensively analyzing the big data based on traffic communication data and weather data in transportation.
Preferably, the big data score in transportation is obtained, specifically:
based on the traveling track points of freight transport personnel, road traffic data of a traffic department and meteorological data of a meteorological bureau, extracting and integrating the data: performing data extraction and integration on the road corresponding to all track points in the track at the corresponding time, and performing data extraction and integration on meteorological data; extracting relations and entities from the relation: associating the track point with road traffic data and meteorological data and defining the track point as an entity; through operations such as association, aggregation and the like, data are stored according to a uniformly defined format, and the stored data are analyzed, classified and summarized by using a distributed database or a distributed computing cluster: and passive analysis is carried out on each entity, and the influence of extreme weather such as fog, rain, snow and the like on road traffic is analyzed.
Preferably, the meteorological data includes relative humidity, solar radiation, wind speed, precipitation, temperature, daily average wind speed, daily maximum and minimum temperature, daily average solar radiation, daily average relative humidity, daily cumulative precipitation, and the like.
Preferably, the passivity degree of each entity is obtained according to the influence degree of weather and roads, and the passivity degree is classified and summarized; therefore, the information scores of the 'track point' road traffic state, weather condition and the like are obtained.
As shown in fig. 8, it is a weight score assignment table based on the influence degree of weather and roads. And determining an information score c3j according to the table, and obtaining a big data analysis comprehensive score according to the weight configuration.
Preferably, when calculating the big data analysis score, the relationship and the entity are extracted from the road data and the meteorological data, and a series of analysis such as the weather condition and the road passing condition in the freight transportation trajectory is analyzed.
The embodiment of the present application provides a freight order taking recommendation device, which is used for executing the freight order taking recommendation method according to the content of the present invention, and the freight order taking recommendation device is described in detail below.
As shown in fig. 9, the freight order receiving recommendation device includes:
a track acquiring module 101, configured to acquire track data of a freight carrier and a truck in transportation;
a credibility determination module 102, configured to determine a credibility of the freight person based on the trajectory data;
and the credible recommendation module 103 is used for determining the order taking sequence of the freight personnel based on the credibility of the freight personnel and recommending the order taking sequence to the user.
According to the objective track data, the credibility of the freight transport personnel is determined, so that the determined credibility is strongly related to the track data; order receiving recommendation is carried out based on the credibility strongly related to the track data, so that a better recommendation effect can be achieved, and possible risks of untrustworthy freight transport personnel are avoided.
Preferably, the trust determination module 102 is further configured to: acquiring road network data; determining road network scores according to the road network data and the track data of the trucks; determining a distance score according to the trajectory data of the freight personnel and the trajectory data of the truck; determining the credibility of the freight carrier.
Preferably, the trust determination module 102 is further configured to: carrying out deviation rectification processing on the track data of the truck; calculating the fitting degree of the corrected track data and the road network data; determining the road network score based on the fitness.
Preferably, the trust determination module 102 is further configured to: acquiring track data of a truck and track data of a freight carrier when the freight is loaded; and determining the distance score according to the track data of the truck and the track data of the freight carrier.
Preferably, the trust determination module 102 is further configured to: and acquiring a first watermark photo and a second watermark photo of different channels, and determining the photo score.
Preferably, the trust determination module 102 is further configured to: acquiring the first watermark photo, and generating a first authentication character string based on a preset mode; acquiring the second watermark photo, and generating a second counterfeit identification character string based on the preset mode; determining the photo score according to the first and second pseudonymous authentication strings.
Preferably, the trust determination module 102 is further configured to: and acquiring a big data score in transportation.
The freight receipt recommendation device provided by the above embodiment of the present application and the freight receipt recommendation method provided by the embodiment of the present application have the same inventive concept and have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the device.
An electronic device is provided in the embodiment of the present application, and as shown in fig. 10, the electronic device includes a computer-readable storage medium 301 storing a computer program and a processor 302, where the computer program is read by the processor and executed by the processor, so as to implement the method described above.
According to the objective track data, the credibility of the freight transport personnel is determined, so that the determined credibility is strongly related to the track data; order receiving recommendation is carried out based on the credibility strongly related to the track data, so that a better recommendation effect can be achieved, and possible risks of untrustworthy freight transport personnel are avoided.
An embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is read and executed by a processor, the computer program implements the method as described above.
The technical solution of the embodiment of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be an air conditioner, a refrigeration device, a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method of the embodiment of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
According to the objective track data, the credibility of the freight transport personnel is determined, so that the determined credibility is strongly related to the track data; order receiving recommendation is carried out based on the credibility strongly related to the track data, so that a better recommendation effect can be achieved, and possible risks of untrustworthy freight transport personnel are avoided.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "...," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the application are described in a relevant manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. Reference is made to the preceding description of the embodiments.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (7)
1. A freight order taking recommendation method is characterized by comprising the following steps:
acquiring track data of freight transport personnel and trucks in transportation;
determining a confidence level of the freight person based on the trajectory data;
determining order receiving sequence of the freight transport personnel based on the credibility of the freight transport personnel, and recommending the order receiving sequence to a user;
the determining the credibility of the freight carrier based on the track data comprises:
acquiring road network data;
determining road network scores according to the road network data and the track data of the trucks;
determining a distance score according to the trajectory data of the freight personnel and the trajectory data of the truck;
determining the credibility of the freight personnel;
determining a road network score according to the road network data and the trajectory data of the truck, comprising:
carrying out deviation rectification processing on the track data of the truck and generating effective track data;
calculating the fitting degree of the corrected effective track data and the road network data, and determining the road network score based on the fitting degree when the fitting degree does not exceed a specified threshold A; the fitting degree calculation formula is as follows:
in the formula, tr1 is an effective route, and n is an effective route length; tr2 is road network data, and m is the length of the road network data; head (tr) is the first point of the route, rest (tr) is the subsequence of all points except the first point; a is a minimum distance threshold, and when the distance between two points is less than A, the two points are regarded as the same point;
when the fitting degree exceeds a specified threshold value A, acquiring empirical route data, continuously calculating the coincidence rate of the corrected effective track data and the empirical route data, and determining the road network score based on the coincidence rate;
the determining the credibility of the freight carrier based on the trajectory data further comprises:
acquiring a big data score in transportation; and when calculating the big data score, extracting the relation and the entity from the road data and the meteorological data, and analyzing the weather and the road passing condition in the freight transport personnel track.
2. The method of claim 1, wherein determining a distance score based on trajectory data of a human freight and the trajectory data of a truck comprises:
acquiring track data of a truck and track data of a freight carrier when the freight is loaded;
and determining the distance score according to the track data of the truck and the track data of the freight carrier.
3. The method of any of claims 1-2, wherein determining the trustworthiness of the freight forwarder based on the trajectory data further comprises:
and acquiring a first watermark photo and a second watermark photo of different channels, and determining the photo score.
4. The method of claim 3, wherein the obtaining the first watermark photo and the second watermark photo of different channels and determining the photo score comprises:
acquiring the first watermark photo, and generating a first authentication character string based on a preset mode;
acquiring the second watermark photo, and generating a second counterfeit identification character string based on the preset mode;
and determining the photo score according to the first and second anti-counterfeiting character strings.
5. A freight order taking recommendation device, comprising:
the track acquisition module is used for acquiring track data of the freight transport personnel and the freight wagon in the transportation process;
a credibility determination module for determining the credibility of the freight personnel based on the trajectory data;
the credible recommendation module is used for determining the order taking sequence of the freight personnel based on the credibility of the freight personnel and recommending the order taking sequence to the user;
the trust determination module is further to: acquiring road network data; determining road network scores according to the road network data and the track data of the trucks; determining a distance score according to trajectory data of a freight carrier and the trajectory data of a truck; determining the credibility of the freight personnel; determining road network scores according to the road network data and the track data of the trucks, and performing deviation rectification processing on the track data of the trucks to generate effective track data; calculating the fitting degree of the corrected effective track data and the road network data, and determining the road network score based on the fitting degree when the fitting degree does not exceed a specified threshold A; the fitting degree calculation formula is as follows:
in the formula, tr1 is an effective route, and n is an effective route length; tr2 is road network data, and m is the length of the road network data; head (tr) is the first point of the route, rest (tr) is a subsequence of all points except the first point; a is a minimum distance threshold, and when the distance between two points is less than A, the two points are regarded as the same point;
when the fitting degree exceeds a specified threshold value A, acquiring empirical route data, continuously calculating the coincidence rate of the corrected effective track data and the empirical route data, and determining the road network score based on the coincidence rate;
the trust determination module is further to: acquiring a big data score in transportation; and when calculating the big data score, extracting the relation and the entity from the road data and the meteorological data, and analyzing the weather and the road passing condition in the freight transport personnel track.
6. An electronic device, comprising a processor and a computer readable storage medium storing a computer program, which when read and executed by the processor, implements the method of any one of claims 1-4.
7. A computer-readable storage medium, characterized in that it stores a computer program which, when read and executed by a processor, implements the method according to any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210013441.XA CN114493188B (en) | 2022-01-06 | 2022-01-06 | Freight order receiving recommendation method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112700201A (en) * | 2021-01-12 | 2021-04-23 | 上海斑马来拉物流科技有限公司 | Goods source recommendation method, electronic device and storage medium |
| WO2021151137A1 (en) * | 2020-04-28 | 2021-08-05 | Spotvessels Gmbh | Method for optimizing transport parameters |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9571983B1 (en) * | 2015-07-28 | 2017-02-14 | International Business Machines Corporation | Freight vehicle monitoring using telecommunications data |
| CN106296065A (en) * | 2016-07-21 | 2017-01-04 | 北京京东尚科信息技术有限公司 | The monitoring method of order correct-distribute based on GIS technology exception, Apparatus and system |
| CN110991967A (en) * | 2019-12-05 | 2020-04-10 | 惠龙易通国际物流股份有限公司 | Logistics transportation method and device |
| CN112734219B (en) * | 2021-01-05 | 2023-07-21 | 中交智运有限公司 | Vehicle transportation running behavior analysis method and system |
| CN112926929A (en) * | 2021-04-13 | 2021-06-08 | 车主邦（北京）科技有限公司 | Carrier recommendation method, device, equipment and storage medium |
| CN113610247A (en) * | 2021-07-22 | 2021-11-05 | 北京中交兴路信息科技有限公司 | Fault help seeking method and device for freight vehicle, storage medium and terminal |
| CN113822190B (en) * | 2021-09-16 | 2024-02-20 | 北京中交兴路信息科技有限公司 | Factory road network data fitting method and device, electronic equipment and storage medium |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021151137A1 (en) * | 2020-04-28 | 2021-08-05 | Spotvessels Gmbh | Method for optimizing transport parameters |
| CN112700201A (en) * | 2021-01-12 | 2021-04-23 | 上海斑马来拉物流科技有限公司 | Goods source recommendation method, electronic device and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| Md Mostafizur Rahman Komol 等.Deep Transfer Learning Based Intersection Trajectory Movement Classification for Big Connected Vehicle Data.《IEEE Access》.2021,第9卷141830-141842. * |
| 秦继鹏 等.基于GPS数据的深圳市危险品货运特征分析.《综合运输》.2021,第43卷(第12期),45-50. * |
| 詹起林 ; 高峻 ; 王磊 ; .基于货运车辆GPS数据的交通调查技术研究.地理与地理信息科学.2011,第27卷(第03期),30-33+37. * |
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