CN116341161B - Digital twinning-based cross-border logistics transportation line simulation method and system - Google Patents
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Abstract
The invention relates to the field of logistics transportation, and discloses a digital twinning-based cross-border logistics transportation line simulation method and a digital twinning-based cross-border logistics transportation line simulation system, wherein the method comprises the following steps: step 1: establishing a database, collecting available route data in the Internet, converting the available route data into a machine-readable language, and storing implemented historical transportation route data; step 2: receiving logistics transportation material information, identifying key characteristics, preliminarily acquiring starting point and end point data, butting and operating a map simulation model; according to the method, all available network points are obtained through carding the current logistics transportation information, planning of a path is carried out, comprehensive analysis of historical data and real-time acquisition data is carried out, risk assessment is carried out, and therefore limitation of a final determined line scheme is reduced, various indexes serving as references are comprehensive, dynamic display of the line scheme and measures of speed change adjustment are carried out, and a user can intuitively know a transportation line.
Description
Technical Field
The invention relates to the technical field of logistics transportation, in particular to a digital twinning-based cross-border logistics transportation line simulation method and system.
Background
The cross-border logistics is used as international logistics, namely, goods are transported from one country to another country or region through sea, land, air and the like, and the international commodity transaction process is completed through local delivery of destination countries, the delivery time of the cross-border logistics depends on the distance between the place and the destination of the goods, and the adopted transportation mode, generally, the delivery time of the sea transportation is longer than that of the air, different routes also influence the delivery time, the temperature, box placement, vibration and transportation of the goods in a logistics link are strictly controlled, continuous data transmission is realized through sensors in the application in the delivery process, data recovery and monitoring are carried out, digital twin generation can be based on historical data and real-time transmitted data, data monitoring and simulation are carried out, some problems of the historical logistics link are timely found and corrected, and later avoided;
however, there are problems in planning and simulating existing cross-border logistics transportation routes, such as:
1. the combination comparison of the historical data of the selectable line and the current real-time acquisition data is difficult, so that the generation of the recommended line has certain limitation and is not comprehensive, the reference in the planning process is also deficient, and the integration of the existing data is difficult;
2. in the aspect of cargo dispatching, problems easily occur, and the scheduled journey is changed, but the prior art lacks remedial measures for sudden conditions, so that secondary adjustment of a follow-up scheme is limited, and further, the overall transportation is frustrated, the dispatching is disturbed, and the income is influenced.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects existing in the prior art, the invention provides a digital twinning-based cross-border logistics transportation line simulation method and system, which can effectively solve the problems in the prior art.
(II) technical scheme
In order to achieve the above object, the present invention is realized by the following technical scheme,
the invention discloses a digital twinning-based cross-border logistics transportation line simulation method, which comprises the following steps of:
step 1: establishing a database, collecting available route data in the Internet, converting the available route data into a machine-readable language, and storing implemented historical transportation route data;
step 2: receiving logistics transportation material information, identifying key characteristics, preliminarily acquiring starting point and end point data, butting and operating a map simulation model;
step 3: analyzing available transportation network points between a starting point and a finishing point of the current transportation material, planning the transportation network points, obtaining a plurality of line scheme data connected by a plurality of network points, and respectively inputting the plurality of line scheme data into a map simulation model for display;
step 4: analyzing all line scheme data, respectively calling historical data of corresponding network point lines, collecting real-time data of corresponding line schemes in the current transportation period, and analyzing road conditions, weather and transportation means in a combined mode;
step 5: risk prediction is carried out among the station lines in real time, a simulation model is built on the basis of a map simulation model, and dynamic transportation track display is carried out;
step 6: generating a risk quality index of each line between adjacent network point lines, carrying out overall analysis by combining a global line of a single scheme, deriving priority parameters of each line scheme, acquiring a line scheme with a first priority, taking the line scheme as a to-be-implemented scheme, and outputting cargo scheduling information;
step 7: identifying various characteristics of the goods scheduling information in real time at the stage of putting the implementation scheme into use;
step 8: tracking the transport means in real time, identifying current line information, and generating a standby line with a distinguishing failure characteristic based on different regional characteristics when a certain characteristic in the cargo scheduling information fails.
Still further, the data categories of the risk prediction in the step 5 include: collision, moisture, high and low temperature data.
Furthermore, the dynamic transportation track display in the step 5 is to perform simulation operation on a simulation model by acquiring a planned route, and perform variable speed adjustment, where the variable speed rhythm includes: acceleration adjustment, deceleration adjustment, and conventional speed adjustment.
Furthermore, in the process of displaying the dynamic transportation track in the step 5, real-time voice broadcasting is performed on the simulation data, the type of broadcasting content is edited by manual self-definition, after the editing is finished, corresponding collection is performed in the obtained line data, conversion of the corresponding language format is performed, and the text is simplified.
Further, the characteristics of the cargo scheduling information in step 6 include: the method comprises the steps of receiving network point data, transferring network point associated area travel data, weather data of associated transportation areas, cargo parameters and transportation means data.
Furthermore, before confirming implementation, the to-be-implemented embodiment in the step 6 is interconnected and communicated with the e-commerce logistics distribution management platform, the e-commerce logistics warehouse management platform and the e-commerce express management platform.
The system comprises a digital twin-based cross-border logistics transportation line simulation system, a control module, a global function module, a control module and a control module, wherein the control module is used for editing and sending control instructions;
the storage module is used for storing all analysis and acquisition data and supporting data reading and data writing of external equipment;
the network distribution module is used for providing network authority and carrying out transmission support of network data after passing verification;
the acquisition module is used for acquiring target data of corresponding attributes and converting the target data into a machine-readable language;
the matching module is used for indexing in the storage module, capturing hit data, and marking and analyzing;
the simulation module is used for simulating the simulation operation of the line data in real time and supporting variable speed regulation;
the evaluation module is used for comprehensively grading based on the historical data and the real-time collected data and giving out evaluation indexes of the existing line schemes;
the tracking module is used for tracking the network points and the transportation means of the line scheme in the implemented state and acquiring the transportation state;
the identification module is used for identifying key data of tracking information of the tracking module and confirming whether abnormality exists or not;
and the correction module is used for marking the abnormal data and correcting the abnormal data.
Further, the identification module marks the implemented line scheme when judging that the abnormality exists in the process of judging the abnormality, opens the correction editing authority of the correction module, and continuously operates according to the preset setting when judging that the abnormality does not exist;
the correction triggering of the correction module takes the influence degree of failure characteristics as an index, and the influence degree of the failure characteristics of the original line scheme is represented by the following formula:
wherein F represents the influence degree in units of;
g represents the number of influencing factors.
Furthermore, after the correction module finishes operation, the tracking module and the identification module are reset, the operation buffer is cleared, and the circuit scheme is re-docked.
Still further, the control module is connected with the storage module through wireless network interaction, the control module is connected with the distribution network module through wireless network interaction, the control module is connected with the acquisition module through wireless network interaction, the control module is connected with the matching module through electric signal communication, the control module is connected with the simulation module through electric signal communication, the simulation module is connected with the evaluation module through electric signal communication, the control module is connected with the tracking module through wireless network interaction, the tracking module is connected with the identification module through wireless network interaction, and the identification module is connected with the correction module through wireless network interaction.
(III) beneficial effects
Compared with the prior art, the technical proposal provided by the invention has the following beneficial effects,
1. according to the method, all available network points are obtained through combing the current logistics transportation information, planning of the path is carried out, comprehensive analysis of historical data and real-time acquisition data is carried out, risk assessment is carried out, and therefore limitation of a final determined line scheme is reduced, and various indexes serving as references are comprehensive.
2. According to the invention, through the measures of dynamic display and speed change adjustment of the line scheme, a user can intuitively know the transportation line, so that the user is helped to know the transportation process preliminarily, and the dispatching is facilitated.
3. According to the invention, by tracking and identifying the abnormal situation, when the accident occurs in the implementation process of the line scheme, the risk avoidance can be timely carried out, the remedy is carried out by using the alternative scheme, the transportation loss is reduced, and the transportation stability is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flow diagram of a digital twinning-based cross-border logistics transportation line simulation method;
FIG. 2 is a schematic diagram of a framework of a digital twinning-based cross-border logistics transportation circuit simulation system;
reference numerals in the drawings represent respectively, 1, a control module; 2. a storage module; 3. a distribution network module; 4. an acquisition module; 5. a matching module; 6. a simulation module; 7. an evaluation module; 8. a tracking module; 9. an identification module; 10. and a correction module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of 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. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described below with reference to examples.
Example 1
The digital twinning-based cross-border logistics transportation line simulation method and system in the embodiment, as shown in fig. 1, comprise the following steps:
step 1: establishing a database, collecting available route data in the Internet, converting the available route data into a machine-readable language, and storing implemented historical transportation route data;
step 2: receiving logistics transportation material information, identifying key characteristics, preliminarily acquiring starting point and end point data, butting and operating a map simulation model;
step 3: analyzing available transportation network points between a starting point and a finishing point of the current transportation material, planning the transportation network points, obtaining a plurality of line scheme data connected by a plurality of network points, and respectively inputting the plurality of line scheme data into a map simulation model for display;
step 4: analyzing all line scheme data, respectively calling historical data of corresponding network point lines, collecting real-time data of corresponding line schemes in the current transportation period, and analyzing road conditions, weather and transportation means in a combined mode;
step 5: risk prediction is carried out among the station lines in real time, a simulation model is built on the basis of a map simulation model, and dynamic transportation track display is carried out;
step 6: generating a risk quality index of each line between adjacent network point lines, carrying out overall analysis by combining a global line of a single scheme, deriving priority parameters of each line scheme, acquiring a line scheme with a first priority, taking the line scheme as a to-be-implemented scheme, and outputting cargo scheduling information;
step 7: identifying various characteristics of the goods scheduling information in real time at the stage of putting the implementation scheme into use;
step 8: tracking the transport means in real time, identifying current line information, and generating a standby line with a distinguishing failure characteristic based on different regional characteristics when a certain characteristic in the cargo scheduling information fails.
The data categories of the risk prediction in the step 5 include: collision, moisture, high and low temperature data.
Setting a judging model, and judging the vehicle stability or cargo condition of the vehicle; recording the number of times of sudden braking of the vehicle asThe number of sharp turns of the vehicle is +.>The method comprises the steps of carrying out a first treatment on the surface of the Calculating risk value->=s (ax+by)/(x+y+z), wherein a is a sudden braking weight coefficient, B is a sudden turning weight coefficient, S is a safety coefficient, and the values of A, B and S are all equal to or greater than 1; the magnitude of the risk value can reflect the risk degree of the stability of the transport vehicle.
In addition, the cargo condition judges the acquisition condition according to the expert score.
And (3) displaying the dynamic transportation track in the step (5), namely, performing simulation operation on a simulation model by acquiring a planned line, and performing variable speed adjustment, wherein the variable speed rhythm comprises: acceleration adjustment, deceleration adjustment, and conventional speed adjustment.
In the process of displaying the dynamic transportation track in the step 5, real-time voice broadcasting is performed on the simulation data, the type of broadcasting content is edited by manual self-definition, after the editing is finished, corresponding collection is performed in the obtained line data, conversion of a corresponding language format is performed, and texts are simplified.
The characteristics of the cargo scheduling information in the step 6 include: the method comprises the steps of receiving network point data, transferring network point associated area travel data, weather data of associated transportation areas, cargo parameters and transportation means data.
Before confirmation of implementation, the to-be-implemented implementation in the step 6 is interconnected and communicated with the e-commerce logistics distribution management platform, the e-commerce logistics warehouse management platform and the e-commerce express management platform.
Through the arrangement, all available network points are obtained through carding the current logistics transportation information, planning of a path is carried out, comprehensive analysis of historical data and real-time acquisition data is carried out, risk assessment is carried out, and therefore limitation of a final determined line scheme is reduced, and the limitation of the final determined line scheme is used as a measure of dynamic display and variable speed adjustment of the line scheme, so that a user can intuitively know a transportation line, further, the user is helped to carry out primary knowledge on the transportation process, and scheduling is facilitated;
the abnormal condition is tracked and identified, so that the risk avoidance can be timely carried out when the unexpected condition occurs in the implementation process of the circuit scheme, the replacement scheme is used for remedying, the transportation loss is reduced, and the transportation stability is improved.
Example 2
The embodiment also provides a digital twinning-based cross-border logistics transportation line simulation system, as shown in fig. 2, which comprises: the control module is used for controlling the global functional module in a total way and editing and sending the control instruction;
the storage module is used for storing all analysis and acquisition data and supporting data reading and data writing of external equipment;
the network distribution module is used for providing network authority and carrying out transmission support of network data after passing verification;
the acquisition module is used for acquiring target data of corresponding attributes and converting the target data into a machine-readable language;
the matching module is used for indexing in the storage module, capturing hit data, and marking and analyzing;
the simulation module is used for simulating the simulation operation of the line data in real time and supporting variable speed regulation;
the evaluation module is used for comprehensively grading based on the historical data and the real-time collected data and giving out evaluation indexes of the existing line schemes;
the tracking module is used for tracking the network points and the transportation means of the line scheme in the implemented state and acquiring the transportation state;
the identification module is used for identifying key data of tracking information of the tracking module and confirming whether abnormality exists or not;
and the correction module is used for marking the abnormal data and correcting the abnormal data.
As shown in fig. 2, the identification module marks the implemented line scheme when judging that the line scheme exists in the process of judging the abnormality, and opens the correction editing authority of the correction module, and if judging that the line scheme does not exist, the identification module continuously operates according to the preset setting;
the correction triggering of the correction module takes the influence degree of failure characteristics as an index, and the influence degree of the failure characteristics of the original line scheme is represented by the following formula:
wherein F represents the influence degree in units of;
g represents the number of influencing factors.
And after the correction module finishes running, the tracking module and the identification module are reset, the running buffer is cleared, and the circuit scheme is docked again.
The control module is in interactive connection with the storage module through a wireless network, the control module is in interactive connection with the distribution network module through the wireless network, the control module is in interactive connection with the acquisition module through the wireless network, the control module is in communication connection with the matching module through electric signals, the control module is in communication connection with the simulation module through electric signals, the simulation module is in communication connection with the evaluation module through electric signals, the control module is in interactive connection with the tracking module through the wireless network, the tracking module is in interactive connection with the identification module through the wireless network, and the identification module is in interactive connection with the correction module through the wireless network.
When the system is carried, the overall function module is controlled through the control module, all data are stored through the storage module, redundant data are removed regularly, the network is connected through the distribution network module, the line data are read and simulated through the simulation module, the line data are collected in real time through the collection module, the matching of the related data is carried out in the storage module through the matching module, the existing scheme is evaluated through the evaluation module, the preferred scheme is selected, when the scheme is implemented, the tracking module is used for tracking logistics information, the identification module is used for identifying the logistics data, whether abnormal data exist is judged, the correction module is used for correcting the abnormal data, and the standby scheme is provided.
The method comprises the steps of acquiring all available network points through carding current logistics transportation information, planning a path, comprehensively analyzing historical data and real-time acquisition data, performing risk assessment, further reducing limitation of a final determined line scheme, and taking various indexes as references as measures of dynamic display and variable speed adjustment of the line scheme, so that a user can intuitively know a transportation line, further helping the user to primarily know the transportation process, and facilitating scheduling;
the abnormal condition is tracked and identified, so that the risk avoidance can be timely carried out when the unexpected condition occurs in the implementation process of the circuit scheme, the replacement scheme is used for remedying, the transportation loss is reduced, and the transportation stability is improved.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; while the invention has been described in detail with reference to the foregoing embodiments, it will be appreciated by those skilled in the art that variations may be made in the techniques described in the foregoing embodiments, or equivalents may be substituted for elements thereof; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (3)
1. Digital twinning-based cross-border logistics transportation line simulation system is characterized by comprising:
the control module is used for controlling the global functional module in a total way and editing and sending the control instruction;
the storage module is used for storing all analysis and acquisition data and supporting data reading and data writing of external equipment;
the network distribution module is used for providing network authority and carrying out transmission support of network data after passing verification;
the acquisition module is used for acquiring target data of corresponding attributes and converting the target data into a machine-readable language;
the matching module is used for indexing in the storage module, capturing hit data, and marking and analyzing;
the simulation module is used for simulating the simulation operation of the line data in real time and supporting variable speed regulation;
the evaluation module is used for comprehensively grading based on the historical data and the real-time collected data and giving out evaluation indexes of the existing line schemes;
the tracking module is used for tracking the network points and the transportation means of the line scheme in the implemented state and acquiring the transportation state;
the identification module is used for identifying key data of tracking information of the tracking module and confirming whether abnormality exists or not;
the correction module is used for marking the abnormal data and correcting the abnormal data;
the identification module marks the implemented line scheme when judging that the abnormality exists in the process of judging the abnormality, opens the correction editing authority of the correction module, and continuously operates according to preset settings when judging that the abnormality does not exist;
the correction triggering of the correction module takes the influence degree of failure characteristics as an index, and the influence degree of the failure characteristics of the original line scheme is represented by the following formula:
wherein F represents the influence degree in units of;
g represents the number of influencing factors.
2. The digital twinning-based cross-border logistics transportation line simulation system of claim 1, wherein the correction module resets the tracking module and the identification module after the operation is completed, clears the operation buffer, and re-docks the line scheme.
3. The digital twinning-based cross-border logistics transportation line simulation system according to claim 1, wherein the control module is in interactive connection with the storage module through a wireless network, the control module is in interactive connection with the distribution network module through a wireless network, the control module is in interactive connection with the acquisition module through a wireless network, the control module is in communication connection with the matching module through an electric signal, the control module is in communication connection with the simulation module through an electric signal, the simulation module is in communication connection with the evaluation module through an electric signal, the control module is in interactive connection with the tracking module through a wireless network, the tracking module is in interactive connection with the identification module through a wireless network, and the identification module is in interactive connection with the correction module through a wireless network.
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| CN116542592B (en) * | 2023-07-03 | 2023-10-10 | 深圳市华运国际物流有限公司 | International logistics transportation path analysis and assessment system |
| CN116664031B (en) * | 2023-07-26 | 2023-09-29 | 云南师范大学 | Intelligent port logistics management method, system and storage medium based on Internet of things |
| CN117079220B (en) * | 2023-10-13 | 2023-12-26 | 凌雄技术(深圳)有限公司 | Supply chain intelligent supervision system and method based on Internet of things |
| CN117057703B (en) * | 2023-10-13 | 2024-01-26 | 云南省烟草公司大理州公司 | Logistics robot control system based on virtual map |
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