CN116934194A - Real-time transportation management system based on big data - Google Patents
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Abstract
The application discloses a real-time transportation management system based on big data, and relates to the technical field of transportation management. The application obtains a real-time transportation management system based on big data by integrating the monitoring module, the data acquisition module, the data storage module, the data processing module, the judging and decision-making module and the control module, thereby realizing the functions of monitoring the running state of the vehicle in real time, predicting the traffic jam condition, evaluating the cargo distribution efficiency and predicting weather compliance. Compared with the defects that the traditional transportation management efficiency is low, and meanwhile, the logistics information is unbalanced in transmission, and risk coefficients under various conditions cannot be detected, so that a perfect coping scheme is not available, the method and the system have the advantages that the risk assessment coefficient of the transportation process is calculated by calculating the vehicle running state compliance index, the traffic jam condition compliance index, the cargo distribution efficiency and the weather condition compliance index, so that the transportation risk is assessed more accurately, the transportation risk is reduced, and the transportation efficiency is improved.
Description
Technical Field
The application relates to the technical field of transportation management, in particular to a real-time transportation management system based on big data.
Background
Along with the high-speed development of social science and technology, the large-scale transportation industry is also gradually increased, and along with the expansion of enterprise scale, the transportation management system is also becoming the demand of enterprise development, the construction and the development of logistics companies are more independent of an accurate and effective transportation management system, logistics companies without real-time and accurate transportation management system can generate confusion on goods management, can not track and monitor the goods transportation, further cause untimely logistics state update and goods loss, cause great economic loss, and correspondingly deteriorate user experience.
Through monitoring each goods system of logging in among the prior art, it is convenient to have brought to the lost search of goods, has reduced the condition that the goods transportation was lost. For example, publication No.: in the logistics cargo transportation management system and method disclosed in CN108830358A, each cargo distribution point is provided with a cargo input system, a cargo unloading scanning unit and a cargo loading scanning unit are adopted to scan and upload the upper and lower cargoes of a single cargo distribution point to a main controller for processing, after the cargoes are found to be lost, each cargo distribution point searches through a searching module in the cargo input system, so that the cooperative processing of each link of the cargoes in the transportation process on the lost cargoes is realized, the searching range is greatly reduced, the searching time is saved, and convenience is brought to the searching of the cargoes.
However, in implementation, the above transportation management technology is found to not collect multiple aspects of data in transportation management, and cannot monitor the state of goods in real time in the process of transporting the goods, and the solution of delaying delivery of the goods due to the environment is not considered, so that data update is not timely, management confusion is easy to cause logistic accidents, and no unified platform is available for users to participate in transportation flows.
Therefore, in view of the above problems, there is an urgent need for a real-time transportation management system capable of collecting various data in real time and calculating and processing the data more accurately.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides a real-time transportation management system based on big data, which solves the problems of untimely update of logistics transportation data and poor user experience.
In order to achieve the above purpose, the application is realized by the following technical scheme:
the real-time transportation management system based on big data is characterized by comprising a monitoring module, a data acquisition module, a data processing module, a judgment and decision module and a data storage module, wherein: the monitoring module is used for providing real-time monitoring and displaying vehicle positions and road condition analysis and carrying out real-time tracking and supervision on transportation activities; the data acquisition module is used for acquiring transportation activity data in real time, wherein the transportation activity data comprises vehicle condition data, traffic road section data, transportation cargo data and transportation environment data; the data processing module is used for carrying out real-time analysis and calculation on the transportation activity data fed back by the data acquisition module to obtain a vehicle running state coincidence index, a traffic jam condition coincidence index, cargo distribution efficiency and a meteorological condition coincidence index, and calculating a transportation risk assessment coefficient through the vehicle running state coincidence index, the traffic jam condition coincidence index, the cargo distribution efficiency and the meteorological condition coincidence index; the judging and deciding module is used for comparing and analyzing the transportation risk evaluation value calculated by the data processing module and providing a decision support function for abnormal conditions; the data storage module is used for storing real-time acquisition data, historical data and processing data and carrying out classified management.
Further, the providing real-time monitoring and displaying of the vehicle position and road condition analysis, and the real-time tracking and supervision of the transportation activities are specifically as follows: the monitoring module displays the vehicle position on the map in real time and provides visual display of the vehicle position; the method comprises the steps of monitoring the behavior and the transportation activity of the vehicle in real time, setting an early warning rule and an abnormality detection algorithm, detecting the abnormal behavior and the transportation activity of the vehicle in real time, and sending an alarm notice by a monitoring module in time when abnormal conditions occur.
Further, the vehicle condition data comprise a transportation path and a transportation speed, the transportation path data comprise travel time information of a congestion path, the transportation goods data comprise the quantity of goods delivered on time and the quantity of goods delivered, and the transportation environment data comprise the environment temperature, the environment humidity and the wind speed.
Further, the vehicle running state compliance index calculating step is: the data processing module receives the vehicle condition data acquired by the data acquisition module and numbers the historical transportation times in the data storage module:calculating a vehicle running state coincidence index: />Wherein->The transportation routes of the j-th identical departure place and destination and the transportation average speeds of the j-th identical departure place and destination in the historical transportation process are respectively,expressed as the corresponding transport route of the actual route and the average transport speed from the departure to the current in the actual route,/->The transportation path weight and the transportation speed weight are respectively.
Further, the traffic congestion condition meets an index:wherein->Congestion road section driving time representing j-th same departure place and destination in history transportation process,/>For the travel time through the congested road section in the actual transportation process, e is a natural constant, j represents the historical transportation number, and +.>。
Further, the cargo distribution efficiency:，/>respectively representing the time delivery quantity of the cargoes and the delivery quantity of the cargoes in the actual transportation process; the meteorological conditions conform to an index:wherein->The average temperature of the environment during the j-th identical departure place and destination process, the average humidity of the environment during the j-th identical departure place and destination process, the average wind speed during the j-th identical departure place and destination process, respectively, in the historical transportation process>Respectively the ambient temperature, the ambient humidity, the wind speed and the air speed in the actual transportation process>Respectively an ambient temperature weight, an ambient humidity weight and a wind speed weight, j represents a historical transportation number and +.>。
Further, calculating a transportation risk assessment coefficient according to the vehicle running state compliance index, the traffic jam condition compliance index, the cargo distribution efficiency and the meteorological condition compliance index:wherein->Respectively vehicle running state conforming index and traffic jam condition conforming indexThe number, the cargo distribution efficiency and the meteorological condition accord with the index, and e is a natural constant.
Further, the comparison and analysis of the transportation risk assessment value calculated by the data processing module is specifically as follows: the judging and deciding module evaluates the risk coefficient obtained by the data processing moduleAnd a preset risk analysis index threshold value +.>Performing comparison analysis to determine whether an abnormal condition exists; and according to the analysis result, the judging and deciding module provides decision support functions for the user, including recommending an optimal route or scheme, prompting a region or time period with higher congestion risk, and providing real-time data and advice.
Further, the real-time collected data comprises a transportation path, a transportation speed, travel time information of a congestion road section, the on-time delivery quantity of cargoes, the ambient temperature, the ambient humidity and the wind speed, which are collected by the data collection module; the historical data comprises data recorded in a historical transportation management process; the processing data comprise a vehicle running state compliance index, a traffic jam condition compliance index, cargo distribution efficiency, a meteorological condition compliance index and a risk assessment coefficient calculated by the data processing module; the data storage module receives and processes real-time transportation data through the real-time data processing pipeline; for batch data, the data storage module is provided with a regular data importing and data processing mechanism; the data storage module also has a data backup and recovery mechanism, a data security and privacy protection mechanism and a data cleaning and optimizing mechanism.
Further, the method further comprises the following steps: the visual user connection module displays the data stored in the data storage module in the form of a visual chart, a graph and a map by analyzing the user requirements; the data is updated and displayed in real time through the data interaction with the monitoring module, the data acquisition module, the data processing module, the judgment and decision module and the data storage module.
The application has the following beneficial effects:
(1) According to the real-time transportation management system based on big data, the vehicle running state compliance index is calculated by collecting vehicle condition data and combining historical transportation vehicle condition data, so that the running state of a vehicle is monitored, and the running problem of the vehicle is found in time; calculating traffic jam condition coincidence indexes by collecting traffic road section data and combining historical traffic road section data, helping users to intuitively understand the jam conditions, and reducing the influence of the jam; by collecting the data of the transported goods, the delivery efficiency of the goods is calculated, and the more efficient delivery of the goods is realized; by collecting the transportation environment data and combining the historical transportation environment data, the weather condition compliance index is calculated, the weather influence degree is helped to be estimated, visual understanding of the current environment condition is provided, relevant departments are helped to know the adaptation degree of the current environment, and corresponding adjustment and decision are made, so that the transportation efficiency is improved.
(2) Compared with the route optimization limitation of the traditional transportation management system, the real-time transportation management system based on big data can not detect risk factors under various conditions and further has no perfect coping scheme, the system collects more transportation related data including vehicle condition data, traffic road section data, transportation goods data and transportation environment data through the data collection module, is more perfect and accurate in risk assessment, has corresponding decision schemes and technical support for risk conditions judged through risk assessment coefficient comparison analysis, and a user can select an action scheme most suitable for own conditions according to provided information and suggestions, so that transportation risks are reduced and efficiency is improved.
(3) The real-time transportation management system based on big data has a regular data importing and data processing mechanism, a data backup and recovering mechanism, a data security and privacy protection mechanism and a data cleaning and optimizing mechanism in the aspect of data storage, and can ensure the security and effectiveness of the data; the visual user interface is provided, so that the transportation data can be updated and displayed to the user in real time, and the vehicle position and road condition analysis can be displayed in real time, thereby helping the user to monitor the vehicle position in real time and acquire the latest data in time.
Of course, it is not necessary for any one product to practice the application to achieve all of the advantages set forth above at the same time.
Drawings
FIG. 1 is a flow chart of a real-time transportation management system structure based on big data.
Fig. 2 is a flow chart of processing and calculating the vehicle condition data, the traffic road section data, the transportation goods data and the transportation environment data collected by the data collecting module by the data processing module in the real-time transportation management system based on big data.
Detailed Description
The embodiment of the application realizes the problems that the transportation data is updated timely and the transportation route scheme is preferable by a real-time transportation management system based on big data.
The problems in the embodiment of the application have the following general ideas:
the application discloses a real-time transportation management system based on big data by integrating a monitoring module, a data acquisition module, a data processing module, a judging and deciding module and a data storage module, which realizes the functions of monitoring logistics transportation activities in real time, acquiring transportation process data in real time, processing and calculating the data acquired in the transportation process in real time, making technical decisions and supporting for transportation abnormality in real time and storing effective data in the transportation management process in real time.
Referring to fig. 1, the embodiment of the application provides a technical scheme: a real-time transportation management system based on big data, comprising the steps of:
specifically, referring to fig. 1, the application provides a real-time transportation management system based on big data, which comprises a monitoring module, a data acquisition module, a data processing module, a judgment and decision module and a data storage module, wherein: the monitoring module is used for providing real-time monitoring and displaying vehicle positions and road condition analysis and carrying out real-time tracking and supervision on transportation activities; the data acquisition module is used for acquiring transportation activity data in real time, wherein the transportation activity data comprises vehicle condition data, traffic road section data, transportation cargo data and transportation environment data; the data processing module is used for carrying out real-time analysis and calculation on the transportation activity data fed back by the data acquisition module to obtain a vehicle running state coincidence index, a traffic jam condition coincidence index, cargo distribution efficiency and a meteorological condition coincidence index, and calculating a transportation risk assessment coefficient through the vehicle running state coincidence index, the traffic jam condition coincidence index, the cargo distribution efficiency and the meteorological condition coincidence index; the judging and deciding module is used for comparing and analyzing the transportation risk evaluation value calculated by the data processing module and providing a decision support function for abnormal conditions; the data storage module is used for storing real-time acquisition data, historical data and processing data and carrying out classified management.
In the embodiment, the monitoring module is responsible for monitoring and displaying the vehicle position and road condition analysis in real time, acquiring the vehicle position information through a GPS positioning technology, and carrying out road condition analysis by combining real-time traffic data to help a supervisor to track the vehicle position and monitor transportation activities in real time; the data acquisition module is responsible for acquiring vehicle condition data, traffic road section data, transportation goods data and transportation environment data related to transportation activities in real time, and the data processing module can calculate and process the data in real time by acquiring the data in real time; the data processing module is used for processing the vehicle condition data, the traffic road section data, the transportation goods data and the transportation environment data to obtain a vehicle running state compliance index, a traffic jam condition compliance index, goods distribution efficiency and a meteorological condition compliance index, calculating a transportation risk assessment coefficient according to the indexes, and assessing the risk level of the current transportation activity; the judging and deciding module compares the transportation risk assessment value calculated by the data processing module with a preset risk assessment threshold value for analysis, detects abnormal conditions in transportation activities and triggers corresponding early warning or decision support; the data storage module is used for storing real-time collected data, historical data and processed data, and carrying out classification management, so that backtracking analysis, statistics and report generation of the data are facilitated, and the data storage module is used for providing data query and retrieval functions for other modules.
Specifically, referring to fig. 1, the monitoring module displays the vehicle position on the map in real time, providing a visual display of the vehicle position; the method comprises the steps of monitoring the behavior and the transportation activity of the vehicle in real time, setting an early warning rule and an abnormality detection algorithm, detecting the abnormal behavior and the transportation activity of the vehicle in real time, and sending an alarm notice by a monitoring module in time when abnormal conditions occur.
In the embodiment, the monitoring module displays the position of the vehicle on the map in real time through the map display interface, so that a user can intuitively know the position and distribution condition of the vehicle; the GPS positioning sensor is utilized to collect vehicle information in real time, monitor the vehicle behavior and the transportation activity in real time and automatically identify abnormal conditions; when abnormal conditions are monitored, alarm notification short messages are sent out in time, so that a supervision department can take corresponding measures in time, and the safety of transportation activities is ensured.
Specifically, referring to fig. 2, the vehicle condition data includes a transportation path and a transportation speed, the transportation path data includes travel time information of a congested path, the transportation cargo data includes a time-to-delivery number of cargoes and a shipment number of cargoes, and the transportation environment data includes an environment temperature, an environment humidity and a wind speed.
In the embodiment, the information of the transportation distance, the transportation speed and the running time of the congested road condition is obtained by a GPS positioning radar sensor; the on-time delivery quantity of the goods and the delivery quantity of the goods are obtained through feedback of the quality management part; the ambient temperature is obtained by a temperature sensor; the ambient humidity is obtained by a humidity sensor; the wind speed is obtained through a wind speed sensor, the data acquisition module integrates and installs selected sensor equipment and transportation equipment, a GPS positioning radar sensor is installed on a vehicle to ensure that accurate satellite signals are received, and specific conditions of a traffic road section are monitored; the temperature sensor, the humidity sensor and the air speed sensor are arranged at proper positions outside the vehicle, so that the transportation environment data are accurately collected, the data collected by the data collection module are required to be updated in real time, and the real-time data are transmitted to the cloud platform for data processing and storage through wireless communication calculation.
Specifically, referring to fig. 2, the vehicle running state compliance index calculation step is: the data processing module receives the data acquisition moduleThe collected vehicle condition data are numbered on the historical transportation times in the data storage module:calculating a vehicle running state coincidence index: />Wherein->The transport route of the jth same departure place and destination in the history transport process, the transport average speed of the jth same departure place and destination, and the +.>Represented as the corresponding transport path of the actual route and the average transport speed from the departure to the current in the actual route,the transportation path weight and the transportation speed weight are respectively.
In this embodiment, the data processing module receives the vehicle condition data acquired from the data acquisition module, including the transportation path and the transportation speed, then the data processing module accesses the historical transportation data in the data storage module, numbers the historical transportation times, and calculates the coincidence index of the vehicle running state by summing up the average preset transportation path and the transportation speed and using the data analysis technology; according to the coincidence index, the data processing module generates a state indication to indicate the current running state of the vehicle, and if the coincidence index is high, the running state of the vehicle is good; if the coincidence index is lower, the vehicle is indicated to have problems or needs to be maintained, the running condition of the vehicle is monitored, the problems are found in time, and corresponding measures are taken to ensure the normal running of the vehicle.
Specifically, referring to fig. 2, traffic congestion conditions conform to an index:wherein->Congestion road section driving time representing j-th same departure place and destination in history transportation process,/>For the travel time through the congested road section in the actual transportation process, e is a natural constant, j represents the historical transportation number, and +.>。
In this embodiment, the data processing module receives the travel time information of the congested road section obtained from the data acquisition module, accesses the historical transportation data in the data storage module, numbers the historical transportation times, and obtains the average preset travel time of the congested road section by summing; comparing the historical average running time with the actual running time, and calculating the coincidence index of the congestion condition by using a difference proportion formula to evaluate the degree of the congestion condition; according to the calculated coincidence index, the data processing module can generate an indication for indicating traffic jam conditions; mapping the compliance index to different congestion levels, providing visual understanding of congestion conditions with low, medium, and high levels, so as to take corresponding measures to optimize route selection, adjust travel plans, and the like, thereby reducing congestion effects.
Specifically, referring to fig. 2, cargo distribution efficiency:，/>respectively representing the time delivery quantity of the cargoes and the delivery quantity of the cargoes in the actual transportation process; the meteorological conditions conform to an index:wherein->The average temperature of the environment during the j-th identical departure place and destination in the historic transportation process, the j-thAmbient average humidity during the same origin and destination, average wind speed during the jth same origin and destination,/v>Respectively the ambient temperature, the ambient humidity, the wind speed and the air speed in the actual transportation process>Respectively an ambient temperature weight, an ambient humidity weight and a wind speed weight, j represents a historical transportation number and +.>。
In this embodiment, the data processing module receives the data of the on-time delivery quantity of the goods and the shipment quantity of the goods acquired from the data acquisition module; calculating a cargo distribution efficiency by a ratio of a number of delivered cargo to a number of shipped cargo on time; according to the calculated delivery efficiency, the data processing module generates an indication indicating the delivery efficiency of the goods, and the high-efficiency, general and low-efficiency grades are used for providing visual understanding of the delivery efficiency of the goods so as to discover and solve the problems in delivery in time and realize more efficient delivery of the goods; the data processing module receives the environmental temperature, the environmental humidity and the wind speed data acquired from the data acquisition module, accesses the historical transportation data in the data storage module, numbers the historical transportation times, and average preset environmental temperature, environmental humidity and wind speed through summation; comparing the historical average ambient temperature, humidity and wind speed with the actual ambient temperature, ambient humidity and wind speed, and calculating a weather compliance index by using a difference proportion formula to evaluate the weather influence degree; according to the calculated weather compliance index, the data processing module generates a compliance grade indicating the current environmental condition and a non-compliance grade, provides visual understanding of the current environmental condition, helps related departments to know the adaptation degree of the current environment, and makes corresponding adjustment and decision so as to improve the transportation efficiency.
Specifically, referring to fig. 2, the vehicle running state compliance index, the traffic congestion condition compliance index, and the cargo distribution efficiency are usedCalculating a transportation risk assessment coefficient according to the rate and the meteorological condition coincidence index:wherein->And the running state of the vehicle accords with the index, the traffic jam condition accords with the index, the cargo distribution efficiency and the meteorological condition accords with the index, and e is a natural constant.
In the embodiment, the data processing module integrates the acquired data of the vehicle running state compliance index, the traffic jam condition compliance index, the cargo distribution efficiency and the weather condition compliance index; according to the specific numerical values of the indexes, a transportation risk assessment coefficient is calculated, transportation risks are divided into low risk, medium risk and high risk levels, visualization of the risk degree is facilitated, reference is provided for subsequent decisions and actions, and transportation risks are reduced.
Specifically, referring to fig. 1, the comparison analysis of the transportation risk assessment values calculated by the data processing module is specifically: the judging and deciding module evaluates the risk coefficient obtained by the data processing moduleAnd a preset risk analysis index threshold value +.>Performing comparison analysis to determine whether an abnormal condition exists; and according to the analysis result, the judging and deciding module provides decision support functions for the user, including recommending an optimal route or scheme, prompting a region or time period with higher congestion risk, and providing real-time data and advice.
In this embodiment, the judging and deciding module receives the risk assessment coefficient obtained by the data processing module, compares the risk assessment coefficient with a preset risk analysis index threshold, if the risk assessment coefficient exceeds the preset threshold, judges that the risk assessment coefficient is abnormal, and the judging and deciding module recommends an optimal route or scheme according to specific conditions to help transportation to avoid a high risk area or time period; prompting an area or a time period with higher congestion risk, and reducing the influence of congestion on transportation efficiency; and providing real-time data and advice, so that the user can know the current traffic condition and possible risks and provide corresponding advice and measures, and the user can select an action scheme which is most suitable for the user according to the provided information and advice, thereby reducing transportation risks and improving efficiency.
Specifically, referring to fig. 1, data is collected in real time, including a transportation path, a transportation speed, travel time information of a congested road section, a time-on-time delivery amount of goods, a shipping amount of goods, an ambient temperature, an ambient humidity, and a wind speed, which are collected by a data collection module; the historical data comprises data recorded by a historical transportation management process; the processing data comprise a vehicle running state compliance index, a traffic jam condition compliance index, cargo distribution efficiency, a meteorological condition compliance index and a risk assessment coefficient calculated by the data processing module; the data storage module receives and processes real-time transportation data through the real-time data processing pipeline; for batch data, the data storage module is provided with a regular data importing and data processing mechanism; the data storage module also has a data backup and recovery mechanism, a data security and privacy protection mechanism and a data cleaning and optimizing mechanism.
In this embodiment, the data storage module receives and stores various data collected by the data collection module, including transportation route, transportation speed, travel time information of a congested road section, on-time delivery quantity of goods, ambient temperature, ambient humidity and wind speed, for subsequent analysis and processing; storing data recorded by the historical transportation management process for review and analysis, thereby helping to provide decision support; the storage data processing module calculates a vehicle running state compliance index, a traffic jam condition compliance index, cargo distribution efficiency, a meteorological condition compliance index and a risk assessment coefficient so as to facilitate subsequent analysis and decision support; the data storage module is provided with a regular data importing and data processing mechanism and is used for processing batch data; the data is ensured to be safe and reliable through a data backup and recovery mechanism; through a data security and privacy protection mechanism, the data is ensured not to be accessed by unauthorized; in addition, the data storage module ensures the storage efficiency and high quality of data by utilizing a data cleaning and optimizing mechanism.
Specifically, referring to fig. 1, further comprising: the visual user connection module displays the data stored in the data storage module in the form of a visual chart, a graph and a map by analyzing the user requirements; the data is updated and displayed in real time through the data interaction with the monitoring module, the data acquisition module, the data processing module, the judgment and decision module and the data storage module.
In this embodiment, the visual user connection module is connected with the user, analyzes and understands the user requirement, and displays the data in the data storage module to the user in a visual form through a user interface, including a real-time transportation route map, and the user browses and analyzes the data through the module to obtain useful information; the visual user connection module acquires data acquired in real time and decision support given by the judgment and decision module through data interaction with the monitoring module, the data acquisition module, the data processing module, the judgment and decision module and the data storage module, and updates and displays the data to a user so as to ensure that the user acquires the latest data.
In summary, the present application has at least the following effects:
the real-time transportation management system based on big data solves the problems of untimely update of logistics transportation data and poor user experience, can monitor transportation activities in real time, collect transportation activity data in real time and calculate and process the collected data in real time through the working operation of the monitoring module, the data acquisition module, the data processing module, the judging and deciding module and the data storage module, gives technical decisions and support to abnormal risk conditions in the transportation activities through analysis and calculation, can store effective data in the transportation management period in real time, and effectively manages cargo transportation.
Those skilled in the art will appreciate that embodiments of the present application may be provided as a system. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to block diagrams of systems according to embodiments of the present application. It will be understood that each of the structures in the block diagrams may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the block diagram.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the block diagram.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the block diagram.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The real-time transportation management system based on big data is characterized by comprising a monitoring module, a data acquisition module, a data processing module, a judgment and decision module and a data storage module, wherein:
the monitoring module is used for providing real-time monitoring and displaying vehicle positions and road condition analysis and carrying out real-time tracking and supervision on transportation activities;
the data acquisition module is used for acquiring transportation activity data in real time, wherein the transportation activity data comprises vehicle condition data, traffic road section data, transportation cargo data and transportation environment data;
the data processing module is used for carrying out real-time analysis and calculation on the transportation activity data fed back by the data acquisition module to obtain a vehicle running state coincidence index, a traffic jam condition coincidence index, cargo distribution efficiency and a meteorological condition coincidence index, and calculating a transportation risk assessment coefficient through the vehicle running state coincidence index, the traffic jam condition coincidence index, the cargo distribution efficiency and the meteorological condition coincidence index;
the judging and deciding module is used for comparing and analyzing the transportation risk evaluation value calculated by the data processing module and providing a decision support function for abnormal conditions;
the data storage module is used for storing real-time acquisition data, historical data and processing data and carrying out classified management.
2. The real-time transportation management system based on big data according to claim 1, wherein the providing real-time monitoring and displaying vehicle position and road condition analysis, and the real-time tracking and supervision of transportation activities are specifically as follows:
the monitoring module displays the vehicle position on the map in real time and provides visual display of the vehicle position;
the method comprises the steps of monitoring the behavior and the transportation activity of the vehicle in real time, setting an early warning rule and an abnormality detection algorithm, detecting the abnormal behavior and the transportation activity of the vehicle in real time, and sending an alarm notice by a monitoring module in time when abnormal conditions occur.
3. The real-time transportation management system based on big data according to claim 1, wherein the vehicle condition data includes transportation distance, transportation speed;
the traffic road section data comprises the running time information of the congestion road section;
the transportation goods data comprise the quantity of goods delivered on time and the quantity of goods delivery;
the transportation environment data includes an ambient temperature, an ambient humidity, a wind speed.
4. A real-time transportation management system based on big data according to claim 3, wherein said vehicle running state compliance index calculating step is: the data processing module receives the vehicle condition data acquired by the data acquisition module and numbers the historical transportation times in the data storage module:calculating a vehicle running state coincidence index:wherein->The transport route of the jth same departure place and destination in the history transport process, the transport average speed of the jth same departure place and destination, and the +.>Expressed as the corresponding transport route of the actual route and the average transport speed from the departure to the current in the actual route,/->The transportation path weight and the transportation speed weight are respectively.
5. A large-based according to claim 4The real-time data transportation management system is characterized in that the traffic jam condition accords with an index:wherein->Congestion road section driving time representing j-th same departure place and destination in history transportation process,/>For the travel time through the congested road section in the actual transportation process, e is a natural constant, j represents the historical transportation number, and +.>。
6. The big data based real time shipping management system of claim 5, wherein the cargo distribution efficiency:，/>respectively representing the time delivery quantity of the cargoes and the delivery quantity of the cargoes in the actual transportation process;
the meteorological conditions conform to an index:whereinThe average temperature of the environment during the j-th identical departure place and destination process, the average humidity of the environment during the j-th identical departure place and destination process, the average wind speed during the j-th identical departure place and destination process, respectively, in the historical transportation process>Respectively the ambient temperature, the ambient humidity, the wind speed and the air speed in the actual transportation process>Respectively an ambient temperature weight, an ambient humidity weight and a wind speed weight, j represents a historical transportation number and +.>。
7. The real-time transportation management system according to claim 6, wherein the transportation risk assessment coefficient is calculated by a vehicle running state compliance index, a traffic congestion condition compliance index, a cargo distribution efficiency, a weather condition compliance index:wherein->And the running state of the vehicle accords with the index, the traffic jam condition accords with the index, the cargo distribution efficiency and the meteorological condition accords with the index, and e is a natural constant.
8. The real-time transportation management system according to claim 7, wherein the comparison and analysis of the transportation risk assessment value calculated by the data processing module is specifically as follows:
the judging and deciding module evaluates the risk coefficient obtained by the data processing moduleAnd a preset risk analysis index threshold value +.>Performing comparison analysis to determine whether an abnormal condition exists;
and according to the analysis result, the judging and deciding module provides decision support functions for the user, including recommending an optimal route or scheme, prompting a region or time period with higher congestion risk, and providing real-time data and advice.
9. The real-time transportation management system based on big data according to claim 1, wherein the real-time collected data comprises transportation route, transportation speed, travel time information of a congestion road section, number of delivered goods on time, number of delivered goods, ambient temperature, ambient humidity and wind speed collected by the data collection module; the historical data comprises data recorded in a historical transportation management process; the processing data comprise a vehicle running state compliance index, a traffic jam condition compliance index, cargo distribution efficiency, a meteorological condition compliance index and a risk assessment coefficient calculated by the data processing module;
the data storage module receives and processes real-time transportation data through the real-time data processing pipeline;
for batch data, the data storage module is provided with a regular data importing and data processing mechanism;
the data storage module also has a data backup and recovery mechanism, a data security and privacy protection mechanism and a data cleaning and optimizing mechanism.
10. The big data based real time transport management system of claim 1, further comprising: the visual user connection module displays the data stored in the data storage module in the form of a visual chart, a graph and a map by analyzing the user requirements; the data is updated and displayed in real time through the data interaction with the monitoring module, the data acquisition module, the data processing module, the judgment and decision module and the data storage module.
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