WO2016155517A1 - 物流监测方法及设备 - Google Patents

物流监测方法及设备 Download PDF

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Publication number
WO2016155517A1
WO2016155517A1 PCT/CN2016/076697 CN2016076697W WO2016155517A1 WO 2016155517 A1 WO2016155517 A1 WO 2016155517A1 CN 2016076697 W CN2016076697 W CN 2016076697W WO 2016155517 A1 WO2016155517 A1 WO 2016155517A1
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WIPO (PCT)
Prior art keywords
vehicle
speed
vehicle speed
time
road
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Ceased
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PCT/CN2016/076697
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English (en)
French (fr)
Inventor
任继东
梁思苗
王瑜
庞宝辉
闵万里
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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Publication of WO2016155517A1 publication Critical patent/WO2016155517A1/zh
Priority to US15/718,733 priority Critical patent/US10446023B2/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/0838—Historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/015—Detecting movement of traffic to be counted or controlled with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed

Definitions

  • the present application relates to the field of communications and computers, and in particular, to a logistics monitoring method and device.
  • the logistics network is a cargo transportation network composed of logistics nodes 11 (networks, transit stations) and logistics lines 12 (roads, railways, sea lines, air lines) connecting them.
  • logistics nodes 11 networkworks, transit stations
  • logistics lines 12 roads, railways, sea lines, air lines
  • FIG. 2 for a logistics network mainly involving roads, the logistics network can be represented by a topological map for the sake of simplicity.
  • the logistics line 12 can be divided into a plurality of sections 13 according to certain rules.
  • each logistics company passes the existing waybill monitoring system, mainly based on the information returned by each node to monitor the flow of the waybill, the data of the meteorological information in the data structure and the logistics industry. There is not much coupling. Usually, one road section will cross multiple meteorological areas. There is no good solution for how to structure these meteorological areas. In the existing scheme, the monitoring of the waybill is a weak link.
  • the existing scheme either calls the driver to understand the situation, or can only obtain the location of each logistics vehicle through GPS information, and cannot comprehensively consider the traffic flow, weather, vehicle speed and other factors. Segmentation monitoring of each line, and can only refer to the information of one company, can not refer to the information of other logistics companies, resulting in incomplete information is not accurate, it is difficult to provide feasible decision support.
  • the Chinese patent application with the publication number 102256377A, the invention name is a agricultural material logistics monitoring system for agricultural products
  • the publication number is 103458236A
  • the invention name is the intelligent monitoring system for hazardous chemicals logistics.
  • These special commodities of hazardous chemicals are for temperature, Humidity, speed, pressure, etc. are very sensitive, so sensors are used for real-time monitoring, but the two patent applications can only monitor temperature, humidity, speed, pressure, position and other variables in real time, thus avoiding product deterioration, damage, or deviation from transportation. route. It is not possible to combine the weather conditions and weather forecasts to estimate the future running time of logistics commodities, so as to adjust the logistics plan in time.
  • the publication number is 103794053A
  • the invention name is a Chinese short-term logistics single-target delivery time fuzzy prediction method and system Chinese patent application, collecting the transit time of the vehicle in each section by GPS, and then based on the collected historical data. On, the delivery time is predicted, but the patent application does not consider meteorological factors and only applies to short-haul logistics within the city.
  • the purpose of the present application is to provide a logistics monitoring method and apparatus capable of implementing more accurate monitoring and forecasting of logistics vehicles and waybills.
  • a logistics monitoring method including:
  • the logistics monitoring data is obtained based on the estimated vehicle speed.
  • meteorological and vehicle speed data of the road section includes: real-time weather data of the road section, real-time vehicle speed of the vehicle, and historical weather data of the road section and the relationship between the vehicle speed.
  • the relationship between the historical meteorological data of the road segment and the vehicle speed includes an average standard vehicle speed of each vehicle type according to historical meteorological data of the road segment and the time period.
  • the estimated speed of the vehicle on the road section is obtained according to the meteorological and vehicle speed data of the road section, including:
  • the average standard vehicle speed corresponding to the vehicle model under the historical meteorological data of the current road section and time period is obtained;
  • Obtaining logistics monitoring data based on the estimated vehicle speed includes:
  • the current road segment is judged to be congested according to the traffic volume of the vehicle at the current road section, the real-time vehicle speed, and the corresponding average standard vehicle speed.
  • the method further includes:
  • the average vehicle speed of the real-time vehicle speed that has traveled on the road section is taken as the estimated vehicle speed of the vehicle in the remaining part of the road section;
  • the average standard vehicle speed closest to the road segment is matched from the historical meteorological data to the vehicle speed, and the estimated vehicle speed of the vehicle in the remaining portion of the road segment is obtained based on the matched average standard vehicle speed closest to the road segment.
  • the average standard vehicle speed closest to the road section is matched, including one of the following:
  • the average standard vehicle speed closest to the road section is matched, including:
  • the speed of the similar road segment is found from the relationship between the historical meteorological data and the vehicle speed as the closest average standard vehicle speed of the matched road segment.
  • the method further includes:
  • the congestion duration of the road segment is obtained based on the vehicle flow rate of the current road section of the vehicle and the estimated vehicle speed of the remaining portion.
  • the method further includes:
  • the route of the vehicle passing through the road section is adjusted.
  • the average vehicle speed of the real-time vehicle speed of the road section has been taken as the estimated vehicle speed of the vehicle in the remaining part of the road section, or the remaining part of the road section is obtained according to the matched average standard vehicle speed closest to the road section.
  • the estimated speed it also includes:
  • Obtaining logistics monitoring data based on the estimated vehicle speed including:
  • the time at which the vehicle arrives at the node is predicted based on the travel time of each segment before the node.
  • the method further includes:
  • the waybill includes the waybill that has been generated and the predicted waybill based on the sales forecast result.
  • the time when the vehicle arrives at the node is predicted according to the travel time of each road segment before the node, including:
  • the time at which the vehicle reaches the current node is predicted based on the waybill processing speed of the previous node and the travel time of each road segment before the current node.
  • the method further includes:
  • the number of tickets that need to be processed by the node in each future time period is predicted according to the time when the vehicle arrives at the node.
  • an apparatus for logistics monitoring comprising:
  • a first device configured to obtain an estimated vehicle speed of the vehicle on the road section according to meteorological and vehicle speed data of the road section;
  • a second device configured to acquire the logistics monitoring data according to the estimated vehicle speed.
  • meteorological and vehicle speed data of the road section includes: real-time weather data of the road section, real-time vehicle speed of the vehicle, and historical weather data of the road section and the relationship between the vehicle speed.
  • the relationship between the historical meteorological data of the road segment and the vehicle speed includes an average standard vehicle speed of each vehicle type according to historical meteorological data of the road segment and the time period.
  • the first device includes:
  • the first module is configured to obtain an average standard corresponding to the vehicle type under the historical meteorological data of the current road section and the time period according to the meteorological data of the current section of the vehicle and the average standard speed of each vehicle under the historical meteorological data of the road section and the time period.
  • Speed of vehicle is configured to obtain an average standard corresponding to the vehicle type under the historical meteorological data of the current road section and the time period according to the meteorological data of the current section of the vehicle and the average standard speed of each vehicle under the historical meteorological data of the road section and the time period.
  • the second device includes:
  • the second module is configured to determine whether the current road segment is congested according to the traffic volume of the vehicle at the current road section, the real-time vehicle speed, and the corresponding average standard vehicle speed.
  • the first device further includes a first two module, configured to determine whether a time for the remaining portion of the current road segment to travel at an average vehicle speed of the real-time vehicle speed of the part of the current road segment is less than a preset threshold, and if so, The average vehicle speed of the real-time vehicle speed that has been driven by the road segment is used as the estimated vehicle speed of the vehicle in the remaining part of the road section; if not, the historical standard meteorological data and the vehicle speed are matched to match the average standard vehicle speed closest to the road section, according to the matched The closest average standard speed of the section is the estimated speed of the vehicle in the remainder of the section.
  • Standard speed including one of the following:
  • the average standard vehicle speed closest to the road section is matched, including:
  • the speed of the similar road segment is found from the relationship between the historical meteorological data and the vehicle speed as the closest average standard vehicle speed of the matched road segment.
  • the second device further includes:
  • the second two module is configured to obtain the congestion duration of the road segment according to the vehicle flow rate of the current road section of the vehicle and the estimated vehicle speed of the remaining portion.
  • the second device further includes:
  • the second third module is configured to adjust a travel route of the vehicle passing the road section when the congestion duration is greater than a preset threshold.
  • the first device further includes:
  • a first three module for obtaining an estimated vehicle speed of subsequent sections of the vehicle's travel route according to an estimated vehicle speed of the vehicle in the remaining portion of the road section;
  • the second device further includes:
  • the second four module is used to estimate the remaining part of the road section and subsequent road sections based on the vehicle
  • the vehicle speed predicts the travel time of the vehicle in each section
  • the second five module is configured to predict the time when the vehicle arrives at the node according to the travel time of each road segment before the node.
  • the second device further includes:
  • the second six module is used to establish a correspondence between the vehicle and the waybill.
  • the waybill includes the waybill that has been generated and the predicted waybill based on the sales forecast result.
  • the second fifth module is configured to predict a time when the vehicle arrives at the current node according to the waybill processing speed of the previous node and the travel time of each road segment before the current node.
  • the second device further includes:
  • the second seven module is configured to predict the number of the waybills that the node needs to process in the future time period according to the time when the vehicle arrives at the node.
  • the present application obtains the estimated vehicle speed of the vehicle according to the meteorological and vehicle speed data of the road section, and then obtains the logistics monitoring data according to the estimated vehicle speed, and can structure the meteorological information to be used for the logistics monitoring.
  • Form combining meteorological factors and road segment information to obtain accurate logistics monitoring and forecasting data. Due to the large time and space span of long-distance logistics, meteorological factors have a great impact on long-distance logistics. This application is especially applicable to the monitoring of long-distance logistics between cities.
  • the meteorological and vehicle speed data of the road section in the present application includes: real-time weather data of the road section, real-time vehicle speed of the vehicle, and historical meteorological data of the road section and the vehicle speed, wherein the historical meteorological data of the road section and the vehicle speed
  • the relationship includes the average standard speed of each model under the historical meteorological data of the road section and time period, which can achieve accurate logistics monitoring data.
  • the present application determines whether the current road section is congested according to the traffic volume of the vehicle at the current road section, the real-time vehicle speed, and the corresponding average standard vehicle speed. On the one hand, the congestion of the current road section can be accurately monitored, and on the other hand, the subsequent further logistics monitoring can be provided. Analytical basis.
  • the present application uses the average vehicle speed of the real-time vehicle speed of the road section as the estimated vehicle speed of the vehicle in the remaining part of the road section, or the relationship between the historical meteorological data and the vehicle speed.
  • the average standard vehicle speed closest to the road section is obtained from the average standard vehicle speed closest to the road section, and the estimated vehicle speed of the remaining part of the road section is obtained, and the remaining part of the road section where the vehicle is located can be obtained in different situations.
  • the estimated speed of the vehicle provides a data base for subsequent accurate acquisition of logistics monitoring data.
  • the congestion duration of the road segment is obtained according to the vehicle traffic volume of the current road segment of the vehicle and the estimated vehicle speed of the remaining portion, thereby obtaining more accurate logistics status information;
  • the congestion duration is greater than the preset threshold, the route of the vehicle passing through the road section is adjusted to improve the logistics transportation efficiency.
  • the present application predicts the travel time of the vehicle in each section according to the estimated speed of the vehicle in the remaining part of the section and the subsequent sections, and predicts the time when the vehicle arrives at the node according to the travel time of each section before the node, thereby Get more accurate vehicle trajectory estimates.
  • the present application can monitor the flow of the waybill by establishing a correspondence between the vehicle and the waybill, and further predict the time when the vehicle arrives at the current node according to the processing speed of the previous node and the travel time of each section before the current node, thereby It is predicted that the time when the vehicle arrives at the current node is more accurate.
  • the number of the waybills that the node needs to process in the future time period is predicted according to the time when the vehicle arrives at the node, so as to increase the manpower according to the number of the waybill in time to avoid the explosion.
  • Figure 1 shows a schematic diagram of an existing logistics network
  • Figure 2 shows an existing logistics network topology diagram
  • FIG. 3 shows a flow chart of a logistics monitoring method in accordance with an aspect of the present application
  • FIG. 4 is a schematic diagram showing weather information of a road section according to an embodiment of the present application.
  • Figure 5 is a flow chart showing a logistics monitoring method of a preferred embodiment of the present application.
  • Figure 6 is a flow chart showing a logistics monitoring method of another preferred embodiment of the present application.
  • FIG. 7 is a schematic diagram showing the travel of a road between a node 1 and a node 2 of a vehicle according to an embodiment of the present application;
  • Figure 8 is a flow chart showing a logistics monitoring method of still another preferred embodiment of the present application.
  • FIG. 9 is a schematic diagram showing an average vehicle speed for each time period according to an embodiment of the present application.
  • FIG. 10 is a schematic diagram showing the normalized vehicle speed after each period of time according to an embodiment of the present application.
  • Figure 11 is a flow chart showing a logistics monitoring method of still another preferred embodiment of the present application.
  • Figure 12 is a flow chart showing a logistics monitoring method of still another preferred embodiment of the present application.
  • Figure 13 is a flow chart showing a logistics monitoring method of another preferred embodiment of the present application.
  • Figure 14 is a flow chart showing a logistics monitoring method of still another preferred embodiment of the present application.
  • Figure 15 shows a schematic diagram of a specific application embodiment of the present application.
  • Figure 16 shows a schematic diagram of an apparatus for logistics monitoring of another aspect of the present application.
  • Figure 17 shows a schematic diagram of an apparatus for logistics monitoring in accordance with a preferred embodiment of the present application.
  • Figure 18 shows a schematic diagram of an apparatus for logistics monitoring of another preferred embodiment of the present application.
  • Figure 19 is a schematic view of an apparatus for logistics monitoring of still another preferred embodiment of the present application.
  • Figure 20 is a schematic view of an apparatus for logistics monitoring in accordance with still another preferred embodiment of the present application.
  • 21 shows a schematic diagram of an apparatus for logistics monitoring of another preferred embodiment of the present application.
  • Figure 22 shows a schematic diagram of an apparatus for logistics monitoring in accordance with yet another preferred embodiment of the present application.
  • the terminal, the device of the service network, and the trusted party each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
  • processors CPUs
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • the memory may include non-persistent memory, random access memory (RAM), and/or non-volatile memory in a computer readable medium, such as read only memory (ROM) or flash memory.
  • RAM random access memory
  • ROM read only memory
  • Memory is an example of a computer readable medium.
  • Computer readable media includes both permanent and non-persistent, removable and non-removable media.
  • Information storage can be implemented by any method or technology.
  • the information can be computer readable instructions, data structures, modules of programs, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory. (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD) or other optical storage,
  • computer readable media does not include non-transitory computer readable media, such as modulated data signals and carrier waves.
  • the application provides a logistics monitoring method, including:
  • Step S1 Obtain an estimated vehicle speed of the vehicle in the road section according to meteorological and vehicle speed data of the road section; where the meteorological data may include real-time weather data and historical weather data, wherein the real-time weather data includes current weather of each road section.
  • Live data and current weather forecast data which can be obtained through various meteorological channels, such as through the National Weather Service or other meteorological platforms. For example, as shown in Figure 4, national highways and national highways can be subdivided. Provide real-time meteorological data and historical meteorological data for each section of the road at each county level;
  • step S2 the logistics monitoring data is obtained according to the estimated vehicle speed.
  • the meteorological information is structured into a form that can be used for logistics monitoring, and the meteorological factors and the road segment information are combined to obtain accurate logistics monitoring and prediction data. Due to the large time and space span of long-distance logistics, meteorological factors have a great impact on long-distance logistics. This application is especially applicable to the monitoring of long-distance logistics between cities.
  • the meteorological and vehicle speed data of the road section includes: real-time weather data of the road section, real-time vehicle speed of the vehicle, and historical meteorological data of the road section and the vehicle speed, thereby achieving subsequent acquisition.
  • Accurate logistics monitoring data the real-time vehicle speed of the vehicle can be calculated according to the GPS information of the vehicle, and the GPS information can determine where the vehicle is located.
  • the road segment, GPS is the global satellite positioning system. The system calculates the distance between the satellite at the known location and the receiver of the user, and then integrates the data of multiple satellites to calculate the specific location of the receiver, that is, the major logistics in China. Most of the company's transportation vehicles are equipped with GPS receivers to navigate and monitor the location of the vehicle and cargo.
  • the relationship between the historical meteorological data of the road section and the vehicle speed includes an average standard vehicle speed of each vehicle type according to historical meteorological data of the road section and the time period, thereby achieving accurate logistics monitoring subsequently.
  • historical meteorological data can be divided into 9 basic types: normal (cloudy, sunny), small to moderate rain, large to heavy rain, thunderstorms, freezing rain, heavy fog, small to medium snow, large to blizzard, sand dust;
  • the model can be classified according to the national standard of automobile classification (GB9417-89), as shown in Table 1:
  • the average standard speed of each model can be as shown in Table 2:
  • the calculation method of the average standard vehicle speed under historical meteorological data of a certain section of a certain section of time may be as follows:
  • the average standard speed of the historical meteorological data of a certain section of a certain section of time (the sum of the distances of the vehicles in the section under the historical meteorological data) / (the historical weather data, the vehicle of the model is driving in the section The sum of time).
  • the small vehicle 2 is driving on the road section at 12:02-12:50 under normal weather, the driving time is 0.6 hours, and the driving distance is 60km (walking the entire road section);
  • the average standard speed of the historical meteorological data of a certain section of a certain section of time (40+60+%)/(0.52+0.6+).
  • step S1 the estimated vehicle speed of the vehicle in the road section is obtained according to the meteorological and vehicle speed data of the road section, including:
  • Step S11 according to the meteorological data of the current road section of the vehicle and the average standard vehicle speed of each vehicle type according to the historical meteorological data of the road section and the time period, the average standard vehicle speed corresponding to the vehicle type under the historical meteorological data of the current road section and time period is obtained, that is, The first estimated speed of the road section;
  • step S2 acquiring the logistics monitoring data according to the estimated vehicle speed comprises:
  • Step S21 judging whether the current road section is congested according to the traffic volume of the vehicle in the current road section, the real-time vehicle speed, and the corresponding average standard vehicle speed, so that the current road section congestion can be accurately monitored on the one hand.
  • the situation provides an analytical basis for subsequent further logistics monitoring.
  • the factors of the traffic volume of the current road section can be comprehensively considered, and when the real-time vehicle speed is compared with the corresponding average standard vehicle speed by at least a certain preset threshold value, the current judgment is judged.
  • the road section is congested.
  • Those skilled in the art should be able to understand that the description of whether the current road segment is congested is only an example. Other existing or future descriptions for judging whether the current road segment is congested may be applicable to the present application, and should also be included in the scope of protection of the present application. It is hereby incorporated by reference.
  • step S11 according to the meteorological data of the current section of the vehicle and the average standard speed of each vehicle under the historical meteorological data of the road section and the time period, the current standard speed is obtained.
  • the average standard speed corresponding to the vehicle model under the historical meteorological data of the road section and time period it also includes:
  • Step S12 determining whether the time of traveling the remaining part of the current road section by the average vehicle speed of the real-time vehicle speed of the part of the current road section is less than a preset threshold, and if yes, going to step S13, if no, going to step S14,
  • Step S13 the average vehicle speed of the real-time vehicle speed of the road section has been taken as the estimated vehicle speed of the remaining part of the road section, that is, the second estimated vehicle speed of the road section is obtained;
  • Step S14 matching the average standard vehicle speed closest to the road section from the relationship between the historical meteorological data and the vehicle speed, and obtaining the estimated vehicle speed of the remaining part of the road section according to the matched average standard vehicle speed closest to the road section, that is, Get the second estimated speed of the road segment.
  • the preset threshold may be set to a shorter time such as 1 hour, and if the time when the vehicle travels the remaining portion of the current road segment at the average speed of the real-time vehicle speed of the portion of the current road segment has been less than a preset threshold, then In the short time of the preset threshold, the probability of weather change is usually small, and the vehicle may drive the estimated speed of the remaining part of the average speed of the real-time vehicle speed of the road section.
  • the average standard vehicle speed closest to the road section can be matched from the relationship between historical meteorological data and vehicle speed, according to the closest match to the road section.
  • the average vehicle speed is obtained from the estimated vehicle speed of the remaining part of the road section, and the vehicle travels the remaining part of the current road section with the estimated speed.
  • the estimated speed of the remaining part of the road section where the vehicle is located is obtained by the above two conditions. It provides a data base for subsequent accurate acquisition of logistics monitoring data.
  • a certain vehicle (assumed to be a medium-sized car) travels on i sections between node 1 and node 2, and sets the current position of the vehicle to F, then:
  • T 0 current time of the current position of the vehicle (if it is an hour);
  • L the length of the part of the vehicle that has been driven on the current section
  • L 0 length of the remainder of the current road segment
  • v 0 average speed of the real-time vehicle speed of the vehicle in the traveled portion L.
  • the average standard vehicle speed closest to the road section can be matched from the relationship between the historical meteorological data and the vehicle speed, and the estimated vehicle speed v 0 ' of the remaining part of the road section is obtained according to the matched average standard vehicle speed closest to the road section.
  • the above description of the estimated vehicle speed of the vehicle in the remainder of the road segment is merely an example, as may other existing or future occurrences of the estimated vehicle speed of the vehicle in the remainder of the road segment. This application is hereby incorporated by reference.
  • step S14 the average standard vehicle speed closest to the road section is matched from the relationship between the historical weather data and the vehicle speed, and includes the following steps S141, S142, and S143.
  • steps S141, S142, and S143 One of them:
  • Step S141 searching for the vehicle speed in the same time segment of the same road segment in the same weather from the relationship between the historical weather data and the vehicle speed as the closest average standard vehicle speed of the matched road segment;
  • Step S142 searching for the vehicle speed of the same time segment of the same road segment from the historical weather data and the vehicle speed as the closest average standard vehicle speed of the matched road segment;
  • Step S143 the vehicle speed searched from the relationship between the historical weather data and the vehicle speed is taken as the closest average standard vehicle speed of the matched road section.
  • the step S141, the step S142 and the step S143 the more accurate average standard vehicle speed of the road section can be obtained.
  • the above description of the average standard vehicle speed that matches the most similar to the road section is only an example, and other existing or future possible matching descriptions of the average standard vehicle speed closest to the road section can be applied to the present application. It is also intended to be included within the scope of this application and is hereby incorporated by reference.
  • step S14 the average standard vehicle speed closest to the road section is matched from the relationship between the historical weather data and the vehicle speed, including:
  • Step S141 searching for the vehicle speed of the same time segment of the same road segment from the relationship between the historical weather data and the vehicle speed, and if yes, going to step S144, using the vehicle speed of the same time segment of the same weather segment as the closest average standard of the matched road segment. Vehicle speed, if not, go to step S142;
  • Step S142 searching for the vehicle speed of the same time segment of the same road section from the relationship between the historical weather data and the vehicle speed, and if yes, going to step S145, using the vehicle speed of the same weather section of the same road section as the closest average standard of the matched road section. Vehicle speed, if not, go to step S143;
  • Step S143 searching for the vehicle speed of the similar road section from the relationship between the historical weather data and the vehicle speed as the closest average standard vehicle speed of the matched road section.
  • step S141, step S142 and step S143 obtain the accuracy of the average standard vehicle speed which is closest to the road segment in turn, so that the previous step is preferentially applied, and the latter one is not applied. Steps to obtain a more accurate average standard vehicle speed that is closest to the road segment.
  • the above description of the average standard vehicle speed that matches the most similar to the road section is only an example, and other existing or future possible matching descriptions of the average standard vehicle speed closest to the road section can be applied to the present application. It is also intended to be included within the scope of this application and is hereby incorporated by reference.
  • estimating the estimated vehicle speed v 0 ' of the remainder of the road segment can be achieved by the following process:
  • Step 1 Search for the speed of the same time segment of the same road segment in the same weather segment as the closest average standard vehicle speed from the historical weather data and the vehicle speed. For example, find the same road segment from the relationship between historical meteorological data and vehicle speed.
  • v' the average running speed of the same type of vehicle
  • r of the vehicle is calculated.
  • r the average speed of the real-time vehicle speed of the already-traveled portion L of the road section of the vehicle/the average speed of the traveling part L of the same type of vehicle in the road section, wherein the average speed of the real-time vehicle speed of the already-traveled portion L of the vehicle at the road section may
  • the average speed of the section L of the same type of vehicle on the road section can be obtained from the relationship between the historical meteorological data and the vehicle speed, and the calculation of the speed coefficient r is added to make the subsequently obtained vehicle in the section.
  • the estimated speed of the remaining part is more accurate, based on the matching average standard speed that is closest to the section.
  • Estimated vehicle speed v at the remaining portion of the section of the 0 ' rv'; Further, if the same time period through the same weather condition is not present, step II;
  • Step 2 Find the vehicle speed in the same time segment of the same road segment from the historical weather data and the speed of the vehicle as the closest average standard vehicle speed of the road segment, that is, the same road segment in the same time period when the same weather condition occurs in the same time zone.
  • the definition of the similar period may be: taking the data of the most recent period, such as one week, is normal.
  • the difference between the average speed of the two periods in the weather (after normalization), and the difference ⁇ 0.1 is a similar period.
  • Step 3 Find the speed of the similar road segment from the historical meteorological data and the speed of the vehicle as the closest average standard vehicle speed of the matched road segment.
  • step S21 after determining whether the current road segment is congested, the method further includes:
  • step S22 when it is determined that the current road segment is congested, the congestion duration of the road segment is obtained according to the vehicle traffic volume of the current road segment of the vehicle and the estimated vehicle speed of the remaining portion, thereby obtaining more accurate logistics condition information.
  • the congestion time of the road section in addition to taking the vehicle flow rate of the current road section and the estimated vehicle speed of the remaining section as considerations, the vehicle flow rate and the estimated vehicle speed of the adjacent road sections of the current road section may be considered, thereby Get a more accurate congestion duration for the current segment.
  • step S22 obtains the congestion duration of the road segment
  • the method further includes:
  • Step S23 when the congestion duration is greater than the preset threshold, the route of the vehicle passing through the road section is adjusted.
  • the route of the vehicle of the road section may be re-planned and adjusted. To improve the efficiency of logistics and transportation.
  • step S13 the average vehicle speed of the real-time vehicle speed of the road section has been taken as the estimated vehicle speed of the vehicle in the remaining part of the road section, or
  • step S14 after obtaining the estimated vehicle speed of the remaining part of the road section according to the matched average standard vehicle speed that is closest to the road section, the method further includes:
  • Step S15 Obtain an estimated vehicle speed of subsequent sections of the vehicle's travel route according to the estimated vehicle speed of the vehicle in the remaining portion of the road section;
  • step S2 the logistics monitoring data is obtained according to the estimated vehicle speed, including:
  • Step S24 predicting the travel time of the vehicle in each section according to the estimated speed of the vehicle in the remaining part of the section and the subsequent sections;
  • Step S25 predicting the time when the vehicle arrives at the node according to the travel time of each road segment before a node, thereby obtaining a more accurate vehicle trajectory estimation.
  • the node may be any node before the end of the vehicle travel, or may be the end point.
  • predicting the time when the vehicle arrives at the node is merely an example, and other existing or future possible predictions of the time at which the predicted vehicle arrives at the node may be applicable to the present application as well as The scope of the present application is intended to be included herein by reference.
  • the method further includes: establishing a correspondence between the vehicle and the waybill.
  • the waybill is the consignment note issued by the logistics company.
  • a waybill ID corresponds to one or a group of goods (packages) carried by the logistics company.
  • the logistics company monitors the flow of the waybill according to the information returned by each outlet or transfer station.
  • the main function of the waybill monitoring is to allow the consumer to check the waybill tracking record and estimated delivery time based on the waybill ID. For example, the results of the waybill query for a logistics company shown in Table 5.
  • the logistics company can adjust the logistics plan according to the monitoring and forecast results of the waybill, such as changing the driving route of the vehicle, adding manpower to the logistics network, and so on.
  • the road segment where the waybill is located can be determined based on the GPS information of the vehicle.
  • Waybill ID *******, place of delivery: **** Road, Haidian District, Beijing, ***, receiving place: *** Road, Panyu District, Guangzhou
  • the waybill includes the invoice that has been generated and the predicted waybill obtained according to the sales forecast result, so that the processing status of the already existing waybill and the predicted waybill can be monitored by the running status of the vehicle corresponding to the waybill.
  • step S25 the time when the vehicle arrives at the node is predicted according to the travel time of each road segment before a node, including:
  • Step S251 predicting the time when the vehicle arrives at the current node according to the waybill processing speed of the previous node and the travel time of each section before the current node, thereby predicting a more accurate time for the vehicle to reach the current node.
  • the processing speed of the waybill of each node includes the delivery tempo and the processing speed, which can be sorted according to the flow information of the waybill.
  • Some logistics nodes have a small amount of processing, and are shipped by time (logistics shuttle); some logistics nodes have a large amount of processing, so as long as they have enough vehicles to ship at any time, the processing time of the waybill at the transfer station and the amount of the waybill It is closely related to the processing speed.
  • the time when the corresponding waybill arrives at the node can be accurately obtained.
  • the description of the time when the predicted vehicle arrives at the current node is only an example, and other existing or future predicted vehicles may arrive at the current node as applicable to the present application, and should also be included in the present application. It is within the scope of protection and is hereby incorporated by reference.
  • step S251 after the time when the vehicle arrives at the current node is predicted according to the waybill processing speed of the previous node and the travel time of each section before the current node, Also includes:
  • Step S252 predicting the number of the waybills that the node needs to process in each future time period according to the time when the vehicle arrives at the node.
  • the situation of each line can be integrated to predict the amount of each logistics node to be processed in the future, so as to increase the number of personnel according to the number of the waybill in time to avoid the explosion.
  • the description of the number of waybills that need to be processed in the future time periods is only an example, other existing or future A description of the number of waybills that may be processed in each future time period as may be applicable to the present application is also intended to be included within the scope of the present application and is hereby incorporated by reference.
  • the historical data 151 such as the waybill information 1511, the in-transit vehicle information 1512, and the road weather information 1513, etc.
  • the waybill information 1511 may be arranged according to the waybill information 1511 to obtain the nodes in the intermediate data 152.
  • the delivery tempo and processing speed 1521 according to the in-transit vehicle information 1512 and the road segment weather information 1513, can be arranged to obtain the relationship 1522 between the weather and the vehicle speed of each road segment, and then the relationship between the delivery tempo of the node and the processing speed 1521, and the weather and the vehicle speed of each road segment.
  • the combination of the real-time data 153 and the real-time data 153 is analyzed to obtain the waybill live information 1541 and the waybill prediction information 1542 in the waybill monitoring 154, wherein the waybill live information 1541, such as the entered waybill number, can query the road section or node where the waybill is located according to the vehicle GPS information ( The network point, the transfer station), the waybill prediction information 1542, such as: according to the time when the vehicle arrives at the node, the time at which the corresponding waybill arrives at the node can be accurately obtained, or the number of the waybills that need to be processed in the future time period of the node is predicted according to the time when the vehicle arrives at the node. .
  • the real-time data 153 may include: sales forecast result 1531, in-transit vehicle information 1532, vehicle and waybill binding information 1533, and road segment weather forecast 1534.
  • the waybill information 1511 in the historical data 151 may be a waybill in and out of each logistics node.
  • the record can be as shown in Table 6:
  • the in-transit vehicle information 1512, 1532 in the historical data 151 and the real-time data 153 is returned by the GPS once per minute, and the format can be as shown in Table 7:
  • the road weather information in the historical data 151 and the real-time data 153 can be obtained from the National Weather Bureau, and the weather conditions of each road segment at each time (accurate to hour) are displayed.
  • the sales forecast result 1531 in the real-time data 153 may be a forecast of how many waybills will be generated in a certain time period in the future, the delivery city of these waybills, the receiving city, and the transit station.
  • the vehicle and waybill binding information 1533 can show which waybills are loaded on the currently running vehicle.
  • an apparatus 100 for logistics monitoring which includes:
  • the first device 101 is configured to obtain an estimated vehicle speed of the vehicle according to the meteorological and vehicle speed data of the road segment; where the meteorological data may include real-time meteorological data and historical meteorological data, wherein the real-time meteorological data includes each Current weather data of the road segment and current weather forecast data, which can be obtained through various meteorological channels, such as through the National Weather Service or other meteorological platforms.
  • the national highway can be obtained.
  • national roads are subdivided into county-level sections to provide real-time meteorological data and historical meteorological data for each section;
  • the second device 102 is configured to obtain the logistics monitoring data according to the estimated vehicle speed.
  • the meteorological information is structured into a form that can be used for logistics monitoring, and the meteorological factors and the road segment information are combined to obtain accurate logistics monitoring and prediction data. Due to the large time and space span of long-distance logistics, meteorological factors have a great impact on long-distance logistics. This application is especially applicable to the monitoring of long-distance logistics between cities.
  • the meteorological and vehicle speed data of the road section includes: real-time weather data of the road section, real-time vehicle speed of the vehicle, and historical meteorological data of the road section and the speed of the road, thereby realizing Followed by accurate logistics monitoring data.
  • the car The real-time vehicle speed can be calculated based on the GPS information of the vehicle.
  • the GPS information can determine the road segment where the vehicle is located.
  • GPS is the global satellite positioning system. The system measures the distance between the satellite at the known location and the receiver of the user, and then integrates The data of multiple satellites calculates the specific location of the receiver, that is, the vehicle.
  • most of the transportation vehicles of major logistics companies in China have installed GPS receivers to navigate and monitor the location of vehicles and goods.
  • the relationship between the historical meteorological data of the road section and the vehicle speed includes an average standard vehicle speed of each vehicle type under historical meteorological data of the road section and the time period.
  • historical meteorological data can be divided into 9 basic types: normal (cloudy, sunny), small to moderate rain, large to heavy rain, thunderstorms, freezing rain, heavy fog, small to medium snow, large to blizzard, sand dust;
  • the model can be classified according to the national standard of automobile classification (GB9417-89), as shown in Table 1:
  • the average standard speed of each model can be as shown in Table 2:
  • the calculation method of the average standard vehicle speed under historical meteorological data of a certain section of a certain section of time may be as follows:
  • the average standard speed of the historical meteorological data of a certain section of a certain section of time (the sum of the distances of the vehicles in the section under the historical meteorological data) / (the historical weather data, the vehicle of the model is driving in the section The sum of time).
  • the small vehicle 2 is driving on the road section at 12:02-12:50 under normal weather, the driving time is 0.6 hours, and the driving distance is 60km (walking the entire road section);
  • the average standard speed of the historical meteorological data of a certain section of a certain section of time (40+60+%)/(0.52+0.6+).
  • the first device 101 includes:
  • the first module 1011 is configured to obtain an average corresponding to the vehicle model under the historical meteorological data of the current road segment and the time period according to the meteorological data of the current road segment of the vehicle and the average standard vehicle speed of each vehicle type according to the historical weather data of the road segment and the time period.
  • the standard speed that is, the first estimated speed of the road section;
  • the second device 102 includes:
  • the second module 1021 is configured to calculate the traffic flow, the real-time vehicle speed and the pair according to the current segment of the vehicle.
  • the average standard speed should be used to judge whether the current road section is congested, so that on the one hand, the current road section congestion can be accurately monitored, and on the other hand, an analysis basis can be provided for subsequent further logistics monitoring.
  • the traffic flow factor of the current road section can be comprehensively considered.
  • the real-time vehicle speed is smaller than the corresponding average standard vehicle speed by at least a certain preset threshold, it is determined that the current road section is congested.
  • the description of whether the current road segment is congested is only an example. Other existing or future descriptions for judging whether the current road segment is congested may be applicable to the present application, and should also be included in the scope of protection of the present application. It is hereby incorporated by reference.
  • the first apparatus further includes a first two module 1012 for determining that the current section is traveled at an average speed of the real-time vehicle speed of the portion of the vehicle that has traveled at the current section. Whether the time of the remaining part is less than a preset threshold, and if so, the average vehicle speed of the real-time vehicle speed of the road section has been used as the estimated vehicle speed of the remaining part of the road section, that is, the second estimated vehicle speed of the road section is obtained; No, from the relationship between historical meteorological data and vehicle speed, the average standard vehicle speed closest to the road section is matched, and the estimated vehicle speed of the remaining part of the road section is obtained according to the matched average standard vehicle speed closest to the road section.
  • the preset threshold may be set to a shorter time such as 1 hour, and if the time when the vehicle travels the remaining portion of the current road segment at the average speed of the real-time vehicle speed of the portion of the current road segment has been less than a preset threshold, then In the short time of the preset threshold, the probability of weather change is usually small, and the vehicle may drive the estimated speed of the remaining part of the average speed of the real-time vehicle speed of the road section.
  • the average standard vehicle speed closest to the road section can be matched from the relationship between historical meteorological data and vehicle speed, according to the closest match to the road section.
  • the average standard vehicle speed is obtained from the estimated vehicle speed of the rest of the road segment, and the vehicle travels at this estimated speed to the remaining portion of the current road segment.
  • the estimated speed of the remaining portion of the road segment where the vehicle is located is obtained by the above two conditions. It can provide a data foundation for subsequent accurate acquisition of logistics monitoring data. Detailed, as shown in Figure 7, some The vehicle (assumed to be a medium-sized car) travels on i roads between node 1 and node 2, and sets the current position of the vehicle to F, then:
  • T 0 current time of the current position of the vehicle (if it is an hour);
  • L the length of the part of the vehicle that has been driven on the current section
  • L 0 length of the remainder of the current road segment
  • v 0 average speed of the real-time vehicle speed of the vehicle in the traveled portion L.
  • the average standard vehicle speed closest to the road section can be matched from the relationship between the historical meteorological data and the vehicle speed, and the estimated vehicle speed v 0 ' of the remaining part of the road section is obtained according to the matched average standard vehicle speed closest to the road section.
  • the above description of the estimated vehicle speed of the vehicle in the remainder of the road segment is merely an example, as may other existing or future occurrences of the estimated vehicle speed of the vehicle in the remainder of the road segment. This application is hereby incorporated by reference.
  • the average standard vehicle speed closest to the road section is matched from the relationship between historical weather data and vehicle speed, including one of the following:
  • the speed of similar sections is found as the closest average standard speed of the matched section.
  • the most accurate average standard speed of the road section can be obtained by any of the three items.
  • Those skilled in the art should be able to understand that the above description of the average standard vehicle speed that matches the most similar to the road section is only an example, and other existing or future possible matching descriptions of the average standard vehicle speed closest to the road section can be applied to the present application. It is also intended to be included within the scope of this application and is hereby incorporated by reference.
  • the average standard vehicle speed closest to the road section is matched from the relationship between historical weather data and vehicle speed, including:
  • the speed of the similar road segment is found from the relationship between the historical meteorological data and the vehicle speed as the closest average standard vehicle speed of the matched road segment.
  • the accuracy of the above-mentioned three items which are the closest to the average standard vehicle speed of the road section is sequentially reduced, so that the former item is preferentially applied under the premise of the former item, and the latter item is not applied, thereby obtaining The exact average standard speed of the road section is the closest.
  • the above description of the average standard vehicle speed that matches the most similar to the road section is only an example, and other existing or future possible matching descriptions of the average standard vehicle speed closest to the road section can be applied to the present application. It is also intended to be included within the scope of this application and is hereby incorporated by reference.
  • estimating the estimated vehicle speed v 0 ' of the remainder of the road segment can be achieved by the following process:
  • Step 1 Search for the speed of the same time segment of the same road segment in the same weather segment as the closest average standard vehicle speed from the historical weather data and the vehicle speed. For example, find the same road segment from the relationship between historical meteorological data and vehicle speed.
  • v' the average running speed of the same type of vehicle
  • r of the vehicle is calculated.
  • r the average speed of the real-time vehicle speed of the already-traveled portion L of the road section of the vehicle/the average speed of the traveling part L of the same type of vehicle in the road section, wherein the average speed of the real-time vehicle speed of the already-traveled portion L of the vehicle at the road section may
  • the average speed of the section L of the same type of vehicle on the road section can be obtained from the relationship between the historical meteorological data and the vehicle speed, and the calculation of the speed coefficient r is added to make the subsequently obtained vehicle in the section.
  • the estimated speed of the remaining part is more accurate, based on the matching average standard speed that is closest to the section.
  • Estimated vehicle speed v at the remaining portion of the section of the 0 ' rv'; Further, if the same time period through the same weather condition is not present, step II;
  • Step 2 Find the vehicle speed in the same time segment of the same road segment from the historical weather data and the speed of the vehicle as the closest average standard vehicle speed of the road segment, that is, the same road segment in the same time period when the same weather condition occurs in the same time zone.
  • the definition of the similar period may be: taking the data of the most recent period, such as one week, is normal.
  • the difference between the average speed of the two periods in the weather (after normalization), and the difference ⁇ 0.1 is a similar period.
  • Step 3 Find the speed of the similar road segment from the historical meteorological data and the speed of the vehicle as the closest average standard vehicle speed of the matched road segment.
  • the similarity coefficient of these two sections (
  • the corresponding road segment is the matched similar road segment, and the estimated vehicle speed of the vehicle in the remaining portion of the road segment can be obtained according to the matched similar road segment.
  • the second device 102 further includes:
  • the second two module 1022 is configured to obtain the congestion duration of the road segment according to the vehicle flow rate of the current road section of the vehicle and the estimated vehicle speed of the remaining portion, thereby obtaining more accurate logistics condition information.
  • the estimated speed of the remaining part is taken into consideration. It is also possible to consider the vehicle flow and the estimated vehicle speed of the adjacent sections of the current section to obtain a more accurate congestion duration of the current section.
  • the second device 102 further includes:
  • the second three module 1023 is configured to adjust a travel route of the vehicle passing through the road section when the congestion duration is greater than a preset threshold.
  • a preset threshold a certain road section may have a long-term delay
  • the vehicle of the road section may be used.
  • the route is re-planned and adjusted to improve logistics and transportation efficiency.
  • the first device 101 further includes:
  • a first three module 1013 configured to obtain an estimated vehicle speed of subsequent sections of the vehicle's travel route according to an estimated vehicle speed of the vehicle in the remaining portion of the road section;
  • the second device 102 further includes:
  • a second fourth module 1024 configured to predict a travel time of the vehicle in each road segment according to an estimated vehicle speed of the vehicle in the remaining portion of the road section and subsequent road sections;
  • the second five module 1025 is configured to predict the time when the vehicle arrives at the node according to the travel time of each road segment before the node, so as to obtain a more accurate vehicle trajectory estimation.
  • the node may be any node before the end of the vehicle travel, or may be the end point.
  • predicting the time when the vehicle arrives at the node is merely an example, and other existing or future possible predictions of the time at which the predicted vehicle arrives at the node may be applicable to the present application as well as The scope of the present application is intended to be included herein by reference.
  • the second device 102 further includes:
  • the second six module 1026 is configured to establish a correspondence between the vehicle and the waybill.
  • the waybill is the consignment note issued by the logistics company.
  • a waybill ID corresponds to one or a group of goods (packages) carried by the logistics company.
  • the logistics company monitors the flow of the waybill according to the information returned by each outlet or transfer station.
  • the main function of the waybill monitoring is to allow the consumer to check the waybill tracking record and estimated delivery time based on the waybill ID.
  • the logistics company can adjust the logistics plan according to the monitoring and forecast results of the waybill, such as changing the driving route of the vehicle, adding manpower to the logistics network, and so on.
  • the road segment where the waybill is located can be determined based on the GPS information of the vehicle.
  • Waybill ID *******, place of delivery: **** Road, Haidian District, Beijing, ***, receiving place: *** Road, Panyu District, Guangzhou
  • the waybill includes the invoice that has been generated and the predicted waybill based on the sales forecast result, so that the already existing waybill can be monitored by the running status of the vehicle corresponding to the waybill.
  • the predicted processing status of the waybill is the predicted processing status of the waybill.
  • the second five module 1025 is configured to predict the arrival of the vehicle at the current node according to the waybill processing speed of the previous node and the travel time of each section before the current node. time.
  • the processing speed of the waybill of each node includes the delivery tempo and the processing speed, which can be sorted according to the flow information of the waybill.
  • Some logistics nodes do not handle a large amount of time, and ship by time (logistics shuttle); some logistics nodes have a large amount of processing, as long as they have enough vehicles It is always ready to ship. At this time, the processing time of the waybill at the transfer station is closely related to the volume of the waybill and the processing speed.
  • the time when the corresponding waybill arrives at the node can be accurately obtained.
  • the description of the time when the predicted vehicle arrives at the current node is only an example, and other existing or future predicted vehicles may arrive at the current node as applicable to the present application, and should also be included in the present application. It is within the scope of protection and is hereby incorporated by reference.
  • the second device 102 further includes:
  • the second seven module 1027 is configured to predict, according to the time when the vehicle arrives at the node, the number of the waybills that the node needs to process in each future time period.
  • the situation of each line can be integrated to predict the amount of each logistics node to be processed in the future, so as to increase the number of personnel according to the number of the waybill in time to avoid the explosion.
  • the description of the number of the waybills to be processed in the future time periods is only an example, and other existing or future possible future time periods may be applied to the description of the number of waybills to be processed, as applicable to the present application. It is also intended to be included within the scope of this application and is hereby incorporated by reference.
  • the present application obtains the estimated vehicle speed of the vehicle according to the meteorological and vehicle speed data of the road section, and then obtains the logistics monitoring data according to the estimated vehicle speed, and can structure the meteorological information to the form that the logistics monitoring can use. Combine meteorological factors with road segment information to obtain accurate logistics monitoring and forecasting data. Due to the large time and space span of long-distance logistics, meteorological factors have a great impact on long-distance logistics. This application is especially applicable to the monitoring of long-distance logistics between cities.
  • the meteorological and vehicle speed data of the road section in the present application includes: real-time weather data of the road section, real-time vehicle speed of the vehicle, and historical meteorological data of the road section and the vehicle speed, wherein the historical meteorological data of the road section and the vehicle speed
  • the relationship includes the average standard speed of each model under the historical meteorological data of the road section and time period, which can achieve accurate logistics monitoring data.
  • the present application determines whether the current road section is congested according to the traffic volume of the vehicle at the current road section, the real-time vehicle speed, and the corresponding average standard vehicle speed. On the one hand, the current road section can be accurately monitored. Blocking the situation, on the other hand, can provide an analytical basis for subsequent further logistics monitoring.
  • the present application uses the average vehicle speed of the real-time vehicle speed of the road section as the estimated vehicle speed of the remaining part of the road section, or matches the average standard vehicle speed closest to the road section from the relationship between the historical weather data and the vehicle speed, according to
  • the matching average standard vehicle speed closest to the road section obtains the estimated vehicle speed of the vehicle in the remaining part of the road section, and the estimated vehicle speed of the remaining part of the road section where the vehicle is located can be obtained in different situations, thereby obtaining the logistics accurately for the subsequent follow-up.
  • Monitoring data provides a data base.
  • the congestion duration of the road segment is obtained according to the vehicle traffic volume of the current road segment of the vehicle and the estimated vehicle speed of the remaining portion, thereby obtaining more accurate logistics status information;
  • the congestion duration is greater than the preset threshold, the route of the vehicle passing through the road section is adjusted to improve the logistics transportation efficiency.
  • the present application predicts the travel time of the vehicle in each section according to the estimated speed of the vehicle in the remaining part of the section and the subsequent sections, and predicts the time when the vehicle arrives at the node according to the travel time of each section before the node, thereby Get more accurate vehicle trajectory estimates.
  • the present application can monitor the flow of the waybill by establishing a correspondence between the vehicle and the waybill, and further predict the time when the vehicle arrives at the current node according to the processing speed of the previous node and the travel time of each section before the current node, thereby It is predicted that the time when the vehicle arrives at the current node is more accurate.
  • the number of the waybills that the node needs to process in the future time period is predicted according to the time when the vehicle arrives at the node, so as to increase the manpower according to the number of the waybill in time to avoid the explosion.
  • the present application can be implemented in software and/or a combination of software and hardware, for example, using an application specific integrated circuit (ASIC), a general purpose computer, or any other similar hardware device.
  • the software program of the present application can be executed by a processor To achieve the steps or functions described above.
  • the software programs (including related data structures) of the present application can be stored in a computer readable recording medium such as a RAM memory, a magnetic or optical drive or a floppy disk and the like.
  • some of the steps or functions of the present application may be implemented in hardware, for example, as a circuit that cooperates with a processor to perform various steps or functions.
  • a portion of the present application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide a method and/or technical solution in accordance with the present application.
  • the program instructions for invoking the method of the present application may be stored in a fixed or removable recording medium, and/or transmitted by a data stream in a broadcast or other signal bearing medium, and/or stored in a The working memory of the computer device in which the program instructions are run.
  • an embodiment in accordance with the present application includes a device including a memory for storing computer program instructions and a processor for executing program instructions, wherein when the computer program instructions are executed by the processor, triggering
  • the apparatus operates based on the aforementioned methods and/or technical solutions in accordance with various embodiments of the present application.

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Abstract

一种物流监测方法及设备，根据路段的气象和车速数据得到车辆在该路段的预估车速(S1)，然后根据所述预估车速获取物流监测数据(S2)，能够将气象信息结构化至物流监控可使用的形式，将气象因素和路段信息结合，获取精确的物流监测和预测数据。由于长途物流的时间和空间跨度大，气象因素对长途物流的影响较大，尤其适用于城市间的长途物流的监控。

Description

物流监测方法及设备 技术领域
本申请涉及通信及计算机领域，尤其涉及一种物流监测方法及设备。
背景技术
随着我国电子商务的高速发展，物流服务体验成为竞争焦点。对物流网络的精细管理(监控，预测，优化)对于优化物流业务，提升电商服务水平，显得非常重要。如图1所示，物流网络是由物流节点11(网点，中转站)以及连结它们之间的物流线路12(公路，铁路，海运线路，航空线路)所构成的货物运输网络。如图2所示，对主要涉及公路的物流网络，为了简化起见物流网络可以用拓扑图表示，图2中，物流线路12可以按一定规则划分为若干路段13。
气象信息在物流网络监控中所发挥的作用日趋重要，误判气象信息的代价也随之升高，往往一个路线的延时影响或许波及范围将是几个省的物流通道。然而，在现有的物流网络的监控方案中，各物流公司通过现有的运单监测系统，主要是根据各节点传回的信息来监测运单流转情况，气象信息在数据结构上与物流行业的数据并无太大耦合关系，通常一个路段会穿越多个气象区域，如何将这些气象区域进行结构化当前尚无很好的解决方案。现有方案中对于在途运单的监测是一个薄弱环节，现有方案要么通过司机打电话了解情况，要么只能通过GPS信息得到各物流车辆所在位置，不能综合考虑车流量，天气，车辆速度等因素对各线路进行分段监控，而且只能参考自己一家公司的信息，不能参考其他物流公司的信息，从而导致信息不全面不准确，难以提供可行的决策支持。
公开号为102256377A、发明名称为一种农资物流监控系统的中国专利申请中针对农产品，公开号为103458236A、发明名称为危化品物流智能监控系统的中国专利申请中针对危化品，农产品和危化品的这些特殊商品对温度， 湿度，速度，压力等非常敏感，因此使用传感器进行了实时监控，但这该两项专利申请只能实时监控温度，湿度，速度，压力，位置等变量，从而避免商品变质、损坏，或者偏离运输路线。并不能结合天气实况和天气预报对物流商品未来的运行时间进行预估，从而及时调整物流计划。
另外，公开号为103794053A、发明名称为一种城市短途物流单目标配送时间模糊预测方法及系统的中国专利申请中，通过GPS采集配送车辆在各路段的通过时间，然后在采集的历史数据的基础上，预测配送时间，但该专利申请不考虑气象因素，只适用于城市内的短途物流。
综上，面对运转情况非常复杂的物流网络，如何综合考虑运单量、路况、天气等各方面因素影响，对运单的状态进行有效监控是目前亟待解决的问题。
发明内容
本申请的目的是提供一种物流监测方法及设备，能够对物流车辆和运单实施更精准的监测和预测。
有鉴于此，本申请提供一种物流监测方法，包括：
根据路段的气象和车速数据得到车辆在该路段的预估车速；
根据所述预估车速获取物流监测数据。
进一步的，所述路段的气象和车速数据包括：路段的实时气象数据、车辆的实时车速和该路段的历史气象数据与车速的关系。
进一步的，所述路段的历史气象数据与车速的关系包括按路段、时段的历史气象数据下各车型的平均标准车速。
进一步的，根据路段的气象和车速数据得到车辆在该路段的预估车速，包括：
根据车辆在当前路段的气象数据和按路段、时段的历史气象数据下各车型的平均标准车速，得到当前路段、时段的历史气象数据下与车辆的车型对应的平均标准车速；
根据所述预估车速获取物流监测数据包括：
根据车辆在当前路段的车流量、实时车速与对应的平均标准车速判断当前路段是否拥堵。
进一步的，得到当前路段、时段的历史气象数据下与车辆的车型对应的平均标准车速之后，还包括：
判断以车辆在当前路段已经行驶部分的实时车速的平均车速行驶当前路段的剩余部分的时间是否小于预设阈值，
若是，将该路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速；
若否，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速。
进一步的，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括如下其中一项：
从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速作为匹配到的该路段最相近的平均标准车速；
从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速作为匹配到的该路段最相近的平均标准车速；
从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。
进一步的，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括：
从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速，若有，将该车速作为匹配到的该路段最相近的平均标准车速；
若无，从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速，若有，将该车速作为匹配到的该路段最相近的平均标准车速；
若无，从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。
进一步的，判断当前路段是否拥堵之后，还包括：
根据当车辆的当前路段的车辆流量、剩余部分的预估车速获取该路段的拥堵持续时间。
进一步的，获取该路段的拥堵持续时间之后还包括：
当拥堵持续时间大于预设阈值时，对经过该路段的车辆的行经路线进行调整。
进一步的，将该路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速之后，或根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速之后，还包括：
根据车辆在该路段的剩余部分的预估车速获取车辆的行经路线的后续各路段的预估车速；
根据所述预估车速获取物流监测数据，包括：
根据车辆在该路段的剩余部分和后续各路段的预估车速预测车辆在各路段的行驶时间；
根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间。
进一步的，根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间之前，还包括：
建立车辆与运单的对应关系。
进一步的，所述运单包括已经产生的运单和根据销量预测结果得到预测运单。
进一步的，根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间，包括：
根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间。
进一步的，根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间之后，还包括：
根据车辆到达节点的时间预测该节点未来各时间段需要处理的运单数量。
根据本申请的另一方面还提供一种用于物流监测的设备，包括：
第一装置，用于根据路段的气象和车速数据得到车辆在该路段的预估车速；
第二装置，用于根据所述预估车速获取物流监测数据。
进一步的，所述路段的气象和车速数据包括：路段的实时气象数据、车辆的实时车速和该路段的历史气象数据与车速的关系。
进一步的，所述路段的历史气象数据与车速的关系包括按路段、时段的历史气象数据下各车型的平均标准车速。
进一步的，所述第一装置，包括：
第一一模块，用于根据车辆在当前路段的气象数据和按路段、时段的历史气象数据下各车型的平均标准车速，得到当前路段、时段的历史气象数据下与车辆的车型对应的平均标准车速；
所述第二装置，包括：
第二一模块，用于根据车辆在当前路段的车流量、实时车速与对应的平均标准车速判断当前路段是否拥堵。
进一步的，所述第一装置还包括第一二模块，用于判断以车辆在当前路段已经行驶部分的实时车速的平均车速行驶当前路段的剩余部分的时间是否小于预设阈值，若是，将该路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速；若否，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速。
进一步的，从历史气象数据与车速的关系中匹配与该路段最相近的平 均标准车速，包括如下其中一项：
从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速作为匹配到的该路段最相近的平均标准车速；
从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速作为匹配到的该路段最相近的平均标准车速；
从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。
进一步的，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括：
从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速，若有，将该车速作为匹配到的该路段最相近的平均标准车速；
若无，从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速，若有，将该车速作为匹配到的该路段最相近的平均标准车速；
若无，从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。
进一步的，所述第二装置，还包括：
第二二模块，用于根据当车辆的当前路段的车辆流量、剩余部分的预估车速获取该路段的拥堵持续时间。
进一步的，所述第二装置，还包括：
第二三模块，用于当拥堵持续时间大于预设阈值时，对经过该路段的车辆的行经路线进行调整。
进一步的，所述第一装置还包括：
第一三模块，用于根据车辆在该路段的剩余部分的预估车速获取车辆的行经路线的后续各路段的预估车速；
所述第二装置还包括：
第二四模块，用于根据车辆在该路段的剩余部分和后续各路段的预估 车速预测车辆在各路段的行驶时间；
第二五模块，用于根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间。
进一步的，所述第二装置还包括：
第二六模块，用于建立车辆与运单的对应关系。
进一步的，所述运单包括已经产生的运单和根据销量预测结果得到预测运单。
进一步的，所述第二五模块，用于根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间。
进一步的，所述第二装置还包括：
第二七模块，用于根据车辆到达节点的时间预测该节点未来各时间段需要处理的运单数量。
与现有技术相比，本申请根据路段的气象和车速数据得到车辆在该路段的预估车速，然后根据所述预估车速获取物流监测数据，能够将气象信息结构化至物流监控可使用的形式，将气象因素和路段信息结合，获取精确的物流监测和预测数据。由于长途物流的时间和空间跨度大，气象因素对长途物流的影响较大，本申请结合尤其适用于城市间的长途物流的监控。
进一步的，本申请中所述路段的气象和车速数据包括：路段的实时气象数据、车辆的实时车速和该路段的历史气象数据与车速的关系，其中，所述路段的历史气象数据与车速的关系包括按路段、时段的历史气象数据下各车型的平均标准车速，可以实现后续得到精确的物流监测数据。
进一步的，本申请根据车辆在当前路段的车流量、实时车速与对应的平均标准车速判断当前路段是否拥堵，一方面可以精确监测当前路段的拥堵情况，另一方面可为后续进一步的物流监测提供分析基础。
进一步的，本申请将路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速，或者从历史气象数据与车速的关系中匹 配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速，可以分不同情况得到精确的车辆所在路段的剩余部分的预估车速，从而为后续精确地获取物流监测数据提供数据基础。
进一步的，本申请中当判断为当前路段为拥堵时，根据当车辆的当前路段的车辆流量、剩余部分的预估车速获取该路段的拥堵持续时间，从而获取到更精确物流状况信息；另外，当拥堵持续时间大于预设阈值时，对经过该路段的车辆的行经路线进行调整，以提高物流运输效率。
进一步的，本申请根据车辆在该路段的剩余部分和后续各路段的预估车速预测车辆在各路段的行驶时间，并根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间，从而获取更为精准的车辆轨迹预估。
进一步的，本申请通过建立车辆与运单的对应关系，可以监控运单的流转情况，另外，根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间，从而预测得到更精确的车辆到达当前节点的时间，此外，根据车辆到达节点的时间预测该节点未来各时间段需要处理的运单数量，以便及时根据运单数量增加人手，避免爆仓。
附图说明
通过阅读参照以下附图所作的对非限制性实施例所作的详细描述，本申请的其它特征、目的和优点将会变得更明显：
图1示出现有的物流网络的示意图；
图2示出现有的物流网络拓扑图；
图3示出根据本申请一个方面的一种物流监测方法的流程图；
图4示出本申请一实施例的路段气象信息的示意图；
图5示出本申请的一优选的实施例的物流监测方法的流程图；
图6示出本申请的另一优选的实施例的物流监测方法的流程图；
图7示出本申请的一实施例的车辆在节点1和节点2之间的路段行驶示意图；
图8示出本申请的又一优选的实施例的物流监测方法的流程图；
图9示出本申请一实施例的各时段平均车速示意图；
图10示出本申请一实施例的各时段归一化后平均车速示意图；
图11示出本申请的再一优选的实施例的物流监测方法的流程图；
图12示出本申请的又一优选的实施例的物流监测方法的流程图；
图13示出本申请的另一优选的实施例的物流监测方法的流程图；
图14示出本申请的再一优选的实施例的物流监测方法的流程图；
图15示出本申请的一具体应用实施例的原理图；
图16示出本申请的另一个方面的一种用于物流监测的设备示意图；
图17示出本申请的一优选的实施例的用于物流监测的设备示意图；
图18示出本申请的另一优选的实施例的用于物流监测的设备示意图；
图19示出本申请的又一优选的实施例的用于物流监测的设备示意图；
图20示出本申请的再一优选的实施例的用于物流监测的设备示意图；
图21示出本申请的另一优选的实施例的用于物流监测的设备示意图；
图22示出本申请的又一优选的实施例的用于物流监测的设备示意图；
附图中相同或相似的附图标记代表相同或相似的部件。
具体实施方式
在本申请一个典型的配置中，终端、服务网络的设备和可信方均包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
内存可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。内存是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括非暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
如图3所示，本申请提供一种物流监测方法，包括：
步骤S1，根据路段的气象和车速数据得到车辆在该路段的预估车速；在此，所述气象数据可包括实时气象数据和历史气象数据，其中，所述实时气象数据包括各路段的当前天气实况数据和当前天气预报数据，所述气象数据可以通过各种气象渠道获取，如通过国家气象局或其它气象平台等获取，例如，如图4所示，可将全国的高速公路和国道细分到县域级别的各路段，提供每个路段的实时气象数据和历史气象数据；
步骤S2，根据所述预估车速获取物流监测数据。本实施例将气象信息结构化至物流监控可使用的形式，将气象因素和路段信息结合，获取精确的物流监测和预测数据。由于长途物流的时间和空间跨度大，气象因素对长途物流的影响较大，本申请结合尤其适用于城市间的长途物流的监控。
本申请的物流监测方法的一优选的实施例中，所述路段的气象和车速数据包括：路段的实时气象数据、车辆的实时车速和该路段的历史气象数据与车速的关系，从而实现后续得到精确的物流监测数据。在此，车辆的实时车速可根据车辆的GPS信息计算得到，GPS信息能够确定车辆所在的 路段，GPS即全球卫星定位系统，该系统通过测量出已知位置的卫星到用户接收机之间的距离，然后综合多颗卫星的数据计算出接收机即车辆的具体位置，目前我国各大物流公司的大部分运输车辆安装了GPS接收机，用来导航和监控车辆、货物所在位置。
本申请的物流监测方法的一优选的实施例中，所述路段的历史气象数据与车速的关系包括按路段、时段的历史气象数据下各车型的平均标准车速，从而实现后续得到精确的物流监测数据。在此，历史气象数据可分为9个基本类型：正常(阴天，晴天)，小到中雨，大到暴雨，雷暴，冻雨，大雾，小到中雪，大到暴雪，沙尘；车型可以依据汽车分类国家标准(GB9417-89)中载货汽车的分类，具体如表1所示：
车型 厂定最大总质量(GA)
微型 GA≤1.8吨
轻型 1.8吨＜GA≤6吨
中型 6吨＜GA≤14吨
重型 GA＞14吨
表1
按路段、时段的历史气象数据下各车型的平均标准车速可以具体如表2所示：
Figure PCTCN2016076697-appb-000001
表2
具体的，例如，某时段某路段某车型的历史气象数据下平均标准车速的计算方式可以如下：
某时段某路段某车型的历史气象数据下平均标准车速＝(该历史气象数据下该时段该车型车辆在该路段行驶距离之和)/(该历史气象数据下该时段该车型车辆在该路段行驶时间之和)。
例如，统计12：00-13：00某路段的平均车速，其中：
小型车辆一在正常天气下，于12：23-13：00在该路段行驶，行驶时间是0.52小时，行驶距离40km；
小型车辆二在正常天气下，于12：02-12：50在该路段行驶，行驶时间0.6小时，行驶距离60km(走完整个路段)；
…
那么，该某时段某路段某车型的历史气象数据下平均标准车速＝(40+60+…)/(0.52+0.6+…)。本领域技术人员应能理解上述路段的历史气象数据与车速的关系的描述仅为举例，其他现有的或今后可能出现的路段的历史气象数据与车速的关系的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
如图5所示，本申请的物流监测方法的一优选的实施例中，步骤S1，根据路段的气象和车速数据得到车辆在该路段的预估车速，包括：
步骤S11，根据车辆在当前路段的气象数据和按路段、时段的历史气象数据下各车型的平均标准车速，得到当前路段、时段的历史气象数据下与车辆的车型对应的平均标准车速，即得到该路段的第一个预估车速；
相应的，步骤S2，根据所述预估车速获取物流监测数据包括：
步骤S21，根据车辆在当前路段的车流量、实时车速与对应的平均标准车速判断当前路段是否拥堵，从而一方面可以精确监测当前路段的拥堵 情况，另一方面为后续进一步的物流监测提供分析基础，在此，可综合考虑当前路段的车流量的因素，比较实时车速比对应的平均标准车速小至少某一预设阈值时，则判断当前路段为拥堵。本领域技术人员应能理解上述判断当前路段是否拥堵的描述仅为举例，其他现有的或今后可能出现的判断当前路段是否拥堵的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
如图6所示，本申请的物流监测方法的一优选的实施例中，步骤S11，根据车辆在当前路段的气象数据和按路段、时段的历史气象数据下各车型的平均标准车速，得到当前路段、时段的历史气象数据下与车辆的车型对应的平均标准车速之后，还包括：
步骤S12，判断以车辆在当前路段已经行驶部分的实时车速的平均车速行驶当前路段的剩余部分的时间是否小于预设阈值，若是，转到步骤S13，若否，转到步骤S14，
步骤S13，将该路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速，即得到该路段的第二个预估车速；
步骤S14，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速，即得到该路段的第二个预估车速。在此，所述预设阈值可以设置为1小时等较短的时间，以车辆在当前路段已经行驶部分的实时车速的平均车速行驶当前路段的剩余部分的时间如果小于预设阈值，那么在该预设阈值的短时间内，通常天气变化的可能性不大，车辆可以该路段已经行驶部分实时车速的平均车速行驶剩余部分的预估车速，相反，如果大于等于预设阈值，那么在大于该预设阈值的较长时间内，通常天气变化的可能性会比较大，那么可从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平 均标准车速得到车辆在该路段的剩余部分的预估车速，车辆以此预估车速行驶当前路段中的剩余部分，这里通过上述两种情况得到精确的车辆所在路段的剩余部分的预估车速，可以为后续精确地获取物流监测数据提供数据基础。详细的，如图7，某一车辆(假设是中型车)在节点1和节点2之间的i个路段行驶，设车辆当前位置为F，则：
T0＝车辆当前位置的当前时刻(如是一整点时刻)；
L＝车辆在当前路段已行驶部分的长度；
L0＝当前路段的剩余部分的长度；
Li＝接下来第i个路段的长度，i＝1，2，3，…；
到达节点2所需时间
t＝t0+t1+t2+…
这里ti是在路段Li行驶时间，i＝0，1，2，3，…。
为了计算t0，假设
v0＝该车辆在已行驶部分L的实时车速的平均速度。
分两种情况：
如果L0/v0<＝1小时，那么t0＝L0/v0。
否则，1小时后，设14：00-15：00L1路段的天气预报如表3：
Figure PCTCN2016076697-appb-000002
表3
那么可从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速v0’。本领域技术人员应能理解上述得到车辆在该路段的剩余部分的预估车速的描述仅为举例，其他现有的或今后可能出现的得到车辆在该路段的剩余部分的预估车速的描述如可适用于本申请，也应 包含在本申请保护范围以内，并在此以引用方式包含于此。
本申请的物流监测方法的一更优选的实施例中，步骤S14中，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括如下步骤S141、步骤S142和步骤S143中的其中一项：
步骤S141，从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速作为匹配到的该路段最相近的平均标准车速；
步骤S142，从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速作为匹配到的该路段最相近的平均标准车速；
步骤S143，从历史气象数据与车速的关系中查找的车速作为匹配到的该路段最相近的平均标准车速。通过步骤S141、步骤S142和步骤S143中的其中任一步骤可以得到较精确的该路段最相近的平均标准车速。本领域技术人员应能理解上述匹配与路段最相近的平均标准车速的描述仅为举例，其他现有的或今后可能出现的匹配与路段最相近的平均标准车速的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
如图8所示，本申请的物流监测方法的一更优选的实施例中，步骤S14中，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括：
步骤S141，从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速，若有，转到步骤S144，将相同天气同一路段同一时段的车速作为匹配到的该路段最相近的平均标准车速，若无，转到步骤S142；
步骤S142，从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速，若有，转到步骤S145，将相同天气同一路段相似时段的车速作为匹配到的该路段最相近的平均标准车速，若无，转到步骤S143；
步骤S143，从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。在此，步骤S141、步骤S142和步骤S143得到该路段最相近的平均标准车速的精确性依次降低，所以在适用于前一步骤的前提下，会优先适用前一步骤，而不会适用后一步骤，从而得到较精确的该路段最相近的平均标准车速。本领域技术人员应能理解上述匹配与路段最相近的平均标准车速的描述仅为举例，其他现有的或今后可能出现的匹配与路段最相近的平均标准车速的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
例如，估算该路段的剩余部分的预估车速v0’具体可通过如下过程实现：
步骤一：从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速作为匹配到的该路段最相近的平均标准车速，例如，从历史气象数据与车速的关系中查找到即同一路段在同一时段(14：00-15：00)最近一次出现相同天气状况时，同类型车辆的平均行驶速度为v’即匹配到的该路段最相近的平均标准车速，计算车辆的速度系数r，r＝该车辆在路段的已经行驶部分L的实时车速的平均车速/同类型车辆在路段的已行驶部分L的平均速度，其中，该车辆在路段的已经行驶部分L的实时车速的平均车速可以根据GPS数据得到，同类型车辆在路段已行驶路段L的平均速度可从历史气象数据与车速的关系中匹配得到，在此加上速度系数r的计算，可使后续得到的车辆在该路段的剩余部分的预估车速更加精确，则根据匹配到的与该路段最相近的平均标准车速可以得到车辆在该路段的剩余部分的预估车速v0’＝rv’；另外，如果同一时段未出现过相同天气状况，执行步骤二；
步骤二：从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速作为匹配到的该路段最相近的平均标准车速，即同一路段在相似时段最近一次出现相同天气状况时，同类型车辆的平均行驶速度为v’ 即匹配到的该路段最相近的平均标准车速，则根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速v0’＝rρv’，这里ρ＝同一时段同一路段的正常天气的平均车速/相似时段同一路段的正常天气的平均车速，在此，相似时段的定义可以是：取最近一段时间如一周的数据，比较正常天气下两个时段的平均车速(归一化处理后)的差值，差值<0.1的是相似时段。
例如，设置有24个时段(时段1(0：00-1：00)，时段2(1：00-2：00)，…，时段24(23：00-24：00))在正常天气下最近一周的平均车速如图9所示，对车速进行归一化处理，即：归一化后车速＝(车速-最小车速)/(最大车速-最小车速)，得到如图10所示的归一化后车速，图10中，时段15(14：00-15：00)的相似时段有时段8，9，10，12，16，如果这些时段均未出现过相同天气状况，那么，就执行步骤三。如果其中一个时段，例如时段8最近出现过相同天气状况。那么车辆在该路段的剩余部分的预估车速v0’＝r*时段8出现相同天气状况时的车速*(时段15正常天气的平均车速/时段8正常天气的平均车速)。
步骤三：从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。路段相似性的计算方法可以是，取最近一段时间如一周相同时段及相似时段的同一车型数据，比较当前路段和其它每个路段在各种天气下的平均速度，定义两个路段的相似系数＝两路段速度差的绝对值/两路段速度之和，再按不同天气取均值，例如，如表4：
天气 路段01 路段02
正常 75 65
小到中雨 64 57
…    
表4
那么这两个路段的相似系数＝(|75-65|/(75+65)+|64-57|/(64+57)…)/天 气种类数量，最后取两个路段的相似系数最小的对应路段即为匹配到的相似路段，后续即可根据匹配到的相似路段得到车辆在该路段的剩余部分的预估车速。
如图11所示，本申请的物流监测方法的一优选的实施例中，步骤S21，判断当前路段是否拥堵之后，还包括：
步骤S22，当判断为当前路段为拥堵时，根据当车辆的当前路段的车辆流量、剩余部分的预估车速获取该路段的拥堵持续时间，从而获取到更精确物流状况信息。在此，在获取该路段的拥堵时间时，除了将当前路段的车辆流量、剩余部分的预估车速作为考虑因素外，还可以考虑当前路段的前后相邻路段的车辆流量和预估车速，从而得到更精确的当前路段的拥堵持续时间。本领域技术人员应能理解上述获取该路段的拥堵持续时间的描述仅为举例，其他现有的或今后可能出现的获取该路段的拥堵持续时间的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
如图12所示，本申请的物流监测方法的一优选的实施例中，步骤S22获取该路段的拥堵持续时间之后还包括：
步骤S23，当拥堵持续时间大于预设阈值时，对经过该路段的车辆的行经路线进行调整，在此，当某路段可能出现长期延误时，可以对该路段的车辆的行经路线进行重新规划调整，以提高物流运输效率。
如图13所示，本申请的物流监测方法的一优选的实施例中，步骤S13中，将该路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速之后，或步骤S14中，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速之后，还包括：
步骤S15，根据车辆在该路段的剩余部分的预估车速获取车辆的行经路线的后续各路段的预估车速；
对应的，步骤S2，根据所述预估车速获取物流监测数据，包括：
步骤S24，根据车辆在该路段的剩余部分和后续各路段的预估车速预测车辆在各路段的行驶时间；
步骤S25，根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间，从而获取更为精准的车辆轨迹预估。在此，所述节点可以车辆行驶的终点之前的任一节点，也可以是所述终点。本领域技术人员应能理解上述预测车辆到达该节点的时间的描述仅为举例，其他现有的或今后可能出现的预测车辆到达该节点的时间的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
本申请的物流监测方法的一优选的实施例中，步骤S25，根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间之前，还包括：建立车辆与运单的对应关系。在此，运单是物流公司开具的货物托运单据。一个运单ID对应物流公司承运的一个或一组货物(包裹)。物流公司根据各网点或中转站回流的信息，监控运单的流转情况。运单监控的主要作用是：可以让消费者根据运单ID查询运单跟踪记录和预计送达时间。例如，表5所示的某物流公司的运单查询结果。物流公司可以根据运单监测和预测结果，调整物流计划，例如改变车辆行驶路线，给物流网点增加人手，等等。根据车辆GPS信息能够确定运单所在的路段。
运单ID：*******，发货地：北京市海淀区****路***号，收货地：广州市番禺区***路***号
Figure PCTCN2016076697-appb-000003
Figure PCTCN2016076697-appb-000004
表5
可选的，所述运单包括已经产生的运单和根据销量预测结果得到预测运单，从而可以通过与运单对应的车辆的运行状态监测已经存在运单和预测到的运单的处理状况。
如图14所示，本申请的物流监测方法的一优选的实施例中，步骤S25，根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间，包括：
步骤S251，根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间，从而预测得到更精确的车辆到达当前节点的时间。在此，各个节点的运单处理速度包括发货节奏和处理速度，具体可根据运单流转信息整理出。有的物流节点处理量不大，按时段发货(物流班车)；有的物流节点处理量大，只要凑够一整车就随时发货，这时候运单在中转站的处理时间就与运单量和处理速度密切相关。相应的，根据车辆到达节点的时间可以精确得到对应的运单到达该节点的时间。本领域技术人员应能理解上述预测车辆到达当前节点的时间的描述仅为举例，其他现有的或今后可能出现的预测车辆到达当前节点的时间如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
如图14所示，本申请的物流监测方法的一优选的实施例中，步骤S251，根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间之后，还包括：
步骤S252，根据车辆到达节点的时间预测该节点未来各时间段需要处理的运单数量。在此，可综合各线路情况，预测各物流节点未来将处理的单量，以便及时根据运单数量增加人手，避免爆仓。本领域技术人员应能理解上述未来各时间段需要处理的运单数量的描述仅为举例，其他现有的或今后 可能出现的未来各时间段需要处理的运单数量的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
如图15，本申请一具体应用实施例中，可从历史数据151如运单流转信息1511、在途车辆信息1512和路段气象信息1513等，根据运单流转信息1511可以整理得到中间数据152中的节点的发货节奏和处理速度1521，根据在途车辆信息1512和路段气象信息1513可以整理得到各路段天气和车速的关系1522，然后将节点的发货节奏和处理速度1521、以及各路段天气和车速的关系1522与实时数据153结合，分析得到运单监控154中的运单实况1541和运单预测信息1542，其中，运单实况1541如：输入的运单号后，根据车辆GPS信息可以查询该运单所在路段或节点(网点，中转站)，运单预测信息1542如：根据车辆到达节点的时间可以精确得到对应的运单到达该节点的时间，或根据车辆到达节点的时间预测该节点未来各时间段需要处理的运单数量等。所述实时数据153可以包括：销量预测结果1531、在途车辆信息1532、车辆和运单绑定信息1533和路段气象预报1534，具体的，历史数据151中的运单流转信息1511可以是运单进出各物流节点的纪录，格式可以如表6：
运单ID 节点ID 时间 出/入
50001 1 2014-5-7 13:25 入
50001 1 2014-5-7 16:12 出
47203 2 2014-5-7 13:27 入
… … … …
表6
历史数据151和实时数据153中的在途车辆信息1512、1532由GPS每分钟回传一次，格式可以如表7：
Figure PCTCN2016076697-appb-000005
Figure PCTCN2016076697-appb-000006
表7
具体的，历史数据151和实时数据153中的路段气象信息，可从国家气象局获取，显示各路段在各个时间(精确到小时)的天气状况。实时数据153中的销量预测结果1531可以是预测未来某时段将会产生多少运单，这些运单的发货城市，收件城市以及途经中转站。车辆和运单绑定信息1533可以显示当前运行的车辆上装载了哪些运单。
如图16所示，根据本申请的另一面还提供一种用于物流监测的设备100，其中，包括：
第一装置101，用于根据路段的气象和车速数据得到车辆在该路段的预估车速；在此，所述气象数据可包括实时气象数据和历史气象数据，其中，所述实时气象数据包括各路段的当前天气实况数据和当前天气预报数据，所述气象数据可以通过各种气象渠道获取，如通过国家气象局或其它气象平台等获取，例如，如图4所示，可将全国的高速公路和国道细分到县域级别的各路段，提供每个路段的实时气象数据和历史气象数据；
第二装置102，用于根据所述预估车速获取物流监测数据。本实施例将气象信息结构化至物流监控可使用的形式，将气象因素和路段信息结合，获取精确的物流监测和预测数据。由于长途物流的时间和空间跨度大，气象因素对长途物流的影响较大，本申请结合尤其适用于城市间的长途物流的监控。
本申请的用于物流监测的设备的一优选的实施例中，所述路段的气象和车速数据包括：路段的实时气象数据、车辆的实时车速和该路段的历史气象数据与车速的关系从而实现后续得到精确的物流监测数据。在此，车 辆的实时车速可根据车辆的GPS信息计算得到，GPS信息能够确定车辆所在的路段，GPS即全球卫星定位系统，该系统通过测量出已知位置的卫星到用户接收机之间的距离，然后综合多颗卫星的数据计算出接收机即车辆的具体位置，目前我国各大物流公司的大部分运输车辆安装了GPS接收机，用来导航和监控车辆、货物所在位置。
本申请的用于物流监测的设备的一优选的实施例中，所述路段的历史气象数据与车速的关系包括按路段、时段的历史气象数据下各车型的平均标准车速。从而实现后续得到精确的物流监测数据。在此，历史气象数据可分为9个基本类型：正常(阴天，晴天)，小到中雨，大到暴雨，雷暴，冻雨，大雾，小到中雪，大到暴雪，沙尘；车型可以依据汽车分类国家标准(GB9417-89)中载货汽车的分类，具体如表1所示：
车型 厂定最大总质量(GA)
微型 GA≤1.8吨
轻型 1.8吨＜GA≤6吨
中型 6吨＜GA≤14吨
重型 GA＞14吨
表1
按路段、时段的历史气象数据下各车型的平均标准车速可以具体如表2所示：
Figure PCTCN2016076697-appb-000007
Figure PCTCN2016076697-appb-000008
表2
具体的，例如，某时段某路段某车型的历史气象数据下平均标准车速的计算方式可以如下：
某时段某路段某车型的历史气象数据下平均标准车速＝(该历史气象数据下该时段该车型车辆在该路段行驶距离之和)/(该历史气象数据下该时段该车型车辆在该路段行驶时间之和)。
例如，统计12：00-13：00某路段的平均车速，其中：
小型车辆一在正常天气下，于12：23-13：00在该路段行驶，行驶时间是0.52小时，行驶距离40km；
小型车辆二在正常天气下，于12：02-12：50在该路段行驶，行驶时间0.6小时，行驶距离60km(走完整个路段)；
…
那么，该某时段某路段某车型的历史气象数据下平均标准车速＝(40+60+…)/(0.52+0.6+…)。本领域技术人员应能理解上述路段的历史气象数据与车速的关系的描述仅为举例，其他现有的或今后可能出现的路段的历史气象数据与车速的关系的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
如图17所示，本申请的用于物流监测的设备的一优选的实施例中，所述第一装置101，包括：
第一一模块1011，用于根据车辆在当前路段的气象数据和按路段、时段的历史气象数据下各车型的平均标准车速，得到当前路段、时段的历史气象数据下与车辆的车型对应的平均标准车速，即得到该路段的第一个预估车速；
所述第二装置102，包括：
第二一模块1021，用于根据车辆在当前路段的车流量、实时车速与对 应的平均标准车速判断当前路段是否拥堵，从而一方面可以精确监测当前路段的拥堵情况，另一方面为后续进一步的物流监测提供分析基础，在此，可综合考虑当前路段的车流量的因素，比较实时车速比对应的平均标准车速小至少某一预设阈值时，则判断当前路段为拥堵。本领域技术人员应能理解上述判断当前路段是否拥堵的描述仅为举例，其他现有的或今后可能出现的判断当前路段是否拥堵的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
本申请的用于物流监测的设备的一优选的实施例中，所述第一装置还包括第一二模块1012，用于判断以车辆在当前路段已经行驶部分的实时车速的平均车速行驶当前路段的剩余部分的时间是否小于预设阈值，若是，将该路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速，即得到该路段的第二个预估车速；若否，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速。，即得到该路段的第二个预估车速。在此，所述预设阈值可以设置为1小时等较短的时间，以车辆在当前路段已经行驶部分的实时车速的平均车速行驶当前路段的剩余部分的时间如果小于预设阈值，那么在该预设阈值的短时间内，通常天气变化的可能性不大，车辆可以该路段已经行驶部分实时车速的平均车速行驶剩余部分的预估车速，相反，如果大于等于预设阈值，那么在大于该预设阈值的较长时间内，通常天气变化的可能性会比较大，那么可从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速，车辆以此预估车速行驶当前路段中的剩余部分，这里通过上述两种情况得到精确的车辆所在路段的剩余部分的预估车速，可以为后续精确地获取物流监测数据提供数据基础。详细的，如图7，某一 车辆(假设是中型车)在节点1和节点2之间的i个路段行驶，设车辆当前位置为F，则：
T0＝车辆当前位置的当前时刻(如是一整点时刻)；
L＝车辆在当前路段已行驶部分的长度；
L0＝当前路段的剩余部分的长度；
Li＝接下来第i个路段的长度，i＝1，2，3，…；
到达节点2所需时间
t＝t0+t1+t2+…
这里ti是在路段Li行驶时间，i＝0，1，2，3，…。
为了计算t0，假设
v0＝该车辆在已行驶部分L的实时车速的平均速度。
分两种情况：
如果L0/v0<＝1小时，那么t0＝L0/v0。
否则，1小时后，设14：00-15：00L1路段的天气预报如表3：
Figure PCTCN2016076697-appb-000009
表3
那么可从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速v0’。本领域技术人员应能理解上述得到车辆在该路段的剩余部分的预估车速的描述仅为举例，其他现有的或今后可能出现的得到车辆在该路段的剩余部分的预估车速的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
本申请的用于物流监测的设备的一优选的实施例中，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括如下其中一项：
从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速作为匹配到的该路段最相近的平均标准车速；
从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速作为匹配到的该路段最相近的平均标准车速；
从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。通过三项中的其中任一项可以得到较精确的该路段最相近的平均标准车速。本领域技术人员应能理解上述匹配与路段最相近的平均标准车速的描述仅为举例，其他现有的或今后可能出现的匹配与路段最相近的平均标准车速的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
本申请的用于物流监测的设备的一优选的实施例中，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括：
从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速，若有，将该车速作为匹配到的该路段最相近的平均标准车速；
若无，从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速，若有，将该车速作为匹配到的该路段最相近的平均标准车速；
若无，从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。在此，上述三项得到该路段最相近的平均标准车速的精确性依次降低，所以在适用于前一项的前提下，会优先适用前一项，而不会适用后一项，从而得到较精确的该路段最相近的平均标准车速。本领域技术人员应能理解上述匹配与路段最相近的平均标准车速的描述仅为举例，其他现有的或今后可能出现的匹配与路段最相近的平均标准车速的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
例如，估算该路段的剩余部分的预估车速v0’具体可通过如下过程实现：
步骤一：从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速作为匹配到的该路段最相近的平均标准车速，例如，从历史气象数据与车速的关系中查找到即同一路段在同一时段(14：00-15：00)最近一次出现相同天气状况时，同类型车辆的平均行驶速度为v’即匹配到的该路段最相近的平均标准车速，计算车辆的速度系数r，r＝该车辆在路段的已经行驶部分L的实时车速的平均车速/同类型车辆在路段的已行驶部分L的平均速度，其中，该车辆在路段的已经行驶部分L的实时车速的平均车速可以根据GPS数据得到，同类型车辆在路段已行驶路段L的平均速度可从历史气象数据与车速的关系中匹配得到，在此加上速度系数r的计算，可使后续得到的车辆在该路段的剩余部分的预估车速更加精确，则根据匹配到的与该路段最相近的平均标准车速可以得到车辆在该路段的剩余部分的预估车速v0’＝rv’；另外，如果同一时段未出现过相同天气状况，执行步骤二；
步骤二：从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速作为匹配到的该路段最相近的平均标准车速，即同一路段在相似时段最近一次出现相同天气状况时，同类型车辆的平均行驶速度为v’即匹配到的该路段最相近的平均标准车速，则根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速v0’＝rρv’，这里ρ＝同一时段同一路段的正常天气的平均车速/相似时段同一路段的正常天气的平均车速，在此，相似时段的定义可以是：取最近一段时间如一周的数据，比较正常天气下两个时段的平均车速(归一化处理后)的差值，差值<0.1的是相似时段。
例如，设置有24个时段(时段1(0：00-1：00)，时段2(1：00-2：00)，…，时段24(23：00-24：00))在正常天气下最近一周的平均车速如图9所示， 对车速进行归一化处理，即：归一化后车速＝(车速-最小车速)/(最大车速-最小车速)，得到如图10所示的归一化后车速，图10中，时段15(14：00-15：00)的相似时段有时段8，9，10，12，16，如果这些时段均未出现过相同天气状况，那么，就执行步骤三。如果其中一个时段，例如时段8最近出现过相同天气状况。那么车辆在该路段的剩余部分的预估车速v0’＝r*时段8出现相同天气状况时的车速*(时段15正常天气的平均车速/时段8正常天气的平均车速)。
步骤三：从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。路段相似性的计算方法可以是，取最近一段时间如一周相同时段及相似时段的同一车型数据，比较当前路段和其它每个路段在各种天气下的平均速度，定义两个路段的相似系数＝两路段速度差的绝对值/两路段速度之和，再按不同天气取均值，例如，如表4：
天气 路段01 路段02
正常 75 65
小到中雨 64 57
…    
表4
那么这两个路段的相似系数＝(|75-65|/(75+65)+|64-57|/(64+57)…)/天气种类数量，最后取两个路段的相似系数最小的对应路段即为匹配到的相似路段，后续即可根据匹配到的相似路段得到车辆在该路段的剩余部分的预估车速。
如图18所示，本申请的用于物流监测的设备的一优选的实施例中，所述第二装置102，还包括：
第二二模块1022，用于根据当车辆的当前路段的车辆流量、剩余部分的预估车速获取该路段的拥堵持续时间，从而获取到更精确物流状况信息。在此，在获取该路段的拥堵时间时，除了将当前路段的车辆流量、剩 余部分的预估车速作为考虑因素外，还可以考虑当前路段的前后相邻路段的车辆流量和预估车速，从而得到更精确的当前路段的拥堵持续时间。本领域技术人员应能理解上述获取该路段的拥堵持续时间的描述仅为举例，其他现有的或今后可能出现的获取该路段的拥堵持续时间的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
如图19所示，本申请的用于物流监测的设备的一优选的实施例中，所述第二装置102，还包括：
第二三模块1023，用于当拥堵持续时间大于预设阈值时，对经过该路段的车辆的行经路线进行调整，在此，当某路段可能出现长期延误时，可以对该路段的车辆的行经路线进行重新规划调整，以提高物流运输效率。
如图20所示，本申请的用于物流监测的设备的一优选的实施例中，所述第一装置101还包括：
第一三模块1013，用于根据车辆在该路段的剩余部分的预估车速获取车辆的行经路线的后续各路段的预估车速；
所述第二装置102还包括：
第二四模块1024，用于根据车辆在该路段的剩余部分和后续各路段的预估车速预测车辆在各路段的行驶时间；
第二五模块1025，用于根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间，从而获取更为精准的车辆轨迹预估。在此，所述节点可以车辆行驶的终点之前的任一节点，也可以是所述终点。本领域技术人员应能理解上述预测车辆到达该节点的时间的描述仅为举例，其他现有的或今后可能出现的预测车辆到达该节点的时间的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
如图21所示，本申请的用于物流监测的设备的一优选的实施例中，所述第二装置102还包括：
第二六模块1026，用于建立车辆与运单的对应关系。在此，运单是物流公司开具的货物托运单据。一个运单ID对应物流公司承运的一个或一组货物(包裹)。物流公司根据各网点或中转站回流的信息，监控运单的流转情况。运单监控的主要作用是：可以让消费者根据运单ID查询运单跟踪记录和预计送达时间。例如，表5所示的某物流公司的运单查询结果。物流公司可以根据运单监测和预测结果，调整物流计划，例如改变车辆行驶路线，给物流网点增加人手，等等。根据车辆GPS信息能够确定运单所在的路段。
运单ID：*******，发货地：北京市海淀区****路***号，收货地：广州市番禺区***路***号
Figure PCTCN2016076697-appb-000010
表5
本申请的用于物流监测的设备的一优选的实施例中，所述运单包括已经产生的运单和根据销量预测结果得到预测运单，从而可以通过与运单对应的车辆的运行状态监测已经存在运单和预测到的运单的处理状况。
本申请的用于物流监测的设备的一优选的实施例中，所述第二五模块1025，用于根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间。在此，各个节点的运单处理速度包括发货节奏和处理速度，具体可根据运单流转信息整理出。有的物流节点处理量不大，按时段发货(物流班车)；有的物流节点处理量大，只要凑够一整车 就随时发货，这时候运单在中转站的处理时间就与运单量和处理速度密切相关。相应的，根据车辆到达节点的时间可以精确得到对应的运单到达该节点的时间。本领域技术人员应能理解上述预测车辆到达当前节点的时间的描述仅为举例，其他现有的或今后可能出现的预测车辆到达当前节点的时间如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
如图22所示，本申请的用于物流监测的设备的一优选的实施例中，所述第二装置102还包括：
第二七模块1027，用于根据车辆到达节点的时间预测该节点未来各时间段需要处理的运单数量。在此，可综合各线路情况，预测各物流节点未来将处理的单量，以便及时根据运单数量增加人手，避免爆仓。本领域技术人员应能理解上述未来各时间段需要处理的运单数量的描述仅为举例，其他现有的或今后可能出现的未来各时间段需要处理的运单数量的描述如可适用于本申请，也应包含在本申请保护范围以内，并在此以引用方式包含于此。
综上所述，本申请根据路段的气象和车速数据得到车辆在该路段的预估车速，然后根据所述预估车速获取物流监测数据，能够将气象信息结构化至物流监控可使用的形式，将气象因素和路段信息结合，获取精确的物流监测和预测数据。由于长途物流的时间和空间跨度大，气象因素对长途物流的影响较大，本申请结合尤其适用于城市间的长途物流的监控。
进一步的，本申请中所述路段的气象和车速数据包括：路段的实时气象数据、车辆的实时车速和该路段的历史气象数据与车速的关系，其中，所述路段的历史气象数据与车速的关系包括按路段、时段的历史气象数据下各车型的平均标准车速，可以实现后续得到精确的物流监测数据。
进一步的，本申请根据车辆在当前路段的车流量、实时车速与对应的平均标准车速判断当前路段是否拥堵，一方面可以精确监测当前路段的拥 堵情况，另一方面可为后续进一步的物流监测提供分析基础。
进一步的，本申请将路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速，或者从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速，可以分不同情况得到精确的车辆所在路段的剩余部分的预估车速，从而为后续精确地获取物流监测数据提供数据基础。
进一步的，本申请中当判断为当前路段为拥堵时，根据当车辆的当前路段的车辆流量、剩余部分的预估车速获取该路段的拥堵持续时间，从而获取到更精确物流状况信息；另外，当拥堵持续时间大于预设阈值时，对经过该路段的车辆的行经路线进行调整，以提高物流运输效率。
进一步的，本申请根据车辆在该路段的剩余部分和后续各路段的预估车速预测车辆在各路段的行驶时间，并根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间，从而获取更为精准的车辆轨迹预估。
进一步的，本申请通过建立车辆与运单的对应关系，可以监控运单的流转情况，另外，根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间，从而预测得到更精确的车辆到达当前节点的时间，此外，根据车辆到达节点的时间预测该节点未来各时间段需要处理的运单数量，以便及时根据运单数量增加人手，避免爆仓。
显然，本领域的技术人员可以对本申请进行各种改动和变型而不脱离本申请的精神和范围。这样，倘若本申请的这些修改和变型属于本申请权利要求及其等同技术的范围之内，则本申请也意图包含这些改动和变型在内。
需要注意的是，本申请可在软件和/或软件与硬件的组合体中被实施，例如，可采用专用集成电路(ASIC)、通用目的计算机或任何其他类似硬件设备来实现。在一个实施例中，本申请的软件程序可以通过处理器执行 以实现上文所述步骤或功能。同样地，本申请的软件程序(包括相关的数据结构)可以被存储到计算机可读记录介质中，例如，RAM存储器，磁或光驱动器或软磁盘及类似设备。另外，本申请的一些步骤或功能可采用硬件来实现，例如，作为与处理器配合从而执行各个步骤或功能的电路。
另外，本申请的一部分可被应用为计算机程序产品，例如计算机程序指令，当其被计算机执行时，通过该计算机的操作，可以调用或提供根据本申请的方法和/或技术方案。而调用本申请的方法的程序指令，可能被存储在固定的或可移动的记录介质中，和/或通过广播或其他信号承载媒体中的数据流而被传输，和/或被存储在根据所述程序指令运行的计算机设备的工作存储器中。在此，根据本申请的一个实施例包括一个装置，该装置包括用于存储计算机程序指令的存储器和用于执行程序指令的处理器，其中，当该计算机程序指令被该处理器执行时，触发该装置运行基于前述根据本申请的多个实施例的方法和/或技术方案。
对于本领域技术人员而言，显然本申请不限于上述示范性实施例的细节，而且在不背离本申请的精神或基本特征的情况下，能够以其他的具体形式实现本申请。因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本申请的范围由所附权利要求而不是上述说明限定，因此旨在将落在权利要求的等同要件的含义和范围内的所有变化涵括在本申请内。不应将权利要求中的任何附图标记视为限制所涉及的权利要求。此外，显然“包括”一词不排除其他单元或步骤，单数不排除复数。装置权利要求中陈述的多个单元或装置也可以由一个单元或装置通过软件或者硬件来实现。第一，第二等词语用来表示名称，而并不表示任何特定的顺序。

Claims (28)

  1. 一种物流监测方法，其中，包括：
    根据路段的气象和车速数据得到车辆在该路段的预估车速；
    根据所述预估车速获取物流监测数据。
  2. 如权利要求1所述的方法，其中，所述路段的气象和车速数据包括：路段的实时气象数据、车辆的实时车速和该路段的历史气象数据与车速的关系。
  3. 如权利要求2所述的方法，其中，所述路段的历史气象数据与车速的关系包括按路段、时段的历史气象数据下各车型的平均标准车速。
  4. 如权利要求3所述的方法，其中，根据路段的气象和车速数据得到车辆在该路段的预估车速，包括：
    根据车辆在当前路段的气象数据和按路段、时段的历史气象数据下各车型的平均标准车速，得到当前路段、时段的历史气象数据下与车辆的车型对应的平均标准车速；
    根据所述预估车速获取物流监测数据包括：
    根据车辆在当前路段的车流量、实时车速与对应的平均标准车速判断当前路段是否拥堵。
  5. 如权利要求4所述的方法，其中，得到当前路段、时段的历史气象数据下与车辆的车型对应的平均标准车速之后，还包括：
    判断以车辆在当前路段已经行驶部分的实时车速的平均车速行驶当前路段的剩余部分的时间是否小于预设阈值，
    若是，将该路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速；
    若否，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速。
  6. 如权利要求5所述的方法，其中，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括如下其中一项：
    从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速作为匹配到的该路段最相近的平均标准车速；
    从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速作为匹配到的该路段最相近的平均标准车速；
    从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。
  7. 如权利要求5所述的方法，其中，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括：
    从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速，若有，将该车速作为匹配到的该路段最相近的平均标准车速；
    若无，从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速，若有，将该车速作为匹配到的该路段最相近的平均标准车速；
    若无，从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。
  8. 如权利要求5至7任一项所述的方法，其中，判断当前路段是否拥堵之后，还包括：
    根据当车辆的当前路段的车辆流量、剩余部分的预估车速获取该路段的拥堵持续时间。
  9. 如权利要求8所述的方法，其中，获取该路段的拥堵持续时间之后还包括：
    当拥堵持续时间大于预设阈值时，对经过该路段的车辆的行经路线进行调整。
  10. 如权利要求5至9任一项所述的方法，其中，将该路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速之后， 或根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速之后，还包括：
    根据车辆在该路段的剩余部分的预估车速获取车辆的行经路线的后续各路段的预估车速；
    根据所述预估车速获取物流监测数据，包括：
    根据车辆在该路段的剩余部分和后续各路段的预估车速预测车辆在各路段的行驶时间；
    根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间。
  11. 如权利要求10所述的方法，其中，根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间之前，还包括：
    建立车辆与运单的对应关系。
  12. 如权利要求11所述的方法，其中，所述运单包括已经产生的运单和根据销量预测结果得到预测运单。
  13. 如权利要求11或12所述的方法，其中，根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间，包括：
    根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间。
  14. 如权利要求13所述的方法，其中，根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间之后，还包括：
    根据车辆到达节点的时间预测该节点未来各时间段需要处理的运单数量。
  15. 一种用于物流监测的设备，其中，包括：
    第一装置，用于根据路段的气象和车速数据得到车辆在该路段的预估车速；
    第二装置，用于根据所述预估车速获取物流监测数据。
  16. 如权利要求15所述的设备，其中，所述路段的气象和车速数据包括：路段的实时气象数据、车辆的实时车速和该路段的历史气象数据与车速的关系。
  17. 如权利要求16所述的设备，其中，所述路段的历史气象数据与车速的关系包括按路段、时段的历史气象数据下各车型的平均标准车速。
  18. 如权利要求17所述的设备，其中，所述第一装置，包括：
    第一一模块，用于根据车辆在当前路段的气象数据和按路段、时段的历史气象数据下各车型的平均标准车速，得到当前路段、时段的历史气象数据下与车辆的车型对应的平均标准车速；
    所述第二装置，包括：
    第二一模块，用于根据车辆在当前路段的车流量、实时车速与对应的平均标准车速判断当前路段是否拥堵。
  19. 如权利要求18所述的设备，其中，所述第一装置还包括第一二模块，用于判断以车辆在当前路段已经行驶部分的实时车速的平均车速行驶当前路段的剩余部分的时间是否小于预设阈值，若是，将该路段已经行驶部分实时车速的平均车速作为车辆在该路段的剩余部分的预估车速；若否，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，根据匹配到的与该路段最相近的平均标准车速得到车辆在该路段的剩余部分的预估车速。
  20. 如权利要求19所述的设备，其中，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括如下其中一项：
    从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速作为匹配到的该路段最相近的平均标准车速；
    从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速作为匹配到的该路段最相近的平均标准车速；
    从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的 该路段最相近的平均标准车速。
  21. 如权利要求19所述的设备，其中，从历史气象数据与车速的关系中匹配与该路段最相近的平均标准车速，包括：
    从历史气象数据与车速的关系中查找相同天气同一路段同一时段的车速，若有，将该车速作为匹配到的该路段最相近的平均标准车速；
    若无，从历史气象数据与车速的关系中查找相同天气同一路段相似时段的车速，若有，将该车速作为匹配到的该路段最相近的平均标准车速；
    若无，从历史气象数据与车速的关系中查找相似路段的车速作为匹配到的该路段最相近的平均标准车速。
  22. 如权利要求19至21任一项所述的设备，其中，所述第二装置，还包括：
    第二二模块，用于根据当车辆的当前路段的车辆流量、剩余部分的预估车速获取该路段的拥堵持续时间。
  23. 如权利要求22所述的设备，其中，所述第二装置，还包括：
    第二三模块，用于当拥堵持续时间大于预设阈值时，对经过该路段的车辆的行经路线进行调整。
  24. 如权利要求19至23任一项所述的设备，其中，所述第一装置还包括：
    第一三模块，用于根据车辆在该路段的剩余部分的预估车速获取车辆的行经路线的后续各路段的预估车速；
    所述第二装置还包括：
    第二四模块，用于根据车辆在该路段的剩余部分和后续各路段的预估车速预测车辆在各路段的行驶时间；
    第二五模块，用于根据一节点之前的各路段的行驶时间预测车辆到达该节点的时间。
  25. 如权利要求24所述的设备，其中，所述第二装置还包括：
    第二六模块，用于建立车辆与运单的对应关系。
  26. 如权利要求25所述的设备，其中，所述运单包括已经产生的运单和根据销量预测结果得到预测运单。
  27. 如权利要求25或26所述的设备，其中，所述第二五模块，用于根据前一节点的运单处理速度和当前节点之前的各路段的行驶时间预测车辆到达当前节点的时间。
  28. 如权利要求27所述的设备，其中，所述第二装置还包括：
    第二七模块，用于根据车辆到达节点的时间预测该节点未来各时间段需要处理的运单数量。
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