CN116882873A - Method and system for determining industry collaboration time based on data processing - Google Patents
Method and system for determining industry collaboration time based on data processing Download PDFInfo
- Publication number
- CN116882873A CN116882873A CN202311147158.7A CN202311147158A CN116882873A CN 116882873 A CN116882873 A CN 116882873A CN 202311147158 A CN202311147158 A CN 202311147158A CN 116882873 A CN116882873 A CN 116882873A
- Authority
- CN
- China
- Prior art keywords
- freight car
- nodes
- neural network
- node
- place
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012545 processing Methods 0.000 title claims abstract description 91
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012544 monitoring process Methods 0.000 claims abstract description 77
- 238000003062 neural network model Methods 0.000 claims abstract description 43
- 238000012546 transfer Methods 0.000 claims description 31
- 238000013527 convolutional neural network Methods 0.000 claims description 12
- 239000004576 sand Substances 0.000 claims description 5
- SAZUGELZHZOXHB-UHFFFAOYSA-N acecarbromal Chemical compound CCC(Br)(CC)C(=O)NC(=O)NC(C)=O SAZUGELZHZOXHB-UHFFFAOYSA-N 0.000 claims description 4
- 230000008569 process Effects 0.000 description 7
- 238000013528 artificial neural network Methods 0.000 description 6
- 238000012549 training Methods 0.000 description 4
- 238000000547 structure data Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004424 eye movement Effects 0.000 description 2
- 230000008921 facial expression Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention provides a method and a system for determining industry collaboration time based on data processing, and relates to the technical field of data processing, wherein the method comprises the steps of determining weather state, road congestion degree and average running speed of a freight car by using an external video processing model based on external monitoring video of the freight car; determining the fatigue degree of a driver by using a cab video processing model based on the monitoring video of the freight car cab; constructing two nodes and an edge between the two nodes; the method comprises the steps of processing two nodes and one side between the two nodes based on a graph neural network model to obtain the transportation time of the freight car from a departure place to a transit place, wherein the input of the graph neural network model is one side between the two nodes, the output of the graph neural network model is the transportation time of the freight car from the departure place to the transit place, and the transportation time of an express company is accurately determined.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a system for determining industrial collaboration time based on data processing.
Background
Currently, with rapid development of information technology, industry collaboration has become one of the important means for competitive power and cost reduction of express enterprises. Industry collaboration refers to the process of achieving a common goal between different enterprises or organizations through collaboration, coordination, and sharing of resources. For example, the express company a may establish a long-term cooperative relationship with the express company B, and after the express company a delivers the goods to a place, the express company B resumes the transportation of the goods. However, since the transportation mode, the transportation personnel and the transportation cooperation mode between different express companies change in real time, and the transportation of the next company also depends on the transportation of the last company, and the transportation data between different companies are often not mutually disclosed, the next company also has difficulty in determining the accurate arrival time of the goods of the last company, which results in that the next company cannot be ready ahead of time, and thus the efficiency of the collaborative transportation of the goods is reduced.
Therefore, how to accurately determine the transportation time of the express company is a current urgent problem to be solved.
Disclosure of Invention
The invention mainly solves the technical problem of accurately determining the transportation time of the express company.
According to a first aspect, the present invention provides a method for determining industry collaboration time based on data processing, including: acquiring an external monitoring video of a freight car, a monitoring video of a freight car cab and a freight car warehouse monitoring image of the freight car, wherein the freight car starts from a departure place to a transit place; determining weather conditions, road congestion degrees and average running speeds of the freight vehicles by using an external video processing model based on the external monitoring video of the freight vehicles; determining the fatigue degree of a driver by using a cab video processing model based on the monitoring video of the freight car cab; constructing two nodes and an edge between the two nodes, wherein the two nodes comprise a departure place node and a transfer place node, each node of the two nodes comprises a plurality of node characteristics, the node characteristics of the departure place node comprise the geographic position of the departure place and a cargo warehouse monitoring image of the freight car, the edge characteristics of the edge comprise the weather state, the road crowding degree, the average running speed of the freight car and the road information from the departure place to the transfer place, and the node characteristics of the transfer place node comprise the geographic position of the transfer place; and processing the two nodes and one side between the two nodes based on a graph neural network model to obtain the transportation time of the freight car from the departure place to the transit place, wherein the input of the graph neural network model is one side between the two nodes, and the output of the graph neural network model is the transportation time of the freight car from the departure place to the transit place.
Still further, the method further comprises: and processing the cargo warehouse monitoring image of the freight car based on a convolutional neural network model to determine a transfer storage space, wherein the input of the convolutional neural network model is the cargo warehouse monitoring image of the freight car, and the output of the convolutional neural network model is the transfer storage space.
Still further, the weather conditions include sunny days, cloudy days, light rain, medium rain, heavy rain, thunder gust, hail, gust, small snow, medium snow, large snow, riot snow, haze, sand, tornado, typhoon.
Furthermore, the external video processing model is a long-short period neural network model, the input of the external video processing model is an external monitoring video of the freight car, and the output of the external video processing model is weather state, road congestion degree and average running speed of the freight car.
Furthermore, the cab video processing model is a long-short period neural network model, the input of the cab video processing model is a monitoring video of the truck cab, and the output of the cab video processing model is the fatigue degree of the driver.
According to a second aspect, the present invention provides a system for determining industry collaboration time based on data processing, comprising: the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring an external monitoring video of a freight car, a monitoring video of a freight car cab and a freight car warehouse monitoring image of the freight car, wherein the freight car starts from a departure place to a transit place; the external video processing module is used for determining weather states, road crowding degrees and average running speeds of the freight vehicles by using an external video processing model based on external monitoring videos of the freight vehicles; the fatigue degree determining module is used for determining the fatigue degree of the driver by using a cab video processing model based on the monitoring video of the freight car cab; a building module, configured to build two nodes and one edge between the two nodes, where the two nodes include a departure node and a destination node, each of the two nodes includes a plurality of node features, the node features of the departure node include a geographic location of the departure node and a cargo space monitoring image of the truck, the edge feature of the one edge includes the weather state, the road congestion degree, the average running speed of the truck, and road information from the departure node to the destination node, and the node features of the destination node include a geographic location of the destination node; and the transportation time determining module is used for processing the two nodes and one edge between the two nodes based on a graph neural network model to obtain the transportation time of the freight car from the departure place to the transit place, wherein the input of the graph neural network model is the two nodes and one edge between the two nodes, and the output of the graph neural network model is the transportation time of the freight car from the departure place to the transit place.
Still further, the system is further configured to: and processing the cargo warehouse monitoring image of the freight car based on a convolutional neural network model to determine a transfer storage space, wherein the input of the convolutional neural network model is the cargo warehouse monitoring image of the freight car, and the output of the convolutional neural network model is the transfer storage space.
Still further, the weather conditions include sunny days, cloudy days, light rain, medium rain, heavy rain, thunder gust, hail, gust, small snow, medium snow, large snow, riot snow, haze, sand, tornado, typhoon.
Furthermore, the external video processing model is a long-short period neural network model, the input of the external video processing model is an external monitoring video of the freight car, and the output of the external video processing model is weather state, road congestion degree and average running speed of the freight car.
Furthermore, the cab video processing model is a long-short period neural network model, the input of the cab video processing model is a monitoring video of the truck cab, and the output of the cab video processing model is the fatigue degree of the driver.
The invention provides a method and a system for determining industry collaboration time based on data processing, wherein the method comprises the steps of obtaining an external monitoring video of a freight car, a monitoring video of a freight car cab and a freight car warehouse monitoring image of the freight car, wherein the freight car starts from a departure place to a transit place; determining weather conditions, road congestion degrees and average running speeds of the freight vehicles by using an external video processing model based on the external monitoring video of the freight vehicles; determining the fatigue degree of a driver by using a cab video processing model based on the monitoring video of the freight car cab; constructing two nodes and an edge between the two nodes, wherein the two nodes comprise a departure place node and a transfer place node, each node of the two nodes comprises a plurality of node characteristics, the node characteristics of the departure place node comprise the geographic position of the departure place and a cargo warehouse monitoring image of the freight car, the edge characteristics of the edge comprise the weather state, the road crowding degree, the average running speed of the freight car and the road information from the departure place to the transfer place, and the node characteristics of the transfer place node comprise the geographic position of the transfer place; the method comprises the steps of processing two nodes and one side between the two nodes based on a graph neural network model to obtain the transportation time of the freight car from a departure place to a transit place, wherein the input of the graph neural network model is one side between the two nodes, and the output of the graph neural network model is the transportation time of the freight car from the departure place to the transit place.
Drawings
FIG. 1 is a flow chart of a method for determining industrial collaboration time based on data processing according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a system for determining industrial collaboration time based on data processing according to an embodiment of the present invention.
Detailed Description
In an embodiment of the present invention, a method for determining an industry collaboration time based on data processing is provided as shown in fig. 1, where the method for determining an industry collaboration time based on data processing includes steps S1 to S5:
step S1, acquiring an external monitoring video of a freight car, a monitoring video of a freight car cab and a freight car warehouse monitoring image of the freight car, wherein the freight car starts from a departure place to a transit place.
The external monitoring video of the freight car refers to video shot by a camera installed outside the freight car. The external monitoring video of the freight car can display various information such as traffic conditions, weather conditions, average running speed of the freight car and the like. As an example, a truck is traveling on a highway, and a camera records conditions of the road, such as traffic flow, changes in the environment outside the truck, the speed at which the truck is traveling, the smoothness of the road, whether an obstacle is present, etc. As an example, the period of the external surveillance video of the truck may be one minute, ten minutes, half an hour, one hour, two hours, five hours, or the like after the truck is driven out from the departure place to the transit place.
The monitoring video of the freight car cab refers to video shot by a camera installed inside the freight car cab. The surveillance video of the freight car cab may be used to monitor the behavior and status of the driver. As an example, a camera in the truck cab captures information of the facial expression, eye movement, hand movements, etc. of the driver, and the information in the monitoring video of the truck cab can be used to determine the fatigue level of the driver. As an example, the time period of the surveillance video of the truck cab may be one minute, ten minutes, half an hour, one hour, two hours, five hours, or the like after the truck is driven out from the departure place to the transit place.
The cargo warehouse monitoring image of the freight car refers to an image photographed by a camera installed inside the cargo warehouse of the freight car. The cargo warehouse monitoring image of the freight car can provide information on the state, the quantity and whether the freight is safely stored or not.
In some embodiments, multiple cameras may be provided at different locations on the truck, such as outside the truck, in the cab, and in the cargo hold. The cameras may capture different video and image data. By installing and configuring proper monitoring equipment, external monitoring video of the freight car, monitoring video of a cab and monitoring images of a warehouse can be obtained. At the same time, these image and video data can also be used for processing and analysis in subsequent steps to extract relevant features and to determine the transit time of the truck from the departure location to the transit location.
And step S2, determining weather states, road congestion degree and average running speed of the freight car by using an external video processing model based on the external monitoring video of the freight car.
The external video processing model is a long-short-period neural network model, the input of the external video processing model is an external monitoring video of the freight car, and the output of the external video processing model is weather state, road congestion degree and average running speed of the freight car.
The external video processing model is a long-short-term neural network model. The Long-Short Term neural network model includes a Long-Short Term neural network (LSTM). The long-term and short-term neural network model can process sequence data with any length, capture sequence information and output results based on the association relationship of front data and rear data in the sequence. The external video processing model comprehensively considers the external monitoring videos of the freight car at each time point, and finally determines weather conditions, road congestion degree and average running speed of the freight car. The external video processing model can be obtained by training the training sample through a gradient descent method.
The weather conditions include sunny days, cloudy days, overcast days, light rain, medium rain, heavy rain, thunder gust, hail, snow gust, small snow, medium snow, large snow, snow storm, haze, sand lifting, tornado, typhoon.
The degree of congestion may be a number between 0 and 1, with a larger number indicating that the road is more congested.
The average running speed of the freight car is the average running speed of the freight car in the time period of the monitoring video, which is obtained by processing the external monitoring video of the freight car by the external video processing model and outputting the processed external monitoring video.
And step S3, determining the fatigue degree of the driver by using a cab video processing model based on the monitoring video of the cab of the freight car.
The cab video processing model is a long-short-period neural network model, the input of the cab video processing model is a monitoring video of the truck cab, and the output of the cab video processing model is the fatigue degree of the driver.
The cab video processing model judges the fatigue degree of the driver by analyzing continuous behaviors of the driver in the monitoring video, such as facial expression recognition, eye movement detection and head posture estimation.
The driver's fatigue is a number between 0 and 1, and the greater the number, the more fatigue the driver is.
And S4, constructing two nodes and an edge between the two nodes, wherein the two nodes comprise a departure place node and a transfer place node, each node of the two nodes comprises a plurality of node characteristics, the node characteristics of the departure place node comprise the geographical position of the departure place and the warehouse monitoring image of the freight car, the edge characteristics of the edge comprise the weather state, the road congestion degree, the average running speed of the freight car and the road information from the departure place to the transfer place, and the node characteristics of the transfer place node comprise the geographical position of the transfer place.
Feature information can be added to each node by constructing a departure node and a transfer node and an edge between the departure node and the transfer node, and the transportation process of the freight car is abstracted into a graph structure so as to determine the accurate transportation time of the freight car from the departure node to the transfer node. The graph structure is a data structure consisting of nodes and edges.
The geographical position of the departure place refers to the specific longitude and latitude coordinates of the departure place of the freight car.
The geographic location of the transfer location refers to the specific latitude and longitude coordinates of the truck transfer location.
An edge between the departure node and the transit node represents a connection line between the departure node and the transit node, and an edge between the departure node and the transit node may represent information during transportation. The side characteristics of the side include the weather state, the road congestion degree, the average running speed of the freight car, and road information from the departure place to the transit place.
In some embodiments, the road information from the departure point to the transit point includes distance, road type, road condition, road speed limit, etc.
Distance: the distance from the departure point to the transit point is expressed, typically in kilometers or meters. The distance can be used to evaluate the length and duration of the entire stroke.
Road type: refers to the kind or class of road connecting the departure place and the transit place, such as expressway, general road, rural road, etc. Different road types may have an impact on the speed of travel and traffic conditions.
Road conditions: traffic conditions on the road from the departure point to the transit destination are described, including conditions of smoothness, congestion, road maintenance, etc.
Road speed limit: and the legal speed limit value of the road along the way from the departure place to the transit place is pointed out.
Weather conditions: weather conditions have a direct impact on the transit time of a truck. Bad weather conditions may cause visibility to be reduced, road surface to be slippery, etc., thereby affecting traveling speed and safety. For example, rainy weather may slow down the vehicle travel speed, thus increasing the transportation time.
In the constructed graph structure, the cargo space monitoring image, weather state, road congestion degree, average running speed and road information of the departure place to the transfer place of the freight car can influence the transportation time of the freight car from the departure place to the transfer place.
Degree of road congestion: the degree of road congestion has a significant impact on the transit time. A highly congested situation can cause the truck to spend more time on the road. If the road is congested, the truck may need to wait for traffic to be dredged or to choose a detour path, which increases the overall transit time.
Average speed of travel of freight car: the average speed of travel of the truck directly affects the transit time. If the average speed of travel of the truck is high, the time from the departure point to the transit point is correspondingly shortened. Conversely, if the travel speed is slow, the transportation time will be prolonged.
Road information of departure place to transit place: the road information from the departure place to the transit place can provide information about road types, road conditions, etc., thereby affecting the transportation time. For example, if the road from the departure point to the transit point is an expressway, the truck may travel at a faster speed and reach the transit point faster than an ordinary road or a rural road.
The cargo space monitoring image of the truck may also affect the transportation time of the truck, for example, if the cargo space monitoring image of the truck shows that the truck is fully loaded, the running speed of the truck needs to be slow to smoothly deliver the cargo, whereas if the cargo space monitoring image of the truck shows that the truck has less cargo, the running speed of the truck may be fast to reduce the transportation time.
And S5, processing the two nodes and one side between the two nodes based on a graph neural network model to obtain the transportation time of the freight car from the departure place to the transit place, wherein the input of the graph neural network model is the two nodes and one side between the two nodes, and the output of the graph neural network model is the transportation time of the freight car from the departure place to the transit place.
The graphic neural network model comprises a graphic neural network (Graph Neural Network, GNN) and a full connection layer, wherein the graphic neural network is a neural network directly acting on graphic structure data, and the graphic structure data is a data structure consisting of nodes and edges.
The input of the graphic neural network model is the two nodes and one side between the two nodes, and the output of the graphic neural network model is the transportation time of the freight car from the departure place to the transit place. The graph neural network model can be obtained by training marked graph structure data in historical data.
The graph neural network model receives two nodes and one edge as input, and processes the two nodes and one edge to determine the transportation time of the truck from the departure place to the transit place.
The graph neural network model can learn the characteristics of nodes and edges, and capture potential rules and modes of transportation time from a departure place to a transit place from input data through the calculation and optimization process of the neural network. The model processes and extracts features from the input nodes and edges and then generates output related to transit time. Since the model obtains the prediction capability of the transportation time through training, the transportation time of the cargo vehicle from the departure place to the transit place can be predicted through the input node and the side information.
Based on the same inventive concept, fig. 2 is a schematic diagram of a system for determining an industrial collaboration time based on data processing according to an embodiment of the present invention, where the system for determining an industrial collaboration time based on data processing includes:
an acquisition module 21, configured to acquire an external monitoring video of a freight car, a monitoring video of a freight car cab, and a freight car warehouse monitoring image of the freight car, where the freight car starts from a departure place to a transit place;
an external video processing module 22, configured to determine weather status, road congestion level, average running speed of the freight car using an external video processing model based on an external monitoring video of the freight car;
a fatigue determination module 23 for determining the fatigue of the driver using a cab video processing model based on the surveillance video of the freight car cab;
a building module 24, configured to build two nodes and an edge between the two nodes, where the two nodes include a departure node and a destination node, each of the two nodes includes a plurality of node features, the node features of the departure node include a geographic location of the departure node and a cargo space monitoring image of the truck, the edge feature of the edge includes the weather status, the road congestion level, the average travel speed of the truck, and road information from the departure node to the destination node, and the node features of the destination node include a geographic location of the destination node;
and the transportation time determining module 25 is configured to process the two nodes and one edge between the two nodes based on a graph neural network model, where an input of the graph neural network model is the transportation time of the freight car from the departure place to the transit place, and an output of the graph neural network model is the transportation time of the freight car from the departure place to the transit place.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The method for determining the industry collaboration time based on data processing is characterized by comprising the following steps:
acquiring an external monitoring video of a freight car, a monitoring video of a freight car cab and a freight car warehouse monitoring image of the freight car, wherein the freight car starts from a departure place to a transit place;
determining weather conditions, road congestion degrees and average running speeds of the freight vehicles by using an external video processing model based on the external monitoring video of the freight vehicles;
determining the fatigue degree of a driver by using a cab video processing model based on the monitoring video of the freight car cab;
constructing two nodes and an edge between the two nodes, wherein the two nodes comprise a departure place node and a transfer place node, each node of the two nodes comprises a plurality of node characteristics, the node characteristics of the departure place node comprise the geographic position of the departure place and a cargo warehouse monitoring image of the freight car, the edge characteristics of the edge comprise the weather state, the road crowding degree, the average running speed of the freight car and the road information from the departure place to the transfer place, and the node characteristics of the transfer place node comprise the geographic position of the transfer place;
and processing the two nodes and one side between the two nodes based on a graph neural network model to obtain the transportation time of the freight car from the departure place to the transit place, wherein the input of the graph neural network model is one side between the two nodes, and the output of the graph neural network model is the transportation time of the freight car from the departure place to the transit place.
2. The method for determining a data processing-based industry collaboration time as specified in claim 1, wherein the method further comprises: and processing the cargo warehouse monitoring image of the freight car based on a convolutional neural network model to determine a transfer storage space, wherein the input of the convolutional neural network model is the cargo warehouse monitoring image of the freight car, and the output of the convolutional neural network model is the transfer storage space.
3. The method of claim 1, wherein the weather conditions include sunny, cloudy, overcast, light, medium, heavy, thunderstorm, hail, snow, light, medium, heavy, snow, haze, sand, tornado, typhoon.
4. The method for determining industrial collaborative time based on data processing according to claim 1, wherein the external video processing model is a long and short term neural network model, the input of the external video processing model is an external monitoring video of the truck, and the output of the external video processing model is weather status, road congestion level, average running speed of the truck.
5. The method for determining industrial collaborative time based on data processing according to claim 1 wherein the cab video processing model is a long and short term neural network model, the input of the cab video processing model is a surveillance video of the truck cab, and the output of the cab video processing model is fatigue of the driver.
6. A determination system of an industry collaboration time of data processing based on the determination method according to any one of claims 1 to 5, characterized by comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring an external monitoring video of a freight car, a monitoring video of a freight car cab and a freight car warehouse monitoring image of the freight car, wherein the freight car starts from a departure place to a transit place;
the external video processing module is used for determining weather states, road crowding degrees and average running speeds of the freight vehicles by using an external video processing model based on external monitoring videos of the freight vehicles;
the fatigue degree determining module is used for determining the fatigue degree of the driver by using a cab video processing model based on the monitoring video of the freight car cab;
a building module, configured to build two nodes and one edge between the two nodes, where the two nodes include a departure node and a destination node, each of the two nodes includes a plurality of node features, the node features of the departure node include a geographic location of the departure node and a cargo space monitoring image of the truck, the edge feature of the one edge includes the weather state, the road congestion degree, the average running speed of the truck, and road information from the departure node to the destination node, and the node features of the destination node include a geographic location of the destination node;
and the transportation time determining module is used for processing the two nodes and one edge between the two nodes based on a graph neural network model to obtain the transportation time of the freight car from the departure place to the transit place, wherein the input of the graph neural network model is the two nodes and one edge between the two nodes, and the output of the graph neural network model is the transportation time of the freight car from the departure place to the transit place.
7. The data processing-based industry collaboration time determination system of claim 6, wherein the system is further configured to: and processing the cargo warehouse monitoring image of the freight car based on a convolutional neural network model to determine a transfer storage space, wherein the input of the convolutional neural network model is the cargo warehouse monitoring image of the freight car, and the output of the convolutional neural network model is the transfer storage space.
8. The data processing based industry collaboration time determination system of claim 6 wherein the weather conditions include sunny, cloudy, overcast, light, medium, heavy, thunderstorm, hail, snow, light, medium, heavy, haze, sand, tornado, typhoon.
9. The data processing-based industry collaboration time determination system of claim 6 wherein the external video processing model is a long and short term neural network model, the input of the external video processing model is an external surveillance video of the truck, and the output of the external video processing model is weather status, road congestion level, average travel speed of the truck.
10. The data processing-based industry collaboration time determination system of claim 6 wherein the cab video processing model is a long and short term neural network model, the input of the cab video processing model is a surveillance video of the truck cab, and the output of the cab video processing model is the fatigue of the driver.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311147158.7A CN116882873A (en) | 2023-09-07 | 2023-09-07 | Method and system for determining industry collaboration time based on data processing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311147158.7A CN116882873A (en) | 2023-09-07 | 2023-09-07 | Method and system for determining industry collaboration time based on data processing |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116882873A true CN116882873A (en) | 2023-10-13 |
Family
ID=88272085
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311147158.7A Pending CN116882873A (en) | 2023-09-07 | 2023-09-07 | Method and system for determining industry collaboration time based on data processing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116882873A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112541638A (en) * | 2020-12-21 | 2021-03-23 | 北京邮电大学 | Method for estimating travel time of vehicle connected with Internet |
| CN112991743A (en) * | 2021-04-22 | 2021-06-18 | 泰瑞数创科技(北京)有限公司 | Real-time traffic risk AI prediction method based on driving path and system thereof |
| WO2021221563A1 (en) * | 2020-04-30 | 2021-11-04 | Grabtaxi Holdings Pte. Ltd. | Method for predicting the destination location of a vehicle |
| CN114445577A (en) * | 2021-12-29 | 2022-05-06 | 武汉中海庭数据技术有限公司 | Method and system for calculating estimated time of arrival based on graph network |
-
2023
- 2023-09-07 CN CN202311147158.7A patent/CN116882873A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021221563A1 (en) * | 2020-04-30 | 2021-11-04 | Grabtaxi Holdings Pte. Ltd. | Method for predicting the destination location of a vehicle |
| CN112541638A (en) * | 2020-12-21 | 2021-03-23 | 北京邮电大学 | Method for estimating travel time of vehicle connected with Internet |
| CN112991743A (en) * | 2021-04-22 | 2021-06-18 | 泰瑞数创科技(北京)有限公司 | Real-time traffic risk AI prediction method based on driving path and system thereof |
| CN114445577A (en) * | 2021-12-29 | 2022-05-06 | 武汉中海庭数据技术有限公司 | Method and system for calculating estimated time of arrival based on graph network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11657708B2 (en) | Large-scale real-time traffic flow prediction method based on fuzzy logic and deep LSTM | |
| CN110376594B (en) | Intelligent navigation method and system based on topological graph | |
| KR102539942B1 (en) | Method and apparatus for training trajectory planning model, electronic device, storage medium and program | |
| CN112380312B (en) | Laser map updating method based on grid detection, terminal and computer equipment | |
| CN103258427A (en) | Urban expressway traffic real-time monitoring system and method based on information physical network | |
| CN109815912B (en) | Highway safety inspection system based on artificial intelligence | |
| CN114489122B (en) | UAV and matching airport-based automatic highway inspection method and system | |
| CN110705452A (en) | Intelligent management and control method and system for facilities of expressway unattended toll station | |
| Kaarthik et al. | Image processing based intelligent parking system | |
| US11645775B1 (en) | Methods and apparatus for depth estimation on a non-flat road with stereo-assisted monocular camera in a vehicle | |
| Pan et al. | A novel vision-based framework for identifying dynamic vehicle loads on long-span bridges: A case study of Jiangyin Bridge, China | |
| Gloudemans et al. | Interstate-24 motion: Closing the loop on smart mobility | |
| CN114299457A (en) | Accumulated water depth detection method and device | |
| CN113808414B (en) | Road load determination method, device and storage medium | |
| Pyykönen et al. | Traffic monitoring and modeling for intersection safety | |
| US20230394694A1 (en) | Methods and apparatus for depth estimation using stereo cameras in a vehicle system | |
| CN116882873A (en) | Method and system for determining industry collaboration time based on data processing | |
| CN111856441A (en) | Train positioning method based on fusion of vision and millimeter wave radar | |
| CN116363088A (en) | Expressway concrete pavement crack detection method based on unmanned aerial vehicle, electronic equipment and storage medium | |
| Marques et al. | An evaluation of machine learning methods for speed-bump detection on a GoPro dataset | |
| Das et al. | Why slammed the brakes on? auto-annotating driving behaviors from adaptive causal modeling | |
| CN114844925A (en) | Unmanned mine universe intelligent monitoring system | |
| CN114492544A (en) | Model training method and device and traffic incident occurrence probability evaluation method and device | |
| KR20220071822A (en) | Identification system and method of illegal parking and stopping vehicle numbers using drone images and artificial intelligence technology | |
| Zeng et al. | Research on recognition technology of vehicle rolling line violation in highway based on visual UAV |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |