CN117314280A

CN117314280A - Method and system for processing capacity data

Info

Publication number: CN117314280A
Application number: CN202311263128.2A
Authority: CN
Inventors: 赵宪泽; 李铁军; 王腾; 樊彬
Original assignee: Cccc Intelligent Transportation Co ltd; China Highway Engineering Consultants Corp; CHECC Data Co Ltd
Current assignee: Cccc Intelligent Transportation Co ltd; China Highway Engineering Consultants Corp; CHECC Data Co Ltd
Priority date: 2023-09-27
Filing date: 2023-09-27
Publication date: 2023-12-29

Abstract

The application discloses a method and a system for processing capacity data. Firstly, establishing a communication connection relation with various data sources, and acquiring various transport capacity original information transmitted by the data sources; then, combining, updating and filling all kinds of original information of the transport capacity, and carrying out associated storage on all kinds of processed original information of the transport capacity according to a service main key; establishing a capacity pool based on the data stored in an associated way, and carrying out information integration analysis on the capacity pool to obtain an analysis result; and finally, packaging the obtained analysis result and the corresponding original data, and providing the packaged analysis result and the corresponding original data for the target object. The invention directly obtains the required complete, accurate and real-time data from the capacity pool dispatching system, reduces the cost and complexity of data processing and improves the overall service efficiency.

Description

Method and system for processing capacity data

Technical Field

The present invention relates to the field of capacity data processing, and in particular, to a capacity data processing method and system.

Background

With the rapid development of the logistics, transportation and sharing economic industries, the management and scheduling demands for transportation resources are increasing. In order to improve transportation efficiency and reduce operation cost, various intelligent transportation scenes need to perform centralized management and scheduling on transportation resources. However, the existing capacity pool management system often has the problems of information fragmentation, limited data processing and service capacity, and the like, which makes the management and scheduling of capacity resources not efficient and cannot meet the increasing market demands.

Existing capacity pool management systems focus mainly on basic management and scheduling of capacity resources, such as vehicle information, driver information, etc. These systems generally collect information such as vehicle position and speed based on vehicle-mounted devices (such as Beidou equipment) and mobile communication equipment (such as smart phones), and perform data aggregation and processing through a cloud server. However, the prior art has limitations in several respects:

(1) Information gathering channel is limited: the prior art mainly relies on an on-board device and a mobile communication device to collect information, and lacks of a docking with a third party service provider, so that information collection is incomplete.

(2) The data processing capacity is insufficient: the prior art is simple to process the collected information, lacks detection and analysis of abnormal conditions, and cannot provide enough insight for a service system to use.

(3) Service function limitation: the service provided by the prior art is mainly limited to basic information inquiry and track planning, and lacks support for advanced functions such as electronic fence, human-vehicle track matching, constant running route analysis and the like after information is highly integrated.

Disclosure of Invention

Based on the above, the embodiments of the present application provide a method and a system for processing capacity data, which specifically implement the processes of capacity data aggregation, integration and service provision, and solve the problems existing in the prior art.

In a first aspect, there is provided a method of handling capacity data, the method comprising:

establishing a communication connection relation with various data sources, and acquiring various transport capacity original information transmitted by the data sources; the data source at least comprises a road transportation electronic license system, a logistics business system, a logistics credit evaluation system, vehicle-mounted equipment and a third party service provider;

combining, updating and complementing all kinds of original information of the transport capacity, and carrying out associated storage on the processed original information of the transport capacity according to a service main key;

establishing a capacity pool based on the data stored in an associated way, and carrying out information integration analysis on the capacity pool to obtain an analysis result; the information integration analysis at least comprises electronic fence analysis, anomaly analysis, man-vehicle track matching analysis and usual running route analysis;

and packaging the obtained analysis result and the corresponding original data, and providing the packaged analysis result and the corresponding original data for the target object.

Optionally, the acquiring the various kinds of capacity original information transmitted by the data source includes:

basic information, credit evaluation, real-time positioning, historical track, multichannel video content and various alarm information of drivers, vehicles and households transmitted by a data source are acquired.

Optionally, when the capacity pool is subjected to electronic fence analysis, the method specifically includes:

defining an electronic fence area in a geographic information system according to service requirements;

judging whether the vehicle and a driver enter or leave the areas or not through the real-time positioning data;

triggering a corresponding event notification when a vehicle or driver enters or leaves the electronic fence area;

wherein the electronic fence area comprises at least a circle, a square and an administrative boundary shape.

Optionally, when the abnormality analysis is performed on the capacity pool, the method specifically includes:

mining abnormal behaviors according to historical data of the vehicle and drivers, and carrying out real-time early warning on the abnormal behaviors to assist related personnel in problem processing; the abnormal behavior at least comprises overspeed, idling, fatigue driving and goods exchange.

Optionally, before mining the abnormal behavior according to the historical data of the vehicle and the driver, the method further comprises:

cleaning and preprocessing the original data; removing abnormal values, filling missing values and normalizing data;

extracting target features, and constructing advanced features according to service requirements and the target features; wherein the target features include speed, acceleration, direction; advanced features include driving duration, average speed, etc.

Optionally, when the man-vehicle track matching analysis is performed on the capacity pool, the method specifically includes:

matching the real-time position information of the driver with the real-time position information of the vehicle, and judging whether the vehicles are consistent; the method comprises the steps of carrying out denoising and smoothing on real-time position data of a driver and a vehicle by using a Kalman filter; the matching of the vehicle track is carried out by a method based on distance, time sequence similarity or probability map model.

Optionally, when the man-vehicle track matching analysis is performed on the capacity pool, the method further comprises:

setting time and space thresholds to exclude positioning errors;

and analyzing in combination with the historical track of the driver and the historical track of the vehicle.

Optionally, when the constant running route analysis is performed on the capacity pool, the method specifically includes:

and determining the constant running route and the hot spot area of the vehicle by carrying out cluster analysis and track mining on the historical track data of the vehicle in the capacity pool.

Optionally, performing cluster analysis and track mining on historical track data of vehicles in the capacity pool, and specifically including:

denoising and smoothing historical track data of the vehicle by using a Kalman filter;

and (3) performing track clustering by adopting a method based on distance, density and hierarchy, calculating track density by adopting a nuclear density estimation method, and determining a hot spot area.

In a second aspect, there is provided a capacity data processing system comprising:

the acquisition module is used for establishing communication connection relation with various data sources and acquiring various transport capacity original information transmitted by the data sources; the data source at least comprises a road transportation electronic license system, a logistics business system, a logistics credit evaluation system, vehicle-mounted equipment and a third party service provider;

the storage module is used for carrying out merging, updating and filling processing on various kinds of original transport capacity information, and carrying out associated storage on the processed various kinds of original transport capacity information according to the service main key;

the analysis module is used for establishing an operation pool based on the data stored in a correlated way, and carrying out information integration analysis on the operation pool to obtain an analysis result; the information integration analysis at least comprises electronic fence analysis, anomaly analysis, man-vehicle track matching analysis and usual running route analysis;

and the service module is used for packaging the obtained analysis result and the corresponding original data and providing the obtained analysis result and the corresponding original data for the target object.

The beneficial effects that technical scheme that this application embodiment provided include at least:

(1) Simplified information acquisition and integration: by highly integrating various data, the invention avoids the complexity that a business system needs to be connected with a plurality of data channels, and greatly simplifies the process of information acquisition and processing. The service system can directly acquire the required complete, accurate and real-time data from the capacity pool scheduling system, so that the cost and complexity of data processing are reduced.

(2) Reducing the calculation pressure of the service system: according to the invention, various services in the capacity pool are integrated into one system, such as electronic fence, anomaly analysis, human-vehicle track matching and the like, so that the complex calculation of the logistics service system is avoided. The service system can be more focused on key links such as transportation safety, quality control and the like, and the overall service efficiency is improved.

(3) Providing more efficient data services: the capacity pool scheduling system can provide various data services for other related systems, such as actual positions, vehicle states, abnormal events and the like. The system provides convenience for a logistics service system, a monitoring system, a safety management system and the like, is beneficial to realizing seamless butt joint among the systems, and improves the data utilization efficiency of enterprises.

(4) Support flexible data customization and expansion: because the invention integrates various data to a high degree, the data service can be flexibly customized and expanded according to the actual business requirement. The enterprise can quickly adjust the functions of the capacity pool scheduling system according to the self requirements so as to adapt to the continuously-changing market environment.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those skilled in the art from this disclosure that the drawings described below are merely exemplary and that other embodiments may be derived from the drawings provided without undue effort.

FIG. 1 is a flow chart of a method for processing capacity data according to an embodiment of the present application;

FIG. 2 is a block diagram of an exemplary capacity data processing system according to an exemplary embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

In the description of the present invention, the terms "comprises," "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements but may include other steps or elements not expressly listed but inherent to such process, method, article, or apparatus or steps or elements added based on further optimization of the inventive concept.

Specifically, please refer to fig. 1, which illustrates a flowchart of a capacity data processing method according to an embodiment of the present application, the method may include the following steps:

and 101, establishing a communication connection relation with various data sources, and acquiring various transport capacity original information transmitted by the data sources.

The data source at least comprises a road transportation electronic license system, a logistics business system, a logistics credit evaluation system, vehicle-mounted equipment and a third party service provider.

The method mainly realizes the collection of various kinds of transport capacity original information, and specifically obtains basic information, credit evaluation, real-time positioning, historical track, multichannel video content and various kinds of alarm information of drivers, vehicles and households through the butt joint with a road transportation electronic license system, a logistics business system, a logistics credit evaluation system, vehicle-mounted equipment, a third party service provider and the like.

And 102, carrying out merging, updating and filling processing on various kinds of original information of the transport capacity, and carrying out associated storage on the processed various kinds of original information of the transport capacity according to the service main key.

In the step, basic information processing is mainly realized, specifically, basic information is combined and updated according to the priority of a customized basic information acquisition channel, missing fields are supplemented, and the credibility and integrity of the basic information are improved; and the basic information and dynamic information of the driver and the vehicle are stored in a correlated way according to the service main key.

And step 103, establishing a capacity pool based on the data stored in a correlated way, and carrying out information integration analysis on the capacity pool to obtain an analysis result.

The information integration analysis at least comprises electronic fence analysis, anomaly analysis, human-vehicle track matching analysis and usual running route analysis.

In this embodiment, the capacity pool refers to a system for centrally managing various transportation resources (such as vehicles, drivers, etc.) inside and outside a company, and is used for providing various services in an intelligent transportation scenario. Information aggregation refers to the collection, integration, and processing of data from disparate sources for use in a capacity pool system. Data processing refers to the cleaning, conversion, analysis, and integration of various types of information collected to generate valuable data and insight. An electronic fence refers to a virtual boundary set in a geographic information system for monitoring and managing the activity of a vehicle in a specific area. Freight anomaly analysis refers to the detection and analysis of collected data to identify possible anomalies such as theft of goods, long stops, etc. The human-vehicle track matching refers to judging the fitting degree of the vehicle track and the driver signaling track by comparing the vehicle track and the driver signaling track so as to assist in judging whether the human-vehicle drives the appointed vehicle or not. The analysis of the normal running route refers to the fact that the normal running route of the vehicle is estimated by utilizing an algorithm according to the historical track of the vehicle and geographic information system data, and the directional inquiry of the transportation capacity is facilitated.

In this step, the information is mainly integrated, including the following technical details:

(1) Electronic fence function: an electronic fence is a virtual boundary based on Geographic Information System (GIS) technology for monitoring the activity of vehicles and drivers in a specific area. In the system, the electronic fence area is defined according to the service requirement, and then whether the vehicle and the driver enter or leave the areas is judged through real-time positioning data. When a vehicle or driver enters or leaves the electronic fence area, the system triggers corresponding event notification for real-time monitoring and management. In addition, different types of electronic fences, such as a round shape, a square shape, an administrative boundary shape and the like, can be arranged, are suitable for various scenes such as areas, stations and the like, and realize more refined area management.

(2) Abnormality analysis: anomaly analysis is an important means of identifying potential problems and risks present in capacity pools. By using big data analysis and machine learning techniques, the present application can mine abnormal behavior, such as overspeed, idle, fatigue driving, cargo theft, etc., from historical data of vehicles and drivers. The system can perform real-time early warning on abnormal behaviors and assist related personnel in problem processing. The method specifically comprises the following specific processes:

a. data preprocessing: before performing anomaly analysis, the raw data is first cleaned and preprocessed. This includes outlier removal, missing value padding, data normalization, etc.

b. Characteristic engineering: meaningful features such as speed, acceleration, direction, etc. are extracted from the raw data, and advanced features constructed according to business requirements such as driving duration, average speed, etc.

c. Anomaly detection algorithm: anomaly detection may be performed using one or more combinations of the following algorithms:

statistical-based methods such as Grubbs test, Z-score methods, and the like;

cluster-based methods such as K-means, DBSCAN, etc.;

classification-based methods such as SVM, decision tree, random forest, etc.;

neural network-based methods such as self-encoder (Autoencoder), long-short-time memory network (LSTM), and the like.

(3) Matching human-vehicle tracks: in an intelligent capacity pool dispatch system, it is necessary to ensure that the driver is driving a designated vehicle. Therefore, the system utilizes data association and space-time analysis technology to match the real-time position information of the driver with the real-time position information of the vehicle, so as to judge whether the vehicles are consistent. In the matching process, a certain time and space threshold can be set, so that misjudgment caused by positioning errors is eliminated. In addition, the historical track of the driver and the historical track of the vehicle can be combined for analysis, so that the accuracy of matching the human and the vehicle is improved. The method specifically comprises the following steps:

a. position data preprocessing: for real-time position data of a vehicle and a driver, denoising and smoothing processes, such as a Kalman filter method, are required.

b. Man-vehicle matching algorithm: the vehicle matching may be performed using one or more combinations of the following algorithms:

distance-based methods such as euclidean distance, manhattan distance, etc.;

methods based on time series similarity, such as Dynamic Time Warping (DTW), etc.;

methods based on probabilistic graphical models, such as Hidden Markov Models (HMMs), and the like.

(4) Constant run route analysis: by performing cluster analysis and track mining on historical track data of vehicles in the capacity pool, the method and the device can find the running routes and hot spot areas. The information is helpful for directional inquiry and scheduling of the capacity, and the capacity utilization rate is improved. Meanwhile, the method and the device can also predict according to the variation trend of the normal running route and the hot spot area, and provide basis for capacity planning. In addition, through analysis of the normal running route and the hot spot area, potential traffic jams and accident risk points can be found, and accordingly corresponding measures are taken to reduce transportation risks.

a. Track data preprocessing: the historical track data of the vehicle is subjected to denoising and smoothing, such as a Kalman filter method.

b. Track clustering algorithm: track clustering may be performed using one or more combinations of the following algorithms:

distance-based methods, such as K-means algorithm for trajectory clustering;

density-based methods, such as DBSCAN algorithms for trajectory clustering;

hierarchical based methods such as the AGNES algorithm of trajectory clustering.

Hot spot area analysis: the track density can be calculated by adopting a method such as Kernel Density Estimation (KDE) and the like, so that a hot spot area can be found out.

And 104, packaging the obtained analysis result and the corresponding original data, and providing the packaged analysis result and the corresponding original data for the target object.

In this step, the service providing function is specifically realized, that is, the processed data is encapsulated into usable service, so as to support the business systems and requirements inside and outside the company. The target object is the business system and the demand inside and outside the company. Specifically, the collected data and the processed data are packaged into services, and support is provided for business systems inside and outside a company, including basic information inquiry, latest position and history track inquiry, electronic fence service, various abnormal callback services, human-vehicle track matching service, constant running route inquiry and the like.

In summary, it can be seen that the present application specifically implements the following functions:

integrated data integration: the invention realizes unified management of various information in the capacity pool by integrating various types and sources of data, including real-time position, state, abnormal event and the like of the vehicle. The innovation solves the difficult problem of the logistics business system in the aspects of data butt joint and processing, and reduces the complexity and cost of information processing.

Intelligent analysis and service module: the invention provides various intelligent analysis and service modules, such as electronic fence, anomaly analysis, human-vehicle track matching and the like, and integrates various services in the capacity pool into one system. The innovation simplifies the calculation pressure of the logistics business system and improves the overall business efficiency.

Flexible data service support: the invention supports flexible customization and expansion of data services to meet the actual business requirements of different enterprises. The innovation ensures that the logistics business system can quickly adjust the functions of the capacity pool scheduling system according to the change of market environment, thereby improving the competitiveness of enterprises.

Real-time and accuracy guarantee: the invention can collect and process various data in the capacity pool in real time, and ensures that the information acquired by the logistics service system has high real-time performance and accuracy. The innovation provides more reliable data support for the logistics business system, and improves the level of transportation safety and quality control.

As shown in fig. 2, the embodiment of the application also provides a capacity data processing system. The system comprises:

The capacity data processing system provided in the embodiments of the present application is configured to implement the capacity data processing method described above, and the specific limitation of the capacity data processing system may be referred to the limitation of the capacity data processing method described above, which is not repeated herein. The various portions of the capacity data processing system described above may be implemented in whole or in part in software, hardware, and combinations thereof. The above modules may be embedded in hardware or independent of a processor in the device, or may be stored in software in a memory in the device, so that the processor may call and execute operations corresponding to the above modules.

The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the claims. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.

Claims

1. A method of handling capacity data, the method comprising:

2. The method for processing capacity data according to claim 1, wherein the obtaining the various kinds of capacity raw information transmitted by the data source includes:

3. The method for processing capacity data according to claim 1, wherein the step of performing the electronic fence analysis on the capacity pool specifically comprises:

4. The method for processing capacity data according to claim 1, wherein the performing of the abnormality analysis on the capacity pool specifically comprises:

5. The capacity data processing method as claimed in claim 4, wherein before mining the abnormal behavior based on the history data of the vehicle and the driver, the method further comprises:

6. The method for processing capacity data according to claim 1, wherein the step of performing a human-vehicle trajectory matching analysis on the capacity pool comprises:

7. The method for processing capacity data according to claim 6, wherein when the capacity pool is subjected to the human-vehicle trajectory matching analysis, further comprising:

setting time and space thresholds to exclude positioning errors;

8. The method for processing capacity data according to claim 1, wherein the performing of the constant running route analysis on the capacity pool specifically comprises:

9. The method for processing capacity data according to claim 8, wherein the cluster analysis and the track mining are performed on the historical track data of the vehicles in the capacity pool, and specifically comprising:

10. A capacity data processing system, the system comprising: