CN117078020A - Logistics transportation data management system based on unmanned aerial vehicle - Google Patents
Logistics transportation data management system based on unmanned aerial vehicle Download PDFInfo
- Publication number
- CN117078020A CN117078020A CN202311317287.6A CN202311317287A CN117078020A CN 117078020 A CN117078020 A CN 117078020A CN 202311317287 A CN202311317287 A CN 202311317287A CN 117078020 A CN117078020 A CN 117078020A
- Authority
- CN
- China
- Prior art keywords
- road condition
- unmanned aerial
- aerial vehicle
- data
- condition data
- 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.)
- Granted
Links
- 238000013523 data management Methods 0.000 title claims abstract description 22
- 238000007726 management method Methods 0.000 claims abstract description 22
- 238000012790 confirmation Methods 0.000 claims abstract description 12
- 238000000034 method Methods 0.000 claims description 29
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000013439 planning Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 abstract description 7
- 238000010219 correlation analysis Methods 0.000 abstract 1
- 238000004458 analytical method Methods 0.000 description 6
- 238000012384 transportation and delivery Methods 0.000 description 6
- 230000005856 abnormality Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000005303 weighing 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Data Mining & Analysis (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Probability & Statistics with Applications (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of transportation management, in particular to a logistics transportation data management system based on an unmanned aerial vehicle. The system comprises a data acquisition module, a control module and a control module, wherein the data acquisition module is used for acquiring real-time air road condition data of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle and the people flow of a transport path; the category classification module is used for acquiring the similar road condition category closest to the real-time air road condition data; the risk confirmation module is used for determining a risk index of the unmanned aerial vehicle; the risk index of the unmanned aerial vehicle is adjusted, and a risk reference value of the unmanned aerial vehicle is obtained; the loss confirmation module is used for determining the loss degree of the unmanned aerial vehicle; and the path management module is used for carrying out transportation path management on the unmanned aerial vehicle according to the loss degree of the unmanned aerial vehicle. The invention can evaluate and early warn various risks, and improves the safety and reliability of unmanned aerial vehicle transportation by carrying out real-time monitoring on unmanned aerial vehicle flight data and carrying out correlation analysis on historical data.
Description
Technical Field
The invention relates to the technical field of transportation management, in particular to a logistics transportation data management system based on an unmanned aerial vehicle.
Background
Under the background that urban ground traffic is increasingly congested and urban underground space development is limited, the advantages of no limitation of terrain, flexible scheduling, high speed, high efficiency and the like of the unmanned aerial vehicle are exerted, the problem of 'last kilometer' of logistics distribution can be effectively solved, and an important way is provided for developing urban low-altitude airspace resources. However, the problem of falling caused by air collision, weather and other reasons in the operation process of the logistics unmanned aerial vehicle is extremely easy to cause risks to the life and property safety of ground personnel, and meanwhile, the transported goods are damaged, so that the loss is larger. How to comprehensively evaluate the risk of logistics transportation of the unmanned aerial vehicle becomes an important problem of large-scale development and application of logistics of the unmanned aerial vehicle.
At present, the logistics data management system can monitor and analyze flight data of the unmanned aerial vehicle in real time, identify abnormal modes and trends, and judge whether dangerous conditions exist according to preset rules and algorithms. However, real-time anomaly monitoring focuses on detecting and responding to the current occurring anomaly, and can quickly discover problems and take timely action. However, this approach may be somewhat delayed for certain special situations, as it only deals with anomalies that have occurred, without predicting future risk in advance, without avoiding potentially high risk areas in advance, and with the risk of being too late.
Disclosure of Invention
In order to solve the technical problem that the real-time abnormality monitoring focuses on detecting and responding to the current abnormality, but is difficult to avoid a potential high-risk area in advance, the invention aims to provide a logistics transportation data management system based on an unmanned aerial vehicle, and the adopted technical scheme is as follows:
the data acquisition module is used for acquiring real-time air road condition data of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle and the people flow of the transport path;
the class classification module is used for comparing the real-time air road condition data of the unmanned aerial vehicle with different air road condition classes to obtain the similar road condition class closest to the real-time air road condition data;
the risk confirmation module is used for determining the risk index of the unmanned aerial vehicle according to the similar conditions of the historical air road condition data and the real-time air road condition data in the similar road condition types, the corresponding time intervals and the unmanned aerial vehicle accident rate of the similar road condition types; the risk index of the unmanned aerial vehicle is adjusted through the difference between the historical air road condition data and the real-time air road condition data, so that a risk reference value of the unmanned aerial vehicle is obtained;
the loss confirmation module is used for determining the loss degree of the unmanned aerial vehicle by combining the risk reference value of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle and the people flow of the transport path;
and the path management module is used for carrying out transportation path management on the unmanned aerial vehicle according to the loss degree of the unmanned aerial vehicle.
Preferably, the method for dividing the air road condition types comprises the following steps:
taking the preset time length as a period length;
acquiring a data characteristic value corresponding to each road condition data in historical air road condition data in a period;
and clustering the historical air road condition data by taking the difference of the data characteristic values corresponding to the road condition data between the periods as the distance to obtain different air road condition types.
Preferably, the obtaining the data characteristic value corresponding to each road condition data in the historical air road condition data in one period includes:
each group of historical air road condition data comprises different road condition data;
for any road condition data, calculating the mode, average value, maximum value and minimum value of the road condition data in one period to be used as the data characteristic value of the road condition data in one period.
Preferably, comparing the real-time air road condition data of the unmanned aerial vehicle with different air road condition types to obtain a similar road condition type closest to the real-time air road condition data, including:
taking a clustering center of the air road condition type as a representative sample of the air road condition type;
calculating the similarity of real-time air road condition data of the unmanned aerial vehicle and representative samples of each air road condition type, and taking the air road condition type corresponding to the maximum similarity as the similar road condition type closest to the real-time air road condition data.
Preferably, the determining the risk index of the unmanned aerial vehicle according to the similar situation of the historical air road condition data and the real-time air road condition data in the similar road condition category, the corresponding time interval and the unmanned aerial vehicle accident rate of the similar road condition category includes:
the calculation formula of the risk index is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,is a risk index;unmanned aerial vehicle accident rates of similar road condition types;the similarity between the s-th historical air road condition data and the real-time air road condition data in the similar road condition types;is a preset similar number;is an exponential function based on a natural constant e;is a preset adjusting value;is the s-th historical air road condition data and the real-time air road condition number in the similar road condition typesAccording to the time interval.
Preferably, the adjusting the risk index of the unmanned aerial vehicle according to the difference between the historical air road condition data and the real-time air road condition data to obtain a risk reference value of the unmanned aerial vehicle includes:
the calculation formula of the risk reference value is as follows:
；
wherein,is a risk reference value;the air complexity in the real-time air road condition data is obtained;the air complexity in the initial air road condition data is used; max is a maximum function;the temperature in the real-time air road condition data is obtained;the temperature in the initial air road condition data is set;the wind speed in the real-time air road condition data is used;the wind speed in the initial air road condition data is used as the wind speed;the rainfall intensity in the real-time air road condition data is obtained;the rainfall intensity in the initial air road condition data is obtained;is a risk index of the unmanned aerial vehicle.
Preferably, the determining the loss degree of the unmanned aerial vehicle by combining the risk reference value of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle and the people flow of the transport path includes:
the calculation formula of the loss degree is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,is the degree of loss;is a risk reference value; e is a natural constant;the value of the goods for transportation;is the traffic of people on the transportation path.
Preferably, the managing the transportation path of the unmanned aerial vehicle according to the loss degree of the unmanned aerial vehicle includes:
when the loss degree of the unmanned aerial vehicle is smaller than a preset loss threshold value, continuing logistics distribution according to the original transportation path planning; and when the loss degree of the unmanned aerial vehicle is greater than or equal to a preset loss threshold value, stopping transportation of the unmanned aerial vehicle, and returning to the path relay point.
Preferably, the method for obtaining the accident rate of the unmanned aerial vehicle with similar road condition type comprises the following steps:
and taking the sum value of the corresponding unmanned aerial vehicle accident rates under the historical air road condition data in the similar road condition category as the unmanned aerial vehicle accident rate in the similar road condition category.
Preferably, the similarity between the historical air road condition data and the real-time air road condition data is: cosine similarity.
The embodiment of the invention has at least the following beneficial effects:
the invention relates to the technical field of transportation management. The system comprises a category dividing module, a real-time air road condition module and a real-time air road condition module, wherein the category dividing module is used for comparing real-time air road condition data of the unmanned aerial vehicle with different air road condition categories to obtain similar road condition categories closest to the real-time air road condition data, and is beneficial to follow-up analysis of data in different modes and trends; the risk confirmation module is used for determining a risk index of the unmanned aerial vehicle according to the similar conditions of the historical air road condition data and the real-time air road condition data in the similar road condition types, the corresponding time intervals and the unmanned aerial vehicle accident rate of the similar road condition types, wherein the risk index reflects the risk degree in the unmanned aerial vehicle transportation acquisition process; the risk index of the unmanned aerial vehicle is adjusted through the difference between the historical air road condition data and the real-time air road condition data, so that a risk reference value of the unmanned aerial vehicle is obtained; the loss confirmation module is used for determining the loss degree of the unmanned aerial vehicle by combining the risk reference value of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle and the people flow of the transport path; and the path management module is used for carrying out transportation path management on the unmanned aerial vehicle according to the loss degree of the unmanned aerial vehicle. Compared with the existing unmanned aerial vehicle management system, the data management system can evaluate and early warn various risks in the unmanned aerial vehicle flight process. Through real-time monitoring of unmanned aerial vehicle flight data and associated analysis with historical data, potential risks can be found timely, corresponding measures are taken to conduct risk management, and unmanned aerial vehicle transportation safety and reliability are improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a system block diagram of a logistic transportation data management system based on an unmanned aerial vehicle according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of the unmanned aerial vehicle-based logistics transportation data management system according to the present invention with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment of the invention provides a specific implementation method of a logistics transportation data management system based on an unmanned aerial vehicle. The unmanned plane network can realize real-time road condition, weather data and other related data query by communicating with a remote data source under the scene. In order to solve the technical problems that real-time abnormality monitoring focuses on detecting and responding to the current abnormality, it is difficult to avoid a potential high-risk area in advance. The invention can evaluate and early warn various risks in the flight process of the unmanned aerial vehicle. Through real-time monitoring of unmanned aerial vehicle flight data and associated analysis with historical data, potential risks can be found timely, corresponding measures are taken to conduct risk management, and unmanned aerial vehicle transportation safety and reliability are improved.
The following specifically describes a specific scheme of the unmanned aerial vehicle-based logistics transportation data management system provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a system block diagram of a system for managing logistics transportation data based on an unmanned aerial vehicle according to an embodiment of the present invention is shown, the system includes the following modules:
the data acquisition module 10 is used for acquiring real-time air road condition data of the unmanned aerial vehicle, cargo value transported by the unmanned aerial vehicle and people flow of a transport path.
The unmanned aerial vehicle network can realize the inquiry of real-time road conditions, weather data and other related data by communicating with a remote data source; wherein remote data sources such as traffic management centers, weather service providers, etc. This may be done by means of an API call or a data query, etc. Through the mode, the unmanned aerial vehicle network can acquire the transportation road condition in the transportation process of each unmanned aerial vehicle; the transportation road conditions comprise real-time traffic flow, air complexity, value of goods transported by the unmanned aerial vehicle, weather information and the like on the ground of the transportation path. When no unmanned aerial vehicle collects data, the called weather information of a weather service provider is used for filling the information of the relay point in different time periods, and when the unmanned aerial vehicle collects the information, the real-time collected data is used for filling.
The unmanned aerial vehicle is equipped with various sensor devices, such as temperature and humidity sensors, anemometers, microwave radars and other sensors. The sensors can sense and measure environmental factors and rainfall intensity weather conditions in real time, wherein the rainfall intensity weather conditions comprise temperature, wind speed, rainfall intensity and rainfall intensity are acquired by a microwave radar. In the embodiment of the invention, four road condition data of air complexity, temperature, wind speed and rainfall intensity are collectively called as air road condition data. Therefore, real-time air road condition data of the unmanned aerial vehicle, cargo value transported by the unmanned aerial vehicle and people flow of a transport path can be obtained through the data acquisition module.
Each of the drones in the drone network may communicate and exchange data over the network. They may share each other's sensor data, history information, and real-time delivery segment information, supporting path planning decisions through the services of the rear processor. The unmanned aerial vehicle can acquire real-time air road condition data during operation, and continuously divide the air road condition data acquired at the previous moment into historical air road condition data.
The category classification module 20 is configured to compare real-time air road condition data of the unmanned aerial vehicle with different air road condition categories, and obtain a similar road condition category closest to the real-time air road condition data.
For logistics networks, the delivery of goods is accomplished by two-stage relay of a carrier vehicle and an unmanned aerial vehicle, wherein the carrier vehicle is responsible for transporting packages from a city distribution center to a relay or directly to truck customers. And the unmanned aerial vehicle is used as a delivery vehicle of the second-stage logistics network and is responsible for transporting packages from the relay to unmanned aerial vehicle clients.
The transport vehicle can be stopped in the relay, and then the unmanned aerial vehicle is released to carry out unmanned aerial vehicle delivery. The background server acquires initial air road condition data and historical air road condition data through API call or data query and other modes. The initial air road condition data can be understood as the air road condition data collected when the unmanned aerial vehicle just begins to work.
When planning the flight path of the unmanned aerial vehicle, the background needs to analyze the road conditions according to real-time air road condition data, the cargo value transported by the unmanned aerial vehicle, the people flow of the transport path and the like, so as to obtain an initial transport path.
The initial path of unmanned aerial vehicle flight can be obtained by calling the air road condition data of the background, the cargo value transported by the unmanned aerial vehicle and the traffic of the transport path. However, errors may exist between the background data and the real-time data after the unmanned aerial vehicle takes off, so that the unmanned aerial vehicle needs to collect the data in real time, analyze the difference between the data, and calculate the risk index of the flight by comparing the historical data. And calculating the loss degree of the unmanned aerial vehicle by combining the risk index, the difference value, the people flow and the cargo value of transportation.
For historical air road condition data of unmanned aerial vehicle logistics, the time series with similar traffic conditions can be divided into the same category through density-based clustering, so that a plurality of different air road condition categories are obtained. Therefore, the method can help us to find different modes and trends of logistics environments, such as severe weather environments or complicated and rugged air traffic environments, and the like, and air road conditions formed by various characteristic combinations, and the states of the logistics data of the unmanned aerial vehicle can be expressed through classification formed by clustering. Through further analysis of each cluster, various air road conditions of traffic flow can be excavated; further, the risk reference value can be calculated by counting the probability of occurrence of the risk event under the state through each air road condition.
Firstly, the historical air road condition data is divided into a plurality of air road condition types, and the similar road condition type closest to the real-time air road condition data is obtained.
The method for dividing the types of the air road conditions comprises the following steps: taking the preset time length as a period length; acquiring a data characteristic value corresponding to each road condition data in historical air road condition data in a period; and clustering the historical air road condition data by taking the difference of the data characteristic values corresponding to the road condition data between the periods as the distance to obtain different air road condition types. In the embodiment of the invention, the preset time length is 10 minutes, and in other embodiments, the value can be adjusted by an implementer according to actual conditions, only the air road condition data between 6 a.m. and 10 a.m. are analyzed, the historical air road condition data are preprocessed, the data of each dimension in the historical air road condition data are ensured to be subjected to proper standardized processing, and the situation that the influence of certain dimensions on the clustering result is too large is avoided. And analyzing the historical air road condition data in a unit of day.
And forming a data characteristic value set corresponding to the historical air road condition data by the data characteristic values corresponding to each road condition data in the historical air road condition data in each period. Specific: and clustering all data by using a density-based clustering algorithm (DBSCAN) by taking a data characteristic value set corresponding to historical air road condition data in each period as input, and clustering the historical air road condition data by taking differences of data characteristic values corresponding to the road condition data among the periods as distances to obtain different air road condition types. The method and the device can divide the historical air road condition data with similar road conditions into the same class. Each category represents an air road condition category, a clustering result is represented as a set formed by a plurality of historical air road condition data, a representative sample of each air road condition category is obtained, and the representative sample is data of a clustering center corresponding to the air road condition category.
Each set of historical air road condition data comprises different road condition data, and in the embodiment of the invention, the road condition data comprises air complexity, temperature, wind speed and rainfall intensity.
The method for acquiring the data characteristic value corresponding to each road condition data in the historical air road condition data in one period comprises the following steps: for any road condition data, calculating the mode, average value, maximum value and minimum value of the road condition data in one period to be used as the data characteristic value of the road condition data in one period.
The mode, average value, maximum value and minimum value of the road condition data in one period are calculated and used as the data characteristic value of the road condition data, so that the mode, average value, maximum value and minimum value are averaged to avoid overlarge influence of certain abnormal data on the whole data, the representativeness and stability of the data can be improved, meanwhile, the data distribution in each time period can be considered comprehensively, the change trend of the data in the time period is better reflected, the complexity of the data is reduced, the distribution situation of the data is comprehensively represented, and the method is beneficial to better identifying and dividing similar road condition when clustering is carried out by using a clustering algorithm subsequently.
Therefore, the data characteristic value set corresponding to the historical air road condition data of each time period in each day is obtained, and because the historical air road condition data has four road condition data in the embodiment of the invention, the corresponding data characteristic value set corresponding to the historical air road condition data of each time period has four data characteristic values.
The method comprises the steps of carrying out a first treatment on the surface of the Wherein,a data characteristic value set corresponding to the historical air road condition data of the t-th period;the data characteristic value corresponding to the complexity in the hollow of the historical air road condition data in the t-th period;the data characteristic value corresponding to the temperature in the historical air road condition data of the t-th period;the data characteristic value corresponding to the wind speed in the historical air road condition data of the t-th period;and the data characteristic value corresponding to rainfall intensity in the historical air road condition data of the t-th period. 96 is derived from the number of cycles contained in each of the 16 hours each day of which the analysis is required, with a cycle length of 10 minutes.
The data characteristic value reflects the overall level of road condition data in each period, and the main trend, average level and extreme condition in the time period can be comprehensively considered through the data characteristic value, so that the characteristics in the time period can be more comprehensively known.
Extracting data characteristic values corresponding to air road condition data 30 days before a relay point of the unmanned aerial vehicle, and calculating a data characteristic value set every dayThe method comprises the steps of carrying out a first treatment on the surface of the Wherein,is the data characteristic value set on the m day.The method comprises the steps of carrying out a first treatment on the surface of the Wherein,the method is a data characteristic value set corresponding to historical air road condition data of the 1 st period in the m day;the method is a data characteristic value set corresponding to historical air road condition data of the 2 nd period in the m day;and the set of data characteristic values corresponding to the 96 th period of historical air road condition data in the m day.
When unmanned aerial vehicle carries out logistics distribution, weather factor produces important influence to unmanned aerial vehicle flight. Temperature, wind speed and rainfall intensity can affect the battery life, stability and flying ability of the unmanned aerial vehicle. Meanwhile, the complexity in the air also needs to be considered, such as the influence of buildings and telegraph poles on the obstacle avoidance difficulty of the unmanned aerial vehicle. Therefore, in the unmanned aerial vehicle delivery process, the four characteristics need to be comprehensively considered to ensure safe and efficient delivery.
The unmanned aerial vehicle starts to analyze after starting from a certain relay point, the unmanned aerial vehicle takes off from the relay, the road section position of the relay is determined, and the air road condition of the road section is acquired.
Comparing the real-time air road condition data of the unmanned aerial vehicle with different air road condition types to obtain the similar road condition type closest to the real-time air road condition data, and specifically: taking a clustering center of the air road condition type as a representative sample of the air road condition type; calculating the similarity of real-time air road condition data of the unmanned aerial vehicle and representative samples of each air road condition type, and taking the air road condition type corresponding to the maximum similarity as the similar road condition type closest to the real-time air road condition data. In the embodiment of the invention, the similarity between the real-time air road condition data and the representative samples of different air road condition types is cosine similarity.
The risk confirmation module 30 is configured to determine a risk index of the unmanned aerial vehicle according to similar conditions of the historical air road condition data and the real-time air road condition data in the similar road condition types, corresponding time intervals, and unmanned aerial vehicle accident rates of the similar road condition types; and adjusting the risk index of the unmanned aerial vehicle according to the difference between the historical air road condition data and the real-time air road condition data to obtain a risk reference value of the unmanned aerial vehicle.
After obtaining the similar road condition types, traversing all the historical air road condition data in the similar road condition types, respectively calculating the similarity of each historical air road condition data in the similar road condition types and the real-time air road condition data, and sequencing the historical air road condition data in the similar road condition types according to the sequence of the similarity from large to small to obtain a similarity sequence. And the high-support sequence is formed by presetting a similar number of historical air road condition data in the similarity sequence. In the embodiment of the invention, the preset similarity number is one-fourth of the number of the historical air road condition data in the similarity sequence, and the result value obtained after the one-fourth is taken as a value, and in other embodiments, the preset similarity number can be set by an implementer according to actual conditions. And obtaining the time interval between each historical air road condition data and the real-time air road condition data in the high-support-degree sequence.
And for the air road condition types, counting the duty ratio of the number of crashes or other accidents of the unmanned aerial vehicle flying for ten minutes under the historical air road condition data in the air road condition types, namely the unmanned aerial vehicle accident rate of the historical air road condition data. And taking the sum value of the unmanned aerial vehicle accident rate corresponding to the historical air road condition data in the air road condition type as the unmanned aerial vehicle accident rate of the air road condition type. And regarding the corresponding sum value of the unmanned aerial vehicle accident rates under the historical air road condition data in the similar road condition types as the unmanned aerial vehicle accident rate of the similar road condition types.
And determining the risk index of the unmanned aerial vehicle according to the similar conditions of the historical air road condition data and the real-time air road condition data in the similar road condition types, the corresponding time intervals and the unmanned aerial vehicle accident rate of the similar road condition types. The risk index is calculated for unmanned aerial vehicles taking off in real time.
The calculation formula of the risk index is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,is a risk index;unmanned aerial vehicle accident rates of similar road condition types;the similarity between the s-th historical air road condition data and the real-time air road condition data in the similar road condition types;is a preset similarity numberAn amount of;is an exponential function based on a natural constant e;is a preset adjusting value;is the time interval between the s-th historical air road condition data and the real-time air road condition data in the similar road condition types. The preset adjustment value is used for adjusting the convergence rate of the curve, 0.4 is taken in the embodiment of the invention, and in other embodiments, the value is adjusted by an implementer according to the actual situation.
Wherein the unmanned aerial vehicle accident rate in the calculation formula of the risk index is used for reflecting the safety of the air road condition,reflecting the freshness of the data that can be extracted, wherein the decay factor of the time interval is used to adjust the impact of the time interval on the risk index. The risk index is calculated by considering factors such as similarity, time interval, accident rate and the like, and risks of the unmanned aerial vehicle taking off are quantified by weighing the influences of different factors. The greater the risk index, the greater the probability of an accident occurring with the unmanned aerial vehicle. Multiplying the accident rate by 10-5 in the calculation formula of the risk index, and amplifying the accident rate to a proper range, wherein the amplified index is a super-parameter empirical value, and an implementer can adjust the accident rate according to the scene. The accident rate and the risk index are in a direct proportion, and the larger the accident rate is, the larger the corresponding risk index of the unmanned aerial vehicle is.The part calculates the similarity of the real-time data and the whole historical data, the higher the similarity is, the more the similar road condition type is reflected, the real-time data can be reflected,and the risk index is also in direct proportion to the risk index. Third partThe freshness value of the data, that is, the timeliness of the data is reflected, because when the risk of the real-time data is reflected by the historical data, the influence of the timeliness of the historical data on the risk index needs to be considered. A larger freshness value indicates that the data is fresher, i.e., the time interval is shorter, and the influence on the risk index is larger; while a smaller freshness value indicates that the data is relatively older, i.e., the time interval is longer, with less impact on the risk index. For unmanned aerial vehicles taking off, the fresher data have a greater impact on the risk index because they more reflect the current air traffic and risk level.
After the risk index of the unmanned aerial vehicle is obtained, the risk index of the unmanned aerial vehicle can be adjusted by comparing the difference between the historical air road condition data and the real-time air road condition data, and a risk reference value of the unmanned aerial vehicle is obtained.
The calculation formula of the risk reference value is as follows:
；
wherein,is a risk reference value;the air complexity in the real-time air road condition data is obtained;the air complexity in the initial air road condition data is used; max is a maximum function;the temperature in the real-time air road condition data is obtained;the temperature in the initial air road condition data is set;the wind speed in the real-time air road condition data is used;the wind speed in the initial air road condition data is used as the wind speed;the rainfall intensity in the real-time air road condition data is obtained;the rainfall intensity in the initial air road condition data is obtained;is a risk index of the unmanned aerial vehicle.
The risk reference value is obtained by weighting the risk index through the difference of the air road condition data.，，，The four formulas have the significance of calculating the difference degree between the real-time air road condition data and the initial air road condition data, and if the difference value of a certain characteristic value is larger, the characteristic real-time data and the initial data are obviously different, and the risk is possibly greatly influenced; conversely, if the variance value is small, indicating that the variance of the feature is small, the impact on risk may be relatively small. Likewise, the larger the number of categories with large variance values, the more temperatures that indicate a large variance between the real-time data and the initial air road condition data, which means a potential increase in risk. Wherein the influence of temperature on the risk reference value is more specific,representation fetchIs a positive value in (c). The function of this formula is to letThe value of the temperature is a corresponding value that the real-time temperature is higher than 35 degrees or the negative value of the real-time temperature is higher than 0; when (when)When the temperature is larger than 0, the flying state of the unmanned aerial vehicle is dangerous in a high-temperature or low-temperature flying state, and the higher or lower the temperature of the high-temperature or low-temperature flying state is, the more dangerous is indicated. When (when)When 0 is taken, the real-time temperature is within a reasonable operation range, and the risk reference value is not influenced.
Whether the difference value of the real-time air road condition data and the initial air road condition data is larger or not, whether the larger quantity is larger or not, and reflecting the ratio of the difference value and the corresponding initial air road condition dataThe larger the value is, the larger the difference between the real-time air road condition data and the initial air road condition data is reflected. It should be noted that, max is a maximum function, that is, when the real-time air road condition data is greater than the initial air road condition data in the calculation formula of the risk reference value, the difference between the maximum function and the risk reference value is taken; and taking 0 when the real-time air road condition data is not greater than the initial air road condition data.
The loss confirmation module 40 is configured to determine a loss degree of the unmanned aerial vehicle by combining the risk reference value of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle, and the people flow of the transportation path.
When an unmanned aerial vehicle has an accident, two parameters are required to be considered besides the risk reference value VR, and one parameter is that the unmanned aerial vehicle possibly damages passers-by when falling, so that the traffic of the unmanned aerial vehicle on the transportation path is required to be considered; and secondly, the loss of goods after the unmanned aerial vehicle falls down is considered, so that the value of the goods transported by the unmanned aerial vehicle is required to be considered.
And further determining the loss degree of the unmanned aerial vehicle by combining the risk reference value of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle and the people flow of the transport path.
The calculation formula of the loss degree is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,is the degree of loss;is a risk reference value; e is a natural constant;the value of the goods for transportation;is the traffic of people on the transportation path.
Wherein, the risk reference value and the people flow rate and the loss degree of the transportation path during transportationIn proportion, the greater the risk reference value is, the greater the loss degree is, and the greater the flow of people is, the greater the loss degree is; this is because in areas with large traffic, the unmanned aerial vehicle falling may cause damage to more pedestrians, requiring more reimbursement; in the area with larger traffic, the increase speed of the damage degree of the pedestrians caused by the falling of the unmanned aerial vehicle can be increased, so that an index model is adopted; wherein 0.2 is the adjustment influencing factorEmpirical values of convergence speed. The greater the value of the cargo, the greater the degree of loss of the drone, the greater the amount it needs to be reimbursed.
And obtaining the loss degree of the continuous goods transportation of the unmanned aerial vehicle in the current state of the current road section according to the calculation formula of the loss function.
The path management module 50 is configured to manage a transportation path of the unmanned aerial vehicle according to a loss degree of the unmanned aerial vehicle.
And when the loss degree of the unmanned aerial vehicle is smaller than a preset loss threshold value, continuing logistics distribution according to the original transportation path planning. In the embodiment of the present invention, the preset loss threshold value is 320, and in other embodiments, the implementer adjusts the value according to different transportation scenarios.
And when the loss degree of the unmanned aerial vehicle is greater than or equal to a preset loss threshold value, stopping transportation of the unmanned aerial vehicle, and returning to the path relay point. And selecting a proper transportation strategy according to the real-time air road condition data and the performance of the unmanned aerial vehicle on the transportation vehicle where the relay point stays. For example, for a task with moderate risk, a small unmanned aerial vehicle can be selected for transportation, and the task is completed quickly by adopting the shortest flight path; for high-risk tasks, large unmanned aerial vehicles or unmanned aerial vehicles with higher performance can be selected for transportation, and the safest flight path is adopted, so that dangerous areas are avoided, and risks are reduced. And synchronously updating the air road conditions in the database while obtaining the loss degree of the unmanned aerial vehicle.
In summary, the present invention relates to the technical field of transportation management. The system comprises a data acquisition module, a control module and a control module, wherein the data acquisition module is used for acquiring real-time air road condition data of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle and the people flow of a transport path; the class classification module is used for comparing the real-time air road condition data of the unmanned aerial vehicle with different air road condition classes to obtain the similar road condition class closest to the real-time air road condition data; the risk confirmation module is used for determining the risk index of the unmanned aerial vehicle according to the similar conditions of the historical air road condition data and the real-time air road condition data in the similar road condition types, the corresponding time intervals and the unmanned aerial vehicle accident rate of the similar road condition types; the risk index of the unmanned aerial vehicle is adjusted through the difference between the historical air road condition data and the real-time air road condition data, so that a risk reference value of the unmanned aerial vehicle is obtained; the loss confirmation module is used for determining the loss degree of the unmanned aerial vehicle by combining the risk reference value of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle and the people flow of the transport path; and the path management module is used for carrying out transportation path management on the unmanned aerial vehicle according to the loss degree of the unmanned aerial vehicle. Compared with the existing unmanned aerial vehicle management system, the data management system can evaluate and early warn various risks in the unmanned aerial vehicle flight process. Through real-time monitoring of unmanned aerial vehicle flight data and associated analysis with historical data, potential risks can be found timely, corresponding measures are taken to conduct risk management, and unmanned aerial vehicle transportation safety and reliability are improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. Logistics transportation data management system based on unmanned aerial vehicle, characterized by that, this system includes following module:
the data acquisition module is used for acquiring real-time air road condition data of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle and the people flow of the transport path;
the class classification module is used for comparing the real-time air road condition data of the unmanned aerial vehicle with different air road condition classes to obtain the similar road condition class closest to the real-time air road condition data;
the risk confirmation module is used for determining the risk index of the unmanned aerial vehicle according to the similar conditions of the historical air road condition data and the real-time air road condition data in the similar road condition types, the corresponding time intervals and the unmanned aerial vehicle accident rate of the similar road condition types; the risk index of the unmanned aerial vehicle is adjusted through the difference between the historical air road condition data and the real-time air road condition data, so that a risk reference value of the unmanned aerial vehicle is obtained;
the loss confirmation module is used for determining the loss degree of the unmanned aerial vehicle by combining the risk reference value of the unmanned aerial vehicle, the cargo value transported by the unmanned aerial vehicle and the people flow of the transport path;
and the path management module is used for carrying out transportation path management on the unmanned aerial vehicle according to the loss degree of the unmanned aerial vehicle.
2. The unmanned aerial vehicle-based logistics transportation data management system of claim 1, wherein the method for classifying the air road condition categories comprises:
taking the preset time length as a period length;
acquiring a data characteristic value corresponding to each road condition data in historical air road condition data in a period;
and clustering the historical air road condition data by taking the difference of the data characteristic values corresponding to the road condition data between the periods as the distance to obtain different air road condition types.
3. The unmanned aerial vehicle-based logistics transportation data management system of claim 2, wherein the acquiring the data characteristic value corresponding to each road condition data in the historical air road condition data in one period comprises:
each group of historical air road condition data comprises different road condition data;
for any road condition data, calculating the mode, average value, maximum value and minimum value of the road condition data in one period to be used as the data characteristic value of the road condition data in one period.
4. The unmanned aerial vehicle-based logistics transportation data management system of claim 1, wherein comparing the real-time air traffic data of the unmanned aerial vehicle with different air traffic categories to obtain a similar traffic category closest to the real-time air traffic data comprises:
taking a clustering center of the air road condition type as a representative sample of the air road condition type;
calculating the similarity of real-time air road condition data of the unmanned aerial vehicle and representative samples of each air road condition type, and taking the air road condition type corresponding to the maximum similarity as the similar road condition type closest to the real-time air road condition data.
5. The unmanned aerial vehicle-based logistics transportation data management system of claim 1, wherein the determining the risk index of the unmanned aerial vehicle from the similar situation of the historical air road condition data and the real-time air road condition data in the similar road condition category, the corresponding time interval, and the unmanned aerial vehicle accident rate of the similar road condition category comprises:
the calculation formula of the risk index is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is a risk index; />Unmanned aerial vehicle accident rates of similar road condition types; />The similarity between the s-th historical air road condition data and the real-time air road condition data in the similar road condition types; />Is a preset similar number; />Is an exponential function based on a natural constant e; />Is a preset adjusting value; />Is the time interval between the s-th historical air road condition data and the real-time air road condition data in the similar road condition types.
6. The unmanned aerial vehicle-based logistics transportation data management system of claim 1, wherein the adjusting the risk index of the unmanned aerial vehicle to obtain the risk reference value of the unmanned aerial vehicle by the difference between the historical air road condition data and the real-time air road condition data comprises:
the calculation formula of the risk reference value is as follows:
；
wherein,is a risk reference value; />The air complexity in the real-time air road condition data is obtained; />The air complexity in the initial air road condition data is used; max is a maximum function; />The temperature in the real-time air road condition data is obtained; />The temperature in the initial air road condition data is set; />The wind speed in the real-time air road condition data is used; />The wind speed in the initial air road condition data is used as the wind speed; />In real-time air road condition dataIs a rainfall intensity of (2); />The rainfall intensity in the initial air road condition data is obtained; />Is a risk index of the unmanned aerial vehicle.
7. The unmanned aerial vehicle-based logistics transportation data management system of claim 1, wherein the determining the degree of loss of the unmanned aerial vehicle in combination with the risk reference value of the unmanned aerial vehicle, the cargo value of the unmanned aerial vehicle transportation, and the traffic of the transportation path comprises:
the calculation formula of the loss degree is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is the degree of loss; />Is a risk reference value; e is a natural constant; />The value of the goods for transportation; />Is the traffic of people on the transportation path.
8. The unmanned aerial vehicle-based logistics transportation data management system of claim 1, wherein the performing transportation path management for the unmanned aerial vehicle according to the loss degree of the unmanned aerial vehicle comprises:
when the loss degree of the unmanned aerial vehicle is smaller than a preset loss threshold value, continuing logistics distribution according to the original transportation path planning; and when the loss degree of the unmanned aerial vehicle is greater than or equal to a preset loss threshold value, stopping transportation of the unmanned aerial vehicle, and returning to the path relay point.
9. The unmanned aerial vehicle-based logistics transportation data management system of claim 1, wherein the method for acquiring the unmanned aerial vehicle accident rate of the similar road condition type comprises the following steps:
and taking the sum value of the corresponding unmanned aerial vehicle accident rates under the historical air road condition data in the similar road condition category as the unmanned aerial vehicle accident rate in the similar road condition category.
10. The unmanned aerial vehicle-based logistics transportation data management system of claim 5, wherein the similarity of the historical air road condition data and the real-time air road condition data is: cosine similarity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311317287.6A CN117078020B (en) | 2023-10-12 | 2023-10-12 | Logistics transportation data management system based on unmanned aerial vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311317287.6A CN117078020B (en) | 2023-10-12 | 2023-10-12 | Logistics transportation data management system based on unmanned aerial vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117078020A true CN117078020A (en) | 2023-11-17 |
| CN117078020B CN117078020B (en) | 2024-01-30 |
Family
ID=88717307
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311317287.6A Active CN117078020B (en) | 2023-10-12 | 2023-10-12 | Logistics transportation data management system based on unmanned aerial vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117078020B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117670198A (en) * | 2023-12-13 | 2024-03-08 | 江苏菲达宝开电气股份有限公司 | Smart bin Chu Yunwei method and system |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110113155A1 (en) * | 2008-06-24 | 2011-05-12 | Tsia Kuznetsov | Methods and systems for dynamically adaptive road network hierarchy and routing |
| CN102353380A (en) * | 2011-06-30 | 2012-02-15 | 福建慧翰信息技术有限公司 | Road condition prediction and query system and method |
| CN103106813A (en) * | 2013-01-18 | 2013-05-15 | 南京航空航天大学 | Traffic situation complexity assessment method based on index system |
| CN104021671A (en) * | 2014-05-16 | 2014-09-03 | 浙江银江研究院有限公司 | Real-time road condition determination method through combined svm and fuzzy determination mode |
| WO2016047949A1 (en) * | 2014-09-24 | 2016-03-31 | 삼성에스디에스 주식회사 | Method and apparatus for logistics risk prediction |
| CN107657412A (en) * | 2017-10-23 | 2018-02-02 | 天津职业技术师范大学 | Towards the unmanned plane and automobile combined type delivery system and allocator of remote districts |
| CN108900993A (en) * | 2018-05-09 | 2018-11-27 | 顺丰科技有限公司 | A kind of wireless data sending network system and its working method |
| KR20190126756A (en) * | 2016-04-26 | 2019-11-12 | 김명준 | Method and system for logistics transportation using an unmanned aircraft based on the muilti-relay station environment |
| CN111240229A (en) * | 2020-03-20 | 2020-06-05 | 智慧航海（青岛）科技有限公司 | Fault information processing system based on intelligent ship berthing simulation test |
| CN112185151A (en) * | 2020-09-15 | 2021-01-05 | 上海思寒环保科技有限公司 | Intelligent path planning method and system |
| CN112735189A (en) * | 2020-12-24 | 2021-04-30 | 朱上翔 | Method and system for ground-air mode conversion and intelligent air management of flying vehicle |
| CN113011809A (en) * | 2021-01-26 | 2021-06-22 | 浙江双友物流器械股份有限公司 | Cargo displacement monitoring method for multi-path condition transportation |
| CN113065804A (en) * | 2021-04-27 | 2021-07-02 | 山东交通学院 | Hazardous chemical substance road transportation risk assessment method and system |
| CN113359834A (en) * | 2021-06-22 | 2021-09-07 | 中国民用航空总局第二研究所 | Unmanned aerial vehicle operation monitoring method, system and monitoring platform |
| CN114818056A (en) * | 2022-04-06 | 2022-07-29 | 深圳市同鑫科技有限公司 | Traffic data integration method, device, equipment and medium based on BIM technology |
| CN114971217A (en) * | 2022-05-07 | 2022-08-30 | 南京航空航天大学 | Unmanned aerial vehicle ground risk assessment method and system |
| CN115489336A (en) * | 2022-09-28 | 2022-12-20 | 同济大学 | Motor torque output optimization method and device based on big data |
| CN115946886A (en) * | 2022-12-31 | 2023-04-11 | 山东龙翼航空科技有限公司 | Large-scale unmanned aerial vehicle that carries |
-
2023
- 2023-10-12 CN CN202311317287.6A patent/CN117078020B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110113155A1 (en) * | 2008-06-24 | 2011-05-12 | Tsia Kuznetsov | Methods and systems for dynamically adaptive road network hierarchy and routing |
| CN102353380A (en) * | 2011-06-30 | 2012-02-15 | 福建慧翰信息技术有限公司 | Road condition prediction and query system and method |
| CN103106813A (en) * | 2013-01-18 | 2013-05-15 | 南京航空航天大学 | Traffic situation complexity assessment method based on index system |
| CN104021671A (en) * | 2014-05-16 | 2014-09-03 | 浙江银江研究院有限公司 | Real-time road condition determination method through combined svm and fuzzy determination mode |
| WO2016047949A1 (en) * | 2014-09-24 | 2016-03-31 | 삼성에스디에스 주식회사 | Method and apparatus for logistics risk prediction |
| KR20190126756A (en) * | 2016-04-26 | 2019-11-12 | 김명준 | Method and system for logistics transportation using an unmanned aircraft based on the muilti-relay station environment |
| CN107657412A (en) * | 2017-10-23 | 2018-02-02 | 天津职业技术师范大学 | Towards the unmanned plane and automobile combined type delivery system and allocator of remote districts |
| CN108900993A (en) * | 2018-05-09 | 2018-11-27 | 顺丰科技有限公司 | A kind of wireless data sending network system and its working method |
| CN111240229A (en) * | 2020-03-20 | 2020-06-05 | 智慧航海（青岛）科技有限公司 | Fault information processing system based on intelligent ship berthing simulation test |
| CN112185151A (en) * | 2020-09-15 | 2021-01-05 | 上海思寒环保科技有限公司 | Intelligent path planning method and system |
| CN112735189A (en) * | 2020-12-24 | 2021-04-30 | 朱上翔 | Method and system for ground-air mode conversion and intelligent air management of flying vehicle |
| CN113011809A (en) * | 2021-01-26 | 2021-06-22 | 浙江双友物流器械股份有限公司 | Cargo displacement monitoring method for multi-path condition transportation |
| CN113065804A (en) * | 2021-04-27 | 2021-07-02 | 山东交通学院 | Hazardous chemical substance road transportation risk assessment method and system |
| CN113359834A (en) * | 2021-06-22 | 2021-09-07 | 中国民用航空总局第二研究所 | Unmanned aerial vehicle operation monitoring method, system and monitoring platform |
| CN114818056A (en) * | 2022-04-06 | 2022-07-29 | 深圳市同鑫科技有限公司 | Traffic data integration method, device, equipment and medium based on BIM technology |
| CN114971217A (en) * | 2022-05-07 | 2022-08-30 | 南京航空航天大学 | Unmanned aerial vehicle ground risk assessment method and system |
| CN115489336A (en) * | 2022-09-28 | 2022-12-20 | 同济大学 | Motor torque output optimization method and device based on big data |
| CN115946886A (en) * | 2022-12-31 | 2023-04-11 | 山东龙翼航空科技有限公司 | Large-scale unmanned aerial vehicle that carries |
Non-Patent Citations (2)
| Title |
|---|
| ZHENHAO, L.V. ET AL: "Four-Rotor UAV Safe Landing Risk Assessment Method", <4TH INTERNATIONAL CONFERENCE ON INSULATING MATERIALS, MATERIAL APPLICATION AND ELECTRICAL ENGINEERING (IMMAEE 2019)>, vol. 677, pages 0320005 * |
| 汪?;: "亿航：打通"路空一体"大载重空中物流航线 构建数字化空中生态体系", 交通建设与管理, no. 03 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117670198A (en) * | 2023-12-13 | 2024-03-08 | 江苏菲达宝开电气股份有限公司 | Smart bin Chu Yunwei method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117078020B (en) | 2024-01-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113240909B (en) | Vehicle monitoring method, equipment, cloud control platform and vehicle road cooperative system | |
| US12008896B2 (en) | System and method for detection and quantification of irregular traffic congestion | |
| CN109923595B (en) | Urban road traffic abnormity detection method based on floating car data | |
| CN109686088B (en) | Traffic video alarm method, equipment and system | |
| CN110807930B (en) | Dangerous vehicle early warning method and device | |
| CN117078020B (en) | Logistics transportation data management system based on unmanned aerial vehicle | |
| CN110718057B (en) | Road network operation state evaluation method and device, electronic equipment and medium | |
| KR102508367B1 (en) | Control server that supports safety monitoring and operational design domain of connected autonomous vehicle | |
| CN110310501B (en) | Method for monitoring and forecasting mass fog of motor vehicle expressway | |
| Tak et al. | Development of AI‐Based Vehicle Detection and Tracking System for C‐ITS Application | |
| CN111460920A (en) | Target tracking and segmenting system for complex scene of airport | |
| CN118278841B (en) | Large-piece transportation supervision and early warning method, system and device based on driving track | |
| CN115329865A (en) | Driving condition identification method based on multi-core learning support vector machine | |
| CN113256014B (en) | Intelligent detection system for 5G communication engineering | |
| CN118038674A (en) | Monitoring system real-time dynamic analysis warning system based on big data | |
| CN115691223A (en) | Cloud edge-end cooperation-based collision early warning method and system | |
| CN118485370A (en) | Track verification method and device for large-piece transportation route and electronic equipment | |
| CN113284337B (en) | OD matrix calculation method and device based on vehicle track multidimensional data | |
| CN112447042B (en) | Traffic incident detection system and method | |
| CN117314280A (en) | Method and system for processing capacity data | |
| CN113393011B (en) | Method, device, computer equipment and medium for predicting speed limit information | |
| CN113053100A (en) | Method and device for estimating bus arrival time | |
| CN118366312B (en) | Traffic detection system and method | |
| Hayashi et al. | Prioritization of Lane-based Traffic Jam Detection for Automotive Navigation System utilizing Suddenness Index Calculation Method for Aggregated Values | |
| CN118522151B (en) | Traffic flow prediction method, device, equipment and medium |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |