CN117354374B - Data transmission method and system based on Internet of Things - Google Patents
Data transmission method and system based on Internet of Things Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/12—Shortest path evaluation
- H04L45/123—Evaluation of link metrics
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
- H04L67/61—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources taking into account QoS or priority requirements
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- H—ELECTRICITY
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- H04L67/50—Network services
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- H04L67/63—Routing a service request depending on the request content or context
Abstract
The application relates to the technical field of the Internet of things and discloses a data transmission method and system based on the Internet of things. The method comprises the following steps: updating the data packet downloading state of the target vehicle; matching a plurality of candidate downloading modes and acquiring downloading procedure information; analyzing the flow information to obtain a downloading path and analyzing downloading nodes to obtain a plurality of downloading nodes; obtaining an average download success rate and average download time consumption; performing success rate evaluation index conversion according to the average downloading success rate to obtain a target success rate evaluation index; calculating time consumption scores according to the average download time consumption to obtain target time consumption scores of each candidate download mode; and optimally analyzing the plurality of candidate downloading modes according to the target success rate evaluation index and the target time-consuming value to obtain a target downloading mode.
Description
Technical Field
The application relates to the field of internet of things, in particular to a data transmission method and system based on the internet of things.
Background
With the rapid development of internet of vehicles and the advent of the smart car age, the data exchange demands of vehicles and the outside world are increasing. In particular, for the downloading and updating of the vehicle-mounted map data, this is directly related to driving safety and driving efficiency. In this context, an efficient and reliable data transmission method is particularly important. The internet of things technology provides a richer and real-time data exchange capability for vehicles by building intelligent connection between the vehicles and related infrastructure.
However, how to cope with complex and variable network environments and data volumes while ensuring downloading efficiency and data accuracy becomes a problem to be solved urgently.
Disclosure of Invention
The application provides a data transmission method and system based on the Internet of things.
In a first aspect, the present application provides a data transmission method based on the internet of things, where the data transmission method based on the internet of things includes:
acquiring a vehicle starting state of a target vehicle, controlling the target vehicle to download vehicle-mounted map data of a preset initial area from a preset internet of things vehicle-mounted map database according to the vehicle starting state, and simultaneously updating the data package downloading state of the target vehicle in real time;
matching a plurality of candidate downloading modes of the target vehicle according to the data packet downloading state, and acquiring downloading procedure information of each candidate downloading mode;
carrying out flow information analysis on the download flow information of each candidate download mode to obtain a download path of each candidate download mode, carrying out download node analysis on the download path to obtain a plurality of download nodes of each download path, and obtaining the average download success rate and the average download time consumption of each candidate download mode;
Sorting the plurality of candidate downloading modes according to the average downloading success rate to obtain a first sorting result, and converting success rate evaluation indexes of the plurality of candidate downloading modes according to the first sorting result to obtain target success rate evaluation indexes of each candidate downloading mode;
sorting the plurality of candidate downloading modes according to the average downloading time consumption to obtain a second sorting result, and calculating time consumption scores of the plurality of candidate downloading modes according to the second sorting result to obtain a target time consumption value of each candidate downloading mode;
and carrying out optimization analysis on the plurality of candidate downloading modes according to the target success rate evaluation index and the target time consumption value to obtain a target downloading mode.
In a second aspect, the present application provides a data transmission system based on the internet of things, where the data transmission system based on the internet of things includes:
the acquisition module is used for acquiring the vehicle starting state of the target vehicle, controlling the target vehicle to download the vehicle-mounted map data of the preset initial area from the preset internet of things vehicle-mounted map database according to the vehicle starting state, and simultaneously updating the data packet downloading state of the target vehicle in real time;
The matching module is used for matching a plurality of candidate downloading modes of the target vehicle according to the data packet downloading state and acquiring downloading flow information of each candidate downloading mode;
the processing module is used for analyzing the flow information of the download flow information of each candidate download mode to obtain a download path of each candidate download mode, analyzing the download nodes of the download paths to obtain a plurality of download nodes of each download path, and acquiring the average download success rate and the average download time consumption of each candidate download mode;
the conversion module is used for sequencing the plurality of candidate downloading modes according to the average downloading success rate to obtain a first sequencing result, and converting success rate evaluation indexes of the plurality of candidate downloading modes according to the first sequencing result to obtain target success rate evaluation indexes of each candidate downloading mode;
the calculation module is used for sequencing the plurality of candidate downloading modes according to the average downloading time consumption to obtain a second sequencing result, and calculating time consumption scores of the plurality of candidate downloading modes according to the second sequencing result to obtain a target time consumption score of each candidate downloading mode;
And the analysis module is used for carrying out optimization analysis on the plurality of candidate downloading modes according to the target success rate evaluation index and the target time consumption value to obtain a target downloading mode.
According to the technical scheme, the method effectively reduces invalid or repeated data transmission by intelligently acquiring the starting state of the target vehicle and controlling the target vehicle to download the vehicle-mounted map data of the preset initial area from the internet vehicle-mounted map database, so that the overall data transmission efficiency is improved. Meanwhile, the real-time updated data packet downloading state ensures the flexibility and adaptability of the transmission process, and further optimizes the management of the data stream. The method remarkably improves the success rate of data transmission by analyzing the average download success rate and the average download time consumption of a plurality of candidate download modes and optimizing and sequencing and evaluating index conversion according to the average download success rate and the average download time consumption. The download node analysis further ensures the integrity and accuracy of the data in the transmission process, thereby enhancing the reliability of the overall transmission process. The candidate download mode is matched according to the download state, and the flow information of the mode is deeply analyzed, so that the data transmission strategy can be dynamically adjusted according to the real-time situation. By double evaluation of the downloading success rate and the downloading time consumption, the optimal downloading mode can be more accurately selected, so that the data transmission speed is ensured, and the data quality is ensured. And intelligently selecting the most appropriate data transmission mode according to the real-time state and the network condition of the vehicle. The self-adaptive capability shows high intellectualization of the system, and can effectively cope with various complex practical application scenes, thereby improving the data transmission efficiency of vehicle-mounted data.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained based on these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an embodiment of a data transmission method based on the internet of things in an embodiment of the present application;
fig. 2 is a schematic diagram of an embodiment of a data transmission system based on the internet of things in an embodiment of the present application.
Detailed Description
The embodiment of the application provides a data transmission method and system based on the Internet of things. The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation 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 expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, the following describes a specific flow of an embodiment of the present application, referring to fig. 1, and one embodiment of a data transmission method based on the internet of things in the embodiment of the present application includes:
step 101, acquiring a vehicle starting state of a target vehicle, controlling the target vehicle to download vehicle-mounted map data of a preset initial area from a preset internet of things vehicle-mounted map database according to the vehicle starting state, and simultaneously updating a data packet downloading state of the target vehicle in real time;
it may be understood that the execution body of the application may be a data transmission system based on the internet of things, and may also be a terminal or a server, which is not limited herein. The embodiment of the present application will be described by taking a server as an execution body.
Specifically, first, the start-up state of the target vehicle is acquired. This is typically accomplished by internal sensors of the vehicle that are able to detect whether the engine is started and the activation state of the electronic system. Then, after confirming that the vehicle has started, the system establishes a network connection between the target vehicle and a preset internet of things vehicle-mounted map database. This typically involves using a wireless communication module of the vehicle, such as a cellular network, wi-Fi, or other wireless technology. Then, once the network connection is successfully established, the system controls the target vehicle to start downloading the vehicle-mounted map data of the preset initial area from the internet-of-things vehicle-mounted map database. In the downloading process, the system adopts an optimized data transmission protocol to ensure the downloading efficiency and reliability, and meanwhile, network bandwidth and connection stability are required to be considered. And simultaneously, the download state of the target vehicle is monitored and updated in real time. This includes monitoring the receipt of the data packet, identifying and handling any potential problems such as a connection interruption or data error, and tracking the download progress to ensure that the data packet is intact and undamaged. Finally, the system provides feedback to the vehicle system so that the vehicle can immediately use the newly downloaded map data. The real-time feedback mechanism ensures that the vehicle can access the latest map data at any time, and provides accurate navigation support for driving.
102, matching a plurality of candidate downloading modes of a target vehicle according to the downloading state of the data packet, and acquiring downloading flow information of each candidate downloading mode;
specifically, first, the candidate download pattern of the target vehicle is updated in real time based on the current packet download status. By analyzing the current network connection quality, download speed and other relevant parameters of the vehicle. A downloading mode list is preset in the system, wherein the downloading mode list comprises a plurality of downloading schemes under different network environments and vehicle states. According to the real-time data, the system dynamically selects the mode which is most matched with the current download state, so that the high efficiency and stability of data transmission are ensured. Meanwhile, a plurality of candidate downloading modes corresponding to the target vehicle are recorded in the process, so that subsequent processing and analysis are facilitated. Next, the system obtains key information of each candidate download mode, including a download start time, a download end time, and a download amount. Such data is helpful in understanding the efficiency and feasibility of each download mode. The recording of the start and end times of the download can help the system analyze the overall time consumption of each mode, while the downloaded amount of data reflects the efficiency of data transmission over a particular period of time. Then, the system extracts download flow information corresponding to each candidate download mode according to the collected data. The goal is to identify the characteristics and potential advantages and disadvantages of each mode. For example, some modes perform better in situations where the network connection is stable but slow, while others are suitable for environments where the network fluctuates but peak speeds are high.
Step 103, analyzing the flow information of the download flow information of each candidate download mode to obtain a download path of each candidate download mode, analyzing the download nodes of the download paths to obtain a plurality of download nodes of each download path, and obtaining the average download success rate and the average download time consumption of each candidate download mode;
specifically, first, the download flow information of each candidate download mode is analyzed in detail. The data flow and intermediate steps in the transmission process are analyzed to derive a specific download path for each mode. The establishment of the download path directly affects the efficiency and stability of the data transmission. On this basis, the system further performs a node cluster calculation on the download path, identifying nodes similar to the group by analyzing each point through which the data stream passes. The result of the node clustering reveals the relative positions and roles of each node in the whole download path, and provides a basis for subsequent analysis. And then, the system calculates the importance degree of the nodes of the downloading path according to the clustering result, so as to determine the influence of each node on the whole downloading process. The importance of the nodes is determined based on their role and location in data transmission, for example, some nodes are key hubs for data transmission, while other nodes affect the transmission process relatively little. According to the importance of the nodes, the system executes the dependency calculation, so as to understand the interdependence and action relationship among different nodes, and thus obtain the dependency information of each node. The system then uses the dependency information to analyze the download paths and identify a plurality of download nodes on each download path. These download nodes are key points in the data transmission process, and they directly affect the flow and transmission efficiency of data. On the basis, the system collects the download success rate data of each download node in each candidate download mode. By analyzing the success rate of each node, the system can calculate the average download success rate for each candidate download mode, which is an important indicator for measuring the efficiency and reliability of each mode. Finally, the time-consuming data of each download node in each candidate download mode is obtained. The download time is another key indicator that directly reflects the time efficiency of the data transfer process. The system calculates the average download time consumption for each candidate download mode by analyzing the time consumption for each node. By combining the download success rate and the time-consuming data, the system can comprehensively evaluate the performance of each download mode, thereby providing data support for the final selection of the optimal download mode.
Step 104, sorting the plurality of candidate downloading modes according to the average downloading success rate to obtain a first sorting result, and converting success rate evaluation indexes of the plurality of candidate downloading modes according to the first sorting result to obtain target success rate evaluation indexes of each candidate downloading mode;
specifically, first, sorting is performed according to the average download success rate of each candidate download mode, and a first sorting result is generated. The average download success rate is derived through historical data statistics, which reflects the overall performance of each mode under different network conditions and vehicle conditions. In this way, the system can objectively evaluate which download modes are more reliable and efficient in practical applications. Then, the system acquires a conversion relation table between the candidate download mode and the preset success rate index. The conversion relation table is a part of system design, and establishes the corresponding relation between success rates of different download modes and evaluation indexes based on a large amount of data analysis in the past. The correspondence helps the system to convert the raw success rate data into more targeted evaluation indexes, thereby analyzing the performance of each mode more carefully. And then, the system converts the success rate evaluation index of each candidate downloading mode according to the conversion relation table to obtain the initial success rate evaluation index of each mode. The abstract success rate data is converted into specific and operable evaluation indexes, and more visual and practical references are provided for subsequent decisions. And then, carrying out weight assignment on the candidate downloading modes according to the first sorting result. This process takes into account the ordering position of each pattern and its performance and reliability in practical applications, so that each pattern is assigned a corresponding weight value. The purpose of the weight assignment is to reflect the relative importance of each mode in the whole evaluation system, and the fairness and accuracy of the evaluation process are ensured. And finally, carrying out weighting operation on the initial success rate evaluation index according to the weight allocated to each mode, thereby obtaining the target success rate evaluation index of each candidate downloading mode. The weighting operation is performed based on the weight of each mode and its initial evaluation index, and aims to comprehensively consider the historical performance and the current sequencing result of the modes, thereby obtaining a more comprehensive and accurate evaluation result.
Step 105, sorting the plurality of candidate downloading modes according to the average downloading time consumption to obtain a second sorting result, and calculating time consumption scores of the plurality of candidate downloading modes according to the second sorting result to obtain a target time consumption score of each candidate downloading mode;
specifically, first, the average download time consumption of the plurality of candidate download modes is subjected to size sorting, and a second sorting result is generated. The goal is to determine which download modes are less time consuming in actual use and thus exhibit greater efficiency. The ranking is based on historical data, which reflects the average download time consumption of each mode under different network conditions, providing a basis for subsequent evaluation. Then, a conversion relation table between the candidate download mode and the preset time-consuming value is acquired. This conversion table is built based on historical data and predefined criteria, which converts the raw time-consuming data into more operable time-consuming values. Through this transformation, the system is able to translate the abstract time-consuming data into more specific and practical scoring criteria. And then, the system performs time-consuming value conversion on each candidate downloading mode according to the time-consuming value conversion relation table to obtain an initial time-consuming value of each mode. The original time-consuming data is converted into an initial and standardized grading value, and a reference is provided for subsequent weighting operation and comprehensive evaluation. And then, the system carries out weight assignment on the candidate downloading modes according to the second sorting result. This step takes into account the position of each pattern in the time-consuming ordering, assigning a corresponding weight value. The weight assignment reflects the relative importance of each mode in the whole evaluation system, and the comprehensiveness and accuracy of the evaluation process are ensured. And finally, the system performs weighted operation on the initial time-consuming scores according to the weight allocated to each mode to obtain the target time-consuming score of each candidate downloading mode. The weighting operation is performed based on the weight of each pattern and its initial time-consuming value, aiming at comprehensively considering the historical time-consuming performance of the patterns and the current sequencing result to obtain a more comprehensive and accurate time-consuming evaluation.
And 106, performing optimization analysis on the plurality of candidate downloading modes according to the target success rate evaluation index and the target time-consuming value to obtain a target downloading mode.
Specifically, first, a success rate relation curve between a target success rate evaluation index and a plurality of candidate download modes is constructed. The curve is based on the target success rate evaluation index of each download mode, and shows the change trend of the success rate under different modes. The purpose of the success rate relation curve is to visually represent the performance of different download modes in terms of success rate, providing an intuitive basis for subsequent analysis. Next, a time-consuming relationship curve between the target time-consuming value and the plurality of candidate download patterns is constructed. Similar to the success rate relationship, the time-consuming relationship is drawn according to the target time-consuming score for each download mode, demonstrating the time-consuming changes in the different modes. This curve helps the system understand the behavior of the various modes in terms of time efficiency, so that time costs can be fully accounted for when selecting the download mode. The system then converts the two relationship curves into the same coordinate system and identifies them as curve intersections. The key to this step is to find those download modes that perform well in terms of both success rate and time consumption, which usually appear as intersections of two curves. Identification of curve intersections is a critical data analysis process that requires an accurate determination of at which points two curves intersect, revealing those download patterns that perform well in both evaluation dimensions. Once these target curve intersections are determined, the system then obtains two candidate download patterns corresponding to these intersections. This step involves in-depth analysis of the download patterns behind each intersection to determine their performance and applicability in practical applications. And finally, the system optimally selects two candidate download modes corresponding to the target curve intersection points to obtain a final target download mode. This selection process is not only based on the data analysis results, but also takes into account the actual application scenario and requirements to ensure that the selected download mode is both efficient and reliable. By the method, the system can select the downloading mode which is most suitable for the current network environment and the vehicle state from a plurality of candidates, so that the overall performance and the user experience of data transmission are effectively improved.
According to the method, invalid or repeated data transmission is effectively reduced by intelligently acquiring the starting state of the target vehicle and controlling the target vehicle to download the vehicle-mounted map data of the preset initial area from the internet-of-things vehicle-mounted map database, so that the overall data transmission efficiency is improved. Meanwhile, the real-time updated data packet downloading state ensures the flexibility and adaptability of the transmission process, and further optimizes the management of the data stream. The method remarkably improves the success rate of data transmission by analyzing the average download success rate and the average download time consumption of a plurality of candidate download modes and optimizing and sequencing and evaluating index conversion according to the average download success rate and the average download time consumption. The download node analysis further ensures the integrity and accuracy of the data in the transmission process, thereby enhancing the reliability of the overall transmission process. The candidate download mode is matched according to the download state, and the flow information of the mode is deeply analyzed, so that the data transmission strategy can be dynamically adjusted according to the real-time situation. By double evaluation of the downloading success rate and the downloading time consumption, the optimal downloading mode can be more accurately selected, so that the data transmission speed is ensured, and the data quality is ensured. And intelligently selecting the most appropriate data transmission mode according to the real-time state and the network condition of the vehicle. The self-adaptive capability shows high intellectualization of the system, and can effectively cope with various complex practical application scenes, thereby improving the data transmission efficiency of vehicle-mounted data.
In a specific embodiment, the process of executing step 101 may specifically include the following steps:
(1) Acquiring a vehicle starting state of a target vehicle, wherein the vehicle starting state is as follows: the vehicle is started and the vehicle is not started;
(2) If the vehicle starting state is that the vehicle is started, establishing network connection between the target vehicle and a preset Internet of things vehicle-mounted map database, and acquiring a target connection state of the network connection;
(3) When the target connection state is that the connection is successful, the target vehicle is controlled to download vehicle-mounted map data of a preset initial area from an internet-of-things vehicle-mounted map database;
(4) And monitoring the download state of the target vehicle, and updating the download state of the data packet of the target vehicle in real time.
Specifically, first, the start-up state of the target vehicle is acquired, and this state is classified into two cases of "the vehicle is started" and "the vehicle is not started". This may be achieved by using a sensor system inside the vehicle that is able to detect key signals at vehicle start-up, such as engine start-up, activation of an electronic ignition system, etc. These signals are transmitted to the central processing unit of the vehicle, from which the starting state of the vehicle is analyzed and determined. And if the vehicle starting state is that the vehicle is started, establishing network connection between the target vehicle and a preset Internet of things vehicle-mounted map database. Wireless communication modules of the vehicle are used, such as 4G or 5G cellular networks, wi-Fi, etc. The system attempts to connect to the internet through these communication modules and further accesses a preset map database. During this process, the system will constantly try to establish a connection and monitor the status of the connection. For example, a vehicle equipped with an intelligent navigation system will automatically attempt to connect to a remote map database server via its cellular network module after start-up. And after the system successfully establishes network connection, namely the target connection state is 'connection success', downloading the vehicle-mounted map data of the preset initial area from the internet vehicle-mounted map database. This download process is dynamic and the system will select the appropriate map data to download based on the current geographic location of the vehicle and the predetermined route. The download state of the target vehicle is monitored in real time while the map data is downloaded. This includes tracking the download progress, detecting any interruption or error that occurs, and adjusting the download process accordingly. For example, if the network connection becomes unstable during the download, the system may pause the download, wait for the connection to stabilize and then continue, or find other available network connections to continue the download. Meanwhile, the system can update the downloading state of the data packet of the vehicle in real time so as to ensure that the downloaded data is up-to-date and complete.
In a specific embodiment, the process of executing step 102 may specifically include the following steps:
(1) Based on the data packet downloading state, updating the candidate downloading mode of the target vehicle in real time through a preset downloading mode list, and recording a plurality of candidate downloading modes corresponding to the target vehicle;
(2) Acquiring the downloading start time, the downloading end time and the downloading amount of each candidate downloading mode;
(3) And extracting the download procedure information corresponding to each candidate download mode according to the download start time, the download end time and the download amount.
Specifically, first, the candidate download mode is updated in real time according to the download status of the data packet of the target vehicle. A download mode list is preset in the system, and this list contains a plurality of different download configurations, each of which is suitable for a specific network environment and vehicle status. For example, some download modes are suitable for high-speed stable network environments, while others perform better in situations where the network fluctuations are large. The system dynamically selects the most appropriate download mode based on the current download status of the vehicle, download speed, network stability, and historical download records. Then, a plurality of candidate download modes corresponding to the target vehicle are recorded. These records include not only the currently selected mode, but also those modes that are alternatives. The purpose is to be able to switch quickly to another more suitable mode when a change of the download environment occurs. For example, a vehicle may initially use a mode that is suitable for high-speed downloading, but if an area is entered where the network signal is weak, the system switches to another mode that is more suitable for low-speed stable downloading. Then, key information of each candidate download mode is acquired, including download start time, download end time and download amount. This information directly reflects the efficiency and applicability of each download mode. The recording of the start and end times of the download can help the system analyze the overall time consumption of each mode, while the downloaded amount of data demonstrates the amount of data transferred in a particular time. And finally, extracting the downloading flow information corresponding to each candidate downloading mode according to the collected data by the system. The start time, end time, and download amount data are analyzed to identify characteristics and potential advantages and disadvantages of each download mode. For example, one download mode completes downloading of a large amount of data in a short time, but this needs to be done under a good network condition; while the other mode is downloaded at a slower speed but is more suitable for use when network conditions are unstable. In this way, the system can understand in detail the behavior of each download mode under different conditions and make a more accurate selection accordingly. This not only improves the efficiency of data transmission, but also increases the adaptability and flexibility of the overall system.
In a specific embodiment, the process of executing step 103 may specifically include the following steps:
(1) Carrying out flow information analysis on the download flow information corresponding to each candidate download mode to obtain a download path;
(2) Performing node clustering calculation on the downloading path to obtain a node clustering result, and performing node importance calculation on the downloading path according to the node clustering result to obtain node importance;
(3) Performing dependency calculation on the download path according to the node importance degree to obtain node dependency information;
(4) According to the node affiliation information, carrying out download node identification on the download paths to obtain a plurality of download nodes of each download path;
(5) Acquiring the download success rate corresponding to each download node in each candidate download mode, and calculating the average download success rate of each candidate download mode according to the download success rate corresponding to each download node;
(6) And acquiring the downloading time consumption corresponding to each downloading node in each candidate downloading mode, and calculating the average downloading time consumption of each candidate downloading mode according to the downloading time consumption corresponding to each downloading node.
Specifically, first, the download path information corresponding to each candidate download mode is parsed to obtain a specific download path. This analyzes the data transmission manner and procedure of each mode, including the start point and end point of data, passing route nodes, and the like. For example, one download mode involves data transfer through multiple relay servers, while another mode is directly from the source server to the target device. The system analyzes the information to determine the data transmission path for each mode. Then, node clustering calculation is performed on these download paths. The purpose is to classify and group nodes on the download path according to their characteristics and roles. Through a clustering algorithm, the system can identify which nodes play a similar role in the downloading process, for example, some nodes are mainly used for data caching, and other nodes are responsible for data forwarding. For example, if a download pattern is often transmitting large amounts of data through a particular network node, that node will be identified as an important node in the cluster analysis. And then, calculating the importance degree of the nodes on the downloading path according to the node clustering result. The importance of a node is assessed based on its role and location throughout the download process. For example, some nodes are located at critical network intersections, which is important to maintain the continuity and stability of the download process. The system analyzes the functions and network status of these nodes to determine their importance. Then, the system uses the importance degree of the nodes to perform subordinate relation calculation, and understand the mutual dependence and action relation between different nodes. The membership calculation helps the system identify which nodes are important to the download process and which are relatively minor. For example, some nodes are responsible for initial data requests, while other nodes act as relays during data transmission. And then, the system identifies the download nodes of the download path according to the node affiliation information. A critical data transfer node is identified throughout the download path. These download nodes are key points in the data transmission process, and they directly affect the flow and transmission efficiency of data. For example, if a node is often the bottleneck for data transfer in a download path, that node is identified as a critical download node. Finally, the system obtains the download success rate of each download node in each candidate download mode, and calculates the average download success rate of each mode according to the download success rate. The historical performance of each node is analyzed, including its success rate and stability in data transmission. Meanwhile, the system also collects the downloading time-consuming data of each node and calculates the average downloading time-consuming of each candidate downloading mode. For example, if a node in a download mode frequently experiences transmission failures or delays, this can affect the overall success rate and time consumption of the mode.
In a specific embodiment, the process of executing step 104 may specifically include the following steps:
(1) Sequencing a plurality of candidate downloading modes according to the average downloading success rate to obtain a first sequencing result;
(2) Acquiring a success rate evaluation index conversion relation table between a plurality of candidate downloading modes and preset success rate indexes;
(3) Performing success rate evaluation index conversion on the plurality of candidate downloading modes according to the success rate evaluation index conversion relation table to obtain initial success rate evaluation indexes of each candidate downloading mode;
(4) According to the first sorting result, carrying out weight assignment on the plurality of candidate downloading modes to obtain first weight data of each candidate downloading mode;
(5) And carrying out weighted operation on the initial success rate evaluation index according to the first weight data to obtain a target success rate evaluation index of each candidate downloading mode.
Specifically, first, a plurality of candidate download modes are ranked according to an average download success rate, so as to obtain a first ranking result. The average download success rate is a key indicator that reflects the reliability of each mode in actual operation. For example, if one download mode is successful in completing a data transfer in most cases, its average download success rate will be high, whereas if another mode is frequently failed to transfer or damaged, its average download success rate will be low. The system ranks the download modes by comparing their average download success rates, ensuring that those modes that perform best are ranked in front. Then, a success rate evaluation index conversion relation table between a plurality of candidate download modes and a preset success rate index is obtained. The conversion relation table is established according to historical data and predefined standards, and converts the original download success rate into more detailed and specific evaluation indexes. For example, this relationship table specifies that if the download success rate reaches 90% or more, the corresponding evaluation index is "excellent", whereas if the success rate is lower than 60%, the evaluation index is "poor". And then, the system converts the success rate evaluation index of each candidate downloading mode according to the conversion relation table to obtain the initial success rate evaluation index of each mode. The process converts the original success rate data into an evaluation index with more practical application value, so that the evaluation result is more visual and comparable. For example, two download modes have download success rates of 85% and 95%, respectively, which are converted into "good" and "excellent" evaluation indexes according to the conversion relation table. Then, the system performs weight assignment on the plurality of candidate download modes according to the first sorting result. The relative importance of each mode in the overall evaluation system is determined based on the ranking position of the mode. For example, the front-ranked mode may be given a higher weight due to its higher download success rate, while the rear-ranked mode may be given a lower weight. This weight assignment reflects the priority and reliability of the different download modes in the actual application. And finally, the system carries out weighting operation on the initial success rate evaluation index according to the first weight data allocated to each mode to obtain the target success rate evaluation index of each candidate downloading mode. The weighting operation combines the original download success rate, the converted evaluation index and the weight of the mode to obtain an evaluation result comprehensively considering various factors.
In a specific embodiment, the process of executing step 105 may specifically include the following steps:
(1) Performing size sorting on average download time consumption of a plurality of candidate download modes to obtain a second sorting result;
(2) Obtaining a time-consuming value conversion relation table between a plurality of candidate downloading modes and preset time-consuming values;
(3) Performing time-consuming value conversion on the plurality of candidate downloading modes according to the time-consuming value conversion relation table to obtain initial time-consuming values of each candidate downloading mode;
(4) According to the second sorting result, carrying out weight assignment on the plurality of candidate downloading modes to obtain second weight data of each candidate downloading mode;
(5) And carrying out weighted operation on the initial time-consuming values according to the second weight data to obtain the target time-consuming value of each candidate downloading mode.
Specifically, first, the average download time consumption of the plurality of candidate download modes is subjected to size sorting, so that a second sorting result is obtained. Average download time is the average time required to complete a data download task within a certain period of time, and is an important indicator for evaluating download efficiency. For example, if a download mode is to complete a download of a large amount of data, typically in a short period of time, then the average download time will be short; conversely, if another mode takes longer to download an equal amount of data, its average download takes longer. The system can determine which modes perform better in time efficiency by comparing the average download time consumption of the different modes. Then, a time-consuming value conversion relation table between the candidate download mode and the preset time-consuming value is acquired. This table is built from historical data and predefined criteria, which converts the raw time-consuming data into more refined and specific time-consuming scores. This conversion allows the time consuming comparison and evaluation of the different download modes by a unified standard. For example, the conversion relationship table specifies that if the average download takes less than a certain threshold, such as 30 minutes, the time-consuming score is defined as "efficient", and if it takes more than one hour, the score is "inefficient". And then, the system performs time-consuming value conversion on each candidate downloading mode according to the time-consuming value conversion relation table to obtain an initial time-consuming value of each mode. The raw time-consuming data is converted into a more standardized and comparable form, thereby providing a basis for subsequent weighting operations and comprehensive evaluation. For example, two download modes have average download time consumption of 40 minutes and 70 minutes, respectively, which are converted into "medium efficiency" and "low efficiency" time consumption scores according to a conversion relationship table. And then, the system carries out weight assignment on the plurality of candidate downloading modes according to the second sequencing result. The relative importance of each pattern in the overall evaluation system is determined based on its position in the time-consuming ordering. For example, a shorter time consuming pattern may be given a higher weight due to its higher time efficiency, while a longer time consuming pattern may be given a lower weight. This weight assignment reflects the priority and efficiency of the different download modes in the actual application. And finally, the system carries out weighted operation on the initial time-consuming values according to the second weight data distributed to each mode to obtain the target time-consuming value of each candidate downloading mode. The weighting operation combines the original time consumption of downloading, the time consumption value after conversion and the weight of the mode to obtain a time consumption evaluation result comprehensively considering various factors.
In a specific embodiment, the process of executing step 106 may specifically include the following steps:
(1) Constructing a success rate relation curve between a target success rate evaluation index and a plurality of candidate downloading modes;
(2) Constructing a time-consuming relation curve between the target time-consuming value and a plurality of candidate downloading modes;
(3) Converting the success rate relation curve and the time-consuming relation curve into the same coordinate system, and identifying curve intersection points of the success rate relation curve and the time-consuming relation curve to obtain target curve intersection points;
(4) And obtaining two candidate download modes corresponding to the target curve intersection point, and optimally selecting the two candidate download modes corresponding to the target curve intersection point to obtain the target download mode.
Specifically, first, a success rate relation curve between a target success rate evaluation index and a plurality of candidate download modes is constructed. The aim is to visually display the success rate performance of different download modes, thereby providing an intuitive basis for comparison and analysis. For example, each download mode may have a different success rate performance based on its different network conditions and packet sizes, which are summarized and plotted to show the performance of each mode under different conditions. Next, a time-consuming relationship curve between the target time-consuming value and the plurality of candidate download patterns is constructed. Similar to the success rate relationship, the time-consuming relationship is drawn according to the average time consumption of each download mode, demonstrating the time-consuming changes in the different modes. For example, some download modes perform tasks quickly when network conditions are good, and time consumption increases when the network is unstable, all of which are reflected in the time-consuming relationship curve. The system then converts the two relationship curves into the same coordinate system and identifies them as curve intersections. The intersection of the curves represents the optimal balance between success rate and time consumption. For example, an intersection point indicates that a certain download pattern has a high success rate while maintaining reasonable time consumption, and such a pattern is often the most desirable choice. Once these target curve intersections are determined, the system then obtains two candidate download patterns corresponding to these intersections. The download patterns behind each intersection are analyzed in depth to determine their performance and applicability in practical applications. For example, two modes corresponding to the intersection point perform best under different network environments or data packet sizes, and the system judges which mode is more suitable according to actual application scenes and requirements. And finally, the system optimally selects two candidate download modes corresponding to the target curve intersection points to obtain a final target download mode. This selection process is not only based on the data analysis results, but also takes into account the actual application scenario and requirements to ensure that the selected download mode is both efficient and reliable. For example, if a vehicle is about to enter an area where network conditions are poor, the system may select a download mode that will still maintain a high success rate in such an environment.
The data transmission method based on the internet of things in the embodiment of the present application is described above, and the data transmission system based on the internet of things in the embodiment of the present application is described below, referring to fig. 2, an embodiment of the data transmission system based on the internet of things in the embodiment of the present application includes:
the acquiring module 201 is configured to acquire a vehicle start state of a target vehicle, control the target vehicle to download vehicle-mounted map data of a preset initial area from a preset internet of things vehicle-mounted map database according to the vehicle start state, and update a data packet download state of the target vehicle in real time;
a matching module 202, configured to match a plurality of candidate download modes of the target vehicle according to the data packet download status, and obtain download procedure information of each candidate download mode;
the processing module 203 is configured to perform flow information analysis on the download flow information of each candidate download mode to obtain a download path of each candidate download mode, perform download node analysis on the download path to obtain a plurality of download nodes of each download path, and obtain an average download success rate and an average download time consumption of each candidate download mode;
The conversion module 204 is configured to sort the plurality of candidate download modes according to the average download success rate to obtain a first sorting result, and convert success rate evaluation indexes of the plurality of candidate download modes according to the first sorting result to obtain target success rate evaluation indexes of each candidate download mode;
the calculating module 205 is configured to sort the plurality of candidate download modes according to the average download time consumption, obtain a second sorting result, and calculate time consumption scores of the plurality of candidate download modes according to the second sorting result, so as to obtain a target time consumption score of each candidate download mode;
and the analysis module 206 is configured to perform an optimization analysis on the plurality of candidate download modes according to the target success rate evaluation index and the target time consumption value, so as to obtain a target download mode.
Through the cooperative cooperation of the components, the method effectively reduces invalid or repeated data transmission by intelligently acquiring the starting state of the target vehicle and controlling the target vehicle to download the vehicle-mounted map data of the preset initial area from the internet-of-things vehicle-mounted map database, thereby improving the overall data transmission efficiency. Meanwhile, the real-time updated data packet downloading state ensures the flexibility and adaptability of the transmission process, and further optimizes the management of the data stream. The method remarkably improves the success rate of data transmission by analyzing the average download success rate and the average download time consumption of a plurality of candidate download modes and optimizing and sequencing and evaluating index conversion according to the average download success rate and the average download time consumption. The download node analysis further ensures the integrity and accuracy of the data in the transmission process, thereby enhancing the reliability of the overall transmission process. The candidate download mode is matched according to the download state, and the flow information of the mode is deeply analyzed, so that the data transmission strategy can be dynamically adjusted according to the real-time situation. By double evaluation of the downloading success rate and the downloading time consumption, the optimal downloading mode can be more accurately selected, so that the data transmission speed is ensured, and the data quality is ensured. And intelligently selecting the most appropriate data transmission mode according to the real-time state and the network condition of the vehicle. The self-adaptive capability shows high intellectualization of the system, and can effectively cope with various complex practical application scenes, thereby improving the data transmission efficiency of vehicle-mounted data.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, systems and units may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (2)
1. The data transmission method based on the Internet of things is characterized by comprising the following steps of:
acquiring a vehicle starting state of a target vehicle, controlling the target vehicle to download vehicle-mounted map data of a preset initial area from a preset internet of things vehicle-mounted map database according to the vehicle starting state, and simultaneously updating the data package downloading state of the target vehicle in real time; the method specifically comprises the following steps: acquiring a vehicle starting state of a target vehicle, wherein the vehicle starting state is as follows: the vehicle is started and the vehicle is not started; if the vehicle starting state is that the vehicle is started, establishing network connection between the target vehicle and a preset Internet of things vehicle-mounted map database, and acquiring a target connection state of the network connection; when the target connection state is that the connection is successful, controlling the target vehicle to download vehicle-mounted map data of a preset initial area from the internet of things vehicle-mounted map database; monitoring the download state of the target vehicle, and updating the download state of the data packet of the target vehicle in real time;
Matching a plurality of candidate downloading modes of the target vehicle according to the data packet downloading state, and acquiring downloading procedure information of each candidate downloading mode; the method specifically comprises the following steps: based on the data packet downloading state, updating the candidate downloading mode of the target vehicle in real time through a preset downloading mode list, and recording a plurality of candidate downloading modes corresponding to the target vehicle; acquiring the downloading start time, the downloading end time and the downloading amount of each candidate downloading mode; extracting download procedure information corresponding to each candidate download mode according to the download start time, the download end time and the download amount;
carrying out flow information analysis on the download flow information of each candidate download mode to obtain a download path of each candidate download mode, carrying out download node analysis on the download path to obtain a plurality of download nodes of each download path, and obtaining the average download success rate and the average download time consumption of each candidate download mode; the method specifically comprises the following steps: carrying out flow information analysis on the download flow information corresponding to each candidate download mode to obtain a download path; performing node clustering calculation on the downloading path to obtain a node clustering result, and performing node importance calculation on the downloading path according to the node clustering result to obtain node importance; performing dependency calculation on the download path according to the node importance degree to obtain node dependency information; according to the node affiliation information, carrying out download node identification on the download paths to obtain a plurality of download nodes of each download path; acquiring the download success rate corresponding to each download node in each candidate download mode, and calculating the average download success rate of each candidate download mode according to the download success rate corresponding to each download node; acquiring the downloading time consumption corresponding to each downloading node in each candidate downloading mode, and calculating the average downloading time consumption of each candidate downloading mode according to the downloading time consumption corresponding to each downloading node;
Sorting the plurality of candidate downloading modes according to the average downloading success rate to obtain a first sorting result, and converting success rate evaluation indexes of the plurality of candidate downloading modes according to the first sorting result to obtain target success rate evaluation indexes of each candidate downloading mode; the method specifically comprises the following steps: sorting the plurality of candidate downloading modes according to the average downloading success rate to obtain a first sorting result; acquiring a success rate evaluation index conversion relation table between the candidate download modes and preset success rate indexes; performing success rate evaluation index conversion on the plurality of candidate downloading modes according to the success rate evaluation index conversion relation table to obtain initial success rate evaluation indexes of each candidate downloading mode; performing weight assignment on the plurality of candidate downloading modes according to the first sequencing result to obtain first weight data of each candidate downloading mode; weighting operation is carried out on the initial success rate evaluation index according to the first weight data, and a target success rate evaluation index of each candidate downloading mode is obtained;
sorting the plurality of candidate downloading modes according to the average downloading time consumption to obtain a second sorting result, and calculating time consumption scores of the plurality of candidate downloading modes according to the second sorting result to obtain a target time consumption value of each candidate downloading mode; the method specifically comprises the following steps: sorting the average download time consumption of the plurality of candidate download modes to obtain a second sorting result; acquiring a time-consuming value conversion relation table between the plurality of candidate downloading modes and preset time-consuming values; performing time-consuming score conversion on the plurality of candidate downloading modes according to the time-consuming score conversion relation table to obtain initial time-consuming scores of each candidate downloading mode; performing weight assignment on the plurality of candidate downloading modes according to the second sorting result to obtain second weight data of each candidate downloading mode; weighting operation is carried out on the initial time consumption values according to the second weight data, and target time consumption values of each candidate downloading mode are obtained;
Performing optimization analysis on the plurality of candidate downloading modes according to the target success rate evaluation index and the target time consumption value to obtain a target downloading mode; the method specifically comprises the following steps: constructing a success rate relation curve between the target success rate evaluation index and the plurality of candidate downloading modes; constructing a time-consuming relation curve between the target time-consuming value and the plurality of candidate downloading modes; converting the success rate relation curve and the time-consuming relation curve into the same coordinate system, and identifying curve intersection points of the success rate relation curve and the time-consuming relation curve to obtain target curve intersection points; and obtaining two candidate download modes corresponding to the target curve intersection point, and optimally selecting the two candidate download modes corresponding to the target curve intersection point to obtain a target download mode.
2. The data transmission system based on the Internet of things is characterized by comprising:
the acquisition module is used for acquiring the vehicle starting state of the target vehicle, controlling the target vehicle to download the vehicle-mounted map data of the preset initial area from the preset internet of things vehicle-mounted map database according to the vehicle starting state, and simultaneously updating the data packet downloading state of the target vehicle in real time; the method specifically comprises the following steps: acquiring a vehicle starting state of a target vehicle, wherein the vehicle starting state is as follows: the vehicle is started and the vehicle is not started; if the vehicle starting state is that the vehicle is started, establishing network connection between the target vehicle and a preset Internet of things vehicle-mounted map database, and acquiring a target connection state of the network connection; when the target connection state is that the connection is successful, controlling the target vehicle to download vehicle-mounted map data of a preset initial area from the internet of things vehicle-mounted map database; monitoring the download state of the target vehicle, and updating the download state of the data packet of the target vehicle in real time;
The matching module is used for matching a plurality of candidate downloading modes of the target vehicle according to the data packet downloading state and acquiring downloading flow information of each candidate downloading mode; the method specifically comprises the following steps: based on the data packet downloading state, updating the candidate downloading mode of the target vehicle in real time through a preset downloading mode list, and recording a plurality of candidate downloading modes corresponding to the target vehicle; acquiring the downloading start time, the downloading end time and the downloading amount of each candidate downloading mode; extracting download procedure information corresponding to each candidate download mode according to the download start time, the download end time and the download amount;
the processing module is used for analyzing the flow information of the download flow information of each candidate download mode to obtain a download path of each candidate download mode, analyzing the download nodes of the download paths to obtain a plurality of download nodes of each download path, and acquiring the average download success rate and the average download time consumption of each candidate download mode; the method specifically comprises the following steps: carrying out flow information analysis on the download flow information corresponding to each candidate download mode to obtain a download path; performing node clustering calculation on the downloading path to obtain a node clustering result, and performing node importance calculation on the downloading path according to the node clustering result to obtain node importance; performing dependency calculation on the download path according to the node importance degree to obtain node dependency information; according to the node affiliation information, carrying out download node identification on the download paths to obtain a plurality of download nodes of each download path; acquiring the download success rate corresponding to each download node in each candidate download mode, and calculating the average download success rate of each candidate download mode according to the download success rate corresponding to each download node; acquiring the downloading time consumption corresponding to each downloading node in each candidate downloading mode, and calculating the average downloading time consumption of each candidate downloading mode according to the downloading time consumption corresponding to each downloading node;
The conversion module is used for sequencing the plurality of candidate downloading modes according to the average downloading success rate to obtain a first sequencing result, and converting success rate evaluation indexes of the plurality of candidate downloading modes according to the first sequencing result to obtain target success rate evaluation indexes of each candidate downloading mode; the method specifically comprises the following steps: sorting the plurality of candidate downloading modes according to the average downloading success rate to obtain a first sorting result; acquiring a success rate evaluation index conversion relation table between the candidate download modes and preset success rate indexes; performing success rate evaluation index conversion on the plurality of candidate downloading modes according to the success rate evaluation index conversion relation table to obtain initial success rate evaluation indexes of each candidate downloading mode; performing weight assignment on the plurality of candidate downloading modes according to the first sequencing result to obtain first weight data of each candidate downloading mode; weighting operation is carried out on the initial success rate evaluation index according to the first weight data, and a target success rate evaluation index of each candidate downloading mode is obtained;
the calculation module is used for sequencing the plurality of candidate downloading modes according to the average downloading time consumption to obtain a second sequencing result, and calculating time consumption scores of the plurality of candidate downloading modes according to the second sequencing result to obtain a target time consumption score of each candidate downloading mode; the method specifically comprises the following steps: sorting the average download time consumption of the plurality of candidate download modes to obtain a second sorting result; acquiring a time-consuming value conversion relation table between the plurality of candidate downloading modes and preset time-consuming values; performing time-consuming score conversion on the plurality of candidate downloading modes according to the time-consuming score conversion relation table to obtain initial time-consuming scores of each candidate downloading mode; performing weight assignment on the plurality of candidate downloading modes according to the second sorting result to obtain second weight data of each candidate downloading mode; weighting operation is carried out on the initial time consumption values according to the second weight data, and target time consumption values of each candidate downloading mode are obtained;
The analysis module is used for carrying out optimization analysis on the plurality of candidate downloading modes according to the target success rate evaluation index and the target time consumption value to obtain a target downloading mode; the method specifically comprises the following steps: constructing a success rate relation curve between the target success rate evaluation index and the plurality of candidate downloading modes; constructing a time-consuming relation curve between the target time-consuming value and the plurality of candidate downloading modes; converting the success rate relation curve and the time-consuming relation curve into the same coordinate system, and identifying curve intersection points of the success rate relation curve and the time-consuming relation curve to obtain target curve intersection points; and obtaining two candidate download modes corresponding to the target curve intersection point, and optimally selecting the two candidate download modes corresponding to the target curve intersection point to obtain a target download mode.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106850778A (en) * | 2017-01-17 | 2017-06-13 | 无锡清华信息科学与技术国家实验室物联网技术中心 | A kind of multi-source download performance optimization method and device |
| CN115348251A (en) * | 2021-05-14 | 2022-11-15 | 腾讯科技（深圳）有限公司 | File downloading method and device, computer equipment and storage medium |
| CN116684416A (en) * | 2023-07-05 | 2023-09-01 | 中国电子科技集团公司第三十六研究所 | Mirror image distribution method, device and system in network element cluster |
| CN116800796A (en) * | 2023-07-06 | 2023-09-22 | 东软睿驰汽车技术(大连)有限公司 | Method, device, equipment and medium for transmitting internet of vehicles data |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004361723A (en) * | 2003-06-05 | 2004-12-24 | Nec Commun Syst Ltd | System, method and program to distribute music data |
| CN109510851B (en) * | 2017-09-15 | 2022-01-04 | 华为技术有限公司 | Map data construction method and device |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106850778A (en) * | 2017-01-17 | 2017-06-13 | 无锡清华信息科学与技术国家实验室物联网技术中心 | A kind of multi-source download performance optimization method and device |
| CN115348251A (en) * | 2021-05-14 | 2022-11-15 | 腾讯科技（深圳）有限公司 | File downloading method and device, computer equipment and storage medium |
| CN116684416A (en) * | 2023-07-05 | 2023-09-01 | 中国电子科技集团公司第三十六研究所 | Mirror image distribution method, device and system in network element cluster |
| CN116800796A (en) * | 2023-07-06 | 2023-09-22 | 东软睿驰汽车技术(大连)有限公司 | Method, device, equipment and medium for transmitting internet of vehicles data |
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