CN112104996A - Low-cost crowd sensing calculation method applied to shared traffic system - Google Patents
Low-cost crowd sensing calculation method applied to shared traffic system Download PDFInfo
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- CN112104996A CN112104996A CN202010935452.4A CN202010935452A CN112104996A CN 112104996 A CN112104996 A CN 112104996A CN 202010935452 A CN202010935452 A CN 202010935452A CN 112104996 A CN112104996 A CN 112104996A
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Abstract
The invention discloses a low-cost crowd sensing calculation method applied to a shared traffic system, which comprises the following steps: acquiring sensor acquisition data through a front-end device of a shared traffic system; the front-end device sends the data collected by the sensor and a corresponding calculation request to user calculation equipment; the user computing device selects to execute the computing request locally or in the cloud after receiving the sensor acquisition data and the corresponding computing request. According to the invention, the front-end device of the shared traffic system does not need to be additionally provided with additional computing equipment, the required computing task is completed by utilizing the user mobile computing equipment, and the data or the result of the perception operation is converged to the cloud server by the user mobile computing equipment, so that the crowd-sourcing task with higher complexity is completed at lower cost.
Description
Technical Field
The invention belongs to the technical field of Internet of things and edge computing, and particularly relates to a low-cost crowd sensing computing method applied to a shared traffic system.
Background
In recent years, with the great development of technologies such as embedded technology, sensor technology, wireless communication and the like, the internet of things gradually goes from concept to reality, and becomes a continuous research hotspot in the scientific research community and the industrial community. With the continuous development of application schemes and technical means of the internet of things, huge terminal equipment of the internet of things generates massive and diversified ubiquitous computing requirements besides traditional perception and communication. Meanwhile, the artificial intelligence technology is continuously developed at a high speed in recent years, good effect is achieved in multiple fields of computer vision, texts, voice and the like, and breakthroughs are made in the aspects of precision and efficiency. However, the current artificial intelligence related algorithm generally needs to use high-performance computing equipment for model training and deployment, and the internet of things is difficult to effectively utilize various artificial intelligence algorithms due to serious resource limitation on low-power-consumption internet of things equipment, and cannot meet various ever-increasing computing requirements.
Disclosure of Invention
The invention provides a low-cost crowd sensing calculation method applied to a shared traffic system, and aims to solve the technical problem that existing low-power-consumption Internet of things equipment cannot meet various increasing calculation requirements due to resource limitation.
The invention is realized by the following technical scheme:
a low-cost crowd sensing calculation method applied to a shared traffic system comprises the following steps:
acquiring sensor acquisition data through a front-end device of a shared traffic system;
the front-end device sends the data collected by the sensor and a corresponding calculation request to user calculation equipment;
the user computing device selects to execute the computing request locally or in the cloud after receiving the sensor acquisition data and the corresponding computing request.
According to the invention, the front-end device of the shared traffic system does not need to be additionally provided with additional computing equipment, the required computing task is completed by utilizing the user mobile computing equipment, and the data or the result of the perception operation is converged to the cloud server by the user mobile computing equipment, so that the crowd-sourcing task with higher complexity is completed at lower cost.
Preferably, after the step of acquiring the data collected by the sensor by the front-end device is executed, the method further comprises the following steps:
the front-end device divides the acquired data according to a preset length, adds identification information to obtain a current divided data block, packages the current divided data block and then sends the current divided data block to the user computing equipment.
Preferably, the front-end device of the shared traffic system of the present invention employs a low-cost and low-power processor, and the front-end device has insufficient or no data processing capability.
Preferably, the identification information of the present invention includes:
the data uniqueness identifier is used for indicating which piece of data transmitted the current divided data block belongs to;
the corresponding request service name is used for representing the request service name corresponding to the data of the current segmentation data block;
the last block identifier indicates that the current segmentation database is the last block of the data when the value is 1;
and the data identifier is used for representing the content of the transmitted data block.
Preferably, before the step of the front-end device segmenting the acquired data according to the preset length and adding the identification information, the method further comprises:
the front-end device judges whether the size of the transmission data needs to be adjusted or not according to the link quality; if so, processing the acquired data to reduce the transmission data amount, otherwise, not processing.
Preferably, the specific process of acquiring the data acquired by the sensor by the front-end device of the invention is as follows:
the front-end device acquires a data acquisition strategy from user computing equipment; the data acquisition strategy is generated according to the time distribution condition, the space distribution condition, the data quality and the specific data requirement of historical data;
and the front-end device acquires data at a target time or a target place according to the acquired data acquisition strategy and stores the acquired data in the storage equipment.
Preferably, the user computing device of the present invention receives the sensor acquisition data, and the specific process includes:
the user computing equipment receives the current segmented data block which is sent by the front-end device and is packaged, and decapsulates the current segmented data block;
the user computing equipment acquires the received identification information of the current segmented data block and judges whether the identification information of the current segmented data block is an end identification; if yes, the user computing equipment restores the received divided data blocks into a piece of complete data, runs corresponding computing service according to the complete data and continues to execute the step that the user computing equipment receives the packaged current divided data blocks sent by the front-end device; otherwise, the step that the user computing equipment receives the packaged current segmentation data block sent by the front-end device is continuously executed.
Preferably, the user computing device of the present invention selects, when executing the computing request locally: before executing the step of running the corresponding computing service, the method further comprises the following steps:
after the user computing equipment starts the corresponding computing service, judging whether the requested service exists in the user computing equipment or not, and if the service does not exist, establishing the service and running the service; if the current state exists, the operation is directly carried out;
after the step of executing the corresponding computing service, the method further comprises the following steps:
the user computing device determines whether there are other requests for the computing service, and if not, closes the service.
Preferably, the user computing device sends the computing service operation result to the cloud server.
Preferably, the user computing device of the present invention selects, when the computing request is executed in the cloud: and the user computing equipment directly sends the received data to the cloud server for corresponding computing service.
According to the invention, the last block identifier lastLock is set, and the value is 1 to be used as the end identifier, so that the user computing equipment can acquire complete data for subsequent processing.
The invention has the following advantages and beneficial effects:
1. aiming at the problems of low data transmission efficiency and unreliable data, the invention provides a self-adaptive data blocking method, establishes concurrent execution between data transmission and operation, reduces delay overhead brought by transmission and improves system reliability. The data acquisition, transmission and processing of the invention are executed in parallel, thereby greatly improving the resource utilization rate and reducing the transmission delay.
2. The invention provides a self-adaptive task unloading strategy aiming at the problem of network resource limitation, and minimizes the data transmission quantity while ensuring that the key characteristic value of the data is not damaged by using a lightweight data sampling method based on the system time-space characteristics.
3. Aiming at the problem that resources of user computing equipment are limited, the invention provides a service optimization mechanism based on dependency analysis, so that the computing overhead of each service is reduced, the common dependency resident memory of each service is extracted, and the delay overhead caused by cold start of the service is greatly reduced.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
The embodiment provides a low-cost crowd sensing calculation method applied to a shared traffic system. The method of the embodiment provides a self-adaptive data blocking method aiming at the problems of low data transmission efficiency and unreliability, establishes concurrent execution between data transmission and operation, reduces delay overhead brought by transmission, and improves system reliability.
Specifically, as shown in fig. 1, the method of this embodiment includes the front-end device executing steps, which specifically include:
1. the front-end device acquires sensor acquisition data and sends the sensor acquisition data and a corresponding calculation request to the user calculation equipment.
The front-end device of the embodiment is deployed on a shared vehicle, and adopts a processor with low cost and low power consumption, so that the self calculation and storage capacity is limited, the data processing capacity is insufficient, the algorithm precision and efficiency which are the same as those of a high-performance platform cannot be realized, or the data processing capacity is not available, or even one picture cannot be transmitted at one time.
The specific process of the front-end device acquiring data collected by the sensor in this embodiment is as follows:
1.1, the front-end device acquires a data acquisition strategy from user computing equipment; the data acquisition strategy is generated according to the time distribution condition, the space distribution condition, the data quality and the specific data requirement of historical data;
and 1.2, the front-end device collects data at a target time or a target place according to the acquired data collection strategy and stores the collected data into a storage device (such as an SD card or other external storage devices).
The embodiment further includes the following steps after acquiring the data collected by the sensor:
and 2, the front-end device carries out adaptive processing on the acquired data according to actual conditions.
The front-end device of this embodiment determines whether the size of the transmission data needs to be adjusted according to the link quality; if so, processing the acquired data to reduce the transmission data amount, otherwise, not processing.
3, the front-end device divides the acquired data according to a preset length and adds identification information to obtain a current divided data block; the specific process comprises the following steps:
3.1, the front-end device divides one piece of acquired original data to obtain a data block;
3.2, the front-end device adds identification information to the data blocks in sequence; the identification information of this embodiment includes:
a data uniqueness identifier ID for indicating which piece of data the current divided data block belongs to;
a corresponding request service name methodName for indicating a request service name corresponding to the data to which the current divided data block belongs;
the last block is identified with lastLock, and when the value is 1, the current segmentation database is the last block of the data to which the current segmentation database belongs;
and the data identification data is used for representing the content of the transmitted data block.
And 3.3, the front-end device packages the data block added with the identification bit into a specific format to obtain the packaged current segmentation data block.
4. And the front-end device packages the current segmentation data block and then transmits the current segmentation data block to the user computing equipment.
5. The user computing equipment receives the sensor acquisition data and the corresponding computing request sent by the front-end device, selects to execute the computing request locally or at the cloud end, and sends the result to the cloud server.
This embodiment is through setting up user computing equipment (like cell-phone, flat board, bracelet etc.) for use with above-mentioned front end device cooperation, make shared traffic system's front end device need not to install additional computing equipment additional, can realize gathering, handling, storage and analysis to the sensing data on the shared vehicle, carry out high-efficient processing to the calculation demand on the vehicle, finally gather large-scale public area sensing data, provide real-time computational service for the front end device simultaneously.
The process of receiving sensor acquisition data by the user computing device of this embodiment specifically includes:
the user computing equipment receives the current segmented data block which is sent by the front-end device and is packaged, and decapsulates the current segmented data block;
the user computing equipment acquires the received identification information of the current segmented data block and judges whether the identification information of the current segmented data block is an end identification; if yes, the user computing equipment restores the received divided data blocks into a piece of complete data, runs corresponding computing service according to the complete data and continues to execute the step that the user computing equipment receives the packaged current divided data blocks sent by the front-end device; otherwise, the step that the user computing equipment receives the packaged current segmentation data block sent by the front-end device is continuously executed.
(1) After receiving the data collected by the sensor, the user computing device of this embodiment further includes, before executing the step of running the corresponding computing service according to the specific purpose of the collected data:
after the user computing equipment starts the corresponding computing service, judging whether the requested service exists in the user computing equipment or not, and if the service does not exist, establishing the service and running the service; if the current state exists, the operation is directly carried out;
after the step of executing the corresponding computing service according to the specific purpose of the collected data, the embodiment further includes:
the user computing device determines whether there are other requests for the computing service, and if not, closes the service.
The user computing device of the embodiment sends the operation result of the computing service to the cloud server.
(2) The selecting, by the user computing device of this embodiment, a corresponding computing service process at the cloud specifically includes:
and the user computing equipment directly sends the received data to the cloud server to execute corresponding computing service.
Example 2
The present embodiment is a specific application of the calculation method provided in embodiment 1, and includes the following processes:
data is collected from a front-end device deployed on a shared vehicle. The method comprises the steps of firstly obtaining a data acquisition strategy from a server side on user computing equipment, acquiring data at a specific time or a specific place according to the data acquisition strategy, and storing the acquired data in storage equipment. Specifically, the data acquisition strategy is generated based on the spatio-temporal characteristics of the acquired data, that is, the acquisition strategy is generated according to the time distribution and the space distribution of the historical data, the data quality and the specific requirements. For example, based on the time and space distribution in the historical data of the shared bicycle used by the user, the probability of judging where and when the user stops is higher, and the data collection frequency is reduced before the probability, so that the data can be completely transmitted to the user computing device.
And data self-adaptive processing, namely judging whether the size of the transmitted data needs to be adjusted or not according to the link quality, and if the link quality is poor, processing the data, such as compressing the data or selectively transmitting part of the data to reduce the amount of the transmitted data.
And data blocking processing, namely dividing the data to be transmitted into a plurality of data blocks and adding corresponding identification information. Specifically, the identification information includes: the data uniqueness identifier ID indicates which data block is transmitted by the current data block; a corresponding request service name methodName, which represents the request service name corresponding to the piece of data; the last block identification lastLock, when the value is 1, it means that the current data block is the last block of the transmission data; and the data identification data represents the content of the transmitted data block.
The data is sent and received in blocks, the front-end processor sends the data blocks to the transmission module in sequence, the transmission module packages and sends the data, the user computing equipment checks whether a lastLock field is 1 after receiving the data, if the lastLock field is 1, the data blocks of the current data are received, the received data blocks are processed, and the original data is restored.
The server side deployed on the user computing equipment starts corresponding services, the server side judges whether the requested services exist through a service discovery module, and if the services do not exist, the services are created. And after the user computing equipment executes the artificial intelligence algorithm of the request, judging whether other requests for the service exist or not, and if not, closing the service.
And the server side on the user computing equipment uploads the computing result to the cloud server through a wireless network or a mobile network according to the requirement, or transmits the collected sensing data to the cloud server for computing by the cloud server.
And optimizing the service of a server side on the user computing equipment, wherein the server side adopts docker as a container isolation technology to isolate the operating environments of different services. For docker starting optimization and memory optimization, the server side adopts a three-level cache mechanism aiming at docker, and caches the python common packet to a local external memory; establishing a container pool cache, cloning other containers through a Copy on Write mechanism through the container pool, and loading a third square package from an external memory according to a reasonable sequence by analyzing the dependency of containers with different functions on the package; a suspended container cache is established, the suspended container is put into the cache, and when the container is needed again, the suspension is cancelled.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A low-cost crowd sensing calculation method applied to a shared traffic system is characterized by comprising the following steps:
acquiring sensor acquisition data through a front-end device of a shared traffic system;
the front-end device sends the data collected by the sensor and a corresponding calculation request to user calculation equipment;
the user computing device selects to execute the computing request locally or in the cloud after receiving the sensor acquisition data and the corresponding computing request.
2. The method of claim 1, wherein the step of obtaining sensor data by the front-end device further comprises:
the front-end device divides the acquired data according to a preset length, adds identification information to obtain a current divided data block, packages the current divided data block and then sends the current divided data block to the user computing equipment.
3. The method as claimed in claim 1, wherein the front-end device of the shared transportation system employs a low-cost and low-power processor, and the front-end device has insufficient or no data processing capability.
4. The method of claim 2, wherein the identification information comprises:
the data uniqueness identifier is used for indicating which piece of data transmitted the current divided data block belongs to;
the corresponding request service name is used for representing the request service name corresponding to the data of the current segmentation data block;
the last block identifier indicates that the current segmentation database is the last block of the data when the value is 1;
and the data identifier is used for representing the content of the transmitted data block.
5. The method as claimed in claim 2, further comprising, before the step of the front-end device segmenting the acquired data by a preset length and adding the identification information, the step of:
the front-end device judges whether the size of the transmission data needs to be adjusted or not according to the link quality; if so, processing the acquired data to reduce the transmission data amount, otherwise, not processing.
6. The method as claimed in claim 2, wherein the front-end device acquires the sensor data by a specific process comprising:
the front-end device acquires a data acquisition strategy from user computing equipment; the data acquisition strategy is generated according to the time distribution condition, the space distribution condition, the data quality and the specific data requirement of historical data;
and the front-end device acquires data at a target time or a target place according to the acquired data acquisition strategy and stores the acquired data in the storage equipment.
7. The method for computing low-cost crowd-sourcing awareness for use in a shared transportation system according to any one of claims 2-6, wherein the user computing device receives the sensor-collected data by a process comprising:
the user computing equipment receives the current segmented data block which is sent by the front-end device and is packaged, and decapsulates the current segmented data block;
the user computing equipment acquires the received identification information of the current segmented data block and judges whether the identification information of the current segmented data block is an end identification; if yes, the user computing equipment restores the received divided data blocks into a piece of complete data, runs corresponding computing service according to the complete data and continues to execute the step that the user computing equipment receives the packaged current divided data blocks sent by the front-end device; otherwise, the step that the user computing equipment receives the packaged current segmentation data block sent by the front-end device is continuously executed.
8. The method of claim 7, wherein the user computing device selects, when executing the computing request locally: before executing the step of running the corresponding computing service, the method further comprises the following steps:
after the user computing equipment starts the corresponding computing service, judging whether the requested service exists in the user computing equipment or not, and if the service does not exist, establishing the service and running the service; if the current state exists, the operation is directly carried out;
after the step of executing the corresponding computing service, the method further comprises the following steps:
the user computing device determines whether there are other requests for the computing service, and if not, closes the service.
9. The method of claim 8, wherein the user computing device sends the results of the computing service operations to a cloud server.
10. The method of claim 7, wherein the user computing device selects, when executing the computing request in the cloud, to: and the user computing equipment directly sends the received data to the cloud server for corresponding computing service.
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