CN112596894A - Tracking method and device based on edge calculation - Google Patents
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Abstract
The invention relates to a tracking method and a device based on edge calculation, wherein the method comprises the following steps: marking a target position in selected image data of the image data, and modeling the target data according to the target position and a preset data modeling strategy; acquiring an acquisition strategy and a processing strategy of the target image data according to the data processing corresponding relation and the target image data; acquiring target data from the target image data based on an acquisition strategy of the target image data, and processing according to a processing strategy of the target image data to obtain network edge image data; and summarizing the edge image data of each network, and analyzing and processing according to a preset edge image data analysis processing strategy to obtain the corresponding position of the target position in each image frame. The invention solves the problem of network time delay in data tracking, provides near-intelligent service, and can meet the requirements of agile connection, real-time service, data optimization, application intelligence, complete and privacy protection and the like.
Description
Technical Field
The invention relates to the technical field of intelligent communication, in particular to a tracking method and a tracking device based on edge calculation.
Background
At present, the wave of digitized transformation of the industry has been raised worldwide, digitization is the foundation, networking is the support, and intellectualization is the target. The data is generated by digitalizing objects such as characters, environments, processes and the like, the value flow of the data is realized through networking, the data is used as a production element, and economic and social values are created for various industries through intellectualization. The intellectualization is based on intelligent analysis of data, so that intelligent decision and intelligent operation are realized, and continuous intelligent optimization of a business process is realized through closed loop.
The current intelligent data processing mode is a mode of collecting data from various places and carrying out centralized analysis processing by taking a data computing center as a base, the general deployment positions of the data computing centers are far, in various application scenes, the computing tasks need small computing time delay, and the data transmission time is long and the time delay loss is caused by directly passing through the remote data computing center. In industrial production and operation scenes, real-time response to accidents, faults and emergencies is very important, and existing data processing and even cloud computing have a series of problems of large network delay, high cost, potential safety hazards and the like, and cannot meet all requirements for analyzing and processing big data.
In a cloud computing architecture, data are all transmitted and stored to a cloud end through a network, and the problems of monopolarity of IT resources, low service density of a data center, low utilization efficiency of system resources and difficulty in meeting the requirement of rapid development of services in a limited physical space are solved through the dynamic, elastic and flexible characteristics of cloud computing. However, at the same time, a large amount of data required by cloud computing is acquired from related equipment, various terminals transmit the acquired large amount of data to corresponding cloud platforms, analysis results are obtained through centralized operation, and with the wide use of distributed intelligent equipment, a large amount of data transmission causes the blockage of a transmission channel, which causes great difficulty in cloud computing.
Therefore, there is a need in the industry for a solution that can analyze and track the characteristics of the target tracking task.
Disclosure of Invention
The present invention is directed to a tracking method and apparatus based on edge calculation, which overcome the disadvantages of the prior art. The object of the present invention can be achieved by the following technical means.
The invention provides a tracking method based on edge calculation, which comprises the following steps:
the method comprises the steps of marking a target position in selected image data of the image data, and modeling the target data according to the target position and a preset data modeling strategy;
presetting a deployment strategy corresponding relation of the image data and the network edge frame image data thereof according to the frame sequence rule of the image data and the designated target position in the selected image data; setting a data processing corresponding relation between the target image data and a network edge frame image data acquisition and processing strategy;
acquiring a network edge image data deployment strategy of the target image data according to the corresponding relation between the target image data and the deployment strategy; configuring target network edge image data of the target image data according to the network edge image data deployment strategy;
acquiring an acquisition strategy and a processing strategy of the target image data according to the data processing corresponding relation and the target image data; acquiring target data from the target image data based on the acquisition strategy of the target image data, and processing according to the processing strategy of the target image data to obtain network edge image data;
and summarizing the network edge image data, and analyzing and processing according to a preset edge image data analysis processing strategy to obtain the corresponding position of the target position in each image frame.
Optionally, the processing according to the target image data to obtain the network edge image data includes:
acquiring data characteristics of each target image data according to a frame sequence rule of the image data;
setting the search area of the target image data to be 1.2-3 times of the area range based on a filtering algorithm, preprocessing the data characteristics, extracting the characteristics, training a model according to the set area range, and then classifying the data by using the obtained model to obtain the network edge image data.
Optionally, wherein the method further comprises:
in the process of modeling the filtering algorithm, the correlation operation in the space domain is converted into the frequency domain and replaced by the product operation of corresponding elements.
Optionally, wherein the method further comprises:
connecting each target network edge image data to an edge computing control platform;
the edge computing control platform sends a state detection signal to the target network edge image data according to a preset edge image data state detection strategy;
and when the state detection signal is sent within the preset time and the detection feedback signal is not received, configuring the updated target network edge image data of the target object according to the network edge image data deployment strategy.
Optionally, wherein the method further comprises:
connecting each target network edge image data to an edge computing control platform;
the target network edge image data receives a storage search strategy of the edge computing control platform, and target storage equipment in a preset storage equipment range is obtained according to the storage search strategy;
and creating a virtual distributed cache based on the target network edge device according to a preset virtual cache creating strategy, and distributing the virtual distributed cache on each target storage device.
Optionally, wherein, processing the target data in the target network edge image data according to the processing policy of the target image data to obtain network edge image data is:
correcting, eliminating noise and windowing the target data in the target network edge image data according to the processing strategy of the target data to obtain primary processing data;
and according to the attribute type of the target image data, performing feature extraction on the primary processing data based on a feature extraction strategy corresponding to the attribute type to obtain network edge image data.
In another aspect, the present invention further provides an edge-calculation-based tracking apparatus, including: the device comprises a setting module, a network edge image data configuration module and an image data tracking module; wherein,
the setting module is used for marking a target position in selected image data of the image data and modeling the target data according to the target position and a preset data modeling strategy;
presetting a deployment strategy corresponding relation of the image data and the network edge frame image data thereof according to the frame sequence rule of the image data and the designated target position in the selected image data; setting a data processing corresponding relation between the target image data and a network edge frame image data acquisition and processing strategy;
the network edge image data configuration module is connected with the setting module and acquires a network edge image data deployment strategy of the target image data according to the corresponding relation between the target image data and the deployment strategy; configuring target network edge image data of the target image data according to the network edge image data deployment strategy;
acquiring an acquisition strategy and a processing strategy of the target image data according to the data processing corresponding relation and the target image data; acquiring target data from the target image data based on the acquisition strategy of the target image data, and processing according to the processing strategy of the target image data to obtain network edge image data;
the image data tracking module is connected with the network edge image data configuration module, summarizes all the network edge image data, and carries out analysis processing according to a preset edge image data analysis processing strategy to obtain the corresponding position of the target position in each image frame.
Optionally, the network edge image data configuring module includes: a data characteristic acquisition unit and a data characteristic processing unit; wherein,
the data characteristic acquisition unit acquires the data characteristics of each target image data according to the frame sequence rule of the image data;
the data feature processing unit is connected with the data feature acquisition unit, sets the search area of the target image data to be 1.2-3 times of the area range based on a filtering algorithm, performs preprocessing, feature extraction and model training on the data features according to the set area range, and performs data classification by using the obtained model to obtain the network edge image data.
Optionally, wherein the apparatus further comprises: the target network edge image data state detection module is connected with the network edge image data configuration module and is used for connecting each target network edge image data to an edge calculation control platform;
the edge computing control platform sends a state detection signal to the target network edge image data according to a preset edge image data state detection strategy;
and when the state detection signal is sent within the preset time and the detection feedback signal is not received, configuring the updated target network edge image data of the target object according to the network edge image data deployment strategy.
Optionally, wherein the apparatus further comprises: the target network edge image data virtual cache creation module is connected with the network edge image data configuration module and connects each target network edge image data to an edge computing control platform;
the target network edge image data receives a storage search strategy of the edge computing control platform, and target storage equipment in a preset storage equipment range is obtained according to the storage search strategy;
and creating a virtual distributed cache based on the target network edge device according to a preset virtual cache creating strategy, and distributing the virtual distributed cache on each target storage device.
Compared with the prior art, the invention has the beneficial effects that:
the invention develops a tracking method and a tracking device based on edge computing, which improves the network service performance and the open network control capability by utilizing a distributed open platform fusing the core capabilities of network, computing, storage and application on the edge side of a network close to an object or a data source, and stimulates a new network computing processing state similar to the mobile internet. The method solves the problem of network time delay, provides near-intelligent service, and can meet the requirements of agile connection, real-time service, data optimization, application intelligence, complete privacy protection and the like. An intelligent, flexible and elastic network is constructed at the edge of the network, and the intelligent, flexible and elastic network is supplemented with a cloud computing centralized platform, so that the advantages of edge computing in the current scene relative to a traditional data acquisition and analysis system are shown, the data computing processing efficiency is greatly improved, and the detection accuracy is improved. The single-target tracking platform based on edge calculation is similar to a distributed machine learning system in a certain degree, and the distributed machine learning system generally comprises a data and model division module, a single-machine optimization module, a communication module, a model and data aggregation module and the like. The method comprises the steps of dividing training samples in a random sampling or scrambling segmentation mode or the like, distributing the training samples to different working nodes after transverse, longitudinal or random division is carried out on a model, carrying out sub-model training by adopting a single machine optimization module, carrying out information synchronization by a communication module in the period, and finally carrying out integrated aggregation on the trained model by adopting a model and a data aggregation module.
Similarly, the main modules of the tracking platform based on edge computing comprise a task segmentation module, a target tracking module, a communication module and an information fusion module. The terminal nodes and the edge cloud server are respectively provided with a target tracking module, the task segmentation module makes a task segmentation strategy through equipment filling and environmental information, information is received and sent through the communication module, and after a computing task is unloaded to a cloud computing, computing results on two sides of the terminal nodes and the edge cloud are fused through the information fusion module. The tracking processing efficiency and accuracy of the image data are greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart illustrating an edge-based tracking method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a second tracking method based on edge calculation according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a third tracking method based on edge calculation according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a fourth tracking method based on edge calculation according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating a fifth tracking method based on edge calculation according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an edge-based tracking apparatus according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a second tracking apparatus based on edge calculation according to an embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating a third exemplary embodiment of an edge-based tracking device;
FIG. 9 is a schematic structural diagram of a fourth tracking apparatus based on edge calculation according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to specific embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a tracking method based on edge calculation in this embodiment. Specifically, the method comprises the following steps:
102, presetting a deployment strategy corresponding relation of image data and network edge frame image data thereof according to a frame sequence rule of the image data and a calibration target position in the selected image data; and setting a data processing corresponding relation between the target image data and the network edge frame image data acquisition and processing strategy.
103, acquiring a network edge image data deployment strategy of the target image data according to the corresponding relation between the target image data and the deployment strategy; and configuring target network edge image data of the target image data according to the network edge image data deployment strategy.
104, acquiring an acquisition strategy and a processing strategy of target image data according to the data processing corresponding relation and the target image data; and acquiring target data from the target image data based on the acquisition strategy of the target image data, and processing according to the processing strategy of the target image data to obtain network edge image data.
And 105, summarizing the edge image data of each network, and analyzing and processing according to a preset edge image data analysis processing strategy to obtain corresponding positions of the target positions in each image frame.
In some optional embodiments, as shown in fig. 2, which is a schematic flow chart of a second tracking method based on edge calculation in this implementation, different from fig. 1, the network edge image data obtained by processing according to the processing policy of the target image data is:
In the process of modeling the filtering algorithm, the correlation operation in the space domain is converted into the frequency domain and replaced by the product operation of corresponding elements.
In some optional embodiments, as shown in fig. 3, which is a schematic flowchart of a third tracking method based on edge calculation in this implementation, different from fig. 1, the method further includes:
and 301, connecting each target network edge image data to an edge computing control platform.
And 303, configuring the updated target network edge image data of the target object according to the network edge image data deployment strategy when the detection feedback signal is not received within the preset time of sending the state detection signal.
In some optional embodiments, as shown in fig. 4, which is a schematic flowchart of a fourth tracking method based on edge calculation in this implementation, different from fig. 1, the method further includes:
In some optional embodiments, as shown in fig. 5, which is a schematic flow chart of a fifth tracking method based on edge calculation in this implementation, different from fig. 1, the processing policy according to the target image data processes the target data in the target network edge image data to obtain the network edge image data, where the method includes:
In some alternative embodiments, as shown in fig. 6, the schematic diagram of the edge-calculation-based tracking apparatus in this embodiment is used to implement the above-mentioned edge-calculation-based tracking method. Specifically, the apparatus includes: a setup module 601, a network edge image data configuration module 602, and an image data tracking module 603.
The setting module 601 is configured to mark a target location in selected image data of the image data, and model the target data according to the target location and a preset data modeling policy.
Presetting a corresponding relation of deployment strategies of the image data and the network edge frame image data thereof according to a frame sequence rule of the image data and a calibration target position in the selected image data; and setting a data processing corresponding relation between the target image data and the network edge frame image data acquisition and processing strategy.
The network edge image data configuration module 602 is connected to the setting module 601, and acquires a network edge image data deployment policy of the target image data according to the corresponding relationship between the target image data and the deployment policy; and configuring target network edge image data of the target image data according to the network edge image data deployment strategy.
Acquiring an acquisition strategy and a processing strategy of the target image data according to the data processing corresponding relation and the target image data; and acquiring target data from the target image data based on the acquisition strategy of the target image data, and processing according to the processing strategy of the target image data to obtain network edge image data.
The image data tracking module 603 is connected to the network edge image data configuration module 602, and summarizes the network edge image data, and performs analysis processing according to a preset edge image data analysis processing policy to obtain corresponding positions of the target positions in the image frames.
In some alternative embodiments, as shown in fig. 7, which is a schematic diagram of a second tracking apparatus based on edge calculation in this implementation, different from fig. 6, a network edge image data configuration module 602 includes: a data feature acquisition unit 701 and a data feature processing unit 702; wherein,
the data feature acquiring unit 701 acquires the data feature of each target image data according to the frame sequence rule of the image data.
And the data feature processing unit 702 is connected with the data feature acquiring unit 701, sets the search area of the target image data to be 1.2-3 times of the area range based on the filtering algorithm, performs preprocessing, feature extraction and model training on the data features according to the set area range, and performs data classification by using the obtained model to obtain network edge image data.
In some optional embodiments, as shown in fig. 8, which is a schematic diagram of a third tracking apparatus based on edge calculation in this implementation, different from fig. 6, the tracking apparatus further includes: and the target network edge image data state detection module 801 is connected with the network edge image data configuration module and connects each target network edge image data to the edge calculation control platform.
And the edge computing control platform sends a state detection signal to the edge image data of the target network according to a preset edge image data state detection strategy.
And when the detection feedback signal is not received within the preset time of sending the state detection signal, configuring the updated target network edge image data of the target object according to the network edge image data deployment strategy.
In some optional embodiments, as shown in fig. 9, which is a schematic diagram of a fourth tracking apparatus based on edge calculation in this implementation, different from fig. 6, the method further includes: the target network edge image data virtual cache creating module 901 is connected to the network edge image data configuration module 602, and connects each target network edge image data to the edge computing control platform.
And the target network edge image data receives a storage and search strategy of the edge computing control platform, and acquires target storage equipment within a preset storage equipment range according to the storage and search strategy.
And creating a virtual distributed cache based on the target network edge device according to a preset virtual cache creating strategy, and distributing the virtual distributed cache on each target storage device.
The tracking scheme based on the edge calculation is implemented by respectively providing scheme designs facing a data acquisition module and a data preprocessing module under respective environments aiming at different types of data sources and data types and realizing the scheme under corresponding specific scenes. A communication framework of a bottom layer data acquisition and processing module and an upper layer big data processing system is designed, and when different operation requirements of a central data processing system on edge equipment in a specific scene are met, the central data processing system can make a corresponding operation according to specific business requirements. The data analysis and data mining tasks are completed by utilizing the cooperation of the central equipment and the edge equipment on the premise of poor data processing capacity of the edge equipment, so that the defects of poor computing capacity of the edge equipment, slow computing response of the cloud center and the like are overcome. According to the above process, the data acquisition and processing system based on the edge calculation is completed. The system has the characteristics of high efficiency, quick response, low energy consumption and the like, and meets the requirements of most daily data collection and processing scenes. The feasibility and the characteristics of the system are verified by the implementation under the scenes of two different data sources and processing requirements.
The present invention has been further described with reference to specific embodiments, but it should be understood that the detailed description should not be construed as limiting the spirit and scope of the present invention, and various modifications made to the above-described embodiments by those of ordinary skill in the art after reading this specification are within the scope of the present invention.
Claims (10)
1. An edge calculation-based tracking method, comprising:
the method comprises the steps of marking a target position in selected image data of the image data, and modeling the target data according to the target position and a preset data modeling strategy;
presetting a deployment strategy corresponding relation of the image data and the network edge frame image data thereof according to the frame sequence rule of the image data and the designated target position in the selected image data; setting a data processing corresponding relation between the target image data and a network edge frame image data acquisition and processing strategy;
acquiring a network edge image data deployment strategy of the target image data according to the corresponding relation between the target image data and the deployment strategy; configuring target network edge image data of the target image data according to the network edge image data deployment strategy;
acquiring an acquisition strategy and a processing strategy of the target image data according to the data processing corresponding relation and the target image data; acquiring target data from the target image data based on the acquisition strategy of the target image data, and processing according to the processing strategy of the target image data to obtain network edge image data;
and summarizing the network edge image data, and analyzing and processing according to a preset edge image data analysis processing strategy to obtain the corresponding position of the target position in each image frame.
2. The edge-computation-based tracking method according to claim 1, wherein the network edge image data obtained by processing according to the processing policy of the target image data is:
acquiring data characteristics of each target image data according to a frame sequence rule of the image data;
setting the search area of the target image data to be 1.2-3 times of the area range based on a filtering algorithm, preprocessing the data characteristics, extracting the characteristics, training a model according to the set area range, and then classifying the data by using the obtained model to obtain the network edge image data.
3. The edge-computation-based tracking method of claim 2, further comprising:
in the process of modeling the filtering algorithm, the correlation operation in the space domain is converted into the frequency domain and replaced by the product operation of corresponding elements.
4. The edge-computation-based tracking method of claim 1, further comprising:
connecting each target network edge image data to an edge computing control platform;
the edge computing control platform sends a state detection signal to the target network edge image data according to a preset edge image data state detection strategy;
and when the state detection signal is sent within the preset time and the detection feedback signal is not received, configuring the updated target network edge image data of the target object according to the network edge image data deployment strategy.
5. The edge-computation-based tracking method of claim 1, further comprising:
connecting each target network edge image data to an edge computing control platform;
the target network edge image data receives a storage search strategy of the edge computing control platform, and target storage equipment in a preset storage equipment range is obtained according to the storage search strategy;
and creating a virtual distributed cache based on the target network edge device according to a preset virtual cache creating strategy, and distributing the virtual distributed cache on each target storage device.
6. The edge-computation-based tracking method according to claim 1, wherein the processing of the target data in the target network edge image data according to the processing policy of the target image data to obtain network edge image data is:
correcting, eliminating noise and windowing the target data in the target network edge image data according to the processing strategy of the target data to obtain primary processing data;
and according to the attribute type of the target image data, performing feature extraction on the primary processing data based on a feature extraction strategy corresponding to the attribute type to obtain network edge image data.
7. An edge-computation-based tracking device, comprising: the device comprises a setting module, a network edge image data configuration module and an image data tracking module; wherein,
the setting module is used for marking a target position in selected image data of the image data and modeling the target data according to the target position and a preset data modeling strategy;
presetting a deployment strategy corresponding relation of the image data and the network edge frame image data thereof according to the frame sequence rule of the image data and the designated target position in the selected image data; setting a data processing corresponding relation between the target image data and a network edge frame image data acquisition and processing strategy;
the network edge image data configuration module is connected with the setting module and acquires a network edge image data deployment strategy of the target image data according to the corresponding relation between the target image data and the deployment strategy; configuring target network edge image data of the target image data according to the network edge image data deployment strategy;
acquiring an acquisition strategy and a processing strategy of the target image data according to the data processing corresponding relation and the target image data; acquiring target data from the target image data based on the acquisition strategy of the target image data, and processing according to the processing strategy of the target image data to obtain network edge image data;
the image data tracking module is connected with the network edge image data configuration module, summarizes all the network edge image data, and carries out analysis processing according to a preset edge image data analysis processing strategy to obtain the corresponding position of the target position in each image frame.
8. The edge-computing-based tracking device of claim 7, wherein the network edge image data configuration module comprises: a data characteristic acquisition unit and a data characteristic processing unit; wherein,
the data characteristic acquisition unit acquires the data characteristics of each target image data according to the frame sequence rule of the image data;
the data feature processing unit is connected with the data feature acquisition unit, sets the search area of the target image data to be 1.2-3 times of the area range based on a filtering algorithm, performs preprocessing, feature extraction and model training on the data features according to the set area range, and performs data classification by using the obtained model to obtain the network edge image data.
9. The edge-computation-based tracking device of claim 6, further comprising: the target network edge image data state detection module is connected with the network edge image data configuration module and is used for connecting each target network edge image data to an edge calculation control platform;
the edge computing control platform sends a state detection signal to the target network edge image data according to a preset edge image data state detection strategy;
and when the state detection signal is sent within the preset time and the detection feedback signal is not received, configuring the updated target network edge image data of the target object according to the network edge image data deployment strategy.
10. The edge-computation-based tracking device of claim 6, further comprising: the target network edge image data virtual cache creation module is connected with the network edge image data configuration module and connects each target network edge image data to an edge computing control platform;
the target network edge image data receives a storage search strategy of the edge computing control platform, and target storage equipment in a preset storage equipment range is obtained according to the storage search strategy;
and creating a virtual distributed cache based on the target network edge device according to a preset virtual cache creating strategy, and distributing the virtual distributed cache on each target storage device.
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