CN109408466B - Agricultural Internet of things redundant data processing method and device - Google Patents
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Abstract
The invention discloses a method and a device for processing redundant data for agricultural Internet of things, which are characterized in that the optimal sensing nodes in the agricultural Internet of things are selected to collect the same type of sensing data, small files in data streams in a preset time period are combined into a large filing file and are divided into a plurality of files, a plurality of threads are established according to the plurality of files to perform data processing tasks, the data are preprocessed, incomplete data are filled, the hash values between every two data in the complete data files are compared, and the redundant data are deleted, so that the problems of data loss, data errors, redundant data and the like of the agricultural Internet of things sensing data in the prior art are solved.
Description
Technical Field
The invention relates to the technical field of agricultural data processing, in particular to a method and a device for processing redundant data for an agricultural Internet of things.
Background
The agricultural Internet of things uses information sensing equipment, a communication network and an intelligent information processing technology as a core, agricultural scientific management is realized, and the purposes of reasonably using agricultural resources, improving ecological environment, reducing production cost and improving quantity and quality of agricultural products are achieved. Due to the fact that the agricultural production environment is complex and the conditions are severe, higher requirements are provided for the performances of accurate sensing, reliable and stable transmission, low power consumption and the like of the agricultural Internet of things sensor.
The data required by all aspects are acquired in real time through the established wireless sensor network, so that the seed culture of agricultural products is managed finely, and due to the fact that the data information data volume is large, the real-time transmission data is large, and the sensors are exposed to the outdoor high-temperature high-humidity environment for a long time, and the error data is large, redundant data such as data loss, data errors and the like often exist. Therefore, a method for processing the redundant data is needed.
Disclosure of Invention
The embodiment of the invention provides a method and a device for processing redundant data for an agricultural Internet of things, which are used for optimizing data, so that the problems of data loss, data errors, redundant data and the like of agricultural Internet of things sensing data in the prior art are solved.
In order to solve the problems, the invention discloses the following technical scheme:
in a first aspect, a method for processing redundant data for an agricultural internet of things is provided, and is applied to sensing data processing of the agricultural internet of things, and the method includes:
screening sensor nodes of different sensing types, selecting one sensor node as a main node of the sensing type, and collecting sensing data under the sensing type by the main node;
receiving a data stream transmitted by a sensor in real time under each sensing type, setting a data reading time period N, intercepting M filing time periods in the reading time period N, and obtaining M filing large files from the small files in each filing time period according to a filing method of a distributed file system, wherein N, M is a natural number more than or equal to 1;
establishing M data processing threads with the same quantity as M large files to be archived, wherein each data processing thread corresponds to one data processing task;
each data processing task sets the size of the radius of a neighborhood and the number of minimum value points in the neighborhood for a data block in each large filing file, divides the data block into a plurality of clusters, performs data filling according to the calculated value of the similarity between the center point of the cluster and the data in the data block, selects the cluster to which the center point nearest to the data belongs and assigns the data to the cluster to obtain the complete data file of the sensing type;
setting a sliding window, and traversing an existing list according to the obtained hash value by performing hash value calculation on the complete data file to determine whether the same hash value exists or not;
if there is the same hash value, it represents that a record already exists and is deleted in the complete data file.
In a second aspect, a redundant data processing device for an agricultural internet of things is provided, which is applied to sensing data processing of the agricultural internet of things, and the device includes:
the main node processing module is used for screening sensor nodes of different sensing types, selecting one sensor node as a main node of the sensing type, and collecting sensing data under the sensing type by the main node;
the file archiving module receives data streams transmitted by the sensors in real time under each sensing type, sets a data reading time period N, intercepts M archiving time periods in the reading time period N, and obtains M large archiving files from the small files in each archiving time period according to an archiving method of a distributed file system, wherein N, M is a natural number which is more than or equal to 1;
the thread establishing module is used for establishing M data processing threads with the same number as the M large files to be archived, and each data processing thread corresponds to one data processing task;
the data filling module is used for setting the size of the radius of a neighborhood and the number of minimum value points in the neighborhood for a data block in each large filing file, dividing the data block into a plurality of clusters, filling data according to the calculated value of the similarity between the center point of each cluster and the data in the data block, selecting the cluster to which the center point nearest to the data belongs and assigning the data to the cluster to obtain the complete data file of the sensing type;
the traversal module is used for setting a sliding window, performing hash value calculation on the complete data file, traversing the existing list according to the obtained hash value and determining whether the same hash value exists or not;
and the result processing module represents that the record exists and is deleted in the complete data file if the same hash value exists.
The invention discloses a method and a device for processing redundant data for an agricultural Internet of things, which are used for collecting the same type of sensing data by selecting an optimal sensing node in the agricultural Internet of things, merging small files in a data stream in a preset time period into a large filing file, dividing the large filing file into a plurality of files, establishing a plurality of threads according to the plurality of files to perform data processing tasks, preprocessing the data, filling incomplete data, comparing hash values between every two data in the complete data file, and deleting the redundant data, thereby solving the problems of data loss, data errors, redundant data and the like of the sensing data of the agricultural Internet of things in the prior art.
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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 introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart illustrating a method for processing redundant data according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a redundant data processing apparatus according to another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a flowchart of a redundant data processing method for an agricultural internet of things, where the method is applied to data processing of the agricultural internet of things. Various sensor devices are tightly combined with a network in the agricultural internet of things to realize data acquisition, transmission, processing and control, the various sensor devices can be used for monitoring agricultural environment, soil information and the like in real time, such as detecting ambient temperature, humidity, wind power, rainfall, soil pH value, nitrogen and the like, and the types of the sensors are not limited. Generally, each type of sensor senses data sensed by itself and transmits the data in real time, so that each type of data is generally stored in one table record, which results in time and labor consumption in subsequent data processing. Therefore, the invention collects the data stream transmitted by other sensors under the sensing type by setting a sensor main node. Screening sensor nodes of different sensing types, selecting one sensor node as a main node of the sensing type, and collecting sensing data under the sensing type by the main node.
Optionally, the sensor node with the lowest frequency of use in one period is selected and set as the master node of the sensing type, or the sensor node with the highest accuracy in one period is selected and set as the master node of the sensing type.
After the sensor main node collects data streams transmitted in real time, a data reading time period N is set, M filing time periods are intercepted in the data reading time period N, and M filing large files are obtained from small files in each filing time period according to a filing method of a distributed file system, wherein N, M is a natural number which is more than or equal to 1. When the number of the small files is large, a large number of small blocks on the magnetic disk can be read randomly, and in order to avoid frequent input operation caused by processing the small files one by one, all the small files on the logical disk group form a large filing file, and then all the small files on the logical disk group are read in batches to an internal memory, so that the reading speed of the small files is improved.
The data flow data is large, the consumption of the processing time is long when the data flow data are combined, parallel processing can be carried out in order to reduce the processing time, M data processing threads which are the same as M archived large files are established, and each data processing thread corresponds to one data processing task. A plurality of thread parallel processing units are established by the GPU unit, and one thread parallel processing unit in the plurality of thread parallel processing units corresponds to one data processing task. The GPU threads are light-weight threads, zero overhead can be realized by switching between the threads, the thread switching has the advantages that the threads are switched to the ready state threads, the delay of the threads can be hidden by calculation in the threads, and the hidden delay is better if the number of the threads is more; the method for realizing multithreading by the CPU uses coarse-grained multithreading of software, and is characterized in that thread switching generally needs hundreds of clock cycles, and the consumption is very large. In a CPU, there is a multi-core system, which may have 2-8 computing cores, but the improvement of hardware performance is limited, so it is not easy to increase the number of computing cores continuously. In contrast, the number of streaming multiprocessors in the GPU is typically 1-30, and if the GPU is used at full capacity, the floating point computing processing power is very advantageous, so the mainstream GPU performance is 10 times or even higher than the CPU performance. Comparing the GPU and the CPU, it can be seen that, in the two aspects of the bandwidth of the memory and the operation capability, the GPU is several times higher than the CPU in the same period.
Filling incomplete data, setting the size of a neighborhood radius and the number of minimum value points in a neighborhood for a data block in each large filing file, dividing the data block into a plurality of clusters, filling the data according to the calculated value of the similarity between the central point of the cluster and the data in the data block, selecting the cluster to which the central point closest to the data belongs, assigning the data to the cluster, and obtaining a sensing type complete data file.
And setting a sliding window, calculating the hash value of the complete data file, traversing the existing list according to the obtained hash value, and determining whether the same hash value exists. And establishing a window with the length of N, and extracting the data hash value from the current window for filing the large file by using a CPU (central processing unit) of the processor.
If the hash values are the same, representing that the record exists and deleting the record in the complete data file; if there is no identical hash value, the record is retained and traversal continues.
According to the redundant data processing method, the optimal sensing nodes are selected to collect the same type of sensing data, small files in data streams in a preset time period are combined into a large filing file and are divided into a plurality of files, a plurality of threads are established according to the plurality of files to perform data processing tasks, data are preprocessed, incomplete data are filled, the hash values of every two data in the complete data files are compared, and redundant data are deleted, so that the problems of data loss, data errors, redundant data and the like of the sensing data of the Internet of things of agriculture in the prior art are solved.
Fig. 2 is a schematic structural diagram of a redundant data processing apparatus according to another embodiment of the present invention, including: a master node processing module 201, a file archiving module 202, a thread creation module 203, a data population module 204, a traversal module 205, and a result processing module 206.
Various sensor devices are tightly combined with a network in the agricultural internet of things to realize data acquisition, transmission, processing and control, the various sensor devices can be used for monitoring agricultural environment, soil information and the like in real time, such as detecting ambient temperature, humidity, wind power, rainfall, soil pH value, nitrogen and the like, and the types of the sensors are not limited. Generally, each type of sensor senses data sensed by itself and transmits the data in real time, so that each type of data is generally stored in one table record, which results in time and labor consumption in subsequent data processing. Thus, the master node processing module 201 collects the data streams of the transmissions of other sensors in its sensing type. Screening sensor nodes of different sensing types, selecting one sensor node as a main node of the sensing type, and collecting sensing data under the sensing type by the main node.
Optionally, the sensor node with the lowest frequency of use in one period is selected and set as the master node of the sensing type, or the sensor node with the highest accuracy in one period is selected and set as the master node of the sensing type.
The file archiving module 202 sets a data reading time period N after the sensor master node collects the data stream transmitted in real time, intercepts M archiving time periods within the reading time period N, and obtains M archiving large files from the small files in each archiving time period according to an archiving method of a distributed file system, wherein N, M is a natural number greater than or equal to 1. When the number of the small files is large, a large number of small blocks on the magnetic disk can be read randomly, and in order to avoid frequent input operation caused by processing the small files one by one, all the small files on the logical disk group form a large filing file, and then all the small files on the logical disk group are read in batches to an internal memory, so that the reading speed of the small files is improved.
The thread establishing module 203 has large data flow data and long processing time consumption when the data flow data are combined, and can perform parallel processing to reduce the processing time, and establish M data processing threads which are the same as M archived large files, wherein each data processing thread corresponds to one data processing task. A plurality of thread parallel processing units are established by the GPU unit, and one thread parallel processing unit in the plurality of thread parallel processing units corresponds to one data processing task. The GPU threads are light-weight threads, zero overhead can be realized by switching between the threads, the thread switching has the advantages that the threads are switched to the ready state threads, the delay of the threads can be hidden by calculation in the threads, and the hidden delay is better if the number of the threads is more; the method for realizing multithreading by the CPU uses coarse-grained multithreading of software, and is characterized in that thread switching generally needs hundreds of clock cycles, and the consumption is very large. In a CPU, there is a multi-core system, which may have 2-8 computing cores, but the improvement of hardware performance is limited, so it is not easy to increase the number of computing cores continuously. In contrast, the number of streaming multiprocessors in the GPU is typically 1-30, and if the GPU is used at full capacity, the floating point computing processing power is very advantageous, so the mainstream GPU performance is 10 times or even higher than the CPU performance. Comparing the GPU and the CPU, it can be seen that, in the two aspects of the bandwidth of the memory and the operation capability, the GPU is several times higher than the CPU in the same period.
The data filling module 204 is configured to fill incomplete data, set the size of a neighborhood radius and the number of minimum points in a neighborhood for a data block in each large archive file, divide the data block into a plurality of clusters, perform data filling according to a calculated value of similarity between a center point of a cluster and data in the data block, select a cluster to which the center point closest to the data belongs, assign the data to the cluster, and obtain a sensing-type complete data file.
The traversal module 205 sets a sliding window, and traverses an existing list according to the obtained hash value by performing hash value calculation on the complete data file, to determine whether the same hash value exists. And establishing a window with the length of N, and extracting the data hash value from the current window for filing the large file by using a CPU (central processing unit) of the processor.
A result processing module 206, which represents that a record exists and is deleted in the complete data file if the hash values are the same; if there is no identical hash value, the record is retained and traversal continues.
According to the device, the optimal sensing nodes are selected to collect the sensing data of the same type, small files in data streams in a preset time period are combined into a large filing file and are divided into a plurality of files, a plurality of threads are established according to the files to perform data processing tasks, the data are preprocessed, incomplete data are filled, the hash values of every two data in complete data files are compared, redundant data are deleted, and therefore the problems of data loss, data errors, redundant data and the like of the sensing data of the Internet of things of agriculture in the prior art are solved.
For convenience of description, each part of the above-described apparatus is separately described as being functionally divided into various modules or units. Of course, the functionality of the various modules or units may be implemented in the same one or more pieces of software or hardware in practicing the invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
Claims (6)
1. A redundant data processing method for an agricultural Internet of things is applied to sensing data processing of the agricultural Internet of things, and is characterized by comprising the following steps:
screening sensor nodes of different sensing types, selecting one sensor node as a main node of the sensing type, and collecting sensing data under the sensing type by the main node;
receiving a data stream transmitted by a sensor in real time under each sensing type, setting a data reading time period N, intercepting M filing time periods in the reading time period N, and obtaining M filing large files from the small files in each filing time period according to a filing method of a distributed file system, wherein N, M is a natural number more than or equal to 1;
establishing M data processing threads with the same quantity as M large files to be archived, wherein each data processing thread corresponds to one data processing task;
each data processing task sets the size of the radius of a neighborhood and the number of minimum value points in the neighborhood for a data block in each large filing file, divides the data block into a plurality of clusters, performs data filling according to the calculated value of the similarity between the center point of the cluster and the data in the data block, selects the cluster to which the center point nearest to the data belongs and assigns the data to the cluster to obtain the complete data file of the sensing type;
setting a sliding window, and traversing an existing list according to the obtained hash value by performing hash value calculation on the complete data file to determine whether the same hash value exists or not;
if there is the same hash value, it represents that a record already exists and is deleted in the complete data file.
2. The method of claim 1, wherein selecting a sensor node as the master node for the sensing type further comprises: and selecting and setting the sensor node with the lowest use frequency in one period as the main node of the sensing type, or selecting and setting the sensor node with the highest accuracy in one period as the main node of the sensing type.
3. The method of claim 1, further comprising determining whether there is an identical hash value, and if there is no identical hash value, retaining the record and continuing the traversal.
4. The utility model provides an agricultural is redundant data processing apparatus for thing networking, is applied to the sensing data processing of agricultural thing networking, its characterized in that, the device includes:
the main node processing module is used for screening sensor nodes of different sensing types, selecting one sensor node as a main node of the sensing type, and collecting sensing data under the sensing type by the main node;
the file archiving module receives data streams transmitted by the sensors in real time under each sensing type, sets a data reading time period N, intercepts M archiving time periods in the reading time period N, and obtains M large archiving files from the small files in each archiving time period according to an archiving method of a distributed file system, wherein N, M is a natural number which is more than or equal to 1;
the thread establishing module is used for establishing M data processing threads with the same number as the M large files to be archived, and each data processing thread corresponds to one data processing task;
the data filling module is used for setting the size of the radius of a neighborhood and the number of minimum value points in the neighborhood for a data block in each large filing file, dividing the data block into a plurality of clusters, filling data according to the calculated value of the similarity between the center point of each cluster and the data in the data block, selecting the cluster to which the center point nearest to the data belongs and assigning the data to the cluster to obtain the complete data file of the sensing type;
the traversal module is used for setting a sliding window, performing hash value calculation on the complete data file, traversing the existing list according to the obtained hash value and determining whether the same hash value exists or not;
and the result processing module represents that the record exists and is deleted in the complete data file if the same hash value exists.
5. The apparatus of claim 4, wherein the master node processing module further comprises: and selecting and setting the sensor node with the lowest use frequency in one period as the main node of the sensing type, or selecting and setting the sensor node with the highest accuracy in one period as the main node of the sensing type.
6. The apparatus of claim 4, wherein the result processing module further comprises: and confirming whether the same hash value exists, and if the same hash value does not exist, keeping the record and continuing to traverse.
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