CN116980457A - Remote control system based on Internet of things - Google Patents

Remote control system based on Internet of things Download PDF

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CN116980457A
CN116980457A CN202311220153.2A CN202311220153A CN116980457A CN 116980457 A CN116980457 A CN 116980457A CN 202311220153 A CN202311220153 A CN 202311220153A CN 116980457 A CN116980457 A CN 116980457A
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data
key
sequence
value
window
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CN116980457B (en
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高晓波
焦艳
李钟书
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Jiangsu Sairong Technology Co ltd
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Jiangsu Sairong Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/04—Protocols for data compression, e.g. ROHC
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Computer Security & Cryptography (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)

Abstract

The application relates to the technical field of data storage, and provides a remote control system based on the Internet of things, which acquires a product data sequence through a data acquisition module; the product data sequence segmentation module segments the product data sequence into a plurality of small data sequences; the initial dictionary area window acquisition module selects an initial dictionary area window of the small data sequence; the optimal dictionary area window acquisition module acquires an optimal dictionary area window of the small data sequence, and the data compression module acquires compressed data; the remote control module realizes the accurate control of the Internet of things platform server on the remote connection equipment and the active sending of the request notification to the server by the remote connection equipment. The system improves the compression storage efficiency and effect of data, reduces the waste of system space resources, effectively improves the operation efficiency of the system, and further effectively improves the efficiency and quality of management and remote control of equipment by the Internet of things platform.

Description

Remote control system based on Internet of things
Technical Field
The application relates to the technical field of data storage, in particular to a remote control system based on the Internet of things.
Background
With the rapid development of economy, the demands of society for Internet application are growing, and the wide application of the Internet has a great influence on the development of information industry in China. The internet completes the connection of people and people, creates a digital virtual world, the internet of things is the next stage of the internet, and the task of the stage is to digitize the physical world and break through the virtual and reality. The internet of things platform is an internet of things platform for governments and enterprises, and integrates the capabilities of equipment access, equipment full life cycle management, rule engines, scene linkage, message subscription and the like. Downward supporting connection of mass equipment, and collecting equipment data to cloud; the cloud end API is provided upwards, and the server side can send the instruction to the equipment side by calling the cloud end API, so that remote control is realized.
The internet of things platform has the functions of supporting unified equipment access and management, supporting equipment remote control and the like. The remote control system based on the Internet of things is a system for remotely acquiring related data of the access equipment through the Internet of things, analyzing and monitoring the equipment data and further remotely controlling the equipment. The data volume that the system obtained through the thing networking often is great, needs to store the data, and data storage's compression efficiency and effect directly influence thing networking platform's operating efficiency and effect. Therefore, improving the compression efficiency of the data of the internet of things platform becomes a key technology for the internet of things platform to perform equipment management and remote control.
LZ77 is a commonly used lossless data compression algorithm that uses sliding windows and dictionaries to effect compression. In LZ77, the dictionary window size has a direct impact on compression efficiency. Therefore, how to adaptively obtain the optimal LZ77 compression window size becomes one of the key technologies for efficient lossless compression of data.
Disclosure of Invention
In order to solve the technical problems and realize efficient operation of a networking platform, the application provides a remote control system based on the Internet of things.
The provided remote control system based on the Internet of things, the system comprises: the system comprises a data acquisition module, a product data sequence segmentation module, an initial dictionary area window acquisition module, an optimal dictionary area window acquisition module, a data compression module and a remote control module;
the data acquisition module is used for acquiring state data of equipment products remotely connected with the Internet of things platform to obtain a product data sequence;
the product data sequence segmentation module is used for analyzing local data value changes corresponding to data points in the product data sequence, acquiring key data points in the product data sequence, and then segmenting the product data sequence into a plurality of small data sequences based on the key data points;
the initial dictionary area window acquisition module is used for taking the sequence length of the small data sequence as the size of a to-be-selected dictionary area window of the small data sequence, and then selecting an initial dictionary area window of the small data sequence based on the size of the to-be-selected dictionary area window;
the optimal dictionary area window acquisition module is used for adaptively adjusting the initial dictionary area window based on the compression effect of the data in the initial dictionary area window to acquire the optimal dictionary area window of the small data sequence;
the data compression module is used for compressing the platform data of the Internet of things based on the optimal dictionary area window to obtain compressed data;
the remote control module is used for realizing accurate control of the Internet of things platform server on the remote connection equipment and actively sending request notification to the server by the remote connection equipment by using the PRC API based on the compressed data.
In some embodiments of the present application, the product data sequence segmentation module includes a key data point acquisition sub-module, where the key data point acquisition sub-module is configured to analyze local data value changes corresponding to data points in the product data sequence, and acquire key data points in the product data sequence;
the key data point acquisition submodule comprises:
the key value unit is used for analyzing the local data value change corresponding to the data point in the product data sequence to obtain a key value of which the data point is a key data point;
a formulated key data point unit for obtaining formulated key data points in the product data sequence according to the key values;
the optimizing key value unit is used for optimizing the key value corresponding to the data point based on the distribution characteristics of the formulated key data point, and acquiring an optimizing key value of the formulated key data point;
and the key data point unit is used for obtaining key data points in the product data sequence according to the optimized key value.
In some embodiments of the application, the key value unit is further configured to:
based on the data points in the product data sequence, the first ten data points and the last ten data points of the data point adjacent time are acquired and respectively marked as a front adjacent sequence and a rear adjacent sequence;
obtaining a key value of which the data point is a key data point through the data value change of the front adjacent sequence and the rear adjacent sequence, wherein the key value is calculated by the following steps:
in the formula (i),a key value representing a data point as a key data point, +.>Representing the number of data points with equal corresponding position data values in two adjacent product data sequences, +1 is used for preventing meaningless phenomenon that denominator is 0, +.>Representing the pre-proximity sequence->In sequence +.>Are not equal to the data value of the corresponding data pointData value of data point, +.>Represents the post-proximity sequence->In sequence +.>A data value for a data point that is not equal to the data value for the corresponding data point.
In some embodiments of the application, the optimization key unit is further configured to:
obtaining the data fluctuation of the proposed key data points according to the proposed key data points and the data values of the former nearest data points and the latter nearest data points;
based on the data fluctuation, combining the position relation between the planned key data point and other relevant planned key data points to obtain an optimized value of the key value of the planned key data point;
and based on the optimized value, combining the key values to obtain the optimized key value of the planned key data point.
In some embodiments of the application, the data volatility is: the sum of absolute values of the difference between the data values of the key data points and the data values of the previous nearest data point and the next nearest data point is formulated.
In some embodiments of the present application, the method for calculating the optimized value is:
in the formula (i),representation->Fitting the optimal value of the key value corresponding to the key data point,/for the key data point>Representing proposed critical data points +.>The number of key data points is drawn up by correlation of +.>Representing proposed critical data points +.>Sum of distances between related proposed key data points, +.>Representing proposed critical data points +.>Distance between the sequence of key data points is formulated in relation to another correlation, < >>Data volatility representing proposed key data points, < ->Representing the +.f in the sequence of related proposed key data points>The key value of the data point is set,representing the maximum value of the key value of the data points in the sequence of related proposed key data points.
In some embodiments of the application, the optimization key is: the product of the key value and the normalized optimized value.
In some embodiments of the present application, the optimal dictionary area window acquisition module is further configured to:
constructing the compression ratio of the initial dictionary area window based on the compression effect of the data in the initial dictionary area window;
based on the size relation between the compression ratio and a preset compression ratio threshold, the size of an adjustment window is obtained by combining the difference between the maximum value of the successfully matched data offset and the size of the window of the initial dictionary area, and the size of the window of the initial dictionary area is adaptively adjusted;
stopping adjusting the size of the initial dictionary area window until the compression ratio is greater than or equal to a preset compression ratio threshold or the adjustment times reach a preset times threshold;
and selecting the size of the initial dictionary area window corresponding to the maximum compression ratio as the optimal window size, and acquiring the optimal dictionary area window of the small data sequence.
In some embodiments of the present application, the compression ratio calculating method is as follows:
in the formula (i),compression ratio representing initial dictionary area window, +.>Representing the number of successfully matched data in the sequence of small data at the window size of the initial dictionary area,/>Representing the number of overall data in the small data sequence, +.>Representing the initial dictionary area window size, +.>Then indicate +.>The corresponding offset of the successfully matched data, < > is shown in the specification>Indicate->Key value corresponding to data point with successful matching, < ->Indicating a data offset maximum value for which the match was successful.
In some embodiments of the present application, the method for obtaining the adjustment window size is:
when the compression ratio is smaller than the preset compression ratio threshold value, andwhen the size of the adjustment window is more than or equal to 0.1, the size of the adjustment window is the size of the window of the initial dictionary area plus one;
when the compression ratio is smaller than the preset compression ratio threshold value, andand when the size of the adjustment window is smaller than 0.1, the size of the adjustment window is the size of the initial dictionary area window minus one.
As can be seen from the above embodiments, the remote control system based on the internet of things provided by the embodiment of the application has the following beneficial effects:
the application acquires the state data of the product through the data acquisition module to obtain a product data sequence; the product data sequence segmentation module analyzes local data value changes corresponding to data points in the product data sequence, acquires key data points in the product data sequence, and then segments the product data sequence into a plurality of small data sequences based on the key data points; the initial dictionary area window acquisition module takes the sequence length of the small data sequence as the size of a to-be-selected dictionary area window of the small data sequence, and then selects an initial dictionary area window of the small data sequence based on the size of the to-be-selected dictionary area window; and the optimal dictionary area window acquisition module carries out self-adaptive adjustment on the initial dictionary area window based on the compression effect of the data in the initial dictionary area window to acquire the optimal dictionary area window of the small data sequence.
According to the method, related data of a product is remotely obtained based on the Internet of things, key data points of the data are obtained through analysis of the related data, the data sequences are adaptively segmented based on the key data points, a plurality of small data sequences are obtained, the size of a dictionary area window in a preliminary LZ77 compression process is obtained based on the length of the small data sequences, a compression ratio index is adaptively constructed based on the characteristics of the compression process, the size of the dictionary area window is adaptively adjusted based on the compression ratio index, the optimal size of the dictionary area window corresponding to the small data sequences is obtained, the LZ77 algorithm is used for compressing and storing the data, the compression and storage efficiency and effect of the data are improved, the waste of system space resources is reduced, and the operation efficiency of a system is effectively improved.
According to the remote control system based on the Internet of things, which is provided by the application, the efficiency and quality of the management and remote control of equipment by the Internet of things platform are effectively improved, and the space occupation cost of the Internet of things platform is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
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In order to more clearly illustrate the technical solution of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a basic composition schematic diagram of a remote control system based on the internet of things provided by an embodiment of the application.
Detailed Description
In order to make the technical solution of the present application better understood by those skilled in the art, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Fig. 1 is a schematic diagram of basic components of a remote control system based on the internet of things provided in an embodiment of the present application, and in the following, with reference to fig. 1, a detailed description will be given of the remote control system based on the internet of things provided in the embodiment.
The purpose of the application is that: and the window size of the optimal dictionary area in the LZ77 algorithm is obtained in a self-adaptive mode, data are compressed and stored, storage efficiency is improved, memory consumption is reduced, and efficiency and effect of the Internet of things platform on equipment management and remote control are improved.
The specific scene aimed by the application is as follows: in a remote control system based on the Internet of things, the product image and the related information of the product need to be uploaded, more product information is needed, and the product information needs to be compressed and stored in the system so as to reduce the waste of system space.
The internet of things platform is an internet of things platform for governments and enterprises, and integrates the capabilities of equipment access, equipment full life cycle management, rule engines, scene linkage, message subscription and the like. Downward supporting connection of mass equipment, and collecting equipment data to cloud; the cloud end API is provided upwards, and the server side can send the instruction to the equipment side by calling the cloud end API, so that remote control is realized.
The internet of things platform has the following characteristics that support unified equipment to insert and manage: mass device connection is achieved through industry standard internet of things protocols (MQTT, coAP, and HTTP).
Support device remote control: the PRC API can realize accurate control of the server to the device and the device actively sends request notification to the server.
Custom rules engine: flexible rule model configuration, supporting multiple rule models and custom rule models. The equipment alarm, scene linkage and management are all managed by a unified rule engine.
Custom data rights control: flexible non-invasive data rights control. And the data authority control of three-dimensional dimensions of menus, buttons and data can be realized. The operation authority of the single data can be controlled.
Higher security guarantee: supporting MQTT SSL configuration, HTTP SSL configuration and CoAP DTLS configuration; an Access token (Access Tokens) authentication mode is supported.
Based on the purposes and the scenes, the remote control system based on the Internet of things provided by the application comprises the following contents.
As shown in fig. 1, a remote control system based on the internet of things mainly comprises a data acquisition module 10, a product data sequence segmentation module 20, an initial dictionary area window acquisition module 30, an optimal dictionary area window acquisition module 40, a data compression module 50 and a remote control module 60.
Specifically, the data acquisition module 10 is configured to acquire status data of an equipment product remotely connected to the internet of things platform, and obtain a product data sequence. After logging in the internet of things platform, the tenant needs to upload relevant information of the product, equipment of the tenant is directly connected with the internet of things platform, the internet of things platform receives a product data sequence through a data transmission communication method and the like, for example, operation monitoring data of the product, such as temperature data, humidity data and the like in an operation process, and the state of the product is acquired by analyzing and storing the relevant data of the product, so that the product is remotely controlled.
The product data sequence dividing module 20 is configured to analyze local data value changes corresponding to data points in the product data sequence, obtain key data points in the product data sequence, and then divide the product data sequence into a plurality of small data sequences based on the key data points.
The LZ77 compression algorithm is a lossless compression technique based on a dictionary and sliding window. The basic principle of LZ77 is: data items in a dictionary are constructed with frequently occurring letter combinations (or longer strings) and shorter data encodings are used instead of more complex data items. And during data compression, matching the source data read in from the data to be compressed with the data items in the dictionary, retrieving corresponding codes from the data to be compressed and outputting the codes. Thereby completing the compression of the data.
The window of the LZ77 compression algorithm consists of two parts, namely a dictionary area on the left side and a to-be-encoded area on the right side. Compression is performed by comparing whether the dictionary area is consistent with the characters of the area to be encoded. The data reading is completed according to the above steps. Firstly, carrying out overall analysis on the data to obtain the length of a window to be selected.
Firstly, analyzing the acquired data, wherein the acquired data are continuous data, each data point corresponds to a data value, for a data sequence received by an Internet of things remote control system, acquiring key data points in the data sequence based on local data value changes corresponding to the data points, and dividing the acquired sequence into a plurality of small sequences based on the key data points, wherein the length of each small sequence is the length of a dictionary area to be selected.
Further, the product data sequence segmentation module 20 includes a key data point acquisition sub-module 21 and a segmentation sub-module 22. The key data point obtaining sub-module 21 is configured to analyze local data value changes corresponding to data points in the product data sequence, and obtain key data points in the product data sequence; the segmentation sub-module 22 is used for segmenting the product data sequence into a plurality of small data sequences based on the key data points.
Further, in some embodiments of the present application, the critical data point acquisition sub-module 21 includes: a key value unit 211, a formulated key data point unit 212, an optimized key value unit 213, and a key data point unit 214.
The key value unit 211 is configured to analyze local data value changes corresponding to data points in the product data sequence, and obtain a key value with the data points being key data points. Further, the key value unit 211 is further configured to obtain the first ten data points and the last ten data points of the adjacent time points of the data points based on the data points in the product data sequence, and record as a front adjacent sequence and a rear adjacent sequence respectively; and obtaining the key value of which the data point is the key data point through the data value change of the front adjacent sequence and the rear adjacent sequence.
For better illustration, any data point in the product data sequence is usedThe dots are illustrative. For->The point obtains the first ten data points of the adjacent time and the last ten data points of the adjacent time, wherein the setting of the ten data points is set according to the experience value, and the practitioner can adjust the setting of the ten data points to be respectively marked as a front adjacent sequence and a rear adjacent sequence, and the front adjacent sequence is used for processing the data pointsAnd the post-adjacent sequence->Is to obtain->The point is the key value of the key data point +.>. Key value->The calculation method of (1) is as follows:
in the formula (i),a key value representing a data point as a key data point; />Representing the number of data points with equal corresponding position data values in two adjacent product data sequences, wherein the corresponding position data points are +.>First data point in sequence and +.>The first data points in the sequence correspond to each other, +.>Acquisition ofThe method comprises the following steps: let->=0, calculate sequence +.>And sequence->The difference of the data values of the corresponding data points is 0, then +.>=/>+1, otherwise->=/>，/>The larger the data is, the more obvious the data repetition phenomenon is, and the smaller the key value of the key data point is; +1 is to prevent meaningless phenomenon of denominator 0; />Representing the pre-proximity sequence->In sequence +.>Data value of a data point which is not equal to the data value of the corresponding data point, is->Represents the post-proximity sequence->In sequence +.>Data value of a data point which is not equal to the data value of the corresponding data point, is->And->The larger the difference, the more dissimilar the two data sequences are, the larger the key value. Key value->The larger, the description->The greater the time sequence variation before and after the point is, +.>The greater the likelihood that a point is a critical data point.
A formulated key data point unit 212 is used to obtain formulated key data points in the product data sequence based on the key values. The data points in the product data sequence are analyzed according to the key value unit 211, and then each data point can acquire a corresponding key value. According to the size of the key value, the proposed key data points in the obtained product data sequence can be initially judged. That is, the key value of the data point is compared with the first preset key value threshold, the key value is greater than or equal to the first preset key value threshold, the data point is determined to be the proposed key data point, otherwise, the data point is not the proposed key data point, and the first preset key value threshold can be set to 0.8.
The optimizing key value unit 213 is configured to optimize the key value corresponding to the data point based on the distribution characteristics of the proposed key data point, and obtain the optimizing key value of the proposed key data point.
According to the analysis of the data points in the data sequence by the key value unit 211, each data point can acquire a corresponding key value, however, when the key data point is acquired, some wrong key data points may be acquired when the data is obviously changed, so that the efficiency and the precision of the length of the dictionary area to be acquired subsequently are interfered, and therefore, the optimization key value unit 213 in the application optimizes the key value corresponding to the data point based on the distribution characteristics of the planned key data point, and acquires the optimization key value of the planned key data point.
Further, the optimization key-value unit 213 is further configured to: acquiring the data fluctuation of the planned key data points according to the data values of the planned key data points, the front nearest data point and the rear nearest data point; based on the data fluctuation, combining the position relation between the planned key data point and other relevant planned key data points to obtain an optimized value of the key value of the planned key data point; based on the optimized values, the optimized key values of the proposed key data points are obtained in combination with the key values.
For better illustration, key data points are formulated hereAn example is described. From the proposed key data points +.>Data value +.>And developing key data points +.>Data value of the nearest preceding data point +.>Data value +.>Obtaining the data volatility of the planned critical data point as the sum of absolute values of difference values between the data value of the planned critical data point and the data value of the front nearest data point and the data value of the rear nearest data point, namely:
in the formula (i),to formulate the data volatility of the critical data points, +.>Representing proposed critical data points +.>Before nearest neighbor data point->Data value of->Representing proposed critical data points +.>Is->Is a data value of (a). />And->The larger the difference is, the more critical data points are formulated>Data volatility at->The stronger.
Based on the formulated key data pointsData volatility at the point, combined with the fitting of critical data points +.>The position relation of the position and other related planned key data points is obtained, and the planned key data points +.>Optimized value of Key value->Optimized value->The calculation method of (1) is as follows:
in the formula (i),representation->An optimized value of a key value corresponding to the key data point is drawn; />Representing proposed critical data points +.>The number of related planned key data points is obtained by the method of +.>Adjacent data points->(here assumed to be +.>) Judging when the key data point is planned +.>And adjacent data points->When the difference of the key values of (2) is less than or equal to 0.1, the neighboring data point is considered +.>To plan key data point +.>Key data points are drawn up and the data points +.>The judgment is carried out until a new related proposed key data point cannot be obtained, and the number of the related proposed key data points obtained at the moment is +.>A series of related proposed key data points can be obtained, each of which necessarily has one proposed key data point; />Representing proposed critical data points +.>Sum of distances from related proposed key data points, wherein the data points are assumed +.>And draw key data point->Adjacent, then data point->And draw key data point->The distance between them is 1->The smaller the value, the description draws up the key data point +.>The closer to the center of the relevant proposed key data point, the moreThe greater the optimization value, which may be a critical data point; />Representing proposed critical data points +.>Distance between the sequence of key data points is formulated in relation to another correlation, distance +.>The larger the description is, the more critical data points are formulated>The more likely it is a critical data point; />Representing proposed critical data points +.>Data volatility, specifying the proposed key data points +.>The more likely it is a critical data point; />Representing the +.f in the sequence of related proposed key data points>Critical value of data point +_>A maximum value of the key value representing the data points in the sequence of related proposed key data points,/>And->The smaller the difference, the greater the weight at the time of weighted sum optimization.
Then based on the formulated key numberData pointsOptimized value of Key value->In combination with the planning of critical data points +.>Key value of (2)Obtaining proposed key data points +.>Optimization key value +.>. Optimizing key value->The method comprises the following steps: the product of the key value and the normalized optimized value is:
in the formula (i),representing proposed critical data points +.>Is/are optimized key value>Representing proposed critical data points +.>Key value of->Representing proposed critical data points +.>Normalized optimized value of Key value->。
The key data point unit 214 is configured to obtain key data points in the product data sequence according to the optimized key value. Optimizing key valuesThe larger the probability that the explanatory data point is a critical data point is greater. Carrying out the analysis on the data points in the product data sequence received by the remote control system based on the Internet of things to obtain the optimization key value corresponding to the data points>And setting a second preset key value threshold value to be 0.8 according to the experience value, and when the optimized key value is greater than or equal to the second preset key value threshold value of 0.8, considering the data point as a key data point, otherwise, not as a key data point.
The dividing sub-module 22 is configured to divide the product data sequence into a plurality of small data sequences based on the key data points. That is, the product data sequence is divided into a plurality of small data sequences according to the key data points acquired by the key data point acquisition sub-module 21, and the product data sequence may be divided into n+1 small data sequences assuming that N key data points are acquired.
The initial dictionary area window acquisition module 30 is configured to take the sequence length of the small data sequence as the size of the candidate dictionary area window of the small data sequence, and then select the initial dictionary area window of the small data sequence based on the size of the candidate dictionary area window.
Because of the large variation in data values within adjacent small data sequences, dictionary area windows that are suitable for a current small data sequence are often unsuitable for the next adjacent small data sequence. Therefore, for each small data sequence, the window size of the corresponding optimal dictionary area needs to be obtained in a self-adaptive mode.
The initial dictionary area window acquisition module 30 of the application takes the sequence length of each obtained small data sequence as the size of the window of the to-be-selected dictionary area of the small data sequence, and then selects the initial dictionary area window of the small data sequence based on the size of the window of the to-be-selected dictionary area. Taking any small data sequence Q as an example, when the sequence length corresponding to the small data sequence Q is taken as the size of the window of the dictionary area to be selected of the small data sequence Q, as all information of the small data sequence is contained in the window, when data in the window is compressed, the probability of successful matching between the data and the dictionary area is high, however, when the window is matched in each encoding, the data in the sequence is often required to be matched, the matching time is long, and the efficiency of subsequent encoding matching is further affected. And obtaining the window size of the candidate dictionary area corresponding to the small data sequence Q according to the obtained window size. The meaning of acquiring the size of the initial dictionary area window is that the initial dictionary area window with better quality corresponding to a small data sequence can be acquired, so that the adjustment efficiency and the adjustment precision of the size of a subsequent window are improved.
The optimal dictionary area window acquisition module 40 is configured to adaptively adjust the initial dictionary area window based on the compression effect of the data in the initial dictionary area window, and acquire an optimal dictionary area window of the small data sequence.
The optimal dictionary area window acquisition module 40 is further configured to:
based on the compression effect of the data in the initial dictionary area window, constructing the compression ratio of the initial dictionary area window, wherein the compression ratio calculating method comprises the following steps:
in the formula (i),the compression ratio of the initial dictionary area window is represented, and the larger the compression ratio Y is, the better the compression effect and efficiency corresponding to the size of the dictionary area window are explained; />Representing the number of successfully matched data in the sequence of small data at the window size of the initial dictionary area,/>Representing the number of overall data in the small data sequence, +.>And->The larger the ratio of the initial dictionary area window is, the better the matching effect of the initial dictionary area window is; />Representing the window size of the initial dictionary area, window size +.>The larger the matching time length is, the larger the compression ratio is; />Then indicate +.>The larger the offset corresponding to the successfully matched data, the larger the offset is, the larger the compression duration is, and the worse the window compression effect is; />Indicate->The key value corresponding to the data point successfully matched is smaller, which indicates that the more data in the small data sequence are compressed, the better the compression effect is; />Data offset maximum value indicating successful matching, offset maximum value +.>And window size->The larger the difference, the moreThe more useless data in the bright window, the longer the matching time, the worse the compression effect.
Based on the size relation between the compression ratio and a preset compression ratio threshold, the size of an adjustment window is obtained by combining the difference between the maximum value of the successfully matched data offset and the size of the window of the initial dictionary area, and the size of the window of the initial dictionary area is adaptively adjusted. The method for acquiring the size of the adjusting window comprises the following steps: when the compression ratio is smaller than the preset compression ratio threshold value, andwhen the size of the window is more than or equal to 0.1, the size of the window is adjusted to be the size of the window of the initial dictionary area plus one;
when the compression ratio is smaller than the preset compression ratio threshold value, andwhen the size of the window is smaller than 0.1, the size of the window is adjusted to be the size of the window of the initial dictionary area by one; namely:
in the formula (i),to adjust the window size before +.>For the adjusted window size, +.>When the window size is smaller than 0.1, the phenomenon that the window is too large and wastes resources and is smaller in compression exists, so that the window needs to be reduced; />When the data is larger than or equal to 0.1, the window is too small, the data matching compression is less, and the window needs to be enlarged.
Compression ratioThe larger the speakingThe better the compression effect and efficiency corresponding to the dictionary area window size is. When the compression ratio corresponding to the window is greater than or equal to the set threshold value of 0.75 (set according to the experience value), the window is considered to have reached the compression effect requirement, and can be used as the window of the optimal dictionary area of the small data sequence. Otherwise, subsequent adaptive adjustments are required.
Performing self-adaptive adjustment on the size of the window of the initial dictionary area until the compression ratio is greater than or equal to a preset compression ratio threshold or the adjustment times reach a preset adjustment times threshold, and stopping adjusting the size of the window of the initial dictionary area; and selecting the size of the initial dictionary area window corresponding to the maximum compression ratio as the optimal window size, and acquiring the optimal dictionary area window of the small data sequence.
And continuously adjusting the size of the window of the initial dictionary area until the compression ratio corresponding to the adjusted window is greater than or equal to a preset compression ratio threshold value 0.75 or reaches a preset adjustment frequency threshold value 30 times, and at the moment, selecting the size of the window corresponding to the maximum compression ratio as the optimal window size to acquire the optimal dictionary area window of the small data sequence. Wherein the threshold is set based on empirical values, which can be adjusted by the practitioner.
According to the system module, the optimal dictionary area window size of each small data sequence in the data sequences acquired by the remote control system based on the Internet of things is acquired and is used as the dictionary area window in the LZ77 algorithm, wherein the size of the coding area window is set to be one third of the size of the dictionary area window, and the implementation can adjust according to the experience value. The LZ77 algorithm is used for respectively compressing the data in each small data sequence, so that the compression storage of the data is completed, the compression efficiency and the effect of the LZ77 algorithm are improved, and the specific compression process of the LZ77 algorithm is a known technology and is not repeated here.
The data compression module 50 is configured to perform compression processing on the internet of things platform data based on the optimal dictionary area window, and obtain compressed data.
The remote control module 60 is configured to implement, based on the compressed data, accurate control of the remote connection device by the internet of things platform server and active sending of the request notification to the server by the remote connection device using the PRC API.
Through the remote control system based on the Internet of things, efficient lossless compression of the product data of the platform equipment of the Internet of things is achieved. The management and remote control of the Internet of things platform to the networking equipment are more efficient and better. Provides a technical foundation for the application and popularization of the platform of the Internet of things.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
It is noted that unless specified and limited otherwise, relational terms such as "first" and "second", and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such article or apparatus. Without further limitation, the statement "comprises one … …" does not exclude that an additional identical element is present in an article or device that comprises the element. In addition, the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (10)

1. A remote control system based on the internet of things, the system comprising: the system comprises a data acquisition module (10), a product data sequence segmentation module (20), an initial dictionary area window acquisition module (30), an optimal dictionary area window acquisition module (40), a data compression module (50) and a remote control module (60);
the data acquisition module (10) is used for acquiring state data of equipment products remotely connected with the internet of things platform to obtain a product data sequence;
the product data sequence segmentation module (20) is used for analyzing local data value changes corresponding to data points in the product data sequence, acquiring key data points in the product data sequence, and then segmenting the product data sequence into a plurality of small data sequences based on the key data points;
the initial dictionary area window acquisition module (30) is used for taking the sequence length of the small data sequence as the size of a to-be-selected dictionary area window of the small data sequence, and then selecting the initial dictionary area window of the small data sequence based on the size of the to-be-selected dictionary area window;
the optimal dictionary area window acquisition module (40) is used for adaptively adjusting the initial dictionary area window based on the compression effect of the data in the initial dictionary area window to acquire the optimal dictionary area window of the small data sequence;
the data compression module (50) is used for compressing the internet of things platform data based on the optimal dictionary area window to obtain compressed data;
the remote control module (60) is used for realizing accurate control of the Internet of things platform server on the remote connection device and actively sending request notification to the server by the remote connection device by using the PRC API based on the compressed data.
2. The remote control system based on the internet of things according to claim 1, wherein the product data sequence segmentation module (20) comprises a key data point acquisition sub-module (21), and the key data point acquisition sub-module (21) is configured to analyze local data value changes corresponding to data points in the product data sequence and acquire key data points in the product data sequence;
the critical data point acquisition sub-module (21) comprises:
a key value unit (211) for analyzing local data value changes corresponding to data points in the product data sequence, and obtaining a key value of which the data points are key data points;
-a formulated key data point unit (212) for obtaining formulated key data points in the product data sequence based on the key values;
an optimization key value unit (213) for optimizing the key value corresponding to the data point based on the distribution characteristics of the proposed key data point, and obtaining an optimization key value of the proposed key data point;
a key data point unit (214) for obtaining key data points in the product data sequence based on the optimized key values.
3. The internet of things-based remote control system of claim 2, wherein the key value unit (211) is further configured to:
based on the data points in the product data sequence, the first ten data points and the last ten data points of the data point adjacent time are acquired and respectively marked as a front adjacent sequence and a rear adjacent sequence;
obtaining a key value of which the data point is a key data point through the data value change of the front adjacent sequence and the rear adjacent sequence, wherein the key value is calculated by the following steps:
in the formula (i),a key value representing a data point as a key data point, +.>Representing the number of data points with equal corresponding position data values in two adjacent product data sequences, +1 is used for preventing meaningless phenomenon that denominator is 0, +.>Representing the pre-proximity sequence->In sequence +.>Data value of a data point which is not equal to the data value of the corresponding data point, is->Represents the post-proximity sequence->In sequence +.>A data value for a data point that is not equal to the data value for the corresponding data point.
4. The internet of things-based remote control system according to claim 2, wherein the optimization key unit (213) is further configured to:
obtaining the data fluctuation of the proposed key data points according to the proposed key data points and the data values of the former nearest data points and the latter nearest data points;
based on the data fluctuation, combining the position relation between the planned key data point and other relevant planned key data points to obtain an optimized value of the key value of the planned key data point;
and based on the optimized value, combining the key values to obtain the optimized key value of the planned key data point.
5. The internet of things-based remote control system of claim 4, wherein the data volatility is: the sum of absolute values of the difference between the data values of the key data points and the data values of the previous nearest data point and the next nearest data point is formulated.
6. The remote control system based on the internet of things according to claim 4, wherein the optimization value calculating method is as follows:
in the formula (i),representation->Fitting the optimal value of the key value corresponding to the key data point,/for the key data point>Representing proposed critical data points +.>The number of key data points is drawn up by correlation of +.>Representing proposed critical data points +.>Sum of distances between related proposed key data points, +.>Representing proposed critical data points +.>Distance between the sequence of key data points is formulated in relation to another correlation, < >>Data volatility representing proposed key data points, < ->Representing the +.f in the sequence of related proposed key data points>The key value of the data point is set,representing the maximum value of the key value of the data points in the sequence of related proposed key data points.
7. The remote control system based on the internet of things according to claim 4, wherein the optimization key value is: the product of the key value and the normalized optimized value.
8. The internet of things-based remote control system of claim 1, wherein the optimal dictionary area window acquisition module (40) is further configured to:
constructing the compression ratio of the initial dictionary area window based on the compression effect of the data in the initial dictionary area window;
based on the size relation between the compression ratio and a preset compression ratio threshold, the size of an adjustment window is obtained by combining the difference between the maximum value of the successfully matched data offset and the size of the window of the initial dictionary area, and the size of the window of the initial dictionary area is adaptively adjusted;
stopping adjusting the size of the initial dictionary area window until the compression ratio is greater than or equal to a preset compression ratio threshold or the adjustment times reach a preset times threshold;
and selecting the size of the initial dictionary area window corresponding to the maximum compression ratio as the optimal window size, and acquiring the optimal dictionary area window of the small data sequence.
9. The remote control system based on the internet of things according to claim 8, wherein the compression ratio calculating method is as follows:
in the formula (i),compression ratio representing initial dictionary area window, +.>Representing the number of successfully matched data in the sequence of small data at the window size of the initial dictionary area,/>Representing the number of overall data in the small data sequence, +.>Representing the initial dictionary area window size, +.>Then indicate +.>The corresponding offset of the successfully matched data, < > is shown in the specification>Indicate->Key value corresponding to data point with successful matching, < ->Indicating a data offset maximum value for which the match was successful.
10. The remote control system based on the internet of things according to claim 8, wherein the method for acquiring the size of the adjustment window is as follows:
when the compression ratio is smaller than the preset compression ratio threshold value, andwhen the size of the adjustment window is more than or equal to 0.1, the size of the adjustment window is the size of the window of the initial dictionary area plus one;
when the compression ratio is smaller than the preset compression ratio threshold value, andand when the size of the adjustment window is smaller than 0.1, the size of the adjustment window is the size of the initial dictionary area window minus one.
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