CN113610123B - Multi-source heterogeneous data fusion method and system based on Internet of things - Google Patents
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Abstract
According to the multi-source heterogeneous data fusion method and system based on the Internet of things, heterogeneous data interval distribution data, expected heterogeneous data intervals and current heterogeneous data intervals of target heterogeneous operation data are predicted, the total heterogeneous data intervals conform to a data error permission range and are counted to form a data error permission range, a plurality of target heterogeneous operation data global analysis historical average analysis heterogeneous data introduction attributes are fused, the expected average analysis heterogeneous data introduction attributes can be predicted, the relative relevance of the expected average analysis heterogeneous data introduction attributes and the historical average analysis heterogeneous data introduction attributes is predicted, the expected heterogeneous data intervals are checked through the preset relevance data preset error permission range, the target heterogeneous data interval of each target heterogeneous operation data can be accurately determined, and the data error permission range of errors in the use process of the target heterogeneous operation data is effectively reduced.
Description
Technical Field
The application relates to the technical field of data fusion, in particular to a multi-source heterogeneous data fusion method and system based on the Internet of things.
Background
The data fusion technology is an information processing technology which utilizes a computer to automatically analyze and synthesize a plurality of observation information obtained according to time sequence under a certain criterion so as to complete required decision and evaluation tasks.
With the continuous increment of the data volume, the overload of the data processing terminal is caused to work, so that similar related data are required to be integrated, and the working pressure of the data processing terminal can be reduced.
However, there are also some drawbacks in the related data fusion technique.
Disclosure of Invention
In view of the above, the application provides a multi-source heterogeneous data fusion method and system based on the Internet of things.
In a first aspect, a multi-source heterogeneous data fusion method based on the internet of things is provided, the method comprising:
acquiring historical analysis heterogeneous data introduction attributes, expected heterogeneous data intervals and current heterogeneous data intervals of each of a plurality of target heterogeneous operation data, wherein the historical analysis heterogeneous data introduction attributes represent heterogeneous data conditions of the analysis data of the target heterogeneous operation data in a preset historical interval period;
Using the historical analysis heterogeneous data introduction attribute of the plurality of target heterogeneous operation data to calculate the historical average analysis heterogeneous data introduction attribute of the plurality of target heterogeneous operation data;
determining heterogeneous data interval distribution data of each target heterogeneous operation data;
counting the total heterogeneous data interval of each target heterogeneous operation data to be in accordance with the corresponding data error permission range of the expected heterogeneous data interval queue by utilizing the heterogeneous data interval distribution data, the expected heterogeneous data interval and the current heterogeneous data interval of each target heterogeneous operation data;
counting the data error allowable range of each target heterogeneous operation data by utilizing the fact that the total heterogeneous data interval of the target heterogeneous operation data accords with the corresponding data error allowable range of the expected heterogeneous data interval queue;
using the data error allowable range and the historical analysis heterogeneous data introduction attribute of the target heterogeneous operation data to calculate the expected average analysis heterogeneous data introduction attribute of the target heterogeneous operation data;
and verifying the expected heterogeneous data interval based on the relative relevance of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and a preset error permission range of preset relevance data, and determining a target heterogeneous data interval of each target heterogeneous operation data.
Further, the acquiring the historical analysis heterogeneous data introduction attribute of each of the plurality of target heterogeneous operation data includes:
acquiring analysis data analyzed by each of the plurality of target heterogeneous operation data in the preset historical interval time period;
and determining historical analysis heterogeneous data introduction attributes of each target heterogeneous operation data according to the analysis data of each target heterogeneous operation data in a preset historical interval period.
Further, the determining the heterogeneous data interval distribution data of each target heterogeneous operation data includes:
acquiring consumed resources required by each of the plurality of target heterogeneous operation data to analyze the data in each time within the preset historical interval time period;
using the consumed resources required by each analysis of the data of each target heterogeneous operation data in the preset historical interval time period to count the average consumed resources required by each analysis of the data of each target heterogeneous operation data;
determining distribution data heard by heterogeneous data intervals of the target heterogeneous operation data;
and determining the heterogeneous data interval distribution data of each target heterogeneous operation data based on the average consumed resources required by each target heterogeneous operation data analysis data and the distribution data heard by the heterogeneous data interval.
Further, the counting the data error allowable range of the target heterogeneous operation data according to the corresponding expected heterogeneous data interval queue by using the heterogeneous data interval distribution data, the expected heterogeneous data interval and the current heterogeneous data interval of the target heterogeneous operation data includes:
based on the heterogeneous data interval distribution data of each target heterogeneous operation data, counting a first data error permission range of an expected heterogeneous data interval corresponding to each target heterogeneous operation data heterogeneous data;
based on the heterogeneous data interval distribution data of each target heterogeneous operation data, counting a second data error permission range of the current heterogeneous data interval corresponding to each target heterogeneous operation data heterogeneous data;
and counting the total heterogeneous data interval of each target heterogeneous operation data to be in accordance with the data error permission range of the corresponding expected heterogeneous data interval queue according to the first data error permission range and the second data error permission range corresponding to each target heterogeneous operation data.
Further, the verifying the expected heterogeneous data interval based on the relative association degree of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and a preset error allowable range of preset association data, and determining the target heterogeneous data interval of each target heterogeneous operation data includes:
Counting the relative association degree of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute;
when the relative association degree is larger than the preset error allowable range of the preset association data, reducing the current expected average analysis heterogeneous data introduction attribute based on the reduction of the maximum expected heterogeneous data interval in the current expected heterogeneous data intervals of the target heterogeneous operation data, and updating the relative association degree according to the reduced expected average analysis heterogeneous data introduction attribute;
when the relative association degree is smaller than or equal to the preset error permission range of the preset association data, taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data; the expected heterogeneous data interval after each reduction is the maximum value in the current expected heterogeneous data intervals of the target heterogeneous operation data;
wherein, before taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data, the method further comprises:
Counting the difference between the relative association degree and the preset error allowable range of the preset association data; when the difference value is smaller than or equal to a preset error allowable range, executing an operation of taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data;
when the difference value is larger than the preset error allowable range, increasing the current expected average analysis heterogeneous data introduction attribute based on the increase of the minimum expected heterogeneous data interval in the current expected heterogeneous data intervals of the target heterogeneous operation data, and updating the relative association degree according to the increased expected average analysis heterogeneous data introduction attribute; the expected heterogeneous data interval after each increase is the minimum value in the current expected heterogeneous data intervals of the target heterogeneous operation data.
In a second aspect, a multi-source heterogeneous data fusion system based on internet of things is provided, including a data acquisition end and a data processing terminal, the data acquisition end is in communication connection with the data processing terminal, and the data processing terminal is specifically configured to:
acquiring historical analysis heterogeneous data introduction attributes, expected heterogeneous data intervals and current heterogeneous data intervals of each of a plurality of target heterogeneous operation data, wherein the historical analysis heterogeneous data introduction attributes represent heterogeneous data conditions of the analysis data of the target heterogeneous operation data in a preset historical interval period;
Using the historical analysis heterogeneous data introduction attribute of the plurality of target heterogeneous operation data to calculate the historical average analysis heterogeneous data introduction attribute of the plurality of target heterogeneous operation data;
determining heterogeneous data interval distribution data of each target heterogeneous operation data;
counting the total heterogeneous data interval of each target heterogeneous operation data to be in accordance with the corresponding data error permission range of the expected heterogeneous data interval queue by utilizing the heterogeneous data interval distribution data, the expected heterogeneous data interval and the current heterogeneous data interval of each target heterogeneous operation data;
counting the data error allowable range of each target heterogeneous operation data by utilizing the fact that the total heterogeneous data interval of the target heterogeneous operation data accords with the corresponding data error allowable range of the expected heterogeneous data interval queue;
using the data error allowable range and the historical analysis heterogeneous data introduction attribute of the target heterogeneous operation data to calculate the expected average analysis heterogeneous data introduction attribute of the target heterogeneous operation data;
and verifying the expected heterogeneous data interval based on the relative relevance of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and a preset error permission range of preset relevance data, and determining a target heterogeneous data interval of each target heterogeneous operation data.
Further, the data processing terminal is specifically configured to:
acquiring analysis data analyzed by each of the plurality of target heterogeneous operation data in the preset historical interval time period;
and determining historical analysis heterogeneous data introduction attributes of each target heterogeneous operation data according to the analysis data of each target heterogeneous operation data in a preset historical interval period.
Further, the data processing terminal is specifically configured to:
acquiring consumed resources required by each of the plurality of target heterogeneous operation data to analyze the data in each time within the preset historical interval time period;
using the consumed resources required by each analysis of the data of each target heterogeneous operation data in the preset historical interval time period to count the average consumed resources required by each analysis of the data of each target heterogeneous operation data;
determining distribution data heard by heterogeneous data intervals of the target heterogeneous operation data;
and determining the heterogeneous data interval distribution data of each target heterogeneous operation data based on the average consumed resources required by each target heterogeneous operation data analysis data and the distribution data heard by the heterogeneous data interval.
Further, the data processing terminal is specifically configured to:
Based on the heterogeneous data interval distribution data of each target heterogeneous operation data, counting a first data error permission range of an expected heterogeneous data interval corresponding to each target heterogeneous operation data heterogeneous data;
based on the heterogeneous data interval distribution data of each target heterogeneous operation data, counting a second data error permission range of the current heterogeneous data interval corresponding to each target heterogeneous operation data heterogeneous data;
and counting the total heterogeneous data interval of each target heterogeneous operation data to be in accordance with the data error permission range of the corresponding expected heterogeneous data interval queue according to the first data error permission range and the second data error permission range corresponding to each target heterogeneous operation data.
Further, the data processing terminal is specifically configured to:
counting the relative association degree of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute;
when the relative association degree is larger than the preset error allowable range of the preset association data, reducing the current expected average analysis heterogeneous data introduction attribute based on the reduction of the maximum expected heterogeneous data interval in the current expected heterogeneous data intervals of the target heterogeneous operation data, and updating the relative association degree according to the reduced expected average analysis heterogeneous data introduction attribute;
When the relative association degree is smaller than or equal to the preset error permission range of the preset association data, taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data; the expected heterogeneous data interval after each reduction is the maximum value in the current expected heterogeneous data intervals of the target heterogeneous operation data;
wherein, the data processing terminal is specifically further used for:
counting the difference between the relative association degree and the preset error allowable range of the preset association data; when the difference value is smaller than or equal to a preset error allowable range, executing an operation of taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data;
when the difference value is larger than the preset error allowable range, increasing the current expected average analysis heterogeneous data introduction attribute based on the increase of the minimum expected heterogeneous data interval in the current expected heterogeneous data intervals of the target heterogeneous operation data, and updating the relative association degree according to the increased expected average analysis heterogeneous data introduction attribute; the expected heterogeneous data interval after each increase is the minimum value in the current expected heterogeneous data intervals of the target heterogeneous operation data.
According to the multi-source heterogeneous data fusion method and system based on the Internet of things, heterogeneous data interval distribution data, expected heterogeneous data intervals and current heterogeneous data intervals of target heterogeneous operation data are predicted, the total heterogeneous data intervals of the target heterogeneous operation data conform to the corresponding data error allowable ranges of the expected heterogeneous data intervals in a queuing mode, the data error allowable ranges of each target heterogeneous operation data are counted, the data error allowable ranges are fused with historical average analysis heterogeneous data introduction attributes capable of representing global analysis heterogeneous data conditions of a plurality of target heterogeneous operation data, the expected average analysis heterogeneous data introduction attributes can be predicted, the relative relevance of the expected average analysis heterogeneous data introduction attributes and the historical average analysis heterogeneous data introduction attributes is verified through the expected average analysis heterogeneous data introduction attributes and the preset relevant data preset error allowable ranges, the target heterogeneous data intervals of each target heterogeneous operation data can be accurately determined, the target heterogeneous data fusion integrity of the target heterogeneous operation data is achieved, and the data error allowable ranges in the using process of the target heterogeneous operation data are effectively reduced.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a multi-source heterogeneous data fusion method based on the internet of things provided in an embodiment of the present application.
Fig. 2 is a block diagram of a multi-source heterogeneous data fusion device based on the internet of things according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a multi-source heterogeneous data fusion system based on the internet of things according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions described above, the following detailed description of the technical solutions of the present application is provided through the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limit the technical solutions of the present application, and the technical features of the embodiments and embodiments of the present application may be combined with each other without conflict.
Referring to fig. 1, a multi-source heterogeneous data fusion method based on the internet of things is shown, and the method may include the following technical schemes described in steps 100 to 700.
Step 100, obtaining a historical analysis heterogeneous data introduction attribute, an expected heterogeneous data interval and a current heterogeneous data interval of each of a plurality of target heterogeneous operation data, wherein the historical analysis heterogeneous data introduction attribute represents heterogeneous data conditions of the analysis data of the target heterogeneous operation data in a preset historical interval period.
Illustratively, the heterogeneous data conditions are used to characterize traffic flow and heterogeneous data conditions within a set interval.
Step 200, using the historical analysis heterogeneous data introduction attribute of the plurality of target heterogeneous operation data to calculate the historical average analysis heterogeneous data introduction attribute of the plurality of target heterogeneous operation data.
Illustratively, the heterogeneous data introduction attribute is used to characterize heterogeneous formal speeds.
Step 300, determining heterogeneous data interval distribution data of each target heterogeneous operation data.
Illustratively, the heterogeneous data interval distribution data is used to characterize a profile formed by heterogeneous data intervals.
Step 400, counting the data error allowable range of the corresponding expected heterogeneous data interval queue by using the heterogeneous data interval distribution data, the expected heterogeneous data interval and the current heterogeneous data interval of each target heterogeneous operation data.
Step 500, counting the data error allowable range of each target heterogeneous operation data by using the data error allowable range of the total heterogeneous data interval of the target heterogeneous operation data, which accords with the corresponding expected heterogeneous data interval queue.
Step 600, using the data error allowable range and the historical analysis heterogeneous data introduction attribute of the target heterogeneous operation data, counting the expected average analysis heterogeneous data introduction attribute of the target heterogeneous operation data.
And step 700, checking the expected heterogeneous data interval based on the relative association degree of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and a preset error permission range of preset association data, and determining a target heterogeneous data interval of each target heterogeneous operation data.
It may be appreciated that, when the technical solution described in the foregoing steps 100-700 is executed, the heterogeneous data interval distribution data, the expected heterogeneous data interval, and the current heterogeneous data interval of the target heterogeneous operation data are predicted, the total heterogeneous data interval of the target heterogeneous operation data accords with the data error allowable range of the corresponding expected heterogeneous data interval queue, further, the data error allowable range of each target heterogeneous operation data is counted, the data error allowable range is fused with the historical average analysis heterogeneous data introduction attribute capable of representing the global analysis heterogeneous data condition of a plurality of target heterogeneous operation data, the expected average analysis heterogeneous data introduction attribute is predicted, the relative relevance between the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute is verified through the expected average analysis heterogeneous data introduction attribute and the preset relevant data preset error allowable range, the target heterogeneous data interval of each target heterogeneous operation data can be accurately determined, the fusion integrity of the target heterogeneous operation data is realized, and the data error allowable range appearing in the using process of the target heterogeneous operation data is effectively reduced.
In an alternative embodiment, the inventor finds that, when the history analysis heterogeneous data introduction attribute of each of the plurality of target heterogeneous operation data is obtained, there is a problem that analysis data analyzed in the history interval period is inaccurate, so that it is difficult to accurately obtain the history analysis heterogeneous data introduction attribute of each of the plurality of target heterogeneous operation data, and in order to improve the above technical problem, the step of obtaining the history analysis heterogeneous data introduction attribute of each of the plurality of target heterogeneous operation data described in step 100 may specifically include the following technical solutions described in step q1 and step q 2.
And q1, acquiring analysis data analyzed in the preset historical interval time period of each of the target heterogeneous operation data.
And q2, determining historical analysis heterogeneous data introduction attributes of each target heterogeneous operation data according to the analysis data of each target heterogeneous operation data in a preset historical interval time period.
It can be understood that when the technical solutions described in the above steps q1 and q2 are executed, the problem that the analyzed data in the history interval period is inaccurate is improved when the history analysis heterogeneous data introduction attribute of each of the plurality of target heterogeneous operation data is obtained, so that the history analysis heterogeneous data introduction attribute of each of the plurality of target heterogeneous operation data can be accurately obtained.
In an alternative embodiment, the inventor finds that when determining the heterogeneous data interval distribution data of each target heterogeneous operation data, there is a problem that resources required for analyzing the data each time are not accurate, so that it is difficult to accurately determine the heterogeneous data interval distribution data of each target heterogeneous operation data, and in order to improve the technical problem, the step of determining the heterogeneous data interval distribution data of each target heterogeneous operation data described in step 300 may specifically include the following technical solutions described in steps w1 to w 4.
Step w1, obtaining the consumed resources required by each analysis of the data of the target heterogeneous operation data in the preset historical interval time period.
And step w2, counting the average consumed resources required by the analysis data of each target heterogeneous operation data by using the consumed resources required by the analysis data of each target heterogeneous operation data in the preset historical interval time period.
And step w3, determining the distribution data heard by the heterogeneous data intervals of the target heterogeneous operation data.
And step w4, determining the heterogeneous data interval distribution data of each target heterogeneous operation data based on the average consumption resources required by each target heterogeneous operation data analysis data and the distribution data heard by the heterogeneous data interval.
It can be understood that when the technical solution described in the above steps w1 to w4 is executed, the problem of inaccurate resources consumption required for each analysis of data is improved when determining the heterogeneous data interval distribution data of each target heterogeneous operation data, so that the heterogeneous data interval distribution data of each target heterogeneous operation data can be accurately determined.
In an alternative embodiment, the inventor found that, when using the heterogeneous data interval distribution data, the expected heterogeneous data interval, and the current heterogeneous data interval of each target heterogeneous operation data, there is a problem that the first data error allowable range of the expected heterogeneous data interval corresponding to each target heterogeneous operation data is not accurate, so that it is difficult to accurately calculate that the total heterogeneous data interval of each target heterogeneous operation data matches the data error allowable range of the corresponding expected heterogeneous data interval queue, and in order to improve the above technical problem, the step of calculating that the total heterogeneous data interval of each target heterogeneous operation data matches the data error allowable range of the corresponding expected heterogeneous data interval queue by using the heterogeneous data interval distribution data, the expected heterogeneous data interval, and the current heterogeneous data interval of each target heterogeneous operation data described in step 400 may specifically include the following technical scheme described in steps r 1-r 3.
And r1, based on the heterogeneous data interval distribution data of each target heterogeneous operation data, counting a first data error permission range of an expected heterogeneous data interval corresponding to each target heterogeneous operation data heterogeneous data.
And r2, based on the heterogeneous data interval distribution data of each target heterogeneous operation data, counting a second data error permission range of the current heterogeneous data interval corresponding to each target heterogeneous operation data heterogeneous data.
And r3, counting that the total heterogeneous data interval of each target heterogeneous operation data accords with the corresponding expected heterogeneous data interval queued data error permission range according to the first data error permission range and the second data error permission range corresponding to each target heterogeneous operation data.
It can be understood that when the technical schemes described in the steps r1 to r3 are executed, the problem that the first data error allowable range of the expected heterogeneous data interval corresponding to each target heterogeneous operation data is not accurate is improved by utilizing the heterogeneous data interval distribution data, the expected heterogeneous data interval and the current heterogeneous data interval of each target heterogeneous operation data, so that the total heterogeneous data interval of each target heterogeneous operation data can be accurately counted to conform to the data error allowable range of the corresponding expected heterogeneous data interval queue.
In an alternative embodiment, the inventor finds that when the expected heterogeneous data interval is verified based on the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and the preset association data preset error allowable range, there is a problem that the relative association is inaccurate, so that it is difficult to accurately determine the target heterogeneous data interval of each target heterogeneous operation data, in order to improve the above technical problem, the step 700 of verifying the expected heterogeneous data interval based on the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and the preset association data preset error allowable range, and the step of determining the target heterogeneous data interval of each target heterogeneous operation data may specifically include the following technical scheme described in steps y 1-y 3.
And step y1, counting the relative association degree of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute.
And step y2, when the relative association degree is larger than the preset error allowable range of the preset association data, reducing the current expected average analysis heterogeneous data introduction attribute based on the reduction of the maximum expected heterogeneous data interval in the current expected heterogeneous data intervals of the target heterogeneous operation data, and updating the relative association degree according to the reduced expected average analysis heterogeneous data introduction attribute.
And step y3, when the relative association degree is smaller than or equal to the preset error allowable range of the preset association data, taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data.
Illustratively, the expected heterogeneous data interval after each reduction is the maximum value of the current expected heterogeneous data intervals of the plurality of target heterogeneous operation data.
It can be understood that when the technical solutions described in the above steps y1 to y3 are executed, the problem of inaccurate relative association is improved when the expected heterogeneous data interval is verified based on the relative association degree of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and the preset error allowable range of the preset association data, so that the target heterogeneous data interval of each target heterogeneous operation data can be accurately determined.
Based on the above, before taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data, the following technical solutions described in step a 1-step a3 may be further included.
And a1, counting the difference between the relative association degree and the preset error allowable range of the preset association data.
And a step a2 of executing the operation of taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data when the difference value is smaller than or equal to the preset error allowable range.
And a step a3 of increasing the current expected average analysis heterogeneous data introduction attribute based on the increase of the minimum expected heterogeneous data interval in the current expected heterogeneous data intervals of the target heterogeneous operation data when the difference value is larger than the preset error allowable range, and updating the relative relevance according to the increased expected average analysis heterogeneous data introduction attribute.
Illustratively, the expected heterogeneous data interval after each increment is the smallest value among the current expected heterogeneous data intervals of the plurality of target heterogeneous operation data.
It can be appreciated that when the technical solutions described in the above steps a1 to a3 are executed, the accuracy of updating the relative association degree is improved by precisely counting the difference between the relative association degree and the preset error tolerance range of the preset association data.
Based on the above, the preset correlation data preset error allowable range includes the historical data error condition, and may further include the following technical schemes described in step s 1-step s 5.
Step s1, obtaining statistical data error conditions of heterogeneous data areas corresponding to a plurality of current operation data in the preset historical interval time period.
And step s2, taking the statistical data error condition as the historical data error condition.
Step s3, or, obtaining the statistical data error conditions of the heterogeneous data areas corresponding to the current operation data in the preset historical interval time period.
Step s4, obtaining historical monitoring road condition data of the plurality of current operation data in the preset historical interval time period.
And step s5, training the neural network for the statistical data error condition based on the historical monitoring road condition data to obtain the historical data error condition.
It will be appreciated that in performing the technical scheme described in steps s 1-s 5 above, the accuracy of obtaining the historical data error condition is improved by counting the data error condition.
On the basis of the above, please refer to fig. 2 in combination, there is provided a multi-source heterogeneous data fusion device 200 based on internet of things, applied to a data processing terminal, the device includes:
A heterogeneous data analysis model 210, configured to obtain a historical analysis heterogeneous data introduction attribute, an expected heterogeneous data interval, and a current heterogeneous data interval of each of a plurality of target heterogeneous operation data, where the historical analysis heterogeneous data introduction attribute characterizes heterogeneous data conditions of the analysis data of the target heterogeneous operation data in a preset historical interval period;
a data attribute statistics model 220, configured to use historical analysis heterogeneous data introduction attributes of the plurality of target heterogeneous operation data to calculate historical average analysis heterogeneous data introduction attributes of the plurality of target heterogeneous operation data;
a heterogeneous data distribution model 230 for determining heterogeneous data interval distribution data of each target heterogeneous operation data;
an error permission statistical model 240, configured to use the heterogeneous data interval distribution data, the expected heterogeneous data interval and the current heterogeneous data interval of each target heterogeneous operation data to calculate that the total heterogeneous data interval of each target heterogeneous operation data accords with the data error permission range of the corresponding expected heterogeneous data interval queue;
an error permission statistical model 250, configured to utilize a total heterogeneous data interval of the plurality of target heterogeneous operation data to conform to a data error permission range of a corresponding expected heterogeneous data interval queue, and calculate a data error permission range of each target heterogeneous operation data;
A data attribute analysis model 260 for counting expected average analysis heterogeneous data introduction attributes of the plurality of target heterogeneous operation data using the data error allowable range and the history analysis heterogeneous data introduction attributes of the plurality of target heterogeneous operation data;
the data interval determining model 270 is configured to determine a target heterogeneous data interval of each target heterogeneous operation data by checking the expected heterogeneous data interval based on the relative correlation between the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and a preset error tolerance range of preset correlation data.
On the basis of the above, please refer to fig. 3 in combination, there is shown a multi-source heterogeneous data fusion system 300 based on the internet of things, which includes a processor 310 and a database 320 in communication with each other, wherein the processor 310 is configured to read and execute a statistical machine program from the database 320, so as to implement the above method.
On the basis of the method, a computer readable data medium is provided, and a computer program for data on the computer readable data medium realizes the method when running.
In summary, based on the above scheme, the heterogeneous data interval distribution data, the expected heterogeneous data interval and the current heterogeneous data interval of the target heterogeneous operation data predict that the total heterogeneous data interval of the target heterogeneous operation data accords with the corresponding data error permission range of the expected heterogeneous data interval queue, so as to calculate the data error permission range of each target heterogeneous operation data, fuse the data error permission range with the historical average analysis heterogeneous data introduction attribute capable of representing the global analysis data heterogeneous data condition of a plurality of target heterogeneous operation data, predict the expected average analysis heterogeneous data introduction attribute, and verify the expected heterogeneous data interval through the relative association degree of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and the preset association data preset error permission range, thereby accurately determining the target heterogeneous data interval of each target heterogeneous operation data, and effectively reducing the data error permission range of errors in the use process of the target heterogeneous operation data.
It should be appreciated that the systems and modules thereof shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may then be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only with hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also with software, such as executed by various types of processors, and with a combination of the above hardware circuitry and software (e.g., firmware).
It should be noted that, the advantages that may be generated by different embodiments may be different, and in different embodiments, the advantages that may be generated may be any one or a combination of several of the above, or any other possible advantages that may be obtained.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations of the present application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this application, and are therefore within the spirit and scope of the exemplary embodiments of this application.
Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present application may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the invention are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer storage medium may be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
The computer program code necessary for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python, etc., a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, ruby and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.
Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application and are not intended to limit the order in which the processes and methods of the application are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed herein and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the subject application. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the numbers allow for adaptive variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this application is hereby incorporated by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the present application, documents that are currently or later attached to this application for which the broadest scope of the claims to the present application is limited. It is noted that the descriptions, definitions, and/or terms used in the subject matter of this application are subject to such descriptions, definitions, and/or terms if they are inconsistent or conflicting with such descriptions, definitions, and/or terms.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of this application. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present application may be considered in keeping with the teachings of the present application. Accordingly, embodiments of the present application are not limited to only the embodiments explicitly described and depicted herein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. The multi-source heterogeneous data fusion method based on the Internet of things is characterized by comprising the following steps of:
acquiring historical analysis heterogeneous data introduction attributes, expected heterogeneous data intervals and current heterogeneous data intervals of each of a plurality of target heterogeneous operation data, wherein the historical analysis heterogeneous data introduction attributes represent heterogeneous data conditions of the analysis data of the target heterogeneous operation data in a preset historical interval period;
using the historical analysis heterogeneous data introduction attribute of the plurality of target heterogeneous operation data to calculate the historical average analysis heterogeneous data introduction attribute of the plurality of target heterogeneous operation data;
determining heterogeneous data interval distribution data of each target heterogeneous operation data;
counting the total heterogeneous data interval of each target heterogeneous operation data to be in accordance with the corresponding data error permission range of the expected heterogeneous data interval queue by utilizing the heterogeneous data interval distribution data, the expected heterogeneous data interval and the current heterogeneous data interval of each target heterogeneous operation data;
counting the data error allowable range of each target heterogeneous operation data by utilizing the fact that the total heterogeneous data interval of the target heterogeneous operation data accords with the corresponding data error allowable range of the expected heterogeneous data interval queue;
Using the data error allowable range and the historical analysis heterogeneous data introduction attribute of the target heterogeneous operation data to calculate the expected average analysis heterogeneous data introduction attribute of the target heterogeneous operation data;
and verifying the expected heterogeneous data interval based on the relative relevance of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and a preset error permission range of preset relevance data, and determining a target heterogeneous data interval of each target heterogeneous operation data.
2. The method of claim 1, wherein the obtaining historical analysis disparate data introduction attributes for each of the plurality of target disparate operation data comprises:
acquiring analysis data analyzed by each of the plurality of target heterogeneous operation data in the preset historical interval time period;
and determining historical analysis heterogeneous data introduction attributes of each target heterogeneous operation data according to the analysis data of each target heterogeneous operation data in a preset historical interval period.
3. The method of claim 1, wherein determining the heterogeneous data interval distribution data for each target heterogeneous operation data comprises:
Acquiring consumed resources required by each of the plurality of target heterogeneous operation data to analyze the data in each time within the preset historical interval time period;
using the consumed resources required by each analysis of the data of each target heterogeneous operation data in the preset historical interval time period to count the average consumed resources required by each analysis of the data of each target heterogeneous operation data;
determining distribution data heard by heterogeneous data intervals of the target heterogeneous operation data;
and determining the heterogeneous data interval distribution data of each target heterogeneous operation data based on the average consumed resources required by each target heterogeneous operation data analysis data and the distribution data heard by the heterogeneous data interval.
4. The method of claim 1, wherein using the heterogeneous data interval distribution data, the expected heterogeneous data interval, and the current heterogeneous data interval for each target heterogeneous operation data, counting that the total heterogeneous data interval of each target heterogeneous operation data matches the data error allowable range of the corresponding expected heterogeneous data interval queue comprises:
based on the heterogeneous data interval distribution data of each target heterogeneous operation data, counting a first data error permission range of an expected heterogeneous data interval corresponding to each target heterogeneous operation data heterogeneous data;
Based on the heterogeneous data interval distribution data of each target heterogeneous operation data, counting a second data error permission range of the current heterogeneous data interval corresponding to each target heterogeneous operation data heterogeneous data;
and counting the total heterogeneous data interval of each target heterogeneous operation data to be in accordance with the data error permission range of the corresponding expected heterogeneous data interval queue according to the first data error permission range and the second data error permission range corresponding to each target heterogeneous operation data.
5. The method of claim 1, wherein the verifying the expected heterogeneous data interval based on the relative relevance of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and a preset error tolerance range of preset relevance data, determining a target heterogeneous data interval for each target heterogeneous operation data comprises:
counting the relative association degree of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute;
when the relative association degree is larger than the preset error allowable range of the preset association data, reducing the current expected average analysis heterogeneous data introduction attribute based on the reduction of the maximum expected heterogeneous data interval in the current expected heterogeneous data intervals of the target heterogeneous operation data, and updating the relative association degree according to the reduced expected average analysis heterogeneous data introduction attribute;
When the relative association degree is smaller than or equal to the preset error permission range of the preset association data, taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data; the expected heterogeneous data interval after each reduction is the maximum value in the current expected heterogeneous data intervals of the target heterogeneous operation data;
wherein, before taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data, the method further comprises:
counting the difference between the relative association degree and the preset error allowable range of the preset association data;
when the difference value is smaller than or equal to a preset error allowable range, executing an operation of taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data;
when the difference value is larger than the preset error allowable range, increasing the current expected average analysis heterogeneous data introduction attribute based on the increase of the minimum expected heterogeneous data interval in the current expected heterogeneous data intervals of the target heterogeneous operation data, and updating the relative association degree according to the increased expected average analysis heterogeneous data introduction attribute; the expected heterogeneous data interval after each increase is the minimum value in the current expected heterogeneous data intervals of the target heterogeneous operation data.
6. The multi-source heterogeneous data fusion system based on the Internet of things is characterized by comprising a data acquisition end and a data processing terminal, wherein the data acquisition end is in communication connection with the data processing terminal, and the data processing terminal is specifically used for:
acquiring historical analysis heterogeneous data introduction attributes, expected heterogeneous data intervals and current heterogeneous data intervals of each of a plurality of target heterogeneous operation data, wherein the historical analysis heterogeneous data introduction attributes represent heterogeneous data conditions of the analysis data of the target heterogeneous operation data in a preset historical interval period;
using the historical analysis heterogeneous data introduction attribute of the plurality of target heterogeneous operation data to calculate the historical average analysis heterogeneous data introduction attribute of the plurality of target heterogeneous operation data;
determining heterogeneous data interval distribution data of each target heterogeneous operation data;
counting the total heterogeneous data interval of each target heterogeneous operation data to be in accordance with the corresponding data error permission range of the expected heterogeneous data interval queue by utilizing the heterogeneous data interval distribution data, the expected heterogeneous data interval and the current heterogeneous data interval of each target heterogeneous operation data;
counting the data error allowable range of each target heterogeneous operation data by utilizing the fact that the total heterogeneous data interval of the target heterogeneous operation data accords with the corresponding data error allowable range of the expected heterogeneous data interval queue;
Using the data error allowable range and the historical analysis heterogeneous data introduction attribute of the target heterogeneous operation data to calculate the expected average analysis heterogeneous data introduction attribute of the target heterogeneous operation data;
and verifying the expected heterogeneous data interval based on the relative relevance of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute and a preset error permission range of preset relevance data, and determining a target heterogeneous data interval of each target heterogeneous operation data.
7. The system according to claim 6, wherein the data processing terminal is specifically configured to:
acquiring analysis data analyzed by each of the plurality of target heterogeneous operation data in the preset historical interval time period;
and determining historical analysis heterogeneous data introduction attributes of each target heterogeneous operation data according to the analysis data of each target heterogeneous operation data in a preset historical interval period.
8. The system according to claim 6, wherein the data processing terminal is specifically configured to:
acquiring consumed resources required by each of the plurality of target heterogeneous operation data to analyze the data in each time within the preset historical interval time period;
Using the consumed resources required by each analysis of the data of each target heterogeneous operation data in the preset historical interval time period to count the average consumed resources required by each analysis of the data of each target heterogeneous operation data;
determining distribution data heard by heterogeneous data intervals of the target heterogeneous operation data;
and determining the heterogeneous data interval distribution data of each target heterogeneous operation data based on the average consumed resources required by each target heterogeneous operation data analysis data and the distribution data heard by the heterogeneous data interval.
9. The system according to claim 6, wherein the data processing terminal is specifically configured to:
based on the heterogeneous data interval distribution data of each target heterogeneous operation data, counting a first data error permission range of an expected heterogeneous data interval corresponding to each target heterogeneous operation data heterogeneous data;
based on the heterogeneous data interval distribution data of each target heterogeneous operation data, counting a second data error permission range of the current heterogeneous data interval corresponding to each target heterogeneous operation data heterogeneous data;
and counting the total heterogeneous data interval of each target heterogeneous operation data to be in accordance with the data error permission range of the corresponding expected heterogeneous data interval queue according to the first data error permission range and the second data error permission range corresponding to each target heterogeneous operation data.
10. The system according to claim 6, wherein the data processing terminal is specifically configured to:
counting the relative association degree of the expected average analysis heterogeneous data introduction attribute and the historical average analysis heterogeneous data introduction attribute;
when the relative association degree is larger than the preset error allowable range of the preset association data, reducing the current expected average analysis heterogeneous data introduction attribute based on the reduction of the maximum expected heterogeneous data interval in the current expected heterogeneous data intervals of the target heterogeneous operation data, and updating the relative association degree according to the reduced expected average analysis heterogeneous data introduction attribute;
when the relative association degree is smaller than or equal to the preset error permission range of the preset association data, taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data; the expected heterogeneous data interval after each reduction is the maximum value in the current expected heterogeneous data intervals of the target heterogeneous operation data;
wherein, the data processing terminal is specifically further used for:
counting the difference between the relative association degree and the preset error allowable range of the preset association data; when the difference value is smaller than or equal to a preset error allowable range, executing an operation of taking the current expected heterogeneous data interval of each target heterogeneous operation data as the target heterogeneous data interval of each target heterogeneous operation data;
When the difference value is larger than the preset error allowable range, increasing the current expected average analysis heterogeneous data introduction attribute based on the increase of the minimum expected heterogeneous data interval in the current expected heterogeneous data intervals of the target heterogeneous operation data, and updating the relative association degree according to the increased expected average analysis heterogeneous data introduction attribute; the expected heterogeneous data interval after each increase is the minimum value in the current expected heterogeneous data intervals of the target heterogeneous operation data.
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