CN116150132A - Enterprise internet of things data processing method, terminal equipment and storage medium - Google Patents
Enterprise internet of things data processing method, terminal equipment and storage medium Download PDFInfo
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Abstract
The invention discloses an enterprise internet of things data processing method, terminal equipment and a storage medium, and relates to the technical field of data processing, wherein the method comprises the steps of obtaining internet of things data to be processed; identifying first abnormal data in the to-be-processed internet of things data based on a preset algorithm, and correcting the first abnormal data to obtain corrected internet of things data; and identifying second abnormal data in the corrected internet of things data based on a preset training model, and correcting the second abnormal data to obtain target internet of things data. The method can correct various abnormal data in the enterprise internet of things data, and most of data problems are effectively treated.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to terminal equipment and a storage medium of an enterprise internet of things data processing method.
Background
In the production process of enterprises, a large amount of energy sources such as water, electricity, heat, oil and gas are needed, the energy consumption management of most enterprises is simple, and the quality of reported data is poor due to the addition of the problems of poor performance of some metering devices or poor environment of the metering devices. In order to improve the quality of the Internet of things data reported by enterprises and facilitate the monitoring and analysis of the energy consumption in the future, the use of a model for preprocessing the Internet of things data is particularly important.
Because the stability of the current internet of things meter is poor, data transmission breakpoint and unpredictable data abnormality problems often occur, so that the final energy consumption monitoring and analysis are difficult.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an enterprise Internet of things data processing method, terminal equipment and a storage medium.
In a first aspect, a method for processing enterprise internet of things data includes:
acquiring to-be-processed internet of things data;
identifying first abnormal data in the to-be-processed internet of things data based on a preset algorithm, and correcting the first abnormal data to obtain corrected internet of things data;
and identifying second abnormal data in the corrected internet of things data based on a preset training model, and correcting the second abnormal data to obtain target internet of things data.
Preferably, identifying the first abnormal data in the to-be-processed internet of things data based on a preset algorithm includes:
calculating the density distribution value of the to-be-processed internet-of-things data by using a statistical algorithm; calculating a cluster of the to-be-processed internet-of-things data by using a clustering algorithm; calculating the characteristic deviation of the to-be-processed internet-of-things data by using a principal component analysis algorithm;
and identifying first abnormal data according to the density distribution value, the cluster and the characteristic deviation.
Preferably, the method for identifying the first abnormal data according to the density distribution value, the cluster and the characteristic deviation comprises the following steps:
taking the Internet of things data with the density distribution value lower than a first preset threshold value as first abnormal data, taking the Internet of things data with the Internet of things data quantity smaller than a second preset threshold value in a cluster and the Internet of things data with the cluster mean value lower than a third preset threshold value as first abnormal data, taking the Internet of things data with the characteristic deviation larger than a fourth preset threshold value as first abnormal data, and eliminating the first abnormal data.
Preferably, the method for correcting the first abnormal data to obtain corrected internet of things data includes: and clearing the first abnormal data, and filling missing values of the to-be-processed internet of things data after clearing the first abnormal data by using an STL algorithm to obtain corrected internet of things data.
Preferably, before identifying the second abnormal data in the corrected internet of things data based on the preset training model includes:
constructing a first neural network model;
constructing a first sample set, wherein the first sample set comprises historical internet of things data and corresponding abnormal labels thereof, wherein the abnormal labels comprise time abnormal labels and energy consumption abnormal labels, and the first sample set is divided into a first training set and a first verification set;
and training the first neural network model by using the first training set, verifying the first neural network model by using the first verification set, and obtaining an abnormal recognition model when the verification precision reaches a first preset precision.
Preferably, before identifying the second abnormal data in the corrected internet of things data based on the preset training model, the method further includes:
constructing a second neural network model;
constructing a second sample set, wherein the second sample set comprises historical internet of things data and corresponding time labels thereof, and dividing the second sample set into a second training set and a second verification set;
and training the second neural network model by using the second training set, verifying the second neural network model by using the second verification set, and obtaining the Internet of things prediction model when the verification precision reaches a second preset precision.
Preferably, the second abnormal data in the corrected internet of things data is identified based on a preset training model:
inputting the corrected internet of things data into an anomaly identification model to obtain second anomaly data;
acquiring the time characteristics of the second abnormal data;
inputting the time characteristics of the second abnormal data into an Internet of things prediction model to obtain energy consumption data;
and eliminating the second abnormal data in the corrected internet of things data, and supplementing the energy consumption data to the corresponding time to obtain target internet of things data.
Preferably, when the continuous abnormal time of the second abnormal data is greater than a preset time, the multi-terminal alarm is automatically triggered.
In a second aspect, a terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method according to any of the embodiments described above when executing the computer program.
In a third aspect, a computer readable storage medium stores a computer program which, when executed by a processor, implements a method according to any of the embodiments described above.
The beneficial effects of the invention are as follows: according to the enterprise Internet of things data processing method provided by the embodiment of the invention, various abnormal data in enterprise Internet of things data are corrected, and most data problems are effectively treated. In addition, under the condition that a large amount of data are missing, the abnormality can be automatically identified and early warning can be timely made.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flowchart of an enterprise Internet of things data processing method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
Embodiments of the technical scheme of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and thus are merely examples, and are not intended to limit the scope of the present invention.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.
Example 1
Because most enterprises have simpler energy consumption management, and the problems of poor performance or bad environment of some meters (such as water meters and electric meters) are easy to cause the poor quality of the data of the Internet of things reported by the meters, the data collected by the meters need to be processed so as to improve the quality of the data of the Internet of things.
The abnormal data in the internet of things data mainly comprises basic abnormal (first abnormal data) and long-time missing abnormal (periodic abnormal data) caused by the problems of acquisition interruption or signal receiving and the like of the internet of things table, and regular abnormal data (hidden abnormal data) of which the fluctuation of the enterprise internet of things data does not accord with the normal production rule of an enterprise, wherein the periodic abnormal data and the hidden abnormal data are second abnormal data.
Based on the above problems, an embodiment of the present invention provides an enterprise internet of things data processing method, where a flowchart of the method refers to fig. 1, and the enterprise internet of things data processing method includes:
step one, acquiring to-be-processed internet of things data;
specifically, the to-be-processed internet-of-things data comprise water electricity data collected by an ammeter and a water meter, and the like.
Step two, identifying first abnormal data in the to-be-processed internet of things data based on a preset algorithm, and correcting the first abnormal data to obtain corrected internet of things data;
in the embodiment of the present invention, identifying the first abnormal data in the to-be-processed internet of things data based on a preset algorithm includes:
calculating the density distribution value of the to-be-processed internet-of-things data by using a statistical algorithm; calculating a cluster of the to-be-processed internet-of-things data by using a clustering algorithm; calculating the characteristic deviation of the to-be-processed internet-of-things data by using a principal component analysis algorithm;
taking the Internet of things data with the density distribution value lower than a first preset threshold value as first abnormal data, taking the Internet of things data with the Internet of things data quantity smaller than a second preset threshold value in a cluster and the Internet of things data with the cluster mean value lower than a third preset threshold value as first abnormal data, taking the Internet of things data with the characteristic deviation larger than a fourth preset threshold value as first abnormal data, and eliminating the first abnormal data.
The statistical algorithm is to assume that the data obeys a certain probability distribution model, consider the object with low probability as an outlier, and the clustering algorithm is to consider most sample points in a cluster as outliers if the data sample size of some clusters is far less than that of other clusters and the characteristics of the data in the cluster are greatly different from those of other clusters; the Principal Component Analysis (PCA) tracking method is used for solving the problem of accuracy of data recovery of a PCA algorithm under the noisy condition; variance changes in the raw data in different directions reflect its inherent characteristics, which may be indicative of a single data sample being an outlier if the data sample does not exhibit a characteristic that is consistent with the overall data sample, such as a large deviation from other data samples in some directions.
The combination of the methods is adopted to clean the first abnormal data, so that the efficiency and the accuracy of subsequent data processing are improved.
In the embodiment of the invention, the method for correcting the first abnormal data to obtain corrected internet of things data comprises the following steps: and clearing the first abnormal data, and filling missing values of the to-be-processed internet of things data after clearing the first abnormal data by using an STL algorithm to obtain corrected internet of things data.
After the internet of things data is cleaned, filling missing values, corrected internet of things data is finally obtained, accurate identification and correction of obvious abnormal data are achieved, basic support is provided for identification of subsequent hidden abnormal data, and it is required to be noted that the missing correction is abnormal point internet of things data correction, and then the correction of long-time abnormal data is segment data correction.
And thirdly, identifying second abnormal data in the corrected internet of things data based on a preset training model, and correcting the second abnormal data to obtain target internet of things data.
In the embodiment of the present invention, before identifying the second abnormal data in the corrected internet of things data based on a preset training model includes: constructing a first neural network model; constructing a first sample set, wherein the first sample set comprises historical internet of things data and corresponding abnormal labels thereof, wherein the abnormal labels comprise time abnormal labels and energy consumption abnormal labels, and the first sample set is divided into a first training set and a first verification set; and training the first neural network model by using the first training set, verifying the first neural network model by using the first verification set, and obtaining an abnormal recognition model when the verification precision reaches a first preset precision.
According to the model, according to the historical internet of things data, the general trend, the periodicity and the energy utilization rule of enterprises of the historical internet of things data are intelligently learned, abnormal data are accurately positioned, and the loss and branching caused by abnormal missing are avoided.
In the embodiment of the present invention, before identifying the second abnormal data in the corrected internet of things data based on the preset training model, the method further includes: constructing a second neural network model; constructing a second sample set, wherein the second sample set comprises historical internet of things data and corresponding time labels thereof, and dividing the second sample set into a second training set and a second verification set; and training the second neural network model by using the second training set, verifying the second neural network model by using the second verification set, and obtaining the Internet of things prediction model when the verification precision reaches a second preset precision.
According to the model, according to the historical internet of things data, the general trend, the periodicity and the energy utilization rule of enterprises of the historical internet of things data are intelligently learned, and the accurate prediction of the enterprise internet of things data is realized.
In the embodiment of the invention, the second abnormal data in the corrected internet of things data is identified based on a preset training model: inputting the corrected internet of things data into an anomaly identification model to obtain second anomaly data; acquiring the time characteristics of the second abnormal data; inputting the time characteristics of the second abnormal data into an Internet of things prediction model to obtain energy consumption data; and eliminating the second abnormal data in the corrected internet of things data, and supplementing the energy consumption data to the corresponding time to obtain target internet of things data.
The method has the advantages that the use of the prediction anomaly identification model and the Internet of things prediction model realizes the accurate identification of the anomalies such as periodic anomalies and use of the prediction anomaly, and the intelligent correction of the anomalies is realized.
In the embodiment of the invention, when the continuous abnormal time of the second abnormal data is greater than the preset time, the multi-terminal alarm is automatically triggered.
Specifically, the multi-terminal alarm supports short messages, mails, system information and the like, and is convenient for a manager to process abnormity in time.
In summary, the enterprise internet of things data processing method provided by the embodiment of the invention corrects various obvious or hidden abnormal data of enterprise internet of things data, and most of data problems are effectively treated. In addition, under the condition that a large amount of data are missing, the abnormality can be automatically identified and early warning can be timely made.
Referring to fig. 2, fig. 2 is a schematic block diagram of a terminal device according to an embodiment of the present invention. The terminal 600 in the present embodiment as shown in fig. 3 may include: one or more processors 601, one or more input devices 602, one or more output devices 603, and one or more memories 604. The processor 601, the input device 602, the output device 603, and the memory 604 communicate with each other via a communication bus 605. The memory 604 is used to store a computer program comprising program instructions. The processor 601 is operative to execute program instructions stored in the memory 604.
It should be appreciated that in embodiments of the present invention, the processor 601 may be a central processing unit (CentralProcessing Unit, CPU), which may also be other general purpose processors, digital signal processors (DigitalSignal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 602 may include a touch pad, a fingerprint sensor (for collecting fingerprint information of a user and direction information of a fingerprint), a microphone, etc., and the output device 603 may include a display (LCD, etc.), a speaker, etc.
The memory 604 may include read only memory and random access memory and provides instructions and data to the processor 601. A portion of memory 604 may also include non-volatile random access memory. For example, the memory 604 may also store information of device type.
In a specific implementation, the processor 601, the input device 602, and the output device 603 described in the embodiments of the present invention may execute the implementation described in the embodiments of the method for processing enterprise internet of things data provided in the embodiments of the present invention, and may also execute the implementation of the terminal described in the embodiments of the present invention, which is not described herein again.
In another embodiment of the present invention, a computer readable storage medium is provided, where the computer readable storage medium stores a computer program, where the computer program includes program instructions, where the program instructions, when executed by a processor, implement all or part of the procedures in the method embodiments described above, or may be implemented by instructing related hardware by the computer program, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by the processor, implements the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The computer readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, such as a hard disk or a memory of the terminal. The computer readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device. The computer-readable storage medium is used to store a computer program and other programs and data required for the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
Claims (10)
1. The enterprise internet of things data processing method is characterized by comprising the following steps of:
acquiring to-be-processed internet of things data;
identifying first abnormal data in the to-be-processed internet of things data based on a preset algorithm, and correcting the first abnormal data to obtain corrected internet of things data;
and identifying second abnormal data in the corrected internet of things data based on a preset training model, and correcting the second abnormal data to obtain target internet of things data.
2. The method for processing the internet of things data of an enterprise according to claim 1, wherein identifying the first abnormal data in the internet of things data to be processed based on a preset algorithm comprises:
calculating the density distribution value of the to-be-processed internet-of-things data by using a statistical algorithm; calculating a cluster of the to-be-processed internet-of-things data by using a clustering algorithm; calculating the characteristic deviation of the to-be-processed internet-of-things data by using a principal component analysis algorithm;
and identifying first abnormal data according to the density distribution value, the cluster and the characteristic deviation.
3. The method for processing enterprise internet of things data according to claim 2, wherein the method for identifying the first abnormal data according to the density distribution value, the cluster and the characteristic deviation comprises:
taking the Internet of things data with the density distribution value lower than a first preset threshold value as first abnormal data, taking the Internet of things data with the Internet of things data quantity smaller than a second preset threshold value in a cluster and the Internet of things data with the cluster mean value lower than a third preset threshold value as first abnormal data, taking the Internet of things data with the characteristic deviation larger than a fourth preset threshold value as first abnormal data, and eliminating the first abnormal data.
4. The method for processing enterprise internet of things data according to claim 3, wherein the method for correcting the first abnormal data to obtain corrected internet of things data comprises: and clearing the first abnormal data, and filling missing values of the to-be-processed internet of things data after clearing the first abnormal data by using an STL algorithm to obtain corrected internet of things data.
5. The method for processing internet of things data of an enterprise according to claim 1, wherein before identifying the second abnormal data in the corrected internet of things data based on a preset training model comprises:
constructing a first neural network model;
constructing a first sample set, wherein the first sample set comprises historical internet of things data and corresponding abnormal labels thereof, wherein the abnormal labels comprise time abnormal labels and energy consumption abnormal labels, and the first sample set is divided into a first training set and a first verification set;
and training the first neural network model by using the first training set, verifying the first neural network model by using the first verification set, and obtaining an abnormal recognition model when the verification precision reaches a first preset precision.
6. The method for processing internet of things data according to claim 5, further comprising, before identifying the second abnormal data in the corrected internet of things data based on a preset training model:
constructing a second neural network model;
constructing a second sample set, wherein the second sample set comprises historical internet of things data and corresponding time labels thereof, and dividing the second sample set into a second training set and a second verification set;
and training the second neural network model by using the second training set, verifying the second neural network model by using the second verification set, and obtaining the Internet of things prediction model when the verification precision reaches a second preset precision.
7. The method for processing internet of things data of an enterprise according to claim 6, wherein the second abnormal data in the corrected internet of things data is identified based on a preset training model:
inputting the corrected internet of things data into an anomaly identification model to obtain second anomaly data;
acquiring the time characteristics of the second abnormal data;
inputting the time characteristics of the second abnormal data into an Internet of things prediction model to obtain energy consumption data;
and eliminating the second abnormal data in the corrected internet of things data, and supplementing the energy consumption data to the corresponding time to obtain target internet of things data.
8. The method for processing enterprise internet of things according to claim 7, wherein the multi-terminal alarm is automatically triggered when the continuous anomaly time of the second anomaly data is greater than a preset time.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 8 when executing the computer program.
10. A computer readable storage medium storing a computer program, which when executed by a processor performs the method according to any one of claims 1 to 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116777305A (en) * | 2023-08-18 | 2023-09-19 | 河北思极科技有限公司 | Power data quality improving method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116777305A (en) * | 2023-08-18 | 2023-09-19 | 河北思极科技有限公司 | Power data quality improving method and device, electronic equipment and storage medium |
| CN116777305B (en) * | 2023-08-18 | 2023-11-10 | 河北思极科技有限公司 | Power data quality improving method and device, electronic equipment and storage medium |
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