CN115150294A - Data analysis method, equipment and medium for monitoring Internet of things equipment - Google Patents
Data analysis method, equipment and medium for monitoring Internet of things equipment Download PDFInfo
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Abstract
The embodiment of the specification discloses a data analysis method, equipment and a medium for monitoring equipment of the Internet of things, and relates to the technical field of the Internet of things, wherein the method comprises the following steps: the method comprises the steps that equipment data reported by a plurality of Internet of things equipment are obtained based on a preset timing data obtaining task, and data obtaining time is recorded, the timing data obtaining task is to perform data snapshot on the equipment data reported by the Internet of things equipment within a preset time interval through a timing program, equipment data timestamps of the plurality of Internet of things equipment are set according to the data obtaining time of the equipment data, and the equipment data of the plurality of Internet of things equipment and the equipment data timestamps are stored in a specified database; acquiring a plurality of pieces of specified device data with the same time stamp in a specified database; and determining the equipment relevance of the specified Internet of things equipment corresponding to the plurality of specified equipment data respectively, and performing data analysis on the plurality of specified equipment data according to the equipment relevance of the plurality of specified Internet of things equipment.
Description
Technical Field
The present disclosure relates to the field of internet of things technologies, and in particular, to a data analysis method, device, and medium for monitoring an internet of things device.
Background
The technology of the internet of things is continuously developed, more and more intelligent terminal devices of the internet of things begin to enter various application fields, and the terminal devices and products of the internet of things with intelligent functions are visible everywhere. No matter be family, official working, building or wisdom city, all can see thing networking intelligent terminal equipment.
With the increase of the internet of things devices, the device data generated by various internet of things intelligent terminal devices is increased. Through the analysis to the equipment data, can monitor thing networking device. The reporting frequency of the data reported by the internet of things equipment is inconsistent due to the fact that the attributes of the internet of things equipment in different application scenes are different, and if data analysis is performed after all the internet of things equipment report the equipment data at a certain time, the running state of the internet of things equipment cannot be timely and accurately monitored.
Disclosure of Invention
One or more embodiments of the present specification provide a data analysis method, device, and medium for monitoring an internet of things device, so as to solve the following technical problems: the reporting frequency of the data reported by the internet of things equipment is inconsistent due to the fact that the attributes of the internet of things equipment in different application scenes are different, and if data analysis is performed after all the internet of things equipment report the equipment data at a certain time, the running state of the internet of things equipment cannot be timely and accurately monitored.
One or more embodiments of the present disclosure adopt the following technical solutions:
one or more embodiments of the present specification provide a data analysis method for monitoring an internet of things device, the method including: the method comprises the steps that equipment data reported by a plurality of Internet of things equipment are obtained based on a preset timing data obtaining task, and the obtaining time of the equipment data is recorded, wherein the timing data obtaining task is used for carrying out data snapshot on the equipment data reported to a data analysis server by the Internet of things equipment in a preset time interval through a timing program on the data analysis server; setting the device data timestamps of the plurality of Internet of things devices according to the acquisition time of the device data, and storing the device data of the plurality of Internet of things devices and the device data timestamps into a designated database; acquiring a plurality of pieces of specified device data with the same time stamp in the specified database; determining device relevance of designated Internet of things devices respectively corresponding to the designated device data based on the designated device data to determine whether the designated Internet of things devices are applied to the same application scene; and performing data analysis on the designated equipment data according to the equipment correlation of the designated Internet of things equipment so as to realize equipment monitoring on the designated Internet of things equipment.
Further, before acquiring device data reported by a plurality of internet of things devices based on a preset timing data acquisition task, the method further includes: scanning an IP address of each piece of Internet of things equipment to obtain a plurality of IP addresses of a plurality of pieces of Internet of things equipment; mapping the plurality of IP addresses into an appointed IP address list based on a preset mapping mode and each IP address, wherein the appointed IP address list is a completely random IP address list; and detecting each piece of Internet of things equipment based on the specified IP address list, and determining the online state of each piece of Internet of things equipment.
Further, mapping the plurality of IP addresses into a specified IP address list based on a preset mapping manner specifically includes: carrying out random mapping on each IP address according to a preset formula to obtain a preset IP address corresponding to each IP address; the preset formula is as follows: a. The 1 = a (a) mod p, wherein, a 1 For presetting IP addresses, A is each IP address, a is the primitive root of p, and p is more than 2 32 The minimum prime number of; according to each preset IP address, obtainingA list of IP addresses is specified.
Further, before the obtaining of the plurality of specified device data in the specified database by the device data timestamps of the plurality of device data, the method comprises: extracting the characteristics of each piece of equipment data, and determining the equipment characteristics of each piece of equipment of the Internet of things; identifying each piece of Internet of things equipment according to the equipment characteristics of each piece of Internet of things equipment, and determining the equipment type of each piece of Internet of things equipment; the method comprises the steps of storing a plurality of preset device data in an appointed data set of an appointed database so as to obtain a plurality of appointed device data in the appointed data set of the appointed database, wherein the Internet of things devices corresponding to the preset device data belong to the same device type.
Further, determining the device relevance of the designated internet of things devices corresponding to the plurality of designated device data based on the plurality of designated device data, specifically including: performing data screening on the plurality of pieces of specified equipment data to obtain equipment attribute data and equipment state data in each piece of specified equipment data; determining a device type association result between designated internet-of-things devices corresponding to the designated device data according to the device attribute data in each designated device data; obtaining an application scene correlation result between the designated internet of things devices corresponding to the designated device data based on the device state data in each designated device data; and determining the equipment correlation among the specified equipment according to the equipment type correlation result and the application scene correlation result.
Further, determining a device type association result between the designated internet of things devices corresponding to the plurality of designated device data according to the device attribute data in each designated device data, specifically including: according to the device attribute data in each designated device, calculating the device similarity between each designated device and each device in a pre-constructed device fingerprint library, wherein the device fingerprint library comprises device fingerprints of a plurality of preset devices and corresponding preset device attribute data; determining a first device corresponding to the designated device in the device fingerprint library based on the device similarity between each designated device and each device in the device fingerprint library, wherein the device similarity between the designated device and the first device is greater than a preset similarity threshold; taking the device fingerprint of the first device as the device fingerprint of the specified device; and determining a device type association result among the designated Internet of things devices based on the device fingerprint of each designated device.
Further, according to the device correlation of the multiple pieces of specified internet-of-things devices, performing data analysis on the multiple pieces of specified device data specifically includes: performing data fusion on a plurality of pieces of specified equipment data meeting requirements based on the equipment relevance of the plurality of pieces of specified Internet of things equipment, wherein the equipment type correlation result and the application scene correlation result of the plurality of pieces of specified equipment data meeting the requirements both meet preset correlation conditions; and performing data analysis on the fused specified equipment data to generate a data analysis result.
Further, before performing data fusion on multiple pieces of specified device data meeting requirements based on the device correlation of the multiple pieces of specified internet-of-things devices, the method further includes: performing data quality judgment on each piece of specified equipment data to obtain a judgment result of each piece of specified equipment data, wherein the judgment result comprises normal data, error data and suspicious data; and according to the judgment result of each piece of specified equipment data, eliminating error data in the plurality of pieces of specified equipment data in the specified database, manually checking suspicious data in the plurality of pieces of specified equipment data, and eliminating the suspicious data which do not pass the manual checking.
One or more embodiments of the present specification provide a data analysis device for monitoring an internet of things device, including:
at least one processor; and (c) a second step of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to cause the at least one processor to:
the method comprises the steps that equipment data reported by a plurality of Internet of things equipment are obtained based on a preset timing data obtaining task, and the obtaining time of the equipment data is recorded, wherein the timing data obtaining task is used for carrying out data snapshot on the equipment data reported to a data analysis server by the Internet of things equipment in a preset time interval through a timing program on the data analysis server; setting the device data timestamps of the plurality of Internet of things devices according to the acquisition time of the device data, and storing the device data of the plurality of Internet of things devices and the device data timestamps into a designated database; acquiring a plurality of pieces of specified device data with the same time stamp in the specified database; determining device relevance of designated Internet of things devices respectively corresponding to the designated device data based on the designated device data to determine whether the designated Internet of things devices are applied to the same application scene; and performing data analysis on the designated equipment data according to the equipment correlation of the designated Internet of things equipment so as to realize equipment monitoring on the designated Internet of things equipment.
One or more embodiments of the present specification provide a non-transitory computer storage medium storing computer-executable instructions configured to:
the method comprises the steps that equipment data reported by a plurality of Internet of things equipment are obtained based on a preset timing data obtaining task, and the obtaining time of the equipment data is recorded, wherein the timing data obtaining task is used for carrying out data snapshot on the equipment data reported to a data analysis server by the Internet of things equipment in a preset time interval through a timing program on the data analysis server; setting the device data timestamps of the plurality of Internet of things devices according to the acquisition time of the device data, and storing the device data of the plurality of Internet of things devices and the device data timestamps into a designated database; acquiring a plurality of pieces of specified device data with the same time stamp in the specified database; determining device relevance of designated Internet of things devices respectively corresponding to the designated device data based on the designated device data to determine whether the designated Internet of things devices are applied to the same application scene; and performing data analysis on the designated equipment data according to the equipment correlation of the designated Internet of things equipment so as to realize equipment monitoring on the designated Internet of things equipment.
The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects: through the technical scheme, the equipment data are acquired based on the timed task, the data acquisition time is used as the data reporting time, the data are processed again, the attribute values of the equipment are aligned according to the same timestamp, in addition, the equipment data belonging to the same moment are acquired through the equipment data timestamp, the unification of the time dimension of the equipment data is realized, the data are analyzed through the equipment correlation among a plurality of Internet of things equipment, the unification of the equipment dimension is realized, and the accuracy of the data analysis is further ensured.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:
fig. 1 is a schematic flowchart of a data analysis method for monitoring an internet of things device according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a data acquisition method for monitoring an internet of things device according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a data analysis device for monitoring an internet of things device according to an embodiment of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present specification without any creative effort shall fall within the protection scope of the present specification.
When monitoring mass Internet of things equipment in the intelligent manufacturing industry, the method is applied to the Internet of things equipment in different production processes, and mass Internet of things equipment data can be generated along with development of different production processes. Usually, by comparing and analyzing mass internet of things device data generated at a certain moment with standard device data, the internet of things device data with abnormality at a corresponding moment is found in time. If massive internet of things device data are compared with the corresponding standard device data one by one, extra data analysis pressure is increased. In the embodiment of the description, in order to relieve data analysis pressure, data index comparison is performed on massive internet of things equipment data belonging to the same production process at the same time, and after equipment data different from most of the internet of things equipment data are determined, the equipment data are compared with corresponding standard equipment data, so that abnormal internet of things equipment can be found in time.
The embodiment of the present specification provides a data analysis method for monitoring an internet of things device, and it should be noted that an execution subject in the embodiment of the present specification may be a server, or may be any device with data processing capability. Fig. 1 is a schematic flow chart of a data analysis method for monitoring an internet of things device according to an embodiment of the present specification, and as shown in fig. 1, the method mainly includes the following steps:
step S101, acquiring device data reported by a plurality of Internet of things devices based on a preset timing data acquisition task, and recording data acquisition time of the device data.
Specifically, before step S101, IP address scanning is performed on each internet of things device to obtain a plurality of IP addresses of a plurality of internet of things devices; mapping the plurality of IP addresses into an appointed IP address list based on a preset mapping mode and each IP address, wherein the appointed IP address list is a completely random IP address list; and detecting each piece of Internet of things equipment based on the specified IP address list, and determining the online state of each piece of Internet of things equipment.
In an actual application scene, in order to avoid the condition that the equipment of the Internet of things is disconnected, the equipment of the Internet of things can be scanned, so that online detection is realized; when high-speed scanning is carried out, the problem of blocking of a target network needs to be avoided. Therefore, normal operation of the target equipment can be ensured, the condition that the detection packet is identified as malicious traffic and filtered can be avoided, and the accuracy of the detection result is improved.
In an embodiment of the present description, each piece of internet-of-things equipment is scanned for an IP address, so as to obtain address values of a plurality of IP addresses respectively corresponding to a plurality of pieces of internet-of-things equipment. And randomizing the scanned IP addresses, and mapping the plurality of IP addresses into a completely random IP address list based on a preset mapping mode and the address value of each IP address. And detecting each piece of Internet of things equipment based on the designated IP address list, and determining the online state of each piece of Internet of things equipment.
By the technical scheme, the offline equipment in the Internet of things equipment can be found in time, the condition that the equipment data is lost due to the fact that the equipment cannot report the data after being offline is avoided, and the accuracy of data analysis is further improved.
Based on a preset mapping mode, mapping a plurality of IP addresses into a specified IP address list, which specifically comprises the following steps: carrying out random mapping on each IP address according to a preset formula to obtain a preset IP address corresponding to each IP address; the preset formula is as follows: a. The 1 (= a) × (a) mod p, where a 1 For the default IP address, A is each IP address, a is the primitive root of p, and p is greater than 2 32 The minimum prime number of; and obtaining an appointed IP address list according to each preset IP address.
In one embodiment of the present description, a formula is utilizedA 1 = a (a) mod p, an IP address is mapped randomly to a new IP address, where a 1 Is the address value of the preset IP address, i.e. the value of the new IP address, A is the address value of each IP address, a is the primitive root of p, p is more than 2 32 Is the smallest prime number. And obtaining an appointed IP address list according to the address numerical value of each preset IP address. It should be noted that if a is the primitive root of the prime number p, then a × mod p, a 2 mod p、a 3 mod p, and a P-1 mod p is different and comprises an arrangement from 1 to p-1, for example, let a =5, p =7, then the order of 1-6 is randomized followed by the order of 1 × 5mod 7, 2 × 5mod 7, 3 × 5mod 7, 4 × 5mod 7, 5 × 5mod 7, 6 × 5mod 7, i.e. the sequence of 546231.
In an embodiment of the present specification, device data reported by multiple internet of things devices is acquired based on a preset timing data acquisition task, and a data acquisition time of the device data is recorded. The timing data acquisition task is to perform data snapshot on the device data reported to the data analysis server by the internet of things device within a preset time interval through a timing program on the data analysis server. Fig. 2 is a schematic flow chart of a data acquisition method for monitoring an internet of things device according to an embodiment of the present disclosure, and as shown in fig. 2, a state of a device attribute is stored when the device reports device data each time, where the state has absolute real-time performance, and at this time, time corresponding to the device attribute state is in different dimensions. And then, periodically taking a snapshot of the equipment attribute value through a timing program, and forming a new record by the acquired equipment data and a snapshot timestamp to be persisted in a database, so that the equipment data is acquired secondarily through the timing program.
According to the technical scheme, the data is reprocessed, the attribute values of the equipment are aligned according to the same timestamp, and subsequent analysis and processing are facilitated.
Step S102, device data timestamps of the plurality of Internet of things devices are set according to the data acquisition time of the device data, and the device data timestamps of the plurality of Internet of things devices are stored in a designated database.
In an embodiment of the present specification, device data timestamps are set for device data of a plurality of internet of things devices according to a data obtaining time of the device data, that is, a snapshot timestamp for snapshotting the device data by a timing program. It should be noted that, due to different types and configurations of the devices, reporting frequencies of different devices are inconsistent, and the device data and the corresponding device data timestamp are stored in a specified database by using the data acquisition time as the device data timestamp of the device data, so as to perform data statistics and data analysis on the data in the following process.
In step S103, in the specified database, a plurality of pieces of specified device data having the same time stamp are acquired.
The method before step S103 includes: extracting the characteristics of the data of each piece of equipment, and determining the equipment characteristics of each piece of equipment of the Internet of things; identifying each piece of Internet of things equipment according to the equipment characteristics of each piece of Internet of things equipment, and determining the equipment type of each piece of Internet of things equipment; the method comprises the steps of storing a plurality of preset device data in an appointed data set of an appointed database so as to obtain a plurality of appointed device data in the appointed data set of the appointed database, wherein the Internet of things devices corresponding to the preset device data belong to the same device type.
When data analysis is performed, in addition to the unification in time dimension, under certain specific application scenarios and special data analysis requirements, the device types need to be considered, for example, under the application scenarios of the smart park, device data of similar internet of things devices in the park need to be analyzed when data are analyzed, and the obtained data analysis results are associated and compared with the operation of the similar internet of things devices, so that various devices in the park can be monitored.
In an embodiment of the present specification, an online internet of things device identification technology based on a Self-organizing incremental learning neural network (SOINN) may be used, and the SOINN network with incremental learning capability may be combined with a supervised learning method, so that an internet of things device identification model may be continuously updated in an identification process, a device brand may be identified using a trained classification model, and then a text similarity between the device and a device model feature library is calculated to identify a device model, and a device type is determined according to the device brand and the device model.
In an embodiment of the present specification, feature extraction may be further performed on each piece of device data, and based on the extracted data features, the device features of each corresponding piece of internet-of-things device are determined. And identifying each piece of Internet of things equipment according to the equipment characteristics of each piece of Internet of things equipment, and determining the equipment type of each piece of Internet of things equipment. The identification process may be compared to the device characteristics in the device type data, or may be other ways of determining the device characteristics.
In an embodiment of the present specification, in order to improve data processing efficiency during data analysis, preset device data corresponding to internet of things devices belonging to the same device type is stored in a preset data set. It should be noted that, a plurality of data sets are pre-constructed in the designated database, and the device types corresponding to the device data that needs to be stored in each data set are different, and a device type identifier may be set for the data set, and the device type identifier may be stored in the corresponding data set according to the device types corresponding to different device data.
In one embodiment of the present specification, the designated device data having the same time stamp of the plurality of device data is acquired in the designated database by the device data time stamps of the plurality of device data. Therefore, the device data belong to the same time dimension, and the accuracy and the reference degree of the data source are further ensured.
Step S104, determining the device correlation of the designated Internet of things devices corresponding to the designated device data respectively based on the designated device data so as to determine whether the designated Internet of things devices are applied to the same application scene.
Based on the multiple pieces of specified device data, determining device correlations of specified internet-of-things devices corresponding to the multiple pieces of specified device data respectively, specifically including: performing data screening on the plurality of pieces of specified equipment data to obtain equipment attribute data and equipment state data in each piece of specified equipment data; determining a device type association result between designated Internet of things devices corresponding to the designated device data according to the device attribute data in each designated device data; obtaining an application scene correlation result between the designated internet of things devices corresponding to the designated device data based on the device state data in each designated device data; and determining the device correlation between the specified devices according to the device type correlation result and the application scene correlation result.
In an embodiment of the present specification, the device correlation refers to a certain correlation between two internet of things devices, where the correlation may refer to a correlation of device types or a correlation of application scenarios, and is used to indicate whether multiple specified internet of things devices belong to the same application scenario or the same device type. For example, if two pieces of internet-of-things equipment belong to the same type, the two pieces of internet-of-things equipment are considered to have association in the aspect of equipment types; if the two pieces of Internet of things equipment are applied to the field of smart home, the two pieces of Internet of things equipment are determined to have association in the aspect of application scenes, and if the two pieces of Internet of things equipment are applied to the same production process, the two pieces of Internet of things equipment are determined to have association in the aspect of application scenes.
In an embodiment of the present specification, data screening is performed on a plurality of pieces of specified device data, and device attribute data and device status data in each piece of specified device data are obtained. The device attribute data is used for a device attribute of the current device, and may be data such as a device name and a device model, the device state data may include a device state name, an achievable function corresponding to each state, and state data, the device attribute data is used for representing a device attribute of the current device, and the device state data is used for representing a device state of the current device, and a specific data set is not specifically limited herein.
Determining a device type association result between the designated internet of things devices corresponding to the plurality of designated device data according to the device attribute data in each designated device data, specifically comprising: according to the device attribute data in each designated device, calculating the device similarity between each designated device and each device in a pre-constructed device fingerprint library, wherein the device fingerprint library comprises the device fingerprints of a plurality of preset devices and corresponding preset device attribute data; determining a first device corresponding to the designated device in the device fingerprint library based on the device similarity between each designated device and each device in the device fingerprint library, wherein the device similarity between the designated device and the first device is greater than a preset similarity threshold; taking the device fingerprint of the first device as the device fingerprint of the specified device; and determining a device type association result among the plurality of specified Internet of things devices based on the device fingerprint of each specified device.
In an embodiment of the present specification, a device type association result between specified internet-of-things devices corresponding to a plurality of pieces of specified device data is determined according to device attribute data in each piece of specified device data. And pre-constructing an equipment fingerprint library, wherein the equipment fingerprint library comprises the equipment fingerprints of a plurality of preset equipment and corresponding preset equipment attribute data. And calculating the device similarity between the device attribute data in the specified device and the preset device attribute data of a plurality of preset devices in the device fingerprint library, and determining the first device most similar to the specified device in the device fingerprint library based on the device similarity. It should be noted that, here, by setting the similarity threshold, the device similarity between the designated device and the first device is greater than the preset similarity threshold, that is, the first device and the designated device are considered to be most similar. The device fingerprint of the first device is taken as the device fingerprint of the designated device. And determining the device type association result among the plurality of designated Internet of things devices through comparison among the device fingerprints of each designated device. The device type association result herein may be an association or non-association between devices. If the device types of any two devices are consistent, the two devices are considered to have device type association; if the device types of any two devices are not consistent, the two devices are considered to have no device type association.
Based on the device state data in each piece of designated device data, obtaining an application scene correlation result between designated internet-of-things devices corresponding to the plurality of pieces of designated device data, and analyzing the application scene of each piece of device according to the device state data. Determining an application scene correlation result between a plurality of pieces of equipment of the internet of things according to the application scene of each piece of equipment, wherein the application scene correlation result can be whether the application scenes suitable for the plurality of pieces of equipment are consistent or not, and if the application scenes of any two pieces of equipment are consistent, determining that the application scene correlation exists between the two pieces of equipment; if the application scenes of any two devices are not consistent, the two devices are considered to have no application scene association.
In an embodiment of the present specification, weights of the device type correlation result and the application scenario correlation result may also be set according to different data analysis requirements, and the device correlation between the designated devices may be determined according to the device type correlation result and the application scenario correlation result. That is, in calculating the device correlation between any two specified devices, the device type association may be set to 0, and the device type non-association may be set to 1; likewise, the application scene association is set to 0, and the application scene non-association is set to 1. And if the weight of the application scene correlation result required by the data analysis is greater than that of the equipment type correlation result, setting the weight of the application scene correlation result to be a larger value. For example, the weight of the application scenario association result is set to 0.2, the weight of the corresponding device type association result is set to 0.8, and if the device type is associated and the device application scenario is not associated, the device correlation of the two devices can be obtained by calculation to be 0.2 + 1+0.8 +0, and the final device correlation is 0.2, that is, it is determined that there is an association between the two devices but the correlation is small, and the classification calculation of the application scenario is not required during the calculation.
And S105, performing data analysis on the data of the designated equipment according to the equipment relevance of the designated Internet of things equipment so as to realize equipment monitoring on the designated Internet of things equipment.
According to the device correlation of the multiple pieces of specified internet-of-things equipment, performing data analysis on the multiple pieces of specified equipment data, and specifically comprising the following steps: performing data fusion on a plurality of pieces of specified equipment data meeting requirements based on the equipment relevance of the plurality of pieces of specified Internet of things equipment, wherein the equipment type correlation result and the application scene correlation result of the plurality of pieces of specified equipment data meeting the requirements both meet preset correlation conditions; and performing data analysis on the fused specified equipment data to generate a data analysis result.
In an embodiment of the present specification, a correlation threshold is preset, and when the device correlation among multiple specified internet of things devices is greater than the preset threshold, the data needs to be classified and fused according to a device type correlation result and an application scenario correlation result. The preset association condition may be the following condition: when the device correlation among the multiple designated internet-of-things devices is greater than a preset threshold value and any one or more of the device types and the application scenes are associated, classifying the devices according to the application scenes or the device types and fusing data; when the device correlation among the multiple designated internet of things devices is not larger than a preset threshold, no matter whether the device types and the application scenes are related or not, data classification is not needed. And performing data analysis on the fused specified equipment data to generate a data analysis result.
Based on the device correlation of the designated internet of things devices, before data fusion is performed on the designated device data meeting the requirements, the method further includes: judging the data quality of each piece of specified equipment data to obtain a judgment result of each piece of specified equipment data, wherein the judgment result comprises normal data, error data and suspicious data; and according to the judgment result of each piece of specified equipment data, eliminating error data in the plurality of pieces of specified equipment data in the specified database, manually checking suspicious data in the plurality of pieces of specified equipment data, and eliminating the suspicious data which do not pass the manual checking.
In an embodiment of the present specification, since there may be error data or data in the data quality reported by the internet of things device, data quality determination is required to be performed on the data, and quality determination may be performed on the data by performing feature comparison between correct data pre-stored in each kind of internet of things device and the reported data. And eliminating error data in the plurality of pieces of specified equipment data in the specified database, manually verifying suspicious data in the plurality of pieces of specified equipment data, and eliminating the suspicious data which do not pass the manual verification, so that the accuracy of the data in the database is ensured, and the accuracy of a data analysis result is further ensured.
Through the technical scheme, the equipment data are acquired based on the timed task, the data acquisition time is used as the data reporting time, the data are processed again, the attribute values of the equipment are aligned according to the same timestamp, in addition, the equipment data belonging to the same moment are acquired through the equipment data timestamp, the unification of the time dimension of the equipment data is realized, the data are analyzed through the equipment correlation among a plurality of Internet of things equipment, the unification of the equipment dimension is realized, and the accuracy of the data analysis is further ensured.
An embodiment of the present specification further provides a data analysis device for monitoring an internet of things device, and as shown in fig. 3, the device includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
acquiring device data reported by a plurality of Internet of things devices based on a preset timing data acquisition task, and recording the acquisition time of the device data, wherein the timing data acquisition task carries out data snapshot on the device data reported to a data analysis server by the Internet of things devices within a preset time interval through a timing program on the data analysis server; setting the device data timestamps of the plurality of pieces of internet-of-things equipment according to the acquisition time of the device data, and storing the device data of the plurality of pieces of internet-of-things equipment and the device data timestamps into a specified database; acquiring a plurality of pieces of specified device data having the same time stamp in the specified database; determining the device correlation of designated internet of things devices corresponding to the designated device data respectively based on the designated device data to determine whether the designated internet of things devices are applied to the same application scene; and performing data analysis on the data of the designated equipment according to the equipment correlation of the designated Internet of things equipment so as to realize equipment monitoring on the designated Internet of things equipment.
Embodiments of the present description also provide a non-volatile computer storage medium storing computer-executable instructions configured to:
acquiring device data reported by a plurality of Internet of things devices based on a preset timing data acquisition task, and recording the acquisition time of the device data, wherein the timing data acquisition task carries out data snapshot on the device data reported to a data analysis server by the Internet of things devices within a preset time interval through a timing program on the data analysis server; setting the device data timestamps of the plurality of pieces of internet-of-things equipment according to the acquisition time of the device data, and storing the device data of the plurality of pieces of internet-of-things equipment and the device data timestamps into a specified database; acquiring a plurality of pieces of specified device data having the same time stamp in the specified database; determining the device correlation of designated internet of things devices corresponding to the designated device data respectively based on the designated device data to determine whether the designated internet of things devices are applied to the same application scene; and performing data analysis on the data of the designated equipment according to the equipment correlation of the designated Internet of things equipment so as to realize equipment monitoring on the designated Internet of things equipment.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the apparatus, the device, and the nonvolatile computer storage medium, since they are substantially similar to the embodiments of the method, the description is simple, and for the relevant points, reference may be made to the partial description of the embodiments of the method.
The foregoing description of specific embodiments has been presented for purposes of illustration and description. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The above description is merely one or more embodiments of the present disclosure and is not intended to limit the present disclosure. Various modifications and alterations to one or more embodiments of the present description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of one or more embodiments of the present specification should be included in the scope of the claims of the present specification.
Claims (10)
1. A data analysis method for monitoring Internet of things equipment, the method comprising:
acquiring device data reported by a plurality of pieces of Internet of things equipment based on a preset timing data acquisition task, and recording the acquisition time of the device data, wherein the timing data acquisition task is used for carrying out data snapshot on the device data reported to a data analysis server by the Internet of things equipment within a preset time interval through a timing program on the data analysis server;
according to the acquisition time of the equipment data, setting equipment data timestamps of the plurality of Internet of things equipment, and storing the equipment data of the plurality of Internet of things equipment and the equipment data timestamps into a designated database;
acquiring a plurality of pieces of specified device data with the same time stamp in the specified database;
determining device relevance of designated Internet of things devices respectively corresponding to the designated device data based on the designated device data to determine whether the designated Internet of things devices are applied to the same application scene;
and performing data analysis on the designated equipment data according to the equipment correlation of the designated Internet of things equipment so as to realize equipment monitoring on the designated Internet of things equipment.
2. The data analysis method for monitoring internet of things devices according to claim 1, wherein before acquiring device data reported by a plurality of internet of things devices based on a preset timing data acquisition task, the method further comprises:
scanning an IP address of each Internet of things device to obtain a plurality of IP addresses of a plurality of Internet of things devices;
mapping the plurality of IP addresses into an appointed IP address list based on a preset mapping mode and each IP address, wherein the appointed IP address list is a completely random IP address list;
and detecting each piece of Internet of things equipment based on the specified IP address list, and determining the online state of each piece of Internet of things equipment.
3. The data analysis method for monitoring the internet of things equipment according to claim 2, wherein the mapping of the plurality of IP addresses to the designated IP address list based on a preset mapping manner specifically comprises:
carrying out random mapping on each IP address according to a preset formula to obtain a preset IP address corresponding to each IP address;
the preset formula is as follows: a. The 1 (= a) × (a) mod p, where a 1 For the default IP address, A is each IP address, a is the primitive root of p, and p is greater than 2 32 The minimum prime number of;
and obtaining an appointed IP address list according to each preset IP address.
4. The data analysis method for monitoring the devices of the internet of things according to claim 1, wherein before the plurality of specified device data are acquired in the specified database through device data timestamps of the plurality of device data, the method comprises:
extracting the characteristics of each piece of equipment data, and determining the equipment characteristics of each piece of equipment of the Internet of things;
identifying each piece of Internet of things equipment according to the equipment characteristics of each piece of Internet of things equipment, and determining the equipment type of each piece of Internet of things equipment;
the method comprises the steps of storing a plurality of preset device data in an appointed data set of an appointed database so as to obtain a plurality of appointed device data in the appointed data set of the appointed database, wherein the Internet of things devices corresponding to the preset device data belong to the same device type.
5. The data analysis method for monitoring internet of things devices according to claim 1, wherein determining the device correlation of the designated internet of things devices corresponding to the designated device data based on the designated device data specifically comprises:
performing data screening on the plurality of pieces of specified equipment data to obtain equipment attribute data and equipment state data in each piece of specified equipment data;
determining a device type association result between designated internet-of-things devices corresponding to the designated device data according to the device attribute data in each designated device data;
obtaining an application scene correlation result between the designated internet of things devices corresponding to the designated device data based on the device state data in each designated device data;
and determining the device correlation between the specified devices according to the device type correlation result and the application scene correlation result.
6. The data analysis method for monitoring internet of things devices according to claim 5, wherein the determining, according to the device attribute data in each piece of specified device data, the device type association result between the specified internet of things devices corresponding to the plurality of pieces of specified device data specifically includes:
according to the device attribute data in each designated device, calculating the device similarity between each designated device and each device in a pre-constructed device fingerprint library, wherein the device fingerprint library comprises device fingerprints of a plurality of preset devices and corresponding preset device attribute data;
determining a first device corresponding to the designated device in the device fingerprint library based on the device similarity between each designated device and each device in the device fingerprint library, wherein the device similarity between the designated device and the first device is greater than a preset similarity threshold;
taking the device fingerprint of the first device as the device fingerprint of the specified device;
and determining a device type association result among the plurality of specified Internet of things devices based on the device fingerprint of each specified device.
7. The data analysis method for monitoring the internet of things equipment according to claim 1, wherein the data analysis of the multiple pieces of specified equipment data is performed according to the equipment correlation of the multiple pieces of specified internet of things equipment, and specifically comprises:
performing data fusion on a plurality of pieces of specified equipment data meeting requirements based on equipment correlation of the plurality of pieces of specified Internet of things equipment, wherein equipment type correlation results and application scene correlation results of the plurality of pieces of specified equipment data meeting the requirements meet preset correlation conditions;
and performing data analysis on the fused specified equipment data to generate a data analysis result.
8. The data analysis method for monitoring internet of things devices as claimed in claim 7, wherein before performing data fusion on the multiple pieces of specified device data meeting the requirement based on the device correlation of the multiple pieces of specified internet of things devices, the method further comprises:
performing data quality judgment on each piece of specified equipment data to obtain a judgment result of each piece of specified equipment data, wherein the judgment result comprises normal data, error data and suspicious data;
and according to the judgment result of each piece of specified equipment data, eliminating error data in the plurality of pieces of specified equipment data in the specified database, manually checking suspicious data in the plurality of pieces of specified equipment data, and eliminating the suspicious data which do not pass the manual checking.
9. A data analysis device for monitoring Internet of things devices, the device comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
the method comprises the steps that equipment data reported by a plurality of Internet of things equipment are obtained based on a preset timing data obtaining task, and the obtaining time of the equipment data is recorded, wherein the timing data obtaining task is used for carrying out data snapshot on the equipment data reported to a data analysis server by the Internet of things equipment in a preset time interval through a timing program on the data analysis server;
setting the device data timestamps of the plurality of Internet of things devices according to the acquisition time of the device data, and storing the device data of the plurality of Internet of things devices and the device data timestamps into a designated database;
acquiring a plurality of pieces of specified equipment data with the same time stamp in the specified database;
determining the device relevance of designated Internet of things devices respectively corresponding to the designated device data based on the designated device data to determine whether the designated Internet of things devices are applied to the same application scene;
and performing data analysis on the designated equipment data according to the equipment correlation of the designated Internet of things equipment so as to realize equipment monitoring on the designated Internet of things equipment.
10. A non-transitory computer storage medium storing computer-executable instructions configured to:
the method comprises the steps that equipment data reported by a plurality of Internet of things equipment are obtained based on a preset timing data obtaining task, and the obtaining time of the equipment data is recorded, wherein the timing data obtaining task is used for carrying out data snapshot on the equipment data reported to a data analysis server by the Internet of things equipment in a preset time interval through a timing program on the data analysis server;
setting the device data timestamps of the plurality of Internet of things devices according to the acquisition time of the device data, and storing the device data of the plurality of Internet of things devices and the device data timestamps into a designated database;
acquiring a plurality of pieces of specified device data with the same time stamp in the specified database;
determining device relevance of designated Internet of things devices respectively corresponding to the designated device data based on the designated device data to determine whether the designated Internet of things devices are applied to the same application scene;
and performing data analysis on the designated equipment data according to the equipment correlation of the designated Internet of things equipment so as to realize equipment monitoring on the designated Internet of things equipment.
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