CN115150294B - Data analysis method, device and medium for monitoring Internet of things device - Google Patents
Data analysis method, device and medium for monitoring Internet of things device Download PDFInfo
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Abstract
The embodiment of the specification discloses a data analysis method, equipment and 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: 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, wherein the timing data acquisition task is used for carrying out data snapshot on the device data reported by the Internet of things devices in a preset time interval through a timing program, setting device data time stamps of the plurality of Internet of things devices according to the data acquisition time of the device data, and storing the device data of the plurality of Internet of things devices and the device data time stamps into a designated database; acquiring a plurality of pieces of specified device data with the same time stamp from a specified database; and determining the device correlation of the appointed internet of things device corresponding to the appointed device data respectively, and carrying out data analysis on the appointed device data according to the device correlation of the appointed internet of things device.
Description
Technical Field
The present disclosure relates to the field of internet of things, and in particular, to a data analysis method, device and medium for monitoring devices of the internet of things.
Background
The internet of things technology is continuously developed, more and more intelligent terminal devices of the internet of things start to enter various application fields, and the terminal devices and products of the internet of things with intelligent functions are visible everywhere. Whether it is home, office, building or smart city, the internet of things intelligent terminal device can be seen.
Along with the increasing of the Internet of things equipment, equipment data generated by various intelligent terminal equipment of the Internet of things are more and more. Through the analysis of the equipment data, the equipment of the Internet of things can be monitored. The attribute of the internet of things equipment in different application scenes is different, so that reporting frequencies of reporting equipment data of the internet of things equipment are inconsistent, and if the data analysis is performed after all the internet of things equipment report the equipment data at a certain moment, the running state of the internet of things equipment cannot be monitored timely and accurately.
Disclosure of Invention
One or more embodiments of the present disclosure provide a data analysis method, device, and medium for monitoring an internet of things device, which are configured to solve the following technical problems: the attribute of the internet of things equipment in different application scenes is different, so that reporting frequencies of reporting equipment data of the internet of things equipment are inconsistent, and if the data analysis is performed after all the internet of things equipment report the equipment data at a certain moment, the running state of the internet of things equipment cannot be monitored timely and accurately.
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: 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 is used for carrying out data snapshot on the device data reported to a data analysis server by the Internet of things device in a preset time interval through a timing program on the data analysis server; setting device data time stamps 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 time stamps into a designated database; acquiring a plurality of pieces of designated device data with the same time stamp in the designated database; based on the plurality of pieces of designated equipment data, determining equipment relativity of designated internet of things equipment corresponding to the plurality of pieces of designated equipment data respectively so as to determine whether the plurality of pieces of designated internet of things equipment are applied to the same application scene; and according to the device correlation of the plurality of appointed devices, carrying out data analysis on the plurality of appointed device data so as to realize device monitoring of the appointed devices.
Further, before acquiring the device data reported by the plurality of internet of things devices based on the preset timing data acquisition task, the method further includes: carrying out IP address scanning 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 a specified IP address list based on a preset mapping mode and each IP address, wherein the specified IP address list is a completely random IP address list; and detecting each piece of internet of things equipment based on the appointed IP address list, and determining the online state of each piece of internet of things equipment.
Further, mapping the plurality of IP addresses to 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 is that 1 =a×a) mod p, where a 1 For preset IP addresses, A is each IP address, a is p primitive root, p is more than 2 32 Is the smallest prime number of (a); and obtaining a specified IP address list according to each preset IP address.
Further, the method includes, before acquiring the plurality of specified device data in the specified database by device data time stamps of the plurality of device data: 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 Internet of things device according to the device characteristics of each Internet of things device, and determining the device type of each Internet of things device; and storing the plurality of preset device data in a designated data set of a designated database so as to obtain the plurality of designated device data in the designated data set of the designated database, wherein the Internet of things devices corresponding to the plurality of preset device data belong to the same device type.
Further, based on the plurality of pieces of designated device data, determining the device correlation of the designated internet of things device corresponding to the plurality of pieces of designated device data respectively specifically includes: performing data screening on the plurality of specified device data to obtain device attribute data and device state data in each specified device data; according to the equipment attribute data in each piece of appointed equipment data, determining equipment type association results among appointed internet of things equipment corresponding to the plurality of pieces of appointed equipment data; obtaining application scene association results among the specified Internet of things devices corresponding to the plurality of specified device data based on the device state data in the specified device data; and determining the equipment correlation between the appointed equipment through the equipment type correlation result and the application scene correlation result.
Further, according to the device attribute data in each piece of designated device data, determining a device type association result between designated internet of things devices corresponding to the plurality of pieces of designated device data specifically includes: calculating the equipment similarity between each designated equipment and each equipment in an equipment fingerprint library according to the equipment attribute data in each designated equipment, wherein the equipment fingerprint library comprises equipment fingerprints of a plurality of preset equipment and corresponding preset equipment attribute data; determining a first device corresponding to the specified device in the device fingerprint library based on the device similarity between each specified device and each device in the device fingerprint library, wherein the device similarity between the specified device and the first device is larger than a preset similarity threshold; taking the device fingerprint of the first device as the device fingerprint of the designated device; and determining the device type association result among the plurality of appointed internet of things devices based on the device fingerprint of each appointed device.
Further, according to the device correlation of the plurality of designated devices of the internet of things, performing data analysis on the plurality of designated device data specifically includes: based on the device correlation of the plurality of appointed devices of the Internet of things, carrying out data fusion on a plurality of appointed device data meeting the requirements, wherein the device type association results and the application scene association results of the plurality of appointed device data meeting the requirements meet preset association conditions; and carrying out data analysis on the fused designated equipment data to generate a data analysis result.
Further, before data fusion is performed on the plurality of pieces of specified equipment data meeting requirements based on the equipment correlation of the plurality of pieces of specified equipment of the internet of things, the method further comprises: carrying out data quality judgment on each piece of appointed equipment data to obtain a judgment result of each piece of appointed equipment data, wherein the judgment result comprises normal data, error data and suspicious data; and according to the judging result of each piece of appointed equipment data, eliminating error data in the plurality of pieces of appointed equipment data in the appointed database, manually checking suspicious data in the plurality of pieces of appointed equipment data, and eliminating suspicious data which does not pass through 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; the method comprises the steps of,
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:
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 is used for carrying out data snapshot on the device data reported to a data analysis server by the Internet of things device in a preset time interval through a timing program on the data analysis server; setting device data time stamps 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 time stamps into a designated database; acquiring a plurality of pieces of designated device data with the same time stamp in the designated database; based on the plurality of pieces of designated equipment data, determining equipment relativity of designated internet of things equipment corresponding to the plurality of pieces of designated equipment data respectively so as to determine whether the plurality of pieces of designated internet of things equipment are applied to the same application scene; and according to the device correlation of the plurality of appointed devices, carrying out data analysis on the plurality of appointed device data so as to realize device monitoring of the appointed devices.
One or more embodiments of the present specification 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 is used for carrying out data snapshot on the device data reported to a data analysis server by the Internet of things device in a preset time interval through a timing program on the data analysis server; setting device data time stamps 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 time stamps into a designated database; acquiring a plurality of pieces of designated device data with the same time stamp in the designated database; based on the plurality of pieces of designated equipment data, determining equipment relativity of designated internet of things equipment corresponding to the plurality of pieces of designated equipment data respectively so as to determine whether the plurality of pieces of designated internet of things equipment are applied to the same application scene; and according to the device correlation of the plurality of appointed devices, carrying out data analysis on the plurality of appointed device data so as to realize device monitoring of the appointed devices.
The above-mentioned at least one technical scheme that this description embodiment adopted can reach following beneficial effect: according to the technical scheme, the device data are acquired based on the timing task, the data acquisition time is taken as the data reporting time, the attribute values of the devices are aligned according to the same time stamp through the data reprocessing, in addition, the device data belonging to the same time are acquired through the device data time stamp, the unification of the time dimension of the device data is realized, the data are analyzed through the device correlation among a plurality of Internet of things devices, the unification of the device 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 description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some of the embodiments described in the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
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 disclosure;
Fig. 2 is a flow chart 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 the technical solutions in the present specification better understood by those skilled in the art, 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 some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present disclosure.
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 the different production processes. In general, by comparing and analyzing mass internet of things equipment data generated at a certain moment with standard equipment data, abnormal internet of things equipment data at a corresponding moment is found in time. If massive internet of things equipment data are compared with corresponding standard equipment data one by one, additional data analysis pressure is increased. In the embodiment of the description, in order to relieve data analysis pressure, the embodiment of the description compares data indexes of mass internet of things equipment data belonging to the same production process at the same moment, and compares the equipment data with corresponding standard equipment data after equipment data different from most of the internet of things equipment data are determined so as to discover abnormal internet of things equipment in time.
The embodiment of the present disclosure provides a data analysis method for monitoring an internet of things device, and it should be noted that an execution body in the embodiment of the present disclosure may be a server, or any device having data processing capability. Fig. 1 is a flow chart of a data analysis method for monitoring an internet of things device according to an embodiment of the present disclosure, as shown in fig. 1, mainly including 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 the data acquisition time of the device data.
Specifically, before step S101, IP address scanning is performed on each of the devices of the internet of things, so as to obtain a plurality of IP addresses of the devices of the internet of things; mapping a plurality of IP addresses into a specified IP address list based on a preset mapping mode and each IP address, wherein the specified IP address list is a completely random IP address list; and detecting each piece of Internet of things equipment based on the appointed 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; it is also desirable to avoid congestion problems in the target network when scanning at high speeds. Therefore, normal operation of the target equipment can be ensured, the condition that the detection packet is identified as malicious traffic is filtered can be avoided, and accuracy of detection results is improved.
In an embodiment of the present disclosure, IP address scanning is performed on each of the devices of the internet of things, so as to obtain address values of a plurality of IP addresses corresponding to the devices of the internet of things respectively. And carrying out randomization processing on the scanned IP addresses, and mapping a 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 appointed IP address list, and determining the online state of each piece of Internet of things equipment.
Through the technical scheme, the disconnection device in the Internet of things device can be timely found, the situation that the device data is lost due to the fact that the device is disconnected and cannot report the data is avoided, and the accuracy of data analysis is further improved.
Mapping the plurality of IP addresses into a specified IP address list based on a preset mapping mode 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: a is that 1 =a×a) mod p, where a 1 For preset IP addresses, A is each IP address, a is p primitive root, p is more than 2 32 Is the smallest prime number of (a); and obtaining a specified IP address list according to each preset IP address.
In one embodiment of the present specification, equation A is utilized 1 =a×a) mod p, randomly mapping an IP address to a new IP address, wherein a 1 Is the address value of the preset IP address, namely the value of the new IP address, A is the address value of each IP address, a is the primitive root of p, and p is more than 2 32 Is the smallest prime number of (c). And obtaining a specified IP address list according to the address value of each preset IP address. If a is the primitive root of prime number p, a is mod p, a 2 mod p、a 3 mod p, a P-1 mod p is different and comprises an arrangement of 1 to p-1, e.g. let a=5, p=7, then the order of 1-6The sequence of 5mod 7, 2×5mod 7, 3×5mod 7, 4×5mod 7, 5×5mod 7, 6×5mod 7, i.e., 546231, is sequentially 1×5mod 7, 2×5mod 7, 3×5mod 7, 4×5mod 7 after randomization.
In one embodiment of the present disclosure, device data reported by a plurality of devices of the internet of things 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 device data reported to the data analysis server by the Internet of things device in a preset time interval through a timing program on the data analysis server. Fig. 2 is a flow chart of a data acquisition method for monitoring an internet of things device according to an embodiment of the present disclosure, as shown in fig. 2, a state of a device attribute is saved when the device reports device data each time, the state has absolute real-time performance, and at this time, time corresponding to the state of the device attribute is in different dimensions. And then, periodically taking a snapshot of the device attribute value through a timing program, and forming a new record with the acquired device data and the snapshot timestamp into a database for persistence, so that the device data is acquired for the second time through the timing program.
Through the technical scheme, the data are reprocessed, and the attribute values of the equipment are aligned according to the same time stamp, so that subsequent analysis and processing are facilitated.
Step S102, device data time stamps of a plurality of Internet of things devices are set according to the data acquisition time of the device data, and the device data time stamps of the plurality of Internet of things devices are stored in a designated database.
In one embodiment of the present disclosure, device data time stamps are set for device data of a plurality of internet of things devices by data acquisition time of the device data, that is, snapshot time stamps for snapshot of the device data by a timing program. It should be noted that, due to different types and configurations of devices, reporting frequencies of different devices are inconsistent, and the device data and the corresponding device data time stamp are stored in a designated database by taking the data acquisition time as the device data time stamp of the device data, so that subsequent data statistics and data analysis work can be conveniently performed on the data.
Step S103, in the designation database, a plurality of pieces of designation device data having the same time stamp are acquired.
The method before step S103 includes: 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 Internet of things device according to the device characteristics of each Internet of things device, and determining the device type of each Internet of things device; and storing the plurality of preset device data in a designated data set of a designated database so as to acquire the plurality of designated device data in the designated data set of the designated database, wherein the Internet of things devices corresponding to the plurality of preset device data belong to the same device type.
In data analysis, besides unification in time dimension, under certain specific application scenes and special data analysis requirements, the equipment types are required to be considered, for example, in the application scenes of intelligent parks, the equipment data of similar internet of things equipment in the parks are required to be analyzed when the data are analyzed, and the obtained data analysis results are associated and compared with the operation of the similar internet of things equipment, so that the monitoring of various equipment in the parks is realized.
In one embodiment of the present disclosure, an online internet of things device identification technology based on a Self-organizing incremental learning neural network (Self-organizing incremental neural network, SOINN) may be used to combine the SOINN network with incremental learning capability with a supervised learning method, so that an internet of things device identification model may be continuously updated in an identification process, a trained classification model is used to identify a device brand, and then a text similarity between a device and a device model feature library is calculated to identify a model of the device, and the type of the device is determined according to the device brand and the device model.
In an embodiment of the present disclosure, feature extraction may be further performed on each device data, and based on the extracted data features, device features of each corresponding internet of things device are determined. And identifying each Internet of things device according to the device characteristics of each Internet of things device, and determining the device type of each Internet of things device. The identification process herein may be compared to the device characteristics in the device type data, or other ways of determining the device characteristics.
In an embodiment of the present disclosure, in order to improve data processing efficiency during data analysis, preset device data corresponding to an internet of things device belonging to the same device type is stored in a preset designated data set. It should be noted that, a plurality of data sets are pre-constructed in a designated database, the device type corresponding to the device data to be stored in each data set is different, a device type identifier may be set for the data set, and the data set is stored in the corresponding data set according to the device type corresponding to the different device data.
In one embodiment of the present specification, the specified device data having the same plurality of device data time stamps is acquired in the specified 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 referenceable degree of the data source are further ensured.
Step S104, based on the plurality of designated device data, determining the device correlation of the designated Internet of things devices corresponding to the plurality of designated device data respectively, so as to determine whether the plurality of designated Internet of things devices are applied to the same application scene.
Based on the plurality of pieces of designated equipment data, determining the equipment correlation of the designated internet of things equipment corresponding to the plurality of pieces of designated equipment data respectively specifically comprises the following steps: performing data screening on the plurality of specified device data to obtain device attribute data and device state data in each specified device data; according to the equipment attribute data in each piece of appointed equipment data, determining equipment type association results among appointed internet of things equipment corresponding to the plurality of pieces of appointed equipment data; acquiring application scene association results among the specified Internet of things devices corresponding to the plurality of specified device data based on the device state data in the specified device data; and determining the device correlation between the designated devices through the device type correlation result and the application scene correlation result.
In an embodiment of the present disclosure, the device correlation refers to a certain correlation between two devices of the internet of things, where the correlation may refer to a correlation of a device type or a correlation of an application scenario, which is used to indicate whether a plurality of designated devices of the internet of things belong to the same application scenario or belong to the same device type. For example, two internet of things devices are of the same type, and then the two devices are considered to have an association in terms of device type; if the two pieces of Internet of things equipment are applied to the intelligent home field, the two pieces of Internet of things equipment are considered to have the 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 considered to have the association in the aspect of application scenes.
In one embodiment of the present disclosure, data filtering is performed on a plurality of specified device data to obtain device attribute data and device status data in each specified device data. The device attribute data is used for the device attribute of the current device, and may be data such as a device name, a device model, etc., and the device status data may include a device status name, an achievable function corresponding to each status, and status data, where the device attribute data is used for indicating the device attribute of the current device, and the device status data is used for indicating the device status of the current device, and specific data group embodiments are not specifically limited herein.
According to the device attribute data in each piece of designated device data, determining a device type association result between designated internet of things devices corresponding to the plurality of pieces of designated device data, wherein the method specifically comprises the following steps: according to the equipment attribute data in each designated equipment, calculating the equipment similarity between each designated equipment and each equipment in the equipment fingerprint library in a pre-constructed equipment fingerprint library, wherein the equipment fingerprint library comprises equipment fingerprints of a plurality of preset equipment and corresponding preset equipment 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 designated device; based on the device fingerprint of each designated device, a device type association result between the plurality of designated internet of things devices is determined.
In one embodiment of the present disclosure, a device type association result between designated internet of things devices corresponding to a plurality of designated device data is determined according to device attribute data in each designated device data. The method comprises the steps of pre-constructing an equipment fingerprint library, wherein the equipment fingerprint library comprises 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 designated device and the preset device attribute data of a plurality of preset devices in the device fingerprint library, and determining the first device which is most similar to the designated device in the device fingerprint library based on the device similarity. It should be noted that, the similarity between the designated device and the first device may be greater than the preset similarity threshold by setting the similarity threshold, so that the first device may be considered to be the most similar to the designated device. 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 appointed internet of things devices through comparison among the device fingerprints of each appointed device. The device type association result here may be that there is or is no 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 inconsistent, the two devices are considered to have no device type association.
Based on the equipment state data in each piece of appointed equipment data, an application scene association result among appointed internet of things equipment corresponding to a plurality of pieces of appointed equipment data is obtained, and according to the equipment state data, an application scene of each piece of equipment can be analyzed, for example, the state name in the equipment data is a sleep lamp, and the equipment is indicated to be applied to the application scene of the intelligent home. Determining an application scene association result among a plurality of Internet of things devices according to the application scene of each device, wherein the application scene association result can be whether application scenes applicable to the plurality of devices are consistent or not, and if the application scenes of any two devices are consistent, the application scene association exists between the two devices; if the application scenes of any two devices are inconsistent, the two devices are considered to have no application scene association.
In an embodiment of the present disclosure, the weights of the device type association result and the application scenario association result may be set according to different data analysis requirements, and the device correlation between the specified devices may be determined through the device type association result and the application scenario association result. That is, in calculating the device correlation between any two specified devices, the device type-associated may be set to 0 and the device type-unassociated may be set to 1; likewise, the application scenario association is set to 0 and the application scenario non-association is set to 1. If the data analysis requires that the weight of the corresponding application scene association result is greater than the weight of the equipment type association result, setting the weight of the application scene association result to be a larger value. For example, if the weight of the association result of the application scene is set to 0.2, the weight of the association result of the corresponding device type is set to 0.8, and if the device type has an association and the device application scene has no association, the device correlation between the two is calculated to be 0.2x1+0.8x0, and the final device correlation is 0.2, that is, the two are considered to have an association but have a smaller correlation, and the classification calculation of the application scene is not needed in the calculation.
Step S105, according to the device correlation of the plurality of appointed devices of the Internet of things, data analysis is carried out on the plurality of appointed device data so as to realize device monitoring of the appointed devices of the Internet of things.
According to the device correlation of the plurality of appointed devices of the internet of things, carrying out data analysis on the plurality of appointed device data, wherein the method specifically comprises the following steps: based on the device correlation of the plurality of appointed devices of the Internet of things, carrying out data fusion on a plurality of appointed device data meeting the requirements, wherein the device type association results and the application scene association results of the plurality of appointed device data meeting the requirements meet preset association conditions; and carrying out data analysis on the fused designated equipment data to generate a data analysis result.
In an embodiment of the present disclosure, a correlation threshold is preset, and when a device correlation between a plurality of designated internet of things devices is greater than the preset threshold, classification fusion is required to be performed on data according to a device type association result and an application scenario association result. The preset association condition may be the following condition: when the device correlation among the plurality of appointed 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 related, the devices are required to be classified according to the application scenes or the device types, and data fusion is carried out; when the device correlation among the plurality of appointed internet of things devices is not greater than a preset threshold value, no matter whether the device type and the application scene are associated, data classification is not needed. And carrying out data analysis on the fused designated equipment data to generate a data analysis result.
Based on the device correlation of the plurality of appointed devices of the internet of things, before data fusion is carried out on the plurality of appointed device data meeting the requirements, the method further comprises the following steps: carrying out data quality judgment on each piece of appointed equipment data to obtain a judgment result of each piece of appointed equipment data, wherein the judgment result comprises normal data, error data and suspicious data; and according to the judging result of each piece of appointed equipment data, eliminating error data in the plurality of pieces of appointed equipment data in the appointed database, manually checking suspicious data in the plurality of pieces of appointed equipment data, and eliminating suspicious data which does not pass through manual checking.
In an embodiment of the present disclosure, since there may be erroneous data or possible data in the quality of data reported by the internet of things device, it is necessary to perform data quality judgment on the data, and the quality judgment on the data may be performed by performing feature comparison between correct data pre-stored in each internet of things device and the reported data. And eliminating error data in the plurality of pieces of appointed equipment data in the appointed database, manually checking suspicious data in the plurality of pieces of appointed equipment data, and eliminating suspicious data which does not pass through the manual checking, so that the accuracy of the data in the database is ensured, and the accuracy of a data analysis result is further ensured.
According to the technical scheme, the device data are acquired based on the timing task, the data acquisition time is taken as the data reporting time, the attribute values of the devices are aligned according to the same time stamp through the data reprocessing, in addition, the device data belonging to the same time are acquired through the device data time stamp, the unification of the time dimension of the device data is realized, the data are analyzed through the device correlation among a plurality of Internet of things devices, the unification of the device dimension is realized, and the accuracy of the data analysis is further ensured.
The embodiment of the present disclosure further provides a data analysis device for monitoring an internet of things device, as shown in fig. 3, where 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, the instructions being executable by the at least one processor to enable 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 is used for carrying out data snapshot on the device data reported to a data analysis server by the Internet of things device in a preset time interval through a timing program on the data analysis server; setting device data time stamps 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 time stamps into a designated database; acquiring a plurality of pieces of specified device data having the same time stamp in the specified database; based on the plurality of designated device data, determining device correlation of designated internet of things devices corresponding to the plurality of designated device data respectively, so as to determine whether the plurality of designated internet of things devices are applied to the same application scene; and according to the device correlation of the plurality of appointed devices of the Internet of things, carrying out data analysis on the plurality of appointed device data so as to realize device monitoring of the appointed devices of the Internet of things.
The present specification embodiments 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 is used for carrying out data snapshot on the device data reported to a data analysis server by the Internet of things device in a preset time interval through a timing program on the data analysis server; setting device data time stamps 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 time stamps into a designated database; acquiring a plurality of pieces of specified device data having the same time stamp in the specified database; based on the plurality of designated device data, determining device correlation of designated internet of things devices corresponding to the plurality of designated device data respectively, so as to determine whether the plurality of designated internet of things devices are applied to the same application scene; and according to the device correlation of the plurality of appointed devices of the Internet of things, carrying out data analysis on the plurality of appointed device data so as to realize device monitoring of the appointed devices of the Internet of things.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can 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 are also possible or may be advantageous.
The foregoing is merely one or more embodiments of the present description and is not intended to limit the present description. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of one or more embodiments of the present description, is intended to be included within the scope of the claims of the present description.
Claims (10)
1. A data analysis method for monitoring internet of things equipment, the method comprising:
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 is used for carrying out data snapshot on the device data reported to a data analysis server by the Internet of things device in a preset time interval through a timing program on the data analysis server;
setting device data time stamps 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 time stamps into a designated database;
acquiring a plurality of pieces of designated device data with the same time stamp in the designated database;
based on the plurality of pieces of designated equipment data, determining equipment relativity of designated internet of things equipment corresponding to the plurality of pieces of designated equipment data respectively so as to determine whether the plurality of pieces of designated internet of things equipment are applied to the same application scene;
and according to the device correlation of the plurality of appointed devices, carrying out data analysis on the plurality of appointed device data so as to realize device monitoring of the appointed devices.
2. The data analysis method for monitoring an internet of things device 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:
carrying out IP address scanning 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 a specified IP address list based on a preset mapping mode and each IP address, wherein the specified IP address list is a completely random IP address list;
and detecting each piece of internet of things equipment based on the appointed IP address list, and determining the online state of each piece of internet of things equipment.
3. The data analysis method for monitoring an internet of things device according to claim 2, wherein mapping the plurality of IP addresses to 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 is that 1 =a×a) mod p, where a 1 For preset IP addresses, A is each IP address, a is p primitive root, p is more than 2 32 Is the smallest prime number of (a);
and obtaining a specified IP address list according to each preset IP address.
4. The data analysis method for monitoring an internet of things device according to claim 1, wherein, before acquiring a plurality of specified device data in the specified database by device data time stamps 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 Internet of things device according to the device characteristics of each Internet of things device, and determining the device type of each Internet of things device;
and storing the plurality of preset device data in a designated data set of a designated database so as to obtain the plurality of designated device data in the designated data set of the designated database, wherein the Internet of things devices corresponding to the plurality of preset device data belong to the same device type.
5. The data analysis method for monitoring an internet of things device according to claim 1, wherein determining device correlations of a designated internet of things device corresponding to the plurality of designated device data, respectively, based on the plurality of designated device data, specifically comprises:
Performing data screening on the plurality of specified device data to obtain device attribute data and device state data in each specified device data;
according to the equipment attribute data in each piece of appointed equipment data, determining equipment type association results among appointed internet of things equipment corresponding to the plurality of pieces of appointed equipment data;
obtaining application scene association results among the specified Internet of things devices corresponding to the plurality of specified device data based on the device state data in the specified device data;
and determining the equipment correlation between the appointed equipment through the equipment type correlation result and the application scene correlation result.
6. The data analysis method for monitoring the internet of things device according to claim 5, wherein determining, according to the device attribute data in each piece of designated device data, a device type association result between designated internet of things devices corresponding to the plurality of pieces of designated device data specifically includes:
calculating the equipment similarity between each designated equipment and each equipment in an equipment fingerprint library according to the equipment attribute data in each designated equipment, wherein the equipment fingerprint library comprises equipment fingerprints of a plurality of preset equipment and corresponding preset equipment attribute data;
Determining a first device corresponding to the specified device in the device fingerprint library based on the device similarity between each specified device and each device in the device fingerprint library, wherein the device similarity between the specified device and the first device is larger than a preset similarity threshold;
taking the device fingerprint of the first device as the device fingerprint of the designated device;
and determining the device type association result among the plurality of appointed internet of things devices based on the device fingerprint of each appointed device.
7. The data analysis method for monitoring the internet of things device according to claim 1, wherein the data analysis is performed on the plurality of designated device data according to the device correlation of the plurality of designated internet of things devices, specifically comprising:
based on the device correlation of the plurality of appointed devices of the Internet of things, carrying out data fusion on a plurality of appointed device data meeting the requirements, wherein the device type association results and the application scene association results of the plurality of appointed device data meeting the requirements meet preset association conditions;
and carrying out data analysis on the fused designated equipment data to generate a data analysis result.
8. The method for data analysis for monitoring an internet of things device according to claim 7, wherein prior to data fusion of the plurality of specified device data that meets the requirements based on device correlation of the plurality of specified internet of things devices, the method further comprises:
carrying out data quality judgment on each piece of appointed equipment data to obtain a judgment result of each piece of appointed equipment data, wherein the judgment result comprises normal data, error data and suspicious data;
and according to the judging result of each piece of appointed equipment data, eliminating error data in the plurality of pieces of appointed equipment data in the appointed database, manually checking suspicious data in the plurality of pieces of appointed equipment data, and eliminating suspicious data which does not pass through manual checking.
9. A data analysis device for monitoring an internet of things device, the device comprising:
at least one processor; the method comprises the steps of,
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:
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 is used for carrying out data snapshot on the device data reported to a data analysis server by the Internet of things device in a preset time interval through a timing program on the data analysis server;
setting device data time stamps 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 time stamps into a designated database;
acquiring a plurality of pieces of designated device data with the same time stamp in the designated database;
based on the plurality of pieces of designated equipment data, determining equipment relativity of designated internet of things equipment corresponding to the plurality of pieces of designated equipment data respectively so as to determine whether the plurality of pieces of designated internet of things equipment are applied to the same application scene;
and according to the device correlation of the plurality of appointed devices, carrying out data analysis on the plurality of appointed device data so as to realize device monitoring of the appointed devices.
10. A non-transitory 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 is used for carrying out data snapshot on the device data reported to a data analysis server by the Internet of things device in a preset time interval through a timing program on the data analysis server;
setting device data time stamps 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 time stamps into a designated database;
acquiring a plurality of pieces of designated device data with the same time stamp in the designated database;
based on the plurality of pieces of designated equipment data, determining equipment relativity of designated internet of things equipment corresponding to the plurality of pieces of designated equipment data respectively so as to determine whether the plurality of pieces of designated internet of things equipment are applied to the same application scene;
and according to the device correlation of the plurality of appointed devices, carrying out data analysis on the plurality of appointed device data so as to realize device monitoring of the appointed devices.
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