CN111667015B - Method and device for detecting state of equipment of Internet of things and detection equipment - Google Patents
Method and device for detecting state of equipment of Internet of things and detection equipment Download PDFInfo
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Abstract
The invention discloses a method, a device and equipment for detecting the state of equipment of the Internet of things. In the method, a first running state track of first Internet of things equipment and a second running state track of second Internet of things equipment are firstly determined, a state association list between the first Internet of things equipment and the second Internet of things equipment is secondly generated based on the first running state track and the second running state track, then state parameters under an independent node parameter set and under an associated node parameter set of the first Internet of things equipment are migrated when abnormal records exist in equipment running records of the second Internet of things equipment are detected, and further states of the first Internet of things equipment are detected according to parameter index information corresponding to the calculated independent node parameter set. Therefore, the accuracy of state detection of the first Internet of things equipment can be improved, and missed judgment and misjudgment are avoided.
Description
Technical Field
The disclosure relates to the technical field of equipment detection, in particular to an equipment state detection method, an equipment state detection device and detection equipment for the internet of things.
Background
Along with the development of technology, the application of the internet of things equipment is more and more widespread. Taking intelligent home as an example, the intelligent home system formed by the internet of things equipment clusters can provide comfortable and convenient home life for users. However, in the smart home system, the types of the internet of things devices are often complex, and if the operation state of one of the internet of things devices fails, the safe and stable operation of the whole internet of things device may be affected. Therefore, the running state of the internet of things equipment needs to be detected in real time to determine whether the internet of things equipment is in a fault state. However, in the prior art, the accuracy of detecting the state of the internet of things equipment is poor, and missed judgment and misjudgment often occur.
Disclosure of Invention
In order to improve the technical problems in the related art, the disclosure provides a method, a device and a device for detecting the state of equipment of the internet of things.
An internet of things device state detection method, the method comprising:
generating a first running state track corresponding to first Internet of things equipment and a second running state track corresponding to second Internet of things equipment aiming at the first Internet of things equipment and the second Internet of things equipment with communication frequency larger than the set frequency; wherein the first running state track and the second running state track respectively comprise a plurality of state nodes with different track identifiers;
Determining an equipment attribute label of the first Internet of things equipment at any state node of the first running state track, and determining a state node with the minimum track identification degree in the second running state track as a first node; mapping the equipment attribute tag into the first node based on the difference value of longitude and latitude coordinates of the first internet of things equipment and the second internet of things equipment to obtain a target attribute tag in the first node, and generating a state association list between the first internet of things equipment and the second internet of things equipment according to a tag matching coefficient between the equipment attribute tag and the target attribute tag;
when detecting that an abnormal record exists in the equipment operation record of the second internet of things equipment, acquiring an abnormal information list of the abnormal record and each group of state parameters of the first internet of things equipment in a period corresponding to the abnormal record; determining the association degree between each state parameter of the first Internet of things equipment under the associated node parameter set and each state parameter of the first Internet of things equipment under the independent node parameter set according to the state parameter of the first Internet of things equipment under the independent node parameter set and the parameter weight thereof under the condition that the first Internet of things equipment contains the independent node parameter set according to the abnormal information list and the state association list, and migrating the state parameter of the first Internet of things equipment under the associated node parameter set and associated with the state parameter under the independent node parameter set to the independent node parameter set;
And determining the parameter centrality of each group of state parameters under the independent node parameter set, calculating parameter index information corresponding to the independent node parameter set according to the parameter centrality, and judging that the first Internet of things equipment has abnormal state risk when the parameter index information does not meet a set condition.
Optionally, the method further comprises:
under the condition that the associated node parameter set corresponding to the first Internet of things equipment contains a plurality of state parameters, determining the association degree of the first Internet of things equipment among all the state parameters of the first Internet of things equipment under the associated node parameter set according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights thereof;
screening all the state parameters in the associated node parameter set according to the association degree among all the state parameters of the associated node parameter set corresponding to the first Internet of things equipment to obtain a plurality of groups of target state parameters;
and distributing migration grades for each group of target state parameters obtained through screening according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights of the state parameters, and migrating target state parameters with the set number to the independent node parameter set according to the sequence of the migration grades from large to small.
Optionally, determining the parameter centrality of each set of state parameters under the independent node parameter set includes:
acquiring parameter labels of each group of state parameters, listing label attributes of the parameter labels, and establishing a label similarity distribution map of each group of state parameters; the label similarity distribution map is an atlas of the sub-areas, the atlas of each area corresponds to an area identifier, each area identifier has at least one label attribute, and each area of the label similarity distribution map has an incidence relation from small to large;
determining parameter characteristic information of each group of state parameters, extracting a mapping path of the parameter characteristic information corresponding to each group of state parameters from a tag similarity distribution diagram of each group of state parameters based on the parameter characteristic information, and searching the characteristic recognition degree of the parameter characteristic information in the tag similarity distribution diagram according to the mapping path;
establishing a reliability query list between the parameter labels and the label similarity distribution map of each group of state parameters according to the characteristic recognition degree, and generating a characteristic clustering model of each group of state parameters according to the reliability query list; generating a feature cluster model according to the credibility query list, wherein the feature cluster model comprises the following steps: converting each parameter label into a corresponding character code; generating at least one first coding sequence of each string of character codes respectively; acquiring first coding sequences which are not repeated mutually of the parameter labels to form a second coding sequence; mapping each first coding sequence in the second coding sequence into a tag similarity distribution map of each group of state parameters to form a feature clustering model;
Performing traversal matching on the tag attributes contained in the parameter tags of each group of state parameters and the target tag attributes in the feature cluster model; if all the tag attributes of a first coding sequence are matched with the corresponding target tag attributes in the feature cluster model, determining the parameter tag corresponding to the first coding sequence as a trusted tag;
and carrying out associated parameter extraction on each group of state parameters based on the trusted label to obtain an associated parameter set, clustering the associated parameters in the associated parameter set by adopting a multidimensional feature clustering method to obtain a plurality of target clusters, and calculating the parameter centrality of each group of state parameters according to the number of the target clusters.
Optionally, calculating parameter index information corresponding to the independent node parameter set according to the parameter centrality includes:
sequencing the parameter centroids according to the order from big to small of the parameter centroids to obtain a centroids sequencing sequence, and determining the target parameter centroids from the centroids sequencing sequence based on parameter structure information corresponding to the independent node parameter sets; determining a first sequence weight in the centrality of the target parameter;
Judging whether the difference value between the first sequence weight in the target parameter centrality and the second sequence weight of the previous parameter centrality of the target parameter centrality is smaller than a set threshold value;
if so, determining the determined first sequence weight of the target parameter centrality as the first current sequence weight of the target parameter centrality; otherwise, carrying out weighted summation on the determined first sequence weight of the target parameter centrality and the second current sequence weight of the previous parameter centrality, and determining the weight obtained by the weighted summation as the first current sequence weight of the target parameter centrality; and extracting the dimension value of each state parameter in the independent node parameter set based on the first current sequence weight, and calculating parameter index information corresponding to the independent node parameter set according to the extracted dimension value.
An internet of things device state detection apparatus, the apparatus comprising:
the track generation module is used for generating a first running state track corresponding to the first Internet of things equipment and a second running state track corresponding to the second Internet of things equipment aiming at the first Internet of things equipment and the second Internet of things equipment with the communication frequency larger than the set frequency; wherein the first running state track and the second running state track respectively comprise a plurality of state nodes with different track identifiers;
The list determining module is used for determining an equipment attribute label of any state node of the first running state track of the first Internet of things equipment, and determining a state node with the minimum track identification degree in the second running state track as a first node; mapping the equipment attribute tag into the first node based on the difference value of longitude and latitude coordinates of the first internet of things equipment and the second internet of things equipment to obtain a target attribute tag in the first node, and generating a state association list between the first internet of things equipment and the second internet of things equipment according to a tag matching coefficient between the equipment attribute tag and the target attribute tag;
the parameter migration module is used for acquiring an abnormal information list of the abnormal record and each group of state parameters of the first Internet of things equipment in a period corresponding to the abnormal record when detecting that the abnormal record exists in the equipment operation record of the second Internet of things equipment; determining the association degree between each state parameter of the first Internet of things equipment under the associated node parameter set and each state parameter of the first Internet of things equipment under the independent node parameter set according to the state parameter of the first Internet of things equipment under the independent node parameter set and the parameter weight thereof under the condition that the first Internet of things equipment contains the independent node parameter set according to the abnormal information list and the state association list, and migrating the state parameter of the first Internet of things equipment under the associated node parameter set and associated with the state parameter under the independent node parameter set to the independent node parameter set;
The state detection module is used for determining the parameter centrality of each group of state parameters under the independent node parameter set, calculating parameter index information corresponding to the independent node parameter set according to the parameter centrality, and judging that the first Internet of things equipment has state abnormality risks when the parameter index information does not meet the set conditions.
Optionally, the parameter migration module is further configured to:
under the condition that the associated node parameter set corresponding to the first Internet of things equipment contains a plurality of state parameters, determining the association degree of the first Internet of things equipment among all the state parameters of the first Internet of things equipment under the associated node parameter set according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights thereof;
screening all the state parameters in the associated node parameter set according to the association degree among all the state parameters of the associated node parameter set corresponding to the first Internet of things equipment to obtain a plurality of groups of target state parameters;
and distributing migration grades for each group of target state parameters obtained through screening according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights of the state parameters, and migrating target state parameters with the set number to the independent node parameter set according to the sequence of the migration grades from large to small.
Optionally, the state detection module is specifically configured to:
acquiring parameter labels of each group of state parameters, listing label attributes of the parameter labels, and establishing a label similarity distribution map of each group of state parameters; the label similarity distribution map is an atlas of the sub-areas, the atlas of each area corresponds to an area identifier, each area identifier has at least one label attribute, and each area of the label similarity distribution map has an incidence relation from small to large;
determining parameter characteristic information of each group of state parameters, extracting a mapping path of the parameter characteristic information corresponding to each group of state parameters from a tag similarity distribution diagram of each group of state parameters based on the parameter characteristic information, and searching the characteristic recognition degree of the parameter characteristic information in the tag similarity distribution diagram according to the mapping path;
establishing a reliability query list between the parameter labels and the label similarity distribution map of each group of state parameters according to the characteristic recognition degree, and generating a characteristic clustering model of each group of state parameters according to the reliability query list; generating a feature cluster model according to the credibility query list, wherein the feature cluster model comprises the following steps: converting each parameter label into a corresponding character code; generating at least one first coding sequence of each string of character codes respectively; acquiring first coding sequences which are not repeated mutually of the parameter labels to form a second coding sequence; mapping each first coding sequence in the second coding sequence into a tag similarity distribution map of each group of state parameters to form a feature clustering model;
Performing traversal matching on the tag attributes contained in the parameter tags of each group of state parameters and the target tag attributes in the feature cluster model; if all the tag attributes of a first coding sequence are matched with the corresponding target tag attributes in the feature cluster model, determining the parameter tag corresponding to the first coding sequence as a trusted tag;
and carrying out associated parameter extraction on each group of state parameters based on the trusted label to obtain an associated parameter set, clustering the associated parameters in the associated parameter set by adopting a multidimensional feature clustering method to obtain a plurality of target clusters, and calculating the parameter centrality of each group of state parameters according to the number of the target clusters.
Optionally, the state detection module is specifically configured to:
sequencing the parameter centroids according to the order from big to small of the parameter centroids to obtain a centroids sequencing sequence, and determining the target parameter centroids from the centroids sequencing sequence based on parameter structure information corresponding to the independent node parameter sets; determining a first sequence weight in the centrality of the target parameter;
judging whether the difference value between the first sequence weight in the target parameter centrality and the second sequence weight of the previous parameter centrality of the target parameter centrality is smaller than a set threshold value;
If so, determining the determined first sequence weight of the target parameter centrality as the first current sequence weight of the target parameter centrality; otherwise, carrying out weighted summation on the determined first sequence weight of the target parameter centrality and the second current sequence weight of the previous parameter centrality, and determining the weight obtained by the weighted summation as the first current sequence weight of the target parameter centrality; and extracting the dimension value of each state parameter in the independent node parameter set based on the first current sequence weight, and calculating parameter index information corresponding to the independent node parameter set according to the extracted dimension value.
A detection device comprising a processor and a memory in communication with each other, the processor implementing the method by retrieving a computer program from the memory and executing the computer program.
A computer readable storage medium having stored thereon a computer program which, when run, implements the method described above.
The technical scheme provided by the embodiment of the disclosure can include the following beneficial effects.
Firstly, determining a first running state track of first Internet of things equipment and a second running state track of second Internet of things equipment, secondly, generating a state association list between the first Internet of things equipment and the second Internet of things equipment based on the first running state track and the second running state track, and then migrating state parameters under an independent node parameter set and under an associated node parameter set of the first Internet of things equipment when abnormal records exist in equipment running records of the second Internet of things equipment are detected, and further detecting the state of the first Internet of things equipment according to parameter index information corresponding to the calculated independent node parameter set. Therefore, the accuracy of state detection of the first Internet of things equipment can be improved, and missed judgment and misjudgment are avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic diagram of a communication architecture of an internet of things device state detection system according to the present embodiment.
Fig. 2 is a flow chart of a method for detecting an equipment state of the internet of things according to the embodiment.
Fig. 3 is a functional block diagram of an apparatus for detecting a state of an internet of things device according to this embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
Fig. 1 is a schematic diagram of a communication architecture of an internet of things device state detection system 100 according to the present disclosure, where the internet of things device state detection system 100 includes a detection device 200 and a plurality of internet of things devices 300. Wherein the detection device 200 and the internet of things device 300 communicate with each other. In this embodiment, the detection device 200 may be a cloud control center, and the internet of things device 300 may be an intelligent home.
Referring to fig. 2 in combination, a flowchart of a method for detecting a state of an internet of things device is provided, where the method may be applied to the detecting device 200 in fig. 1, and may specifically include the following steps.
Step S21, generating a first running state track corresponding to first Internet of things equipment and a second running state track corresponding to second Internet of things equipment aiming at the first Internet of things equipment and the second Internet of things equipment with communication frequency larger than the set frequency; the first running state track and the second running state track respectively comprise a plurality of state nodes with different track identifiers.
Step S22, determining an equipment attribute label of the first Internet of things equipment at any state node of the first running state track, and determining a state node with the minimum track identification degree in the second running state track as a first node; and mapping the equipment attribute tag into the first node based on the difference value of longitude and latitude coordinates of the first Internet of things equipment and the second Internet of things equipment to obtain a target attribute tag in the first node, and generating a state association list between the first Internet of things equipment and the second Internet of things equipment according to a tag matching coefficient between the equipment attribute tag and the target attribute tag.
Step S23, when an abnormal record exists in the equipment operation record of the second Internet of things equipment, acquiring an abnormal information list of the abnormal record and each group of state parameters of the first Internet of things equipment in a period corresponding to the abnormal record; under the condition that the first Internet of things equipment comprises an independent node parameter set according to the abnormal information list and the state association list, determining association degrees between each state parameter of the first Internet of things equipment under the associated node parameter set and each state parameter of the first Internet of things equipment under the independent node parameter set according to the state parameter of the first Internet of things equipment under the independent node parameter set and the parameter weight of the state parameter of the first Internet of things equipment under the independent node parameter set, and transferring the state parameter of the first Internet of things equipment under the associated node parameter set and associated with the state parameter under the independent node parameter set to the independent node parameter set.
Step S24, determining the parameter centrality of each group of state parameters under the independent node parameter set, calculating parameter index information corresponding to the independent node parameter set according to the parameter centrality, and judging that the first Internet of things equipment has abnormal state risk when the parameter index information does not meet a set condition.
It can be understood that, through the descriptions in the above steps S21 to S24, the first running state track of the first internet of things device and the second running state track of the second internet of things device are firstly determined, then the state association list between the first internet of things device and the second internet of device is generated based on the first running state track and the second running state track, then when the abnormal record exists in the device running record of the second internet of device, the state parameters under the independent node parameter set and under the associated node parameter set of the first internet of things device are migrated, and further the state of the first internet of things device is detected according to the calculated parameter index information corresponding to the independent node parameter set. Therefore, the accuracy of state detection of the first Internet of things equipment can be improved, and missed judgment and misjudgment are avoided.
In one possible implementation manner, in order to ensure the accuracy of the migration of the state parameter, the method may further include, on the basis of step S23, the following sub-steps:
under the condition that the associated node parameter set corresponding to the first Internet of things equipment contains a plurality of state parameters, determining the association degree of the first Internet of things equipment among all the state parameters of the first Internet of things equipment under the associated node parameter set according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights thereof;
Screening all the state parameters in the associated node parameter set according to the association degree among all the state parameters of the associated node parameter set corresponding to the first Internet of things equipment to obtain a plurality of groups of target state parameters;
and distributing migration grades for each group of target state parameters obtained through screening according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights of the state parameters, and migrating target state parameters with the set number to the independent node parameter set according to the sequence of the migration grades from large to small.
In the implementation, the accuracy of the migration of the state parameters can be ensured through the descriptions of the sub-steps.
In practical application, the inventor finds that the parameter characteristics of the state parameters are easy to ignore when the parameter centrality is determined, so that the reliability of the determined parameter centrality is low, and the accuracy of the state detection of the equipment of the Internet of things is difficult to ensure. To improve the above problem, in step S24, the parameter centrality of each group of state parameters under the independent node parameter set is determined, which may specifically include what is described in the following steps S241 to S245.
Step S241, obtaining parameter labels of each group of state parameters, listing label attributes of the parameter labels, and establishing a label similarity distribution diagram of each group of state parameters; the label similarity distribution map is an atlas of the sub-areas, the atlas of each area corresponds to an area identifier, each area identifier has at least one label attribute, and each area of the label similarity distribution map has an association relationship from small to large.
Step S242, determining parameter feature information of each set of state parameters, extracting a mapping path of the parameter feature information corresponding to each set of state parameters from the tag similarity distribution diagram of each set of state parameters based on the parameter feature information, and finding out feature recognition degree of the parameter feature information in the tag similarity distribution diagram according to the mapping path.
Step S243, a reliability inquiry list between the parameter labels and the label similarity distribution diagram of each group of state parameters is established according to the characteristic recognition degree, and a characteristic clustering model of each group of state parameters is generated according to the reliability inquiry list; generating a feature cluster model according to the credibility query list, wherein the feature cluster model comprises the following steps: converting each parameter label into a corresponding character code; generating at least one first coding sequence of each string of character codes respectively; acquiring first coding sequences which are not repeated mutually of the parameter labels to form a second coding sequence; and mapping each first coding sequence in the second coding sequence into a tag similarity distribution map of each group of state parameters to form a characteristic clustering model.
Step S244, performing traversal matching on the tag attributes contained in the parameter tags of each group of state parameters and the target tag attributes in the feature cluster model; and if all the tag attributes of one first coding sequence are matched with the corresponding target tag attributes in the feature cluster model, determining the parameter tag corresponding to the first coding sequence as a trusted tag.
Step S245, carrying out associated parameter extraction on each group of state parameters based on the trusted label to obtain an associated parameter set, clustering the associated parameters in the associated parameter set by adopting a multidimensional feature clustering method to obtain a plurality of target clusters, and calculating the parameter centrality of each group of state parameters according to the number of the target clusters.
In specific implementation, by executing the steps S241-S245, the parameter feature analysis of the state parameter can be performed, so that the clustering number of the associated parameter set corresponding to the state parameter is determined based on the multidimensional feature clustering method, so that the reliability of the determined parameter centrality can be ensured, and the accuracy of the state detection of the internet of things equipment is ensured.
In one possible implementation manner, in order to ensure the integrity of the parameter index information, in step S24, the parameter index information corresponding to the independent node parameter set is calculated according to the parameter centrality, which may specifically include what is described in the following steps a-d.
Step a, sorting the parameter centroids according to the order from the big to the small of the parameter centroids to obtain a centroids sorting sequence, and determining the target parameter centroids from the centroids sorting sequence based on the parameter structure information corresponding to the independent node parameter sets; and determining a first sequence weight in the centrality of the target parameter.
And b, judging whether the difference value between the first sequence weight in the target parameter centrality and the second sequence weight of the parameter centrality before the target parameter centrality is smaller than a set threshold value.
C, if so, determining the determined first sequence weight of the target parameter centrality as the first current sequence weight of the target parameter centrality; otherwise, carrying out weighted summation on the determined first sequence weight of the target parameter centrality and the second current sequence weight of the previous parameter centrality, and determining the weight obtained by the weighted summation as the first current sequence weight of the target parameter centrality; and extracting the dimension value of each state parameter in the independent node parameter set based on the first current sequence weight, and calculating parameter index information corresponding to the independent node parameter set according to the extracted dimension value.
It will be appreciated that the parameter index information can be determined entirely based on the above steps a-c.
On the basis of the above, whether the parameter index information satisfies the set condition may be determined by: and determining a conversion factor of the parameter index information according to the number of the state parameters in the independent node parameter set, converting the parameter index information through the conversion factor to obtain a risk coefficient corresponding to the parameter index information, and judging that the parameter index information does not meet the set condition when the risk coefficient is larger than the set coefficient.
On the basis of the above, please refer to fig. 3 in combination, there is provided an apparatus 400 for detecting a state of an internet of things device, including the following functional modules:
the track generation module 410 is configured to generate, for a first internet of things device and a second internet of things device with communication frequencies greater than a set frequency, a first running state track corresponding to the first internet of things device and a second running state track corresponding to the second internet of things device; wherein the first running state track and the second running state track respectively comprise a plurality of state nodes with different track identifiers;
A list determining module 420, configured to determine an equipment attribute tag of the first internet of things equipment at any state node of the first running state track, and determine a state node with a minimum track identifier in the second running state track as a first node; mapping the equipment attribute tag into the first node based on the difference value of longitude and latitude coordinates of the first internet of things equipment and the second internet of things equipment to obtain a target attribute tag in the first node, and generating a state association list between the first internet of things equipment and the second internet of things equipment according to a tag matching coefficient between the equipment attribute tag and the target attribute tag;
the parameter migration module 430 is configured to, when detecting that an abnormal record exists in the device operation record of the second internet of things device, obtain an abnormal information list of the abnormal record and each set of state parameters of the first internet of things device in a period corresponding to the abnormal record; determining the association degree between each state parameter of the first Internet of things equipment under the associated node parameter set and each state parameter of the first Internet of things equipment under the independent node parameter set according to the state parameter of the first Internet of things equipment under the independent node parameter set and the parameter weight thereof under the condition that the first Internet of things equipment contains the independent node parameter set according to the abnormal information list and the state association list, and migrating the state parameter of the first Internet of things equipment under the associated node parameter set and associated with the state parameter under the independent node parameter set to the independent node parameter set;
The state detection module 440 is configured to determine a parameter centrality of each group of state parameters in the independent node parameter set, calculate parameter index information corresponding to the independent node parameter set according to the parameter centrality, and determine that the first internet of things device has a state abnormality risk when the parameter index information does not satisfy a set condition.
Optionally, the parameter migration module 430 is further configured to:
under the condition that the associated node parameter set corresponding to the first Internet of things equipment contains a plurality of state parameters, determining the association degree of the first Internet of things equipment among all the state parameters of the first Internet of things equipment under the associated node parameter set according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights thereof;
screening all the state parameters in the associated node parameter set according to the association degree among all the state parameters of the associated node parameter set corresponding to the first Internet of things equipment to obtain a plurality of groups of target state parameters;
and distributing migration grades for each group of target state parameters obtained through screening according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights of the state parameters, and migrating target state parameters with the set number to the independent node parameter set according to the sequence of the migration grades from large to small.
Optionally, the state detection module 440 is specifically configured to:
acquiring parameter labels of each group of state parameters, listing label attributes of the parameter labels, and establishing a label similarity distribution map of each group of state parameters; the label similarity distribution map is an atlas of the sub-areas, the atlas of each area corresponds to an area identifier, each area identifier has at least one label attribute, and each area of the label similarity distribution map has an incidence relation from small to large;
determining parameter characteristic information of each group of state parameters, extracting a mapping path of the parameter characteristic information corresponding to each group of state parameters from a tag similarity distribution diagram of each group of state parameters based on the parameter characteristic information, and searching the characteristic recognition degree of the parameter characteristic information in the tag similarity distribution diagram according to the mapping path;
establishing a reliability query list between the parameter labels and the label similarity distribution map of each group of state parameters according to the characteristic recognition degree, and generating a characteristic clustering model of each group of state parameters according to the reliability query list; generating a feature cluster model according to the credibility query list, wherein the feature cluster model comprises the following steps: converting each parameter label into a corresponding character code; generating at least one first coding sequence of each string of character codes respectively; acquiring first coding sequences which are not repeated mutually of the parameter labels to form a second coding sequence; mapping each first coding sequence in the second coding sequence into a tag similarity distribution map of each group of state parameters to form a feature clustering model;
Performing traversal matching on the tag attributes contained in the parameter tags of each group of state parameters and the target tag attributes in the feature cluster model; if all the tag attributes of a first coding sequence are matched with the corresponding target tag attributes in the feature cluster model, determining the parameter tag corresponding to the first coding sequence as a trusted tag;
and carrying out associated parameter extraction on each group of state parameters based on the trusted label to obtain an associated parameter set, clustering the associated parameters in the associated parameter set by adopting a multidimensional feature clustering method to obtain a plurality of target clusters, and calculating the parameter centrality of each group of state parameters according to the number of the target clusters.
Optionally, the state detection module 440 is specifically configured to:
sequencing the parameter centroids according to the order from big to small of the parameter centroids to obtain a centroids sequencing sequence, and determining the target parameter centroids from the centroids sequencing sequence based on parameter structure information corresponding to the independent node parameter sets; determining a first sequence weight in the centrality of the target parameter;
judging whether the difference value between the first sequence weight in the target parameter centrality and the second sequence weight of the previous parameter centrality of the target parameter centrality is smaller than a set threshold value;
If so, determining the determined first sequence weight of the target parameter centrality as the first current sequence weight of the target parameter centrality; otherwise, carrying out weighted summation on the determined first sequence weight of the target parameter centrality and the second current sequence weight of the previous parameter centrality, and determining the weight obtained by the weighted summation as the first current sequence weight of the target parameter centrality; and extracting the dimension value of each state parameter in the independent node parameter set based on the first current sequence weight, and calculating parameter index information corresponding to the independent node parameter set according to the extracted dimension value.
For a description of the above functional modules, reference is made to the description of the method shown in fig. 2, which is not further described herein.
On the basis of the above, there is also provided a detection device comprising a processor and a memory in communication with each other, the processor implementing the above method by retrieving a computer program from the memory and executing the computer program.
Further, a computer readable storage medium is provided, on which a computer program is stored, which computer program, when run, implements the above-mentioned method.
In summary, when the technical scheme provided by the application is applied, a first running state track of the first internet of things device and a second running state track of the second internet of things device are firstly determined, then a state association list between the first internet of things device and the second internet of things device is generated based on the first running state track and the second running state track, then state parameters under an independent node parameter set and under an associated node parameter set of the first internet of things device are migrated when abnormal records exist in device running records of the second internet of device are detected, and further states of the first internet of things device are detected according to parameter index information corresponding to the calculated independent node parameter set. Therefore, the accuracy of state detection of the first Internet of things equipment can be improved, and missed judgment and misjudgment are avoided.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. The method for detecting the state of the equipment of the Internet of things is characterized by comprising the following steps:
Generating a first running state track corresponding to first Internet of things equipment and a second running state track corresponding to second Internet of things equipment aiming at the first Internet of things equipment and the second Internet of things equipment with communication frequency larger than the set frequency; wherein the first running state track and the second running state track respectively comprise a plurality of state nodes with different track identifiers;
determining an equipment attribute label of the first Internet of things equipment at any state node of the first running state track, and determining a state node with the minimum track identification degree in the second running state track as a first node; mapping the equipment attribute tag into the first node based on the difference value of longitude and latitude coordinates of the first internet of things equipment and the second internet of things equipment to obtain a target attribute tag in the first node, and generating a state association list between the first internet of things equipment and the second internet of things equipment according to a tag matching coefficient between the equipment attribute tag and the target attribute tag;
when detecting that an abnormal record exists in the equipment operation record of the second internet of things equipment, acquiring an abnormal information list of the abnormal record and each group of state parameters of the first internet of things equipment in a period corresponding to the abnormal record; determining the association degree between each state parameter of the first Internet of things equipment under the associated node parameter set and each state parameter of the first Internet of things equipment under the independent node parameter set according to the state parameter of the first Internet of things equipment under the independent node parameter set and the parameter weight thereof under the condition that the first Internet of things equipment contains the independent node parameter set according to the abnormal information list and the state association list, and migrating the state parameter of the first Internet of things equipment under the associated node parameter set and associated with the state parameter under the independent node parameter set to the independent node parameter set;
And determining the parameter centrality of each group of state parameters under the independent node parameter set, calculating parameter index information corresponding to the independent node parameter set according to the parameter centrality, and judging that the first Internet of things equipment has abnormal state risk when the parameter index information does not meet a set condition.
2. The method according to claim 1, wherein the method further comprises:
under the condition that the associated node parameter set corresponding to the first Internet of things equipment contains a plurality of state parameters, determining the association degree of the first Internet of things equipment among all the state parameters of the first Internet of things equipment under the associated node parameter set according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights thereof;
screening all the state parameters in the associated node parameter set according to the association degree among all the state parameters of the associated node parameter set corresponding to the first Internet of things equipment to obtain a plurality of groups of target state parameters;
and distributing migration grades for each group of target state parameters obtained through screening according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights of the state parameters, and migrating target state parameters with the set number to the independent node parameter set according to the sequence of the migration grades from large to small.
3. The method according to claim 1 or 2, wherein determining the parameter centrality of each set of state parameters under the independent node parameter set comprises:
acquiring parameter labels of each group of state parameters, listing label attributes of the parameter labels, and establishing a label similarity distribution map of each group of state parameters; the label similarity distribution map is an atlas of the sub-areas, the atlas of each area corresponds to an area identifier, each area identifier has at least one label attribute, and each area of the label similarity distribution map has an incidence relation from small to large;
determining parameter characteristic information of each group of state parameters, extracting a mapping path of the parameter characteristic information corresponding to each group of state parameters from a tag similarity distribution diagram of each group of state parameters based on the parameter characteristic information, and searching the characteristic recognition degree of the parameter characteristic information in the tag similarity distribution diagram according to the mapping path;
establishing a reliability query list between the parameter labels and the label similarity distribution map of each group of state parameters according to the characteristic recognition degree, and generating a characteristic clustering model of each group of state parameters according to the reliability query list; generating a feature cluster model according to the credibility query list, wherein the feature cluster model comprises the following steps: converting each parameter label into a corresponding character code; generating at least one first coding sequence of each string of character codes respectively; acquiring first coding sequences which are not repeated mutually of the parameter labels to form a second coding sequence; mapping each first coding sequence in the second coding sequence into a tag similarity distribution map of each group of state parameters to form a feature clustering model;
Performing traversal matching on the tag attributes contained in the parameter tags of each group of state parameters and the target tag attributes in the feature cluster model; if all the tag attributes of a first coding sequence are matched with the corresponding target tag attributes in the feature cluster model, determining the parameter tag corresponding to the first coding sequence as a trusted tag;
and carrying out associated parameter extraction on each group of state parameters based on the trusted label to obtain an associated parameter set, clustering the associated parameters in the associated parameter set by adopting a multidimensional feature clustering method to obtain a plurality of target clusters, and calculating the parameter centrality of each group of state parameters according to the number of the target clusters.
4. The method of claim 1, wherein calculating parameter index information corresponding to the independent node parameter set according to the parameter centrality comprises:
sequencing the parameter centroids according to the order from big to small of the parameter centroids to obtain a centroids sequencing sequence, and determining the target parameter centroids from the centroids sequencing sequence based on parameter structure information corresponding to the independent node parameter sets; determining a first sequence weight in the centrality of the target parameter;
Judging whether the difference value between the first sequence weight in the target parameter centrality and the second sequence weight of the previous parameter centrality of the target parameter centrality is smaller than a set threshold value;
if so, determining the determined first sequence weight of the target parameter centrality as the first current sequence weight of the target parameter centrality; otherwise, carrying out weighted summation on the determined first sequence weight of the target parameter centrality and the second current sequence weight of the previous parameter centrality, and determining the weight obtained by the weighted summation as the first current sequence weight of the target parameter centrality; and extracting the dimension value of each state parameter in the independent node parameter set based on the first current sequence weight, and calculating parameter index information corresponding to the independent node parameter set according to the extracted dimension value.
5. An internet of things device state detection apparatus, the apparatus comprising:
the track generation module is used for generating a first running state track corresponding to the first Internet of things equipment and a second running state track corresponding to the second Internet of things equipment aiming at the first Internet of things equipment and the second Internet of things equipment with the communication frequency larger than the set frequency; wherein the first running state track and the second running state track respectively comprise a plurality of state nodes with different track identifiers;
The list determining module is used for determining an equipment attribute label of any state node of the first running state track of the first Internet of things equipment, and determining a state node with the minimum track identification degree in the second running state track as a first node; mapping the equipment attribute tag into the first node based on the difference value of longitude and latitude coordinates of the first internet of things equipment and the second internet of things equipment to obtain a target attribute tag in the first node, and generating a state association list between the first internet of things equipment and the second internet of things equipment according to a tag matching coefficient between the equipment attribute tag and the target attribute tag;
the parameter migration module is used for acquiring an abnormal information list of the abnormal record and each group of state parameters of the first Internet of things equipment in a period corresponding to the abnormal record when detecting that the abnormal record exists in the equipment operation record of the second Internet of things equipment; determining the association degree between each state parameter of the first Internet of things equipment under the associated node parameter set and each state parameter of the first Internet of things equipment under the independent node parameter set according to the state parameter of the first Internet of things equipment under the independent node parameter set and the parameter weight thereof under the condition that the first Internet of things equipment contains the independent node parameter set according to the abnormal information list and the state association list, and migrating the state parameter of the first Internet of things equipment under the associated node parameter set and associated with the state parameter under the independent node parameter set to the independent node parameter set;
The state detection module is used for determining the parameter centrality of each group of state parameters under the independent node parameter set, calculating parameter index information corresponding to the independent node parameter set according to the parameter centrality, and judging that the first Internet of things equipment has state abnormality risks when the parameter index information does not meet the set conditions.
6. The apparatus of claim 5, wherein the parameter migration module is further configured to:
under the condition that the associated node parameter set corresponding to the first Internet of things equipment contains a plurality of state parameters, determining the association degree of the first Internet of things equipment among all the state parameters of the first Internet of things equipment under the associated node parameter set according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights thereof;
screening all the state parameters in the associated node parameter set according to the association degree among all the state parameters of the associated node parameter set corresponding to the first Internet of things equipment to obtain a plurality of groups of target state parameters;
and distributing migration grades for each group of target state parameters obtained through screening according to the state parameters of the first Internet of things equipment under the independent node parameter set and the parameter weights of the state parameters, and migrating target state parameters with the set number to the independent node parameter set according to the sequence of the migration grades from large to small.
7. The apparatus according to claim 5 or 6, wherein the status detection module is specifically configured to:
acquiring parameter labels of each group of state parameters, listing label attributes of the parameter labels, and establishing a label similarity distribution map of each group of state parameters; the label similarity distribution map is an atlas of the sub-areas, the atlas of each area corresponds to an area identifier, each area identifier has at least one label attribute, and each area of the label similarity distribution map has an incidence relation from small to large;
determining parameter characteristic information of each group of state parameters, extracting a mapping path of the parameter characteristic information corresponding to each group of state parameters from a tag similarity distribution diagram of each group of state parameters based on the parameter characteristic information, and searching the characteristic recognition degree of the parameter characteristic information in the tag similarity distribution diagram according to the mapping path;
establishing a reliability query list between the parameter labels and the label similarity distribution map of each group of state parameters according to the characteristic recognition degree, and generating a characteristic clustering model of each group of state parameters according to the reliability query list; generating a feature cluster model according to the credibility query list, wherein the feature cluster model comprises the following steps: converting each parameter label into a corresponding character code; generating at least one first coding sequence of each string of character codes respectively; acquiring first coding sequences which are not repeated mutually of the parameter labels to form a second coding sequence; mapping each first coding sequence in the second coding sequence into a tag similarity distribution map of each group of state parameters to form a feature clustering model;
Performing traversal matching on the tag attributes contained in the parameter tags of each group of state parameters and the target tag attributes in the feature cluster model; if all the tag attributes of a first coding sequence are matched with the corresponding target tag attributes in the feature cluster model, determining the parameter tag corresponding to the first coding sequence as a trusted tag;
and carrying out associated parameter extraction on each group of state parameters based on the trusted label to obtain an associated parameter set, clustering the associated parameters in the associated parameter set by adopting a multidimensional feature clustering method to obtain a plurality of target clusters, and calculating the parameter centrality of each group of state parameters according to the number of the target clusters.
8. The apparatus of claim 5, wherein the status detection module is specifically configured to:
sequencing the parameter centroids according to the order from big to small of the parameter centroids to obtain a centroids sequencing sequence, and determining the target parameter centroids from the centroids sequencing sequence based on parameter structure information corresponding to the independent node parameter sets; determining a first sequence weight in the centrality of the target parameter;
Judging whether the difference value between the first sequence weight in the target parameter centrality and the second sequence weight of the previous parameter centrality of the target parameter centrality is smaller than a set threshold value;
if so, determining the determined first sequence weight of the target parameter centrality as the first current sequence weight of the target parameter centrality; otherwise, carrying out weighted summation on the determined first sequence weight of the target parameter centrality and the second current sequence weight of the previous parameter centrality, and determining the weight obtained by the weighted summation as the first current sequence weight of the target parameter centrality; and extracting the dimension value of each state parameter in the independent node parameter set based on the first current sequence weight, and calculating parameter index information corresponding to the independent node parameter set according to the extracted dimension value.
9. A detection device, characterized in that it comprises a processor and a memory in communication with each other, said processor implementing the method of any of the preceding claims 1-4 by retrieving a computer program from said memory and executing said computer program.
10. A computer readable storage medium, characterized in that it has stored thereon a computer program which, when run, implements the method of any of the preceding claims 1-4.
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