CN117609923B - Electronic information processing system and method for Internet of things - Google Patents
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Abstract
The invention discloses an electronic information processing method of the Internet of things, which particularly relates to the field of data information processing, and comprises the steps of firstly, comprehensively processing an energy consumption self-sustaining stable index and a data transmission variant index to create an equipment abnormal compound degree index, comprehensively evaluating the energy source self-sustaining and communication state of equipment, and identifying self-sustaining energy consumption and communication abnormality; secondly, main abnormal characteristics are extracted by adopting a principal component analysis technology, and abnormal conditions are automatically identified, so that the method not only depends on known fault codes and electricity consumption conditions; in addition, the solution is automatically matched, information related to the current problem is retrieved from a solution database, so that the problem processing speed is accelerated, a sorting estimated value is additionally introduced, the priority of the solution is clarified, the most urgent and challenging problems are ensured to be processed first, the problem solving efficiency and priority are improved, the degree of automation of the hidden danger monitoring processing of equipment is improved, the detection and processing of abnormal conditions are accelerated, and the reliability and performance of the Internet of things equipment are finally improved.
Description
Technical Field
The invention relates to the field of data information processing, in particular to an electronic information processing system and method of the Internet of things.
Background
An internet of things device (Internet of Things, ioT) is an intelligent device that connects and communicates over the internet for monitoring, controlling, and interacting with various physical objects and environments. These devices have sensing capabilities that capture environmental data and transmit the data over the internet to a central system for analysis and response. The internet of things device has the functions of realizing real-time monitoring, remote control, automatic decision making and the like, so that the production efficiency and the resource utilization efficiency are improved, and better user experience is provided.
In the existing internet of things equipment, some equipment starts to adopt an autonomous power supply technology, solar energy is utilized to supplement electric energy to execute a monitoring task, data of monitored objects are sent to the background through wireless signals, and then monitored electronic information is stored in a database to carry out analysis summary and decision. Therefore, the whole monitoring process is based on the Internet of things equipment, and the quality of the Internet of things equipment can directly influence the monitoring result and quality.
When the existing method is used for analyzing the vitamins of the Internet of things equipment, the existing method mainly depends on the communication fault code and the electricity consumption condition which are already generated, and has obvious defects. Firstly, the communication fault code can be identified only after the fault occurs, and comprehensive analysis and prediction of data under the daily use condition are lacking, which easily causes incomplete monitoring data caused by power failure and data transmission loss caused by power failure, and further causes the characteristic data of the monitored object to lose representativeness. Secondly, when the state occurs, the existing method does not consider matching based on the state and the historical solution database, so that the hidden danger elimination efficiency is low, hidden danger is aggravated, the integrity of the monitored data is not high, and the working quality is reduced. Therefore, there is a need for a more efficient method to more fully and accurately identify and handle abnormal situations before problems occur, improving the reliability and operating efficiency of the equipment.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, the embodiments of the present invention provide, first, to comprehensively process the energy consumption self-sustaining stable index and the data transfer mutation index to create an equipment abnormal compound degree index, and comprehensively evaluate the energy source self-sustaining and communication state of the equipment, thereby identifying self-sustaining energy consumption and communication abnormality; secondly, main abnormal characteristics are extracted by adopting a principal component analysis technology, and abnormal conditions are automatically identified, so that the method not only depends on known fault codes and electricity consumption conditions; in addition, the solution is automatically matched, information related to the current problem is retrieved from a solution database, so that the problem processing speed is accelerated, a sorting estimated value is additionally introduced, the priority of the solution is clarified, the most urgent and challenging problems are ensured to be processed first, the problem solving efficiency and priority are improved, the degree of automation of the hidden danger monitoring processing of equipment is improved, manual intervention is reduced, the detection and processing of abnormal conditions are accelerated, and finally the reliability and performance of the Internet of things equipment are improved, so that the problems in the background technology are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions: step S1, monitoring data including self state data and data of a monitored object are obtained from an internet of things device, energy consumption regulation information and transmission maintenance information of the self state data are extracted, an anomaly analysis model is built, and an anomaly compound index is generated;
step S2, further analyzing the abnormal compound index to obtain a normal signal and a trigger signal;
Step S3, after the trigger signal is obtained, main component analysis is carried out on the state data of the trigger signal, the similarity index is carried out on the main abnormal characteristic data and the historical characteristic data, and a matching signal and a disagreement signal are obtained;
S4, after the inconsistent signals are obtained, main abnormal characteristic data are recorded, the data of the monitored object are transferred to a recycling station, a sorting model is built based on the abnormal compound degree index and the similarity degree index, and a sorting estimated value is generated;
And S5, according to the data size of the sorting estimation value, sorting the main abnormal characteristic data recorded in the database again.
In a preferred embodiment, step S1 specifically includes the following:
Acquiring energy consumption regulation information and transmission maintenance information when the Internet of things equipment operates;
the energy consumption regulation information comprises an energy consumption self-sustaining stability index, and the transmission maintenance information comprises a data transmission mutation index.
In a preferred embodiment, the energy consumption self-sustaining stability index is obtained by the following steps:
step S1-11, collecting actual energy source holding time data of the Internet of things equipment in unit time, and simultaneously, calling historical energy source holding time data of an upper operation period, wherein the actual energy source holding time data and the historical energy source time number have the same time stamp so as to facilitate point-to-point comparison;
Step S1-12, fitting actual energy source sustaining data and historical energy source sustaining time data respectively by using a curve fitting technology to construct a first curve and a second curve;
S1-13, calculating a difference value between an observed value and a fitting value for each time point, wherein the observed value and the fitting value are obtained from a first curve and a second curve;
s1-14, calculating a difference value between an observed value and a fitting value for each data point;
step S1-15, for each time point, calculating a relative error between the observed value and the fitting value:
Wherein:
y i represents the observed value at the i-th time point;
Representing the fitting value of the ith time point;
step S1-16, for each time point, calculating the square of the relative error:
step S1-17, summing the square errors of all time points:
step S1-18, calculating an energy consumption self-sustaining stability index:
in a preferred embodiment, the data transfer mutation index is obtained by:
step S1-21, recording the starting time and the finishing time of each monitoring and the interruption time in each transmission, and establishing a time sequence comprising all monitoring and transmission time points;
Step S1-22, calculating the time interval between every two monitoring from the time sequence;
Step S1-23, for each transmission, calculating a time difference between the completion time and the start time;
Step S1-24, adding the interrupt time in all transmission;
Step S1-25, calculating a data transmission mutation index, wherein the expression is:
Wherein:
n is the number of monitoring times, which represents the number of monitoring times during the whole evaluation process;
I is an index of the number of monitoring times, from 1 to N;
M I is the number of transmissions each time monitored, representing the number of data transmissions performed in each time monitored;
j is an index of the number of transmissions, from 1 to N;
Break J is the interrupt time for the J-th transmission;
Transfer J is the time difference of the J-th transmission;
t I is the time interval between each monitoring.
In a preferred embodiment, comparing the abnormal equipment combination index with a trigger threshold, and generating a normal signal if the abnormal equipment combination index is greater than or equal to the trigger threshold; and if the equipment abnormal compound degree index is smaller than the trigger threshold value, generating a trigger signal.
In a preferred embodiment, step S3 specifically includes the following:
step S3-1, under the condition of acquiring a trigger signal, retrieving self-state data corresponding to the trigger signal, wherein the self-state data represents the current running condition of the Internet of things equipment;
s3-2, extracting main abnormal characteristic data from the state data by using a principal component analysis method;
e, normalizing the state data of the self to enable the state data to have zero mean and unit variance;
f, calculating a covariance matrix of the standardized data;
g, carrying out eigenvalue decomposition on the covariance matrix to obtain a main component and a corresponding eigenvalue;
h, comparing the characteristic value with a retention threshold value, and selecting to leave the characteristic value which is larger than or equal to the retention threshold value;
e, projecting the original data onto the selected main component to obtain main abnormal characteristic data;
S3-3, comparing the extracted main abnormal characteristic data with historical characteristic data in a database;
And S3-4, calculating a similarity index between each historical characteristic data and the main abnormal characteristic data by using cosine similarity as a similarity measure, wherein the similarity index is expressed as follows:
Wherein:
CS is a similarity index, A is an extracted main abnormal feature vector, B is a historical feature data vector, and A and B respectively represent corresponding norms;
Step S3-5, for the historical characteristic data with the similarity index being greater than or equal to a similarity threshold, associating the main abnormal characteristic data with solutions corresponding to the historical characteristic data, generating a matching signal, and retrieving related solutions from a database, wherein the solutions are used for processing the current abnormal problem;
Step S3-6, generating a disagreement signal for the historical characteristic data with the similarity index smaller than the similarity threshold.
In a preferred embodiment, step S4 specifically includes the following:
under the condition that the disagreement signals are obtained, the database records main abnormal characteristic data, the data of the monitored object are transferred to the recycling station, the equipment abnormal compound degree index and the similarity index are comprehensively processed to obtain a sorting estimated value, and the main abnormal characteristic data are sorted according to the sorting estimated value from small to large.
An electronic information processing system of the Internet of things comprises an abnormality extraction unit, an abnormality definition unit, a matching definition unit, a priority estimation unit and a data ordering unit;
The anomaly extraction unit is used for acquiring monitoring data from the internet of things equipment, including self state data and data of a monitored object, extracting energy consumption regulation information and transmission maintenance information of the self state data, constructing an anomaly analysis model, generating an anomaly compound index, and sending the anomaly compound index to the anomaly clear unit;
The abnormal clear unit is used for further analyzing the abnormal compound degree index to obtain a normal signal and a trigger signal, and sending the normal signal and the trigger signal to the matched clear unit;
The matching clear unit is used for carrying out principal component analysis on the state data after the trigger signal is obtained, extracting the similarity index of the main abnormal characteristic data and the historical characteristic data, obtaining a matching signal and a disagreement signal, and sending the matching signal and the disagreement signal to the priority estimation unit;
The priority estimation unit is used for recording main abnormal characteristic data after the inconsistent signals are obtained, transferring the data of the monitored object to the recycle bin, constructing a sorting model based on the abnormal compound degree index and the similarity degree index, generating a sorting estimation value, and sending the sorting estimation value to the data sorting unit;
The data sorting unit is used for sorting the main abnormal characteristic data recorded in the database according to the data size of the sorting estimated value.
The electronic information processing system and the method for the Internet of things have the technical effects and advantages that:
1. Recording equipment operation data of the monitoring equipment of the Internet of things, refining the equipment operation data to obtain an energy consumption self-sustaining stable index and a data transmission variant index, identifying self-sustaining energy consumption and communication abnormal characteristics, comprehensively processing the energy consumption self-sustaining stable index and the data transmission variant index to obtain an equipment abnormal compound degree index, identifying the equipment operation abnormality of the Internet of things through the equipment abnormal compound degree index, comparing and analyzing the equipment abnormal compound degree with a trigger threshold value to generate a normal signal or a trigger signal, and helping to give out a clear judgment signal, so that operators can quickly take appropriate measures, improve the reliability and performance of the equipment, help monitor and manage large-scale equipment of the Internet of things, and improve the automation degree and response capability of a system;
2. The system extracts main abnormal characteristics by acquiring self state data related to a trigger signal and extracting the main abnormal characteristics by using a principal component analysis method, standardizing the self state data, calculating a covariance matrix, decomposing characteristic values, selecting and retaining principal components larger than or equal to a specific threshold value, comparing the characteristics with historical characteristic data in a solution database, and then, for the historical characteristic data with similarity indexes higher than the similarity threshold value, the system correlates the historical characteristic data with corresponding solutions to generate a matching signal, and retrieves related solutions from the database to process the current abnormal problems, and for the historical characteristic data with similarity indexes lower than the threshold value, generates a disagreement signal, so that the main abnormal characteristics can be automatically identified and the related solutions are matched, the abnormal conditions of equipment can be rapidly processed, and the efficiency of the abnormality identification and maintenance scheme of the Internet of things equipment can be improved;
3. Under the condition of obtaining the disagreement signal, the equipment abnormal compound degree index and the similarity index are comprehensively processed to obtain a sequencing estimation value, the sequencing estimation value is used for definitely sequencing the main abnormal characteristic data in the database so as to define the priority and the importance, and the smaller sequencing estimation value indicates that the problem of the main abnormal characteristic data is serious or complex and needs to be more urgent to be focused and solved. Thus, the descending order of ranking estimates is used to determine the order of execution of the solutions, ensuring that those most urgent or challenging anomalies are handled first. This helps to increase the priority and processing efficiency of problem solving, ensuring that the most important problem is solved first.
Drawings
FIG. 1 is a schematic flow chart of an electronic information processing system and method of the Internet of things according to the present invention;
fig. 2 is a schematic structural diagram of an electronic information processing system and method of the internet of things according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Fig. 1 shows a method for processing electronic information of internet of things, which comprises the following steps:
step S1, monitoring data including self state data and data of a monitored object are obtained from an internet of things device, energy consumption regulation information and transmission maintenance information of the self state data are extracted, an anomaly analysis model is built, and an anomaly compound index is generated;
step S2, further analyzing the abnormal compound index to obtain a normal signal and a trigger signal;
Step S3, after the trigger signal is obtained, main component analysis is carried out on the state data of the trigger signal, the similarity index is carried out on the main abnormal characteristic data and the historical characteristic data, and a matching signal and a disagreement signal are obtained;
S4, after the inconsistent signals are obtained, main abnormal characteristic data are recorded, the data of the monitored object are transferred to a recycling station, a sorting model is built based on the abnormal compound degree index and the similarity degree index, and a sorting estimated value is generated;
And S5, according to the data size of the sorting estimation value, sorting the main abnormal characteristic data recorded in the database again.
The step S1 specifically comprises the following steps:
Acquiring energy consumption regulation information and transmission maintenance information when the Internet of things equipment operates;
the energy consumption regulation information comprises an energy consumption self-sustaining stability index, and the transmission maintenance information comprises a data transmission mutation index.
The energy consumption self-sustaining stable index is obtained by the following steps:
step S1-11, collecting actual energy source holding time data of the Internet of things equipment in unit time, and simultaneously, calling historical energy source holding time data of an upper operation period, wherein the actual energy source holding time data and the historical energy source time number have the same time stamp so as to facilitate point-to-point comparison;
The energy consumption self-sustaining refers to the time that the internet of things equipment can autonomously maintain normal operation under the condition that no external energy is supplied, which is very important for the internet of things equipment which is self-sufficient to use solar energy for supplying power, and the longer energy consumption self-sustaining time refers to the fact that the internet of things equipment can operate for a longer time under the condition that no external energy is supplied, which means that the internet of things equipment has better stability and can and usability under the condition of autonomous function, which is very important for the monitoring task of the internet of things equipment, and important monitoring data is avoided being omitted; the shorter energy consumption self-holding time means that the internet of things equipment can only run for a limited time when no external energy is supplied, which can lead to the fact that the internet of things equipment is missed from important monitoring opportunities, the data integrity of the monitored object is low, the monitored object is not dominant and representative, and the other side also shows that the energy storage and management and control capability of the internet of things equipment is poor.
Step S1-12, fitting actual energy source sustaining data and historical energy source sustaining time data respectively by using a curve fitting technology to construct a first curve and a second curve;
S1-13, calculating a difference value between an observed value and a fitting value for each time point, wherein the observed value and the fitting value are obtained from a first curve and a second curve;
s1-14, calculating a difference value between an observed value and a fitting value for each data point;
step S1-15, for each time point, calculating a relative error between the observed value and the fitting value:
Wherein:
y i represents the observed value at the i-th time point;
The fitting value for the i-th time point is shown.
Step S1-16, for each time point, calculating the square of the relative error:
step S1-17, summing the square errors of all time points:
step S1-18, calculating an energy consumption self-sustaining stability index:
The energy consumption self-sustaining stability index is used for representing the uncontrollable of the energy consumption self-sustaining of the Internet of things equipment, the larger the value is, the larger the difference of the energy consumption self-sustaining of the same operation period as the last time is, namely the more unstable the energy source self-sustaining is, so that the monitoring data of the monitored object is incomplete or interrupted, and the reliability and the monitoring accuracy of the Internet of things equipment are adversely affected; on the contrary, the smaller the value is, the more stable the energy self-maintenance of the internet of things equipment can be maintained in the analysis time, the smaller the energy self-maintenance variation range is, the energy consumption control and the use are maintained in a more controllable range, and the reliability of the monitoring data of the monitored object is higher.
By monitoring the data transmission time differences, it is possible to know whether the devices maintain consistent communication performance over different time periods, thereby identifying potential problems. Analysis of the interruption time of each transmission helps to find obstructions or fault conditions in the communication that can easily affect the integrity of the monitored data. By integrating the information, the monitoring state of the equipment and whether the equipment is affected by the communication problem can be more comprehensively known, measures can be taken in time to solve the problem, the reliability of the equipment and the accuracy of monitoring data are ensured, and meanwhile, the reliability of the monitoring data of the monitored equipment is fed back from the side face.
The data transfer mutation index is obtained by the following steps:
Step S1-21, recording the start time and the completion time of each monitoring, and the interrupt time in each transmission. Establishing a time sequence including all monitoring and transmission time points;
Steps S1-22, calculating the time interval between every two monitoring from the time sequence to ensure that they are consistent;
Step S1-23, for each transmission, calculating the time difference between the completion time and the start time to measure the time performance of each transmission;
step S1-24, adding the interruption time in all transmissions to represent the sum of communication interruption;
Step S1-25, calculating a data transmission mutation index, wherein the expression is:
Wherein:
n is the number of monitoring times, which represents the number of monitoring times during the whole evaluation process;
I is an index of the number of monitoring times, from 1 to N;
M I is the number of transmissions each time monitored, representing the number of data transmissions performed in each time monitored;
j is an index of the number of transmissions, from 1 to N, representing each data transmission performed in each monitoring;
Break J is the interrupt time of the J-th transmission, which refers to the time of any interrupt or communication failure that occurs in the data transmission;
Transfer J is the time difference of the J-th transmission, representing the time taken for the data transmission from start to completion;
T I is the time interval between each monitoring, which refers to the time difference between successive monitoring.
The data transmission mutation index is used for reflecting the communication stability of the Internet of things equipment, the smaller the data transmission mutation index is, the worse the communication stability is, the communication of the equipment is affected by unstable factors, such as communication interruption or unstable transmission time, the communication problem is shown, and measures are needed to be taken to improve the communication performance of the equipment; the greater the data transfer variability index, the better the communication stability, which means that the device's communication remains stable between different monitoring and transmission cycles, typically without significant communication interruption or transmission delay, indicating that the device is able to reliably communicate with the network.
The step S2 specifically includes the following:
after the energy consumption self-sustaining stable index and the data transmission mutation index are obtained, the energy consumption self-sustaining stable index and the data transmission mutation index are comprehensively processed to obtain the equipment abnormal compound degree index, for example, the abnormal compound degree index can be calculated by the following formula:
Wherein:
DACI is a device anomaly complexity index;
RSMI and CSI are respectively an energy consumption self-sustaining stability index and a data transmission mutation index;
f 1 and f 2 are preset proportionality coefficients of the energy consumption self-sustaining stability index and the data transmission mutation index respectively, and are both larger than 0.
The equipment abnormal compound degree index is used for reflecting the comprehensive abnormal degree of the equipment of the Internet of things. The index integrates the energy conservation stability and data transmission variability of the equipment, and can be used for evaluating the overall operation condition of the equipment by balancing the information of the two aspects. The higher the index, the lower the overall level of abnormality of the device, the more normal and stable the operation, and the lower the index, the higher the overall level of abnormality of the device, with possible problems or anomalies, this index being useful in monitoring the state of the device, identifying anomalies and taking necessary maintenance or repair measures to ensure the reliability and performance of the device.
Comparing the equipment abnormal compound degree index with a trigger threshold, if the equipment abnormal compound degree index is larger than or equal to the trigger threshold, indicating that the comprehensive abnormal degree of the equipment of the Internet of things is lower, namely the operation of the equipment of the Internet of things is relatively normal and stable, indicating that the equipment of the Internet of things is operated within an acceptable range, and generating a normal signal without obvious abnormality or problem; if the equipment abnormal compound degree index is smaller than the trigger threshold, the comprehensive abnormal degree of the internet equipment is higher, abnormal conditions exist, further attention and investigation are needed to determine possible problems of the internet equipment, necessary maintenance, repair or adjustment measures are taken to ensure the reliability and performance of the equipment, the monitored data of monitored equipment of misleading analysts are prevented from being given, and a trigger signal is generated.
According to the method, the device operation data of the monitoring device of the Internet of things are recorded, the device operation data are refined to obtain the self-sustaining energy consumption and communication abnormality characteristics, the self-sustaining energy consumption and communication abnormality characteristics are identified, the device abnormality compound degree index is obtained through comprehensive treatment of the self-sustaining energy consumption and the communication abnormality indexes, the device operation abnormality of the Internet of things is identified through the device abnormality compound degree index, and the device abnormality compound degree and the trigger threshold are compared and analyzed to generate normal signals or trigger signals, so that clear judgment signals are facilitated to be given, operators can quickly take appropriate measures, the reliability and the performance of the device are improved, monitoring and management of large-scale Internet of things devices can be facilitated, and the automation degree and the response capability of a system are improved.
The step S3 specifically comprises the following steps:
step S3-1, under the condition of acquiring a trigger signal, retrieving self-state data corresponding to the trigger signal, wherein the self-state data represents the current running condition of the Internet of things equipment;
s3-2, extracting main abnormal characteristic data from the state data by using a principal component analysis method;
i, normalizing the state data of the self to enable the state data to have zero mean and unit variance;
j, calculating a covariance matrix of the standardized data;
k, carrying out eigenvalue decomposition on the covariance matrix to obtain a main component and a corresponding eigenvalue;
Comparing the characteristic value with a retention threshold value, and selecting to leave the characteristic value which is larger than or equal to the retention threshold value;
e, projecting the original data onto the selected main component to obtain main abnormal characteristic data;
S3-3, comparing the extracted main abnormal characteristic data with historical characteristic data in a database;
And S3-4, calculating a similarity index between each historical characteristic data and the main abnormal characteristic data by using cosine similarity as a similarity measure, wherein the similarity index is expressed as follows:
Wherein:
CS is a similarity index, A is an extracted main abnormal feature vector, B is a historical feature data vector, and A and B respectively represent corresponding norms;
Step S3-5, for the historical characteristic data with the similarity index being greater than or equal to a similarity threshold, associating the main abnormal characteristic data with solutions corresponding to the historical characteristic data, generating a matching signal, and retrieving related solutions from a database, wherein the solutions are used for processing the current abnormal problem;
Step S3-6, generating a disagreement signal for the historical characteristic data with the similarity index smaller than the similarity threshold.
According to the invention, the main abnormal characteristics are extracted by acquiring the state data related to the trigger signal and using a principal component analysis method, the main abnormal characteristics are automatically identified and matched with the related solutions by normalizing the state data, calculating a covariance matrix, decomposing the characteristic values, selecting and retaining the principal components larger than or equal to a specific threshold value, comparing the characteristics with the historical characteristic data in a solution database, and then, for the historical characteristic data with the similarity index higher than the similarity threshold value, the system correlates the historical characteristic data with the corresponding solutions to generate a matching signal, and retrieves the related solutions from the database to treat the current abnormal problems, and for the historical characteristic data with the similarity index lower than the threshold value, the non-symbol signal is generated, so that the abnormal conditions of the equipment can be rapidly processed, and the efficiency of the abnormality identification and maintenance scheme of the Internet of things equipment can be improved.
The step S4 specifically includes the following:
In case of obtaining a disagreement signal, this means that the abnormal characteristics of the current state data of the internet of things device do not match with the historical data recorded in the database. Thus, the current state data itself needs to be recorded as new feature data in the database. Furthermore, for the data of the monitored object collected based on the corresponding time, it is necessary to transfer it to the recycling station. The method is characterized in that the reliability of the monitored object data monitored by the Internet of things equipment with poor state is low, and the monitored object data cannot be used or adopted. This process helps maintain the accuracy and reliability of the database, ensuring that only high quality data is used for further analysis and decision making.
Under the condition that the disagreement signal is obtained, the database records main abnormal characteristic data, the data of the monitored object is transferred to the recycle bin, the equipment abnormal compound index and the similarity index are comprehensively processed to obtain a sequencing estimation value, and the sequencing estimation value is used for defining the sequencing of the main abnormal characteristic data in the database, for example, the sequencing estimation value can be obtained through the following calculation formula:
Wherein:
CEV is a ranking estimate;
DACI and TT are the device anomaly complexity index and trigger threshold, respectively;
CS and ST are similarity index and similarity threshold, respectively;
alpha and beta are respectively And/>Is greater than 0.
The ranking estimates, which in this scenario are used to measure the priority of the newly recorded primary anomaly characteristic data and solutions, reflect the difficulty of processing the newly identified anomaly status data of the internet of things device and the complexity of solving the problem, may rank the priorities of different anomalies by ranking estimates to determine which problems should be solved first, as they may have a higher risk or urgency.
In this context, ranking estimates may be used to determine the order of execution of the solutions, with higher ranking estimates indicating that the problem is more serious or complex and may require more attention and resources to resolve. This helps the system focus on the most urgent or challenging anomalies to improve overall efficiency and emergency response. Thus, the use of ranking estimates can be used to help the system to prioritize abnormal situations, ensuring efficient problem resolution and resource allocation.
The smaller the ranking estimate, the higher the priority of the main anomaly characteristic data that indicates the new record, the more important it is.
And sequencing the main abnormal characteristic data according to the sequencing estimation value from small to large.
Under the condition of obtaining the disagreeable signal, the invention obtains the sorting estimated value through comprehensive processing of the equipment abnormal compound degree index and the similarity degree index, and is used for definitely sorting main abnormal characteristic data in the database so as to definitely determine the priority and the importance, and the smaller sorting estimated value indicates that the problem of the main abnormal characteristic data is serious or complex and needs to be more urgent to be concerned with and solved. Thus, the descending order of ranking estimates is used to determine the order of execution of the solutions, ensuring that those most urgent or challenging anomalies are handled first. This helps to increase the priority and processing efficiency of problem solving, ensuring that the most important problem is solved first.
FIG. 2 shows an electronic information processing system of the Internet of things, which comprises an abnormality extraction unit, an abnormality definition unit, a match definition unit, a priority estimation unit and a data ordering unit;
The anomaly extraction unit is used for acquiring monitoring data from the internet of things equipment, including self state data and data of a monitored object, extracting energy consumption regulation information and transmission maintenance information of the self state data, constructing an anomaly analysis model, generating an anomaly compound index, and sending the anomaly compound index to the anomaly clear unit;
The abnormal clear unit is used for further analyzing the abnormal compound degree index to obtain a normal signal and a trigger signal, and sending the normal signal and the trigger signal to the matched clear unit;
The matching clear unit is used for carrying out principal component analysis on the state data after the trigger signal is obtained, extracting the similarity index of the main abnormal characteristic data and the historical characteristic data, obtaining a matching signal and a disagreement signal, and sending the matching signal and the disagreement signal to the priority estimation unit;
The priority estimation unit is used for recording main abnormal characteristic data after the inconsistent signals are obtained, transferring the data of the monitored object to the recycle bin, constructing a sorting model based on the abnormal compound degree index and the similarity degree index, generating a sorting estimation value, and sending the sorting estimation value to the data sorting unit;
The data sorting unit is used for sorting the main abnormal characteristic data recorded in the database according to the data size of the sorting estimated value.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (5)
1. The electronic information processing method of the Internet of things is characterized by comprising the following steps of:
step S1, monitoring data including self state data and data of a monitored object are obtained from an internet of things device, energy consumption regulation information and transmission maintenance information of the self state data are extracted, an anomaly analysis model is built, and an anomaly compound index is generated;
step S2, further analyzing the abnormal compound index to obtain a normal signal and a trigger signal;
Step S3, after the trigger signal is obtained, main component analysis is carried out on the state data of the trigger signal, the similarity index is carried out on the main abnormal characteristic data and the historical characteristic data, and a matching signal and a disagreement signal are obtained;
S4, after the inconsistent signals are obtained, main abnormal characteristic data are recorded, the data of the monitored object are transferred to a recycling station, a sorting model is built based on the abnormal compound degree index and the similarity degree index, and a sorting estimated value is generated;
S5, according to the data size of the sorting estimation value, sorting the main abnormal characteristic data recorded in the database again;
Acquiring energy consumption regulation information and transmission maintenance information when the Internet of things equipment operates;
the energy consumption regulation and control information comprises an energy consumption self-sustaining stability index, and the transmission maintenance and stability information comprises a data transmission mutation index;
The energy consumption self-sustaining stable index is obtained by the following steps:
step S1-11, collecting actual energy source holding time data of the Internet of things equipment in unit time, and simultaneously, calling historical energy source holding time data of an upper operation period, wherein the actual energy source holding time data and the historical energy source time number have the same time stamp so as to facilitate point-to-point comparison;
Step S1-12, fitting actual energy source sustaining data and historical energy source sustaining time data respectively by using a curve fitting technology to construct a first curve and a second curve;
S1-13, calculating a difference value between an observed value and a fitting value for each time point, wherein the observed value and the fitting value are obtained from a first curve and a second curve;
s1-14, calculating a difference value between an observed value and a fitting value for each data point;
step S1-15, for each time point, calculating a relative error between the observed value and the fitting value:
Wherein:
yi represents the observed value at the i-th time point;
Representing the fitting value of the ith time point;
step S1-16, for each time point, calculating the square of the relative error:
step S1-17, summing the square errors of all time points:
step S1-18, calculating an energy consumption self-sustaining stability index:
The data transfer mutation index is obtained by the following steps:
step S1-21, recording the starting time and the finishing time of each monitoring and the interruption time in each transmission, and establishing a time sequence comprising all monitoring and transmission time points;
Step S1-22, calculating the time interval between every two monitoring from the time sequence;
Step S1-23, for each transmission, calculating a time difference between the completion time and the start time;
Step S1-24, adding the interrupt time in all transmission;
Step S1-25, calculating a data transmission mutation index, wherein the expression is:
Wherein:
n is the number of monitoring times, which represents the number of monitoring times during the whole evaluation process;
I is an index of the number of monitoring times, from 1 to N;
M I is the number of transmissions each time monitored, representing the number of data transmissions performed in each time monitored;
j is an index of the number of transmissions, from 1 to N;
Break J is the interrupt time for the J-th transmission;
Transfer J is the time difference of the J-th transmission;
t I is the time interval between each monitoring;
After the energy consumption self-sustaining stable index and the data transmission mutation index are obtained, the energy consumption self-sustaining stable index and the data transmission mutation index are comprehensively processed to obtain the equipment abnormal compound degree index, and the abnormal compound degree index is calculated by the following formula:
Wherein:
DACI is a device anomaly complexity index;
RSMI and CSI are respectively an energy consumption self-sustaining stability index and a data transmission mutation index;
f 1 and f 2 are preset proportionality coefficients of the energy consumption self-sustaining stability index and the data transmission mutation index respectively, and are both larger than 0.
2. The electronic information processing method of the internet of things according to claim 1, wherein the electronic information processing method is characterized by comprising the following steps:
Comparing the equipment abnormal compound degree index with a trigger threshold, and generating a normal signal if the equipment abnormal compound degree index is greater than or equal to the trigger threshold; and if the equipment abnormal compound degree index is smaller than the trigger threshold value, generating a trigger signal.
3. The electronic information processing method of the internet of things according to claim 2, wherein:
The step S3 specifically comprises the following steps:
step S3-1, under the condition of acquiring a trigger signal, retrieving self-state data corresponding to the trigger signal, wherein the self-state data represents the current running condition of the Internet of things equipment;
s3-2, extracting main abnormal characteristic data from the state data by using a principal component analysis method;
a, normalizing the state data of the self to enable the state data to have zero mean and unit variance;
b, calculating a covariance matrix of the standardized data;
c, carrying out eigenvalue decomposition on the covariance matrix to obtain a main component and a corresponding eigenvalue;
d, comparing the characteristic value with a retention threshold value, and selecting to leave the characteristic value which is larger than or equal to the retention threshold value;
e, projecting the original data onto the selected main component to obtain main abnormal characteristic data;
S3-3, comparing the extracted main abnormal characteristic data with historical characteristic data in a database;
And S3-4, calculating a similarity index between each historical characteristic data and the main abnormal characteristic data by using cosine similarity as a similarity measure, wherein the similarity index is expressed as follows:
Wherein:
CS is a similarity index, A is an extracted main abnormal feature vector, B is a historical feature data vector, and A and B respectively represent corresponding norms;
Step S3-5, for the historical characteristic data with the similarity index being greater than or equal to a similarity threshold, associating the main abnormal characteristic data with solutions corresponding to the historical characteristic data, generating a matching signal, and retrieving related solutions from a database, wherein the solutions are used for processing the current abnormal problem;
Step S3-6, generating a disagreement signal for the historical characteristic data with the similarity index smaller than the similarity threshold.
4. The electronic information processing method of the internet of things according to claim 3, wherein:
the step S4 specifically includes the following:
under the condition that the disagreement signals are obtained, the database records main abnormal characteristic data, the data of the monitored object are transferred to the recycling station, the equipment abnormal compound degree index and the similarity index are comprehensively processed to obtain a sorting estimated value, and the main abnormal characteristic data are sorted according to the sorting estimated value from small to large.
5. An electronic information processing system of the internet of things, configured to implement the electronic information processing method of the internet of things according to any one of claims 1 to 4, including an anomaly extraction unit, an anomaly determination unit, a match determination unit, a priority estimation unit, and a data sorting unit;
The anomaly extraction unit is used for acquiring monitoring data from the internet of things equipment, including self state data and data of a monitored object, extracting energy consumption regulation information and transmission maintenance information of the self state data, constructing an anomaly analysis model, generating an anomaly compound index, and sending the anomaly compound index to the anomaly clear unit;
The abnormal clear unit is used for further analyzing the abnormal compound degree index to obtain a normal signal and a trigger signal, and sending the normal signal and the trigger signal to the matched clear unit;
The matching clear unit is used for carrying out principal component analysis on the state data after the trigger signal is obtained, extracting the similarity index of the main abnormal characteristic data and the historical characteristic data, obtaining a matching signal and a disagreement signal, and sending the matching signal and the disagreement signal to the priority estimation unit;
The priority estimation unit is used for recording main abnormal characteristic data after the inconsistent signals are obtained, transferring the data of the monitored object to the recycle bin, constructing a sorting model based on the abnormal compound degree index and the similarity degree index, generating a sorting estimation value, and sending the sorting estimation value to the data sorting unit;
The data sorting unit is used for sorting the main abnormal characteristic data recorded in the database according to the data size of the sorting estimated value.
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