CN110543505A - Monitoring system based on time series data - Google Patents
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- CN110543505A CN110543505A CN201910844017.8A CN201910844017A CN110543505A CN 110543505 A CN110543505 A CN 110543505A CN 201910844017 A CN201910844017 A CN 201910844017A CN 110543505 A CN110543505 A CN 110543505A
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Abstract
the invention provides a monitoring system based on time series data, which comprises a monitoring project dividing subsystem, a monitoring data acquisition subsystem, a monitoring data mining subsystem and a monitoring early warning subsystem, wherein the monitoring project dividing subsystem divides a monitoring project into a single-index monitoring project and a multi-index monitoring project, the monitoring data acquisition subsystem acquires the time series data of indexes in the single-index monitoring project and the multi-index monitoring project, the monitoring data mining subsystem carries out classification mining on the acquired time series data, and the monitoring early warning subsystem carries out monitoring and early warning on the monitoring project based on the classification mining result of the time series data. The monitoring system based on the time sequence data realizes the whole process from time sequence data acquisition to classification mining no matter a single index monitoring project or a multi-index monitoring project, and realizes effective monitoring on the monitoring project.
Description
Technical Field
the invention relates to the technical field of monitoring, in particular to a monitoring system based on time series data.
Background
with the progress of human society and the development of scientific technology, time series data has an increasing role, and various aspects in production and life, such as atmospheric environment monitoring, human health monitoring, national economy monitoring and the like, can be monitored through the time series data, however, how to systematically complete monitoring activities by using the time series data still does not have a complete solution at present.
Disclosure of Invention
in order to solve the technical problems, the invention provides a monitoring system based on time series data, which realizes the whole process from time series data acquisition to classification mining no matter a single index monitoring project or a multi-index monitoring project, and realizes effective monitoring on the monitoring project.
The invention particularly provides a monitoring system based on time series data, which comprises a monitoring project dividing subsystem, a monitoring data acquisition subsystem, a monitoring data mining subsystem and a monitoring early warning subsystem, wherein the monitoring project dividing subsystem divides a monitoring project into a single-index monitoring project and a multi-index monitoring project, the monitoring data acquisition subsystem acquires the time series data of indexes in the single-index monitoring project and the multi-index monitoring project, the monitoring data mining subsystem carries out classification mining on the acquired time series data, and the monitoring early warning subsystem carries out monitoring and early warning on the monitoring project based on the classification mining result of the time series data.
Optionally, the monitoring data mining subsystem includes a data dividing module, a data measuring module and a data classifying module, the data dividing module divides the time series data into single-index time series data and multi-index time series data, the data measuring module is configured to perform similarity measurement on the single-index time series data and the multi-index time series data, and the data classifying module performs classified mining on the single-index time series data and the multi-index time series data based on a result of the similarity measurement.
optionally, the data measurement module includes a first measurement unit and a second measurement unit, the first measurement unit is configured to perform similarity measurement on single-index time-series data, and the second measurement unit is configured to perform similarity measurement on multi-index time-series data;
The first measurement unit is used for carrying out similarity measurement on the single-index time series data and comprises the following steps:
for the single index time-series data a1 ═ (a1, a2, …, an) and a2 ═ b1, b2, …, bn, where n represents the number of data in the single index time-series data a1 and a2, the single index time-series first similarity factor is calculated using the following formula:
wherein S1 represents a single index time-series first similarity factor, ai, bi represent the ith time-series data in the single index time-series data A1 and A2, respectively; the larger the first similarity factor of the single index time series data is, the higher the similarity of the single index time series data A1 and A2 is.
optionally, the first metric unit is configured to perform similarity measurement on the single-index time-series data, and further includes:
For single index time series data a1 ═ a1, a2, …, an and A3 ═ c1, c2, …, cm, where n and m represent the number of data in the single index time series data a1 and A3, respectively, and n ≠ m, then a1 and A3 are first expanded into single index time series data a1 'of length d ═ a1, a2, …, an, an +1, …, ad) and A3' ═ c1, c2, …, cm, m +1, …, cd, where d represents the number of data in the expanded single index time series data a1 'and A3', d > n and d > m, ai, ci represent the ith time series data in the single index time series data a1 and A3, respectively, and the second similarity factor is calculated using the following formula:
Wherein S2 represents a single index time-series second similarity factor, and aj and cj represent jth time-series data in the expanded single index time-series data a '1 and a' 3, respectively; the larger the second similarity factor of the single index time-series data is, the higher the similarity between the single index time-series data A1 and A3 is.
Optionally, the second metric unit is configured to perform similarity measurement on the multi-index time series data, and includes:
for multi-index time-series data E1 ═ X1, X2, …, Xn1} and E2 ═ { Y1, Y2, …, Yn1}, where Xi ═ T (Xi1, Xi2, …, xik) T, Yi ═(Yi1, Yi2, …, yik) T, i ═ 1, 2, …, n1, n1 denote the lengths of multi-index time-series data E1 and E2, Xi and Yi denote the i-th time-series data in multi-index time-series data E1 and E2, respectively, k denotes the number of indices in multi-index time-series data E1 and E2, k ≧ 2, a multi-index time-series first similarity factor is calculated using the following formula:
In the formula, F1 represents a multi-index time-series first similarity factor; the larger the multi-index time-series first similarity factor, the higher the similarity of the multi-index time-series data E1 and E2.
Optionally, the second metric unit is configured to perform similarity measurement on the multi-index time series data, and further includes:
for multi-index time-series data E1 ═ { X1, X2, …, Xn1} and E3 ═ { Z1, Z2, …, Zn2}, where Xi ═ T (Xi1, Xi2, …, xik) T, Zi ═ (Zi1, Zi2, …, Zik) T, n1, and n2 respectively denote the lengths of the multi-index time-series data E1 and E3, and n1 ≠ n2, Xi, and Zi respectively denote the ith time-series data in the multi-index time-series data E1 and E3, k denotes the number of indices in the multi-index time-series data E1 and E3, and k is not less than 2;
first, E1 and E3 are extended into multi-index time-series data of length w, E '1 ═ { X1, X2, …, Xn1, Xn1+1, …, Xw } and E' 3 ═ { Z1, Z2, …, Zn2, Zn2+1, …, Zw }, where w > n1 and w > n2, and a multi-index time-series second similarity factor is calculated using the following formula:
wherein, F2 represents a multi-index time series second similarity factor; the larger the multi-index time-series second similarity factor, the higher the similarity of the multi-index time-series data E1 and E3.
optionally, the monitoring data mining subsystem performs classification mining on the acquired time series data, specifically:
Performing similarity measurement on the two time series data subjected to similarity measurement and known standard time series data respectively by adopting the same similarity measurement method, presetting a difference judgment threshold, if the difference value of the similarity measurement between the two time series data and the known standard time series data is greater than the preset difference judgment threshold, indicating that the difference between the two time series data is larger, setting the difference factor of the two time series data to be P1 and P1 less than 1, and if the difference value of the similarity measurement between the two time series data and the known standard time series data is smaller than the preset difference judgment threshold, indicating that the difference between the two time series data is smaller, setting the difference factor of the two time series data to be P2 and P2 greater than 1;
Multiplying the similarity measurement result of the two time sequence data by the difference factor of the two time sequence data to obtain an improved similarity measurement result of the time sequence data;
And classifying the time series data by adopting a k-nearest neighbor algorithm based on the similarity measurement result of the improved time series data, and mining the data based on the classification result of the time series data.
Optionally, the multi-index monitoring item is a multi-index mechanical equipment monitoring item, and the multi-index includes mechanical equipment vibration data, mechanical equipment temperature data, and mechanical equipment sound data.
additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a schematic view of the structure of the present invention.
reference numerals:
The monitoring project division subsystem 1, the monitoring data acquisition subsystem 2, the monitoring data mining subsystem 3 and the monitoring early warning subsystem 4.
Detailed Description
the invention is further described with reference to the following examples.
with reference to fig. 1, an embodiment of the present invention provides a monitoring system based on time series data, including a monitoring project dividing subsystem 1, a monitoring data collecting subsystem 2, a monitoring data mining subsystem 3, and a monitoring and early warning subsystem 4, where the monitoring project dividing subsystem 1 divides a monitoring project into a single-index monitoring project and a multi-index monitoring project, the monitoring data collecting subsystem 2 collects time series data of indexes in the single-index monitoring project and the multi-index monitoring project, the monitoring data mining subsystem 3 performs classified mining on the collected time series data, and the monitoring and early warning subsystem 4 monitors and early warns the monitoring project based on a classified mining result of the time series data.
In the actual production life, various monitoring projects exist, some monitoring projects only need to collect time series data of a first variable, such as monitoring the body temperature of a fever patient, monitoring the concentration of PM2.5 in the air and monitoring a vibration signal of mechanical equipment, at the moment, the monitoring projects are used as single index monitoring projects, a sensor only needs to collect the time series data of a single index, some monitoring projects need to detect a plurality of variables, such as comprehensively monitoring the body health of blood pressure, blood sugar and blood fat and comprehensively monitoring the weather of temperature, humidity and air pressure, at the moment, the monitoring projects are multi-index monitoring projects, and the sensor needs to collect multi-index time;
for the acquisition of time sequence data, various classification modes can be adopted for classification, such as k-nearest neighbor classification, artificial neural network classification, Bayesian classification and the like, and human body temperature is taken as an example, and the time sequence data can be classified into low body temperature, normal fever, low fever and high fever according to the body temperature.
and acquiring the required data based on the classification result so as to finish data mining, and further monitoring and early warning the corresponding monitoring items according to the data classification mining result.
in the embodiment, the whole process from time sequence data acquisition to classification mining is realized no matter a single index monitoring project or a multi-index monitoring project, and effective monitoring on the monitoring project is realized.
preferably, the monitoring data mining subsystem 3 includes a data dividing module, a data measuring module and a data classifying module, the data dividing module divides the time series data into single-index time series data and multi-index time series data, the data measuring module is used for performing similarity measurement on the single-index time series data and the multi-index time series data, and the data classifying module performs classified mining on the single-index time series data and the multi-index time series data based on a result of the similarity measurement.
in the data mining process, for the reason that the processing methods and the complexity of single-index time series data and multi-index time series data are different, in order to reduce the difficulty of data processing and improve the data processing efficiency, the monitoring data are firstly divided according to different monitoring items, then corresponding similarity measurement is carried out on different data types, and the corresponding data are classified and mined based on the similarity measurement method.
Preferably, the data measurement module comprises a first measurement unit and a second measurement unit, the first measurement unit is used for carrying out similarity measurement on single-index time-series data, and the second measurement unit is used for carrying out similarity measurement on multi-index time-series data;
The first measurement unit is used for carrying out similarity measurement on the single-index time series data and comprises the following steps:
For the single index time-series data a1 ═ (a1, a2, …, an) and a2 ═ b1, b2, …, bn, where n represents the number of data in the single index time-series data a1 and a2, the single index time-series first similarity factor is calculated using the following formula:
Wherein S1 represents a single index time-series first similarity factor, ai, bi represent the ith time-series data in the single index time-series data A1 and A2, respectively; the larger the first similarity factor of the single index time series data is, the higher the similarity of the single index time series data A1 and A2 is.
for the characteristics of time series data in the actual monitoring process, the similarity measurement is performed on the condition that the data lengths in the single-index time series data are the same, the data meeting the conditions can be effectively measured by determining the first similarity factor of the single-index time series, and the first similarity factor of the single-index time series can be used as a similarity measurement result for subsequent calculation.
Preferably, the first metric unit is configured to perform similarity measurement on the single-index time-series data, and further includes:
for single index time series data a1 ═ a1, a2, …, an and A3 ═ c1, c2, …, cm, where n and m represent the number of data in the single index time series data a1 and A3, respectively, and n ≠ m, then a1 and A3 are first expanded into single index time series data a1 'of length d ═ a1, a2, …, an, an +1, …, ad) and A3' ═ c1, c2, …, cm, cm +1, …, cd, where d represents the number of data in the expanded single index time series data a1 'and A3', d > n and d > m, ai, ci represent the ith time series data in the single index time series data a1 and A3, respectively, and the second time index time series data similarity factor is calculated using the following formula:
Wherein S2 represents a single index time-series second similarity factor, and aj and cj represent jth time-series data in the expanded single index time-series data a '1 and a' 3, respectively; the larger the second similarity factor of the single index time-series data is, the higher the similarity between the single index time-series data A1 and A3 is.
In the calculation process of the second similarity factor of the single index time sequence, d is larger than the maximum value of n and m, so that the time sequence data with different lengths can be conveniently expanded.
in the preferred embodiment, the single index time series data with different lengths are expanded, so that the similarity measurement of the single index time series data with different lengths is realized, and the second similarity factor of the single index time series can be used as a similarity measurement result for subsequent calculation.
Preferably, the second metric unit is configured to perform similarity measurement on the multi-index time-series data, and includes:
For multi-index time-series data E1 ═ X1, X2, …, Xn1} and E2 ═ { Y1, Y2, …, Yn1}, where Xi ═ T (Xi1, Xi2, …, xik) T, Yi ═(Yi1, Yi2, …, yik) T, i ═ 1, 2, …, n1, n1 denote the lengths of multi-index time-series data E1 and E2, Xi and Yi denote the i-th time-series data in multi-index time-series data E1 and E2, respectively, k denotes the number of indices in multi-index time-series data E1 and E2, k ≧ 2, a multi-index time-series first similarity factor is calculated using the following formula:
In the formula, F1 represents a multi-index time-series first similarity factor; the larger the multi-index time-series first similarity factor, the higher the similarity of the multi-index time-series data E1 and E2.
For the characteristics of time series data in the actual monitoring process, the similarity measurement is performed on the condition that the data lengths in the multi-index time series data are the same, the data meeting the conditions can be effectively measured by determining the first similarity factor of the multi-index time series, and the first similarity factor of the multi-index time series can be used as a similarity measurement result for subsequent calculation.
Preferably, the second metric unit is configured to perform similarity measurement on the multi-index time-series data, and further includes:
For multi-index time-series data E1 ═ { X1, X2, …, Xn1} and E3 ═ { Z1, Z2, …, Zn2}, where Xi ═ T (Xi1, Xi2, …, xik) T, Zi ═ (Zi1, Zi2, …, Zik) T, n1, and n2 respectively denote the lengths of the multi-index time-series data E1 and E3, and n1 ≠ n2, Xi, and Zi respectively denote the ith time-series data in the multi-index time-series data E1 and E3, k denotes the number of indices in the multi-index time-series data E1 and E3, and k is not less than 2;
first, E1 and E3 are extended into multi-index time-series data of length w, E '1 ═ { X1, X2, …, Xn1, Xn1+1, …, Xw } and E' 3 ═ { Z1, Z2, …, Zn2, Zn2+1, …, Zw }, where w > n1 and w > n2, and a multi-index time-series second similarity factor is calculated using the following formula:
Wherein, F2 represents a multi-index time series second similarity factor; the larger the multi-index time-series second similarity factor, the higher the similarity of the multi-index time-series data E1 and E3.
in the calculation process of the second similarity factor of the multi-index time series, w is greater than the maximum value of n1 and n2, so that the advantage is that the time series data with different lengths can be conveniently expanded, in order to reduce the operation amount, the value of d is usually not much greater than the maximum value of n1 and n2, and in the process of expanding the time series data, the average value of the original time series data is taken as the value of the expanded time series data, so that the distortion degree of the data can be reduced to the greatest extent, and the accuracy of the similarity measurement result can be ensured.
In the preferred embodiment, the similarity measurement of the multi-index time series data sequence with different lengths is realized by expanding the multi-index time series data with different lengths, and the second similarity factor of the multi-index time series can be used as a similarity measurement result for subsequent calculation.
Preferably, the monitoring data mining subsystem performs classification mining on the acquired time series data, specifically:
the two time series data for similarity measurement are respectively subjected to similarity measurement with known standard time series data by adopting the same similarity measurement method, the two time series data for similarity measurement can be single index time series data with the same length, also can be single index time series data with different lengths, also can be multi-index time series data with the same length and multi-index time series data with different lengths, the known standard time series data can be obtained in various ways, can be directly obtained through the Internet, also can be determined empirically or can be determined according to sample data; the standard time-series data can be understood as monitoring data when the monitoring item is normal.
A difference judgment threshold is preset, the difference judgment threshold is larger than zero and can be adjusted according to the actual situation, if the difference value of the similarity measure between the two time data sequences and the known standard time sequence data is larger than the preset difference judgment threshold, the difference between the two time sequence data is larger, at the moment, the difference factor of the two time sequence data is set to be P1, P1 is smaller than 1, if the difference value of the similarity measure between the two time data sequences and the known standard time sequence data is smaller than the preset difference judgment threshold, the difference factor of the two time sequence data is set to be P2, and P2 is larger than 1; the specific value of the difference factor may be determined empirically, or a method in the monitoring data similarity measurement module may be used to determine a similarity measurement value between two time data sequences and known standard time sequence data, and then determine the similarity measurement value by using the similarity measurement value, for example, P1 is 1 — a difference between the similarity measurement value between two time data sequences and known standard time sequence data, and P2 is 1+ a difference between the similarity measurement value between two time data sequences and known standard time sequence data;
The difference judgment threshold can judge the difference between the similarity metric values of the two time series data to be compared and the standard time series data so as to judge the significant difference between the two time series data to be compared, when the difference between the two time series data is larger, the original similarity metric value is reduced through P1, and when the difference between the two time series data is larger, the original similarity metric value is amplified through P2, namely, the similarity metric result of the two time series data is multiplied by the difference factor of the two time series data to obtain the improved similarity metric result of the time series data; by improving the similarity measurement result, a more accurate similarity measurement result can be obtained;
And classifying the time series data by adopting a k-nearest neighbor algorithm based on the similarity measurement result of the improved time series data, and mining the data based on the classification result of the time series data. Other classification algorithms can be adopted to classify the time series data, and the time series data are classified and mined according to classification results, so that corresponding monitoring items can be conveniently monitored subsequently.
preferably, the multi-index monitoring item is a multi-index mechanical equipment monitoring item, and the multi-index monitoring item includes mechanical equipment vibration data, mechanical equipment temperature data and mechanical equipment sound data. The vibration data, the temperature data and the sound data of the mechanical equipment are used as indexes of a multi-index mechanical equipment monitoring project, various problems of the mechanical equipment in the operation process can be accurately reflected, and the mechanical equipment can be effectively monitored.
From the above description of embodiments, it is clear for a person skilled in the art that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For a hardware implementation, a processor may be implemented in one or more of the following units: an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the procedures of an embodiment may be performed by a computer program instructing associated hardware. In practice, the program may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. Computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by the ordinary technical destination in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (8)
1. the monitoring system based on the time series data is characterized by comprising a monitoring project dividing subsystem, a monitoring data collecting subsystem, a monitoring data mining subsystem and a monitoring early warning subsystem, wherein the monitoring project dividing subsystem divides a monitoring project into a single index monitoring project and a multi-index monitoring project, the monitoring data collecting subsystem collects the time series data of indexes in the single index monitoring project and the multi-index monitoring project, the monitoring data mining subsystem carries out classification mining on the collected time series data, and the monitoring early warning subsystem carries out monitoring and early warning on the monitoring project based on the classification mining result of the time series data.
2. the time-series data-based monitoring system of claim 1, wherein the monitoring data mining subsystem comprises a data partitioning module that partitions the time-series data into single-index time-series data and multi-index time-series data, a data measurement module that measures similarity of the single-index time-series data and the multi-index time-series data, and a data classification module that performs classification mining on the single-index time-series data and the multi-index time-series data based on a result of the similarity measurement.
3. the time-series data-based monitoring system of claim 2, wherein the data metric module comprises a first metric unit and a second metric unit, the first metric unit is configured to perform a similarity metric on single-index time-series data, and the second metric unit is configured to perform a similarity metric on multi-index time-series data;
the first measurement unit is used for carrying out similarity measurement on the single-index time series data and comprises the following steps:
for the single index time-series data a1 ═ (a1, a2, …, an) and a2 ═ b1, b2, …, bn, where n represents the number of data in the single index time-series data a1 and a2, the single index time-series first similarity factor is calculated using the following formula:
Wherein S1 represents a single index time-series first similarity factor, ai, bi represent the ith time-series data in the single index time-series data A1 and A2, respectively; the larger the first similarity factor of the single index time series data is, the higher the similarity of the single index time series data A1 and A2 is.
4. The time-series data-based monitoring system of claim 3, wherein the first metric unit is configured to perform a similarity metric on single-index time-series data, further comprising:
for single index time series data a1 ═ a1, a2, …, an and A3 ═ c1, c2, …, cm, where n and m represent the number of data in the single index time series data a1 and A3, respectively, and n ≠ m, then a1 and A3 are first expanded into single index time series data a1 'of length d ═ a1, a2, …, an, an +1, …, ad) and A3' ═ c1, c2, …, cm, cm +1, …, cd, where d represents the number of data in the expanded single index time series data a1 'and A3', d > n and d > m, ai, ci represent the ith time index time series data in the single index time series data a1 and A3, respectively, and the second time index time series data similarity factor is calculated using the following formula:
wherein S2 represents a single index time-series second similarity factor, and aj and cj represent jth time-series data in the expanded single index time-series data a '1 and a' 3, respectively; the larger the second similarity factor of the single index time-series data is, the higher the similarity between the single index time-series data A1 and A3 is.
5. the time-series data-based monitoring system of claim 4, wherein the second metric unit is configured to perform similarity measurement on multi-index time-series data, and comprises:
for multi-index time-series data E1 ═ X1, X2, …, Xn1} and E2 ═ { Y1, Y2, …, Yn1}, where Xi ═ T (Xi1, Xi2, …, xik) T, Yi ═(Yi1, Yi2, …, yik) T, i ═ 1, 2, …, n1, n1 denote the lengths of multi-index time-series data E1 and E2, Xi and Yi denote the i-th time-series data in multi-index time-series data E1 and E2, respectively, k denotes the number of indices in multi-index time-series data E1 and E2, k ≧ 2, a multi-index time-series first similarity factor is calculated using the following formula:
In the formula, F1 represents a multi-index time-series first similarity factor; the larger the multi-index time-series first similarity factor, the higher the similarity of the multi-index time-series data E1 and E2.
6. The time-series data-based monitoring system of claim 5, wherein the second metric unit is configured to perform a similarity metric on multi-index time-series data, further comprising:
For multi-index time-series data E1 ═ { X1, X2, …, Xn1} and E3 ═ { Z1, Z2, …, Zn2}, where Xi ═ T (Xi1, Xi2, …, xik) T, Zi ═ T (Zi1, Zi2, …, zik) T, n1, and n2 denote the lengths of the multi-index time-series data E1 and E3, respectively, and n1 ≠ n2, Xi, and Zi denote the ith time-series data in the multi-index time-series data E1 and E3, respectively, k denotes the number of indices in the multi-index time-series data E1 and E3, and k is not less than or equal to 2;
First, E1 and E3 are extended into multi-index time-series data of length w, E '1 ═ { X1, X2, …, Xn1, Xn1+1, …, Xw } and E' 3 ═ { Z1, Z2, …, Zn2, Zn2+1, …, Zw }, where w > n1 and w > n2, and a multi-index time-series second similarity factor is calculated using the following formula:
Wherein, F2 represents a multi-index time series second similarity factor; the larger the multi-index time-series second similarity factor, the higher the similarity of the multi-index time-series data E1 and E3.
7. the time-series data-based monitoring system of claim 6, wherein the monitoring data mining subsystem performs classification mining on the collected time-series data, specifically:
Performing similarity measurement on the two time series data subjected to similarity measurement and a known standard time series data respectively by using the same similarity measurement method, presetting a difference judgment threshold, if the difference value of the similarity measurement between the two time series data and the known standard time series data is greater than the preset difference judgment threshold, indicating that the difference between the two time series data is larger, at the moment, setting the difference factor of the two time series data to be P1 and P1 less than 1, and if the difference value of the similarity measurement between the two time series data and the known standard time series data is smaller than the preset difference judgment threshold, indicating that the difference between the two time series data is smaller, at the moment, setting the difference factor of the two time series data to be P2 and P2> 1;
Multiplying the similarity measurement result of the two time sequence data by the difference factor of the two time sequence data to obtain an improved similarity measurement result of the time sequence data;
and classifying the time series data by adopting a k-nearest neighbor algorithm based on the similarity measurement result of the improved time series data, and mining the data based on the classification result of the time series data.
8. the time-series data-based monitoring system of claim 1, wherein the multi-index monitoring item is a multi-index mechanical equipment monitoring item, and the multi-index comprises mechanical equipment vibration data, mechanical equipment temperature data, and mechanical equipment sound data.
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