CN114817373A - Intelligent identification method and system for error jump of dam safety monitoring data system - Google Patents
Intelligent identification method and system for error jump of dam safety monitoring data system Download PDFInfo
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- CN114817373A CN114817373A CN202210540034.4A CN202210540034A CN114817373A CN 114817373 A CN114817373 A CN 114817373A CN 202210540034 A CN202210540034 A CN 202210540034A CN 114817373 A CN114817373 A CN 114817373A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K3/00—Thermometers giving results other than momentary value of temperature
- G01K3/02—Thermometers giving results other than momentary value of temperature giving means values; giving integrated values
- G01K3/04—Thermometers giving results other than momentary value of temperature giving means values; giving integrated values in respect of time
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K3/00—Thermometers giving results other than momentary value of temperature
- G01K3/08—Thermometers giving results other than momentary value of temperature giving differences of values; giving differentiated values
- G01K3/10—Thermometers giving results other than momentary value of temperature giving differences of values; giving differentiated values in respect of time, e.g. reacting only to a quick change of temperature
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
Abstract
The invention discloses an intelligent identification method and system for error jump of a dam safety monitoring data system. Calculating the absolute value of the slope of each measurement according to the observation data sequence of the dam safety monitoring physical quantity and the corresponding measurement time sequence thereof to obtain a first data sequence; calculating the difference value of the arithmetic mean value of the observation data in the set time length before and after each measurement based on the observation data sequence, and taking the absolute value of the difference value to obtain a second data sequence; and judging whether the system error jump occurs in the observation data of a certain measured time in the observation data sequence or not according to the first data sequence and the second data sequence. The method can more accurately and efficiently identify the system error jump phenomenon in the mass monitoring data, is suitable for intelligent batch processing of data by a computer, and is suitable for the fields of dam safety monitoring and other engineering safety monitoring.
Description
Technical Field
The invention belongs to the field of dam safety monitoring data processing, and relates to an intelligent identification method and system for error jump of a dam safety monitoring data system.
Background
The system error is the difference between the average value of the measured results and the true value of the measured value, which is measured by repeating the measurement for a plurality of times under a certain measurement condition. The system error has the characteristics of repeatability, unidirectionality and testability. That is, under the same conditions, the measurement will be repeated, so that the measurement result will be higher or lower systematically, and the value will have a certain rule. The reasons for the generation of system errors mainly include three major factors: reasons for measuring instruments, changes in measurement standards, and the effects of external conditions. In dam safety monitoring measurement data, the measurement result often contains system errors due to the influence of the three factors, for example, constant system errors generated before and after replacement of monitoring instruments or equipment or change of a reference cause that the measurement data is not connected before and after, and a measurement process line of the measurement data shows step-type jump. Data containing such system errors cannot be directly analyzed and used, and a certain method is required to identify and process the data.
At present, the jump of the system error is mainly recognized manually, and whether step-type jump exists or not is observed by drawing a time process line of monitoring data. The manual identification method has the defect that a monitoring data process line needs to be drawn for each measuring point and is distinguished one by people. When the number of measuring points is large, the monitoring time is long, and the data volume is large, the efficiency is low by the manual identification method, and sometimes tiny jump is difficult to find only by human eyes, so that misjudgment and missed judgment are easy to occur.
Disclosure of Invention
The invention aims to provide an intelligent identification method and system for error jump of a dam safety monitoring data system, and aims to solve the problems that in the prior art, the efficiency of error jump of a manual identification system is low, and misjudgment and missed judgment are easy to occur.
In order to realize the purpose, the invention adopts the following technical scheme:
an intelligent identification method for error jump of a dam safety monitoring data system comprises the following steps:
acquiring an observation data sequence of the dam safety monitoring physical quantity and a corresponding measurement time sequence thereof;
calculating the absolute value of the slope of each measurement according to the observation data sequence and the measurement time sequence to obtain a first data sequence formed by the absolute values of the slopes of the measurements;
calculating the difference value of the arithmetic mean value of the observation data in the set time length before and after each measurement based on the observation data sequence, and taking the absolute value of the difference value to obtain a second data sequence consisting of the absolute value of the arithmetic mean value difference value of the observation data in the set time length before and after each measurement;
and judging whether the system error jump occurs in the observation data of a certain measured time in the observation data sequence or not according to the first data sequence and the second data sequence.
Further, the calculating an absolute value of a slope of each measurement time according to the observation data sequence and the measurement time sequence includes:
suppose the observed data sequence is y 1 ，y 2 ，…，y n With a corresponding measurement time sequence of { t } 1 ，t 2 ，…，t n Calculating the slope k of each measurement according to the following formula i ：
In the formula, t i And t i-1 Respectively are observed data y i And y i-1 Corresponding measurement time, wherein n is the total measurement time;
slope k for each measurement i Taking an absolute value:
KJ i ＝|k i |
in the formula, KJ i The absolute value of the slope of the ith measurement.
Further, an arithmetic average of the observation data within a set time length before and after each measurement is calculated according to the following steps:
assuming that the set time length is TL, k times of measurement are set in TL time period before the ith time of measurement, and the corresponding observed data is R 1 ，R 2 ，R 3 ，......，R k Then, the arithmetic mean value E1 of the observed data during the TL period before the ith test is calculated according to the following formula i ：
In the formula, R j ＝R 1 ，R 2 ，R 3 ，......，R k ，i＝2，3，......，n-1；
Let p measurements in TL time period after ith measurement and corresponding observed data be S 1 ，S 2 ，S 3 ，......S p Then, the arithmetic mean value E2 of the observed data in the TL period after the ith test is calculated according to the following formula i ：
In the formula, S j ＝S 1 ，S 2 ，S 3 ，......S p ；
Wherein the observed data of the ith test is contained in p tests in the TL time period after the ith test.
Further, the absolute value PJ of the difference of the arithmetic mean of the observation data within a set time length before and after each measurement i Calculated according to the following formula:
PJ i ＝|E1 i -E2 i |(i＝2，3，......n-1)。
further, the determining whether a system error jump occurs in observation data of a certain measurement time in the observation data sequence according to the first data sequence and the second data sequence includes:
and if the absolute value of the slope of a certain measurement is the maximum value in the first data sequence and the absolute value of the difference value of the arithmetic mean values of the observation data in the set time length before and after the measurement is the maximum value in the second data sequence, judging that the system error jump of the observation data of the measurement in the observation data sequence occurs.
An intelligent identification system for error jump of a dam safety monitoring data system comprises:
the acquisition module is used for acquiring an observation data sequence of the dam safety monitoring physical quantity and a corresponding measurement time sequence thereof;
the first calculation module is used for calculating the absolute value of the slope of each measurement according to the observation data sequence and the measurement time sequence to obtain a first data sequence formed by the absolute values of the slopes of the measurements;
the second calculation module is used for calculating the difference value of the arithmetic mean value of the observation data in the set time length before and after each measurement based on the observation data sequence, and taking the absolute value of the difference value to obtain a second data sequence formed by the absolute value of the arithmetic mean value difference value of the observation data in the set time length before and after each measurement;
and the judging module is used for judging whether the system error jump occurs in the observation data of a certain measured time in the observation data sequence according to the first data sequence and the second data sequence.
Further, the first computing module includes:
the slope calculation module is used for calculating the slope of each measurement according to the observation data sequence and the measurement time sequence;
and the first data sequence generation module is used for taking the absolute value of the slope of each measured time calculated by the slope calculation module to obtain a first data sequence formed by the absolute values of the slopes of the measured times.
Further, the second calculation module includes:
the mean difference value calculating module is used for calculating the difference value of the arithmetic mean value of the observation data in the set time length before and after each measurement based on the observation data sequence;
and the second data sequence generating module is used for taking an absolute value of the difference calculated by the mean difference calculating module to obtain a second data sequence formed by absolute values of arithmetic mean differences of the observation data within a set time length before and after each measurement.
A computer-readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to execute a method of intelligent identification of dam safety monitoring data system error jump as described above.
A computing device comprises a storage and a processor, wherein executable codes are stored in the storage, and when the processor executes the executable codes, the intelligent identification method for the error jump of the dam safety monitoring data system is realized.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention considers two characteristics of observation data when system error jumps occur: slope jump and mean level jump. And checking whether the data sequence to be tested has system error jump or not by judging whether the two characteristics appear simultaneously or not.
2. The method has the advantages of clear principle, easy understanding, simple logic and convenient programming, can more accurately and more efficiently identify the system error jump phenomenon in mass monitoring data compared with a manual identification method, is suitable for intelligent batch processing of data by a computer, and is favorable for popularization and application.
Drawings
Fig. 1 is a flowchart of an intelligent identification method for error jump of a dam safety monitoring data system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of dam temperature observation data changing with measurement time in a specific application example;
fig. 3 is a structural block diagram of an intelligent identification system for error jump of a dam safety monitoring data system according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to specific examples. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As mentioned above, in the dam safety monitoring measurement data, the jump of the system error mainly depends on manual identification, and when the number of measuring points is large, the monitoring time is long, and the data volume is large, the efficiency of the method depending on the manual identification is low, and misjudgment and missed judgment are easy to occur. Therefore, the invention provides an intelligent identification method for the error jump of a dam safety monitoring data system.
The method calculates the slope according to the measurement data and the measurement time interval, and the slope corresponding to each measurement reflects the change degree of the measurement data. And then reflecting the difference level of the two sections of measurement data before and after each measurement by calculating the average value difference of the measurement data in a certain time length before and after each measurement. And judging that the system error jump occurs in the measurement data corresponding to a certain measurement time when the variation degree and the difference level reach the maximum simultaneously.
Specifically, referring to fig. 1, an intelligent identification method for error jump of a dam safety monitoring data system includes the following steps:
step S1, acquiring an observation data sequence of the dam safety monitoring physical quantity and a corresponding measurement time sequence thereof;
observed data sequence to be detected { y) for acquiring certain dam safety monitoring physical quantity 1 ，y 2 ，…，y n Their corresponding measurement time series t 1 ，t 2 ，…，t n And n is the total number of measurements.
Step S2, calculating the absolute value of the slope of each measurement according to the observation data sequence and the measurement time sequence to obtain a first data sequence consisting of the absolute values of the slopes of the measurements;
for observation data sequence y 1 ，y 2 ，…，y n T and its corresponding measurement time series 1 ，t 2 ，…，t n Calculating the slope k of each measurement according to the following formula i ：
In the formula, t i And t i-1 Respectively are observed data y i And y i-1 The corresponding measurement time.
Slope k for each measurement i Taking an absolute value: KJ i ＝|k i |，KJ i The absolute value of the slope of the ith measurement.
All KJi calculated form a new first data sequence KJ 2 ，KJ 3 ，…，KJ n-1 }。
Step S3, calculating the difference of the arithmetic mean of the observation data in the set time length before and after each measurement based on the observation data sequence, and taking the absolute value of the difference to obtain a second data sequence formed by the absolute value of the arithmetic mean difference of the observation data in the set time length before and after each measurement;
the method specifically comprises the following steps:
step S31, calculating the difference value of the arithmetic mean value of the observation data in the set time length before and after each measurement based on the observation data sequence;
setting a time length TL (m unit times, m is a positive integer), and calculating the arithmetic mean value E1 of the observation data sequence in the time length TL before and after the ith measurement i 、E2 i (i＝2，3，......，n-1)。
Wherein, E1 i 、E2 i The calculation method of (2) is as follows:
let the ith testK times of measurement exist in the first TL time period, and the corresponding observed data is R 1 ，R 2 ，R 3 ，......，R k Then, the arithmetic mean value E1 of the observed data during the TL period before the ith test is calculated according to the following formula i ：
In the formula, R j ＝R 1 ，R 2 ，R 3 ，......，R k ；
Let p measurements in TL time period after ith measurement and corresponding observed data be S 1 ，S 2 ，S 3 ，......，S p Then, the arithmetic mean value E2 of the observed data in the TL period after the ith test is calculated according to the following formula i ：
In the formula, S j ＝S 1 ，S 2 ，S 3 ，......，S p ；
Wherein the observed data of the ith test is contained in p tests in the TL time period after the ith test and is used for calculating E2 i 。
Then, the difference of the arithmetic mean of the TL period observed data after the ith measurement is calculated: e1 i -E2 i 。
Step S32, obtaining absolute value PJ of the difference value of the arithmetic mean value of the observation data in the set time length before and after the ith measurement i ：
PJ i ＝|E1 i -E2 i |(i＝2，3，......，n-1)
All calculated PJ i Forming a second data sequence PJ 2 ，PJ 3 ，…，PJ n-1 }。
And step S4, judging whether the observed data of a certain measured time in the observed data sequence has the jump of the system error according to the first data sequence and the second data sequence.
Data y to be detected i And carrying out system error jump identification.
If the ith test time simultaneously meets the following two conditions:
KJ i ＝max{KJ 2 ，KJ 3 ，…，KJ n-1 }
PJ i ＝max{PJ 2 ，PJ 3 ，…，PJ n-1 }
then the original observation data sequence y is determined 1 ，y 2 ，…，y n The ith measurement in i A jump in the system error occurs.
The following description will take the 2004 dam temperature observation data sequence and the corresponding measurement time sequence as an example. As shown in FIG. 2, at y i+1 A systematic error jump is generated. Here, the absolute value of the slope and the absolute value of the mean difference are the maximum values of the first data sequence and the second data sequence, respectively, and the systematic error jump is successfully identified (TL is 30 days).
The invention constructs a new data sequence based on the observation data and the corresponding measuring time, fully considers the change characteristics of the dam safety monitoring data and the possible measuring time interval difference, and is suitable for the fields of dam safety monitoring and other engineering safety monitoring. In addition, the method of the present invention may be realized conveniently in computer program, and this raises the accuracy and efficiency of data processing.
In another embodiment, as shown in fig. 3, an intelligent identification system for error jump of a dam safety monitoring data system comprises:
an obtaining module 201, configured to obtain an observation data sequence of a dam safety monitoring physical quantity and a corresponding measurement time sequence thereof;
a first calculating module 202, configured to calculate an absolute value of a slope of each measurement according to the observation data sequence and the measurement time sequence, to obtain a first data sequence formed by the absolute values of the slopes of the measurements;
a second calculating module 203, configured to calculate, based on the observation data sequence, a difference between arithmetic averages of the observation data in set time lengths before and after each measurement, and take an absolute value of the difference to obtain a second data sequence formed by absolute values of arithmetic average differences of the observation data in set time lengths before and after each measurement;
the determining module 204 is configured to determine whether a system error jump occurs in observation data of a certain measurement time in the observation data sequence according to the first data sequence and the second data sequence.
Wherein, the first calculation module comprises:
the slope calculation module is used for calculating the slope of each measurement according to the observation data sequence and the measurement time sequence;
and the first data sequence generation module is used for taking an absolute value of the slope of each measurement calculated by the slope calculation module to obtain a first data sequence consisting of absolute values of the slopes of the measurements.
Wherein, the second calculation module comprises:
the mean difference value calculating module is used for calculating the difference value of the arithmetic mean value of the observation data in the set time length before and after each measurement based on the observation data sequence;
and the second data sequence generating module is used for taking an absolute value of the difference calculated by the mean difference calculating module to obtain a second data sequence formed by absolute values of arithmetic mean differences of the observation data within a set time length before and after each measurement.
In another embodiment, a computer-readable storage medium has a computer program stored thereon, which when executed in a computer causes the computer to execute the aforementioned method for intelligent identification of error jumps in a dam safety monitoring data system.
In another embodiment, a computing device comprises a memory and a processor, wherein the memory stores executable codes, and the processor executes the executable codes to realize the intelligent identification method for the error jump of the dam safety monitoring data system.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention has been disclosed in terms of the preferred embodiment, but is not intended to be limited to the embodiment, and all technical solutions obtained by substituting or converting equivalents thereof fall within the scope of the present invention.
Claims (10)
1. An intelligent identification method for error jump of a dam safety monitoring data system is characterized by comprising the following steps:
acquiring an observation data sequence of the dam safety monitoring physical quantity and a corresponding measurement time sequence thereof;
calculating the absolute value of the slope of each measurement according to the observation data sequence and the measurement time sequence to obtain a first data sequence consisting of the absolute values of the slopes of the measurements;
calculating the difference value of the arithmetic mean value of the observation data in the set time length before and after each measurement based on the observation data sequence, and taking the absolute value of the difference value to obtain a second data sequence consisting of the absolute value of the arithmetic mean value difference value of the observation data in the set time length before and after each measurement;
and judging whether the system error jump occurs in the observation data of a certain measured time in the observation data sequence or not according to the first data sequence and the second data sequence.
2. The method of claim 1, wherein calculating an absolute value of a slope for each measurement from the observation data sequence and the measurement time sequence comprises:
suppose the observed data sequence is { y } 1 ,y 2 ,…,y n With a corresponding measurement time sequence of { t } 1 ,t 2 ,…,t n Calculating the slope k of each measurement according to the following formula i ：
In the formula, t i And t i-1 Respectively are observed data y i And y i-1 Corresponding measurement time, wherein n is the total measurement time;
slope k for each measurement i Taking an absolute value:
KJ i ＝|k i |
in the formula, KJ i The absolute value of the slope of the ith measurement.
3. The method of claim 1, wherein the arithmetic mean of the observation data for a set length of time before and after each measurement is calculated according to the steps of:
assuming that the set time length is TL, k times of measurement are set in TL time period before the ith time of measurement, and the corresponding observed data is R 1 ，R 2 ，R 3 ，……，R k Then, the arithmetic mean value E1 of the observed data during the TL period before the ith test is calculated according to the following formula i ：
In the formula, R j ＝R 1 ，R 2 ，R 3 ，……，R k ，i＝2，3，……，n-1；
Let p measurements in TL time period after ith measurement and corresponding observed data be S 1 ，S 2 ，S 3 ，……S p Then, the arithmetic mean value E2 of the observed data in the TL period after the ith test is calculated according to the following formula i ：
In the formula, S j ＝S 1 ，S 2 ，S 3 ，……S p ；
Wherein the observed data of the ith test is contained in p tests in the TL time period after the ith test.
4. A method according to claim 3, characterised in that eachAbsolute value PJ of difference of arithmetic mean of observation data in set time length before and after each measurement i Calculated according to the following formula:
PJ i ＝|E1 i -E2 i |(i＝2，3，……n-1)。
5. the method of claim 1, wherein the determining whether a system error jump occurs in observed data of a certain measurement in the observed data sequence according to the first data sequence and the second data sequence comprises:
and if the absolute value of the slope of a certain measurement time is the maximum value in the first data sequence and the absolute value of the difference value of the arithmetic mean of the observation data in the set time length before and after the measurement time is the maximum value in the second data sequence, judging that the system error jump occurs in the observation data of the measurement time in the observation data sequence.
6. The utility model provides an intelligent identification system of dam safety monitoring data system error jump which characterized in that includes:
the acquisition module is used for acquiring an observation data sequence of the dam safety monitoring physical quantity and a corresponding measurement time sequence;
the first calculation module is used for calculating the absolute value of the slope of each measurement according to the observation data sequence and the measurement time sequence to obtain a first data sequence formed by the absolute values of the slopes of the measurements;
the second calculation module is used for calculating the difference value of the arithmetic mean value of the observation data in the set time length before and after each measurement based on the observation data sequence, and taking the absolute value of the difference value to obtain a second data sequence formed by the absolute value of the arithmetic mean value difference value of the observation data in the set time length before and after each measurement;
and the judging module is used for judging whether the system error jump occurs in the observation data of a certain measured time in the observation data sequence according to the first data sequence and the second data sequence.
7. The intelligent identification system for the error jump of the dam safety monitoring data system according to claim 6, wherein the first calculation module comprises:
the slope calculation module is used for calculating the slope of each measurement according to the observation data sequence and the measurement time sequence;
and the first data sequence generation module is used for taking an absolute value of the slope of each measurement calculated by the slope calculation module to obtain a first data sequence consisting of absolute values of the slopes of the measurements.
8. The intelligent identification system for the error jump of the dam safety monitoring data system according to claim 6, wherein the second calculation module comprises:
the mean difference value calculating module is used for calculating the difference value of the arithmetic mean value of the observation data in the set time length before and after each measurement based on the observation data sequence;
and the second data sequence generating module is used for taking an absolute value of the difference calculated by the mean difference calculating module to obtain a second data sequence formed by absolute values of arithmetic mean differences of the observation data within a set time length before and after each measurement.
9. A computer-readable storage medium, on which a computer program is stored which, when executed in a computer, causes the computer to carry out the method of any one of claims 1-5.
10. A computing device comprising a memory and a processor, wherein the memory has stored therein executable code that, when executed by the processor, implements the method of any of claims 1-5.
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