CN110751807A - Method for determining visual smoke foreign matter continuous alarm of power transmission line channel - Google Patents
Method for determining visual smoke foreign matter continuous alarm of power transmission line channel Download PDFInfo
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- CN110751807A CN110751807A CN201911012721.3A CN201911012721A CN110751807A CN 110751807 A CN110751807 A CN 110751807A CN 201911012721 A CN201911012721 A CN 201911012721A CN 110751807 A CN110751807 A CN 110751807A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B19/00—Alarms responsive to two or more different undesired or abnormal conditions, e.g. burglary and fire, abnormal temperature and abnormal rate of flow
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/10—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02G—INSTALLATION OF ELECTRIC CABLES OR LINES, OR OF COMBINED OPTICAL AND ELECTRIC CABLES OR LINES
- H02G1/00—Methods or apparatus specially adapted for installing, maintaining, repairing or dismantling electric cables or lines
- H02G1/02—Methods or apparatus specially adapted for installing, maintaining, repairing or dismantling electric cables or lines for overhead lines or cables
Abstract
A method for determining the visual smoke and fire foreign body continuous alarm of the electric transmission line channel, in particular to preprocessing the image continuous alarm sample data of a certain visual inspection device to extract the characteristic data, the real-time alarm data and the corresponding characteristic dataCalculating Pearson product-moment correlation coefficient by using the characteristic data, and passing through a threshold value R0And judging whether the alarm belongs to continuous alarm. According to the method, the Pearson product moment correlation coefficient is extracted and calculated through the characteristics of the sample data, the problems that smoke and fire alarms and foreign matter alarms are low in occurrence frequency and obvious in seasonal periodicity, conventional single comparison and good applicable dimension reduction analysis on machinery cannot be effectively identified are solved, model support is provided for subsequent application scenes such as intelligent alarm level marking, suspected false alarm of an AI image identification model and missed alarm sample identification, and the intelligent level of transmission line operation and detection is further improved.
Description
Technical Field
The invention discloses a method for determining visible smoke and foreign matter type continuous alarm of a power transmission line channel, belongs to the field of intelligent operation and inspection of power transmission lines, and particularly relates to a method for judging whether real-time alarm data belongs to continuous alarm or not based on labeled visible smoke and foreign matter type continuous alarm sample data of the power transmission line channel.
Background
Along with the upgrading of the maintenance technology of the power transmission line, the visual inspection of the power transmission line channel is widely applied, and the automatic identification of visual information and the marking of alarm objects such as machines, fireworks and foreign matters appearing in images are realized at present. Besides basic statistical analysis report, data mining can be performed based on alarm data, for example, continuous alarm judgment is performed on real-time alarm data, alarm level intelligent labeling, suspected false alarm of an image recognition model, sample recognition missing report and the like are performed based on a determination result, however, continuous alarm recognition technology support is required for application of the scenes, smoke and foreign matter alarms cannot be effectively recognized due to low occurrence frequency and obvious seasonal periodicity, and conventional single comparison and well-applicable dimension reduction analysis on machinery cannot be used for effectively recognizing the smoke and foreign matter alarms.
The visualized smoke and fire foreign matter type continuous alarm of the power transmission line channel is discrete data, so how to find a data processing method can efficiently and accurately determine the visualized smoke and fire foreign matter type continuous alarm is a technical direction which is researched by the applicant all the time. In the related art, data processing by using the pearson product moment correlation coefficient is conceivable for continuous data and waveform comparison, however, the pearson product moment correlation coefficient is difficult to be applied to discrete data for processing the visual smoke foreign matter type continuous alarm in the invention.
In summary, how to provide a feasible and accurate method for determining continuous alarms of fireworks and foreign matters to provide data support for the intelligent maintenance scene of the power transmission line is one of the problems to be solved by those skilled in the art.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for determining the visual smoke and fire foreign matter continuous alarm of the power transmission line channel, which solves the problems that the smoke and fire and foreign matter alarms cannot be effectively identified due to low occurrence frequency and obvious seasonal periodicity, and the conventional single comparison and the well-applicable dimensionality reduction analysis on the machinery cannot be realized by extracting the characteristics of sample data and calculating the Pearson product moment correlation coefficient.
The technical scheme adopted by the invention for realizing the purpose is as follows:
a method for determining continuous alarm of visual smoke and fire foreign matters of a power transmission line channel is characterized by comprising the following steps: the method comprises the following steps:
a. preprocessing image smoke and fire and foreign matter continuous alarm sample data of certain visual inspection equipment of the power transmission line and acquiring characteristic data; in the invention, the determination of smoke, fire and foreign body alarms in the image acquired by the equipment is identified by using an AI image identification model and is not in the functional range of the patent model; whether the alarms form continuous alarms or not is determined manually, and compared with mechanical alarms, due to the fact that the smoke and fire and foreign matter are few in occurrence frequency and large in time and region randomness, at present, an effective machine learning means is not found in the existing method;
b. b, processing smoke and fire and foreign matter real-time alarm data, and then performing Pearson product moment correlation coefficient calculation on the processed smoke and fire and foreign matter real-time alarm data and corresponding data in the characteristic data obtained in the step a to obtain a correlation coefficient R;
c. c, comparing R with a threshold value R by using the Pearson product moment correlation coefficient R obtained in the step b0Size, e.g. satisfies R>R0If the alarm data is a continuous alarm, otherwise, the alarm data is not a continuous alarm.
Preferably, according to the present invention, the step a comprises the following detailed steps:
a 1: preprocessing continuous alarm sample data and real-time alarm data, and only reserving a time field;
a 2: constructing a two-dimensional array with all values of 0, wherein the number of columns is the number of sampling points per day, the behavior is 12 or the multiple N12 of 12, and the two-dimensional array sequentially corresponds to 1-N12 months, wherein N is a natural number; the invention needs to be limited to 12 months, because the smoke, fire and foreign body alarms have strong periodicity, and a complete natural period year needs to be covered. For example, the fireworks are more in wheat harvesting season and dry season, and foreign matters are more in windy season; in addition, for consecutive months, the span needs to be a multiple of 12 months, for example, from the last 7 months 1 to the present 6 months 30, there is no data of the last three months, so periodicity cannot be represented;
a 3: traversing the continuous alarm sample data in the step a, and assigning values to the groups in the step a2, wherein the assignment principle is as follows: firstly, determining the corresponding row position in the two-dimensional array according to the month to which each piece of data belongs, secondly, determining the column position in the two-dimensional array by considering the sampling point corresponding to the time, adding 1 to the position value after determination, and obtaining the characteristic data after traversing is completed.
Preferably, according to the present invention, the step b comprises the following detailed steps:
b 1: acquiring a characteristic sequence of real-time data according to the number of sampling points per day, wherein the acquisition method comprises the steps of constructing a full 0 value sequence with the same number of elements as the number of the sampling points per day, and adding 1 to the position value of each alarm data according to the sampling point corresponding to the time of each alarm data;
b 2: and c, carrying out Pearson product moment correlation coefficient calculation on the feature sequence obtained in the step b1 and the data of the month corresponding to the feature data obtained in the step a3 to obtain a correlation coefficient R:
0,0 for all X set elements or 0 for all Y set elements
In the formula (1), an X set is a characteristic sequence corresponding to real-time data, and a Y set is a row sequence corresponding to a real-time data month in the characteristic data corresponding to the continuous alarm sample; the design has the advantages that in order to ensure that the input data can be judged, the Pearson product moment correlation coefficient is improved, and judgment of two conditions that X or Y set elements are 0 and X and Y sets are equal is added.
According to the present invention, preferably, the continuous alarm sample data in step a refers to alarm data of smoke, fire, foreign matter and the like of the same device with a time span of 2 years. In the invention, the time span has influence on the determination accuracy of the invention, and experiments show that the span time is suitable for 2-5 years, and the data is suitable for 2 years in consideration of the data acquisition difficulty. If the time span is too long, errors are introduced into the data in the past period due to change of landforms, and if the span is short, the randomness is large, so that the time span of the sample data determined by the method is selected to be 2 years.
According to a preferred embodiment of the invention, the threshold value R0The general evaluation standard of the correlation coefficient of the Pearson product moment is that the corresponding meanings of all the value ranges are as follows: 0.8-1.0 strong correlation, 0.6-0.8 strong correlation, 0.4-0.6 moderate correlation, 0.2-0.4 weak correlation, 0.0-0.2 weak correlation or no correlation; the threshold value R of the step c0The value is 0.6.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the idea of the invention is that a final solution is found after the data is specially reduced:
the invention is a complete model, wherein the Pearson correlation coefficient is a bright point, and the serialization and the feature extraction are both difficult to think. The weighted serialization of the discrete data results in characteristic data, and the analysis using pearson coefficients is only possible on the basis of such data. Meanwhile, the method carries out definition optimization on the calculation of the Pearson product moment correlation coefficient so as to meet the similarity calculation of the sequences, and solves the problem that the correlation coefficient cannot be calculated due to low occurrence frequency of smoke and fire and foreign matters, 0 characteristic data row elements in some cases and the fact that the real-time data characteristics are equal to the corresponding row sequence set of the characteristic data in some cases. And (4) the calculated result accords with the practical significance by defining optimization. The patent selects an improved operating method of the Pearson product-moment correlation coefficient, and solves the difficult point of an algorithm that the conventional comparison waveform is similar but the positions are not corresponding so that the dissimilarity is judged.
(2) The invention can carry out continuous alarm judgment on real-time alarm data of fireworks and foreign matters, and provides technology and model support for subsequent application scenes such as intelligent alarm level marking, suspected false alarm of an image recognition model and label of a sample which is not reported.
(3) The invention is based on a specific sample data feature extraction method and calculates the Pearson product moment correlation coefficient, because of special data reduction means and feature extraction method, the similarity judgment of the Pearson product moment correlation coefficient on discrete data becomes a feasible scheme, and unsupervised learning is carried out based on a model in a patent, thereby solving the problems that smoke and fire alarms and foreign alarms can not be effectively identified due to low occurrence frequency and obvious seasonal periodicity.
Drawings
Fig. 1 is a schematic flow chart of the determination method in the present invention.
Detailed Description
The present invention will be further described with reference to the following examples.
Examples 1,
A visual inspection device with the device ID of 99000843025795 is arranged on a power transmission line in a certain place, fireworks alarms are identified in an image shot by 2019-9-9, historical fireworks alarm data of 2017-9-9-2019-8 of the visual inspection device are inquired, 187 labeled continuous alarm sample data exist, and the time span is 2017-9-1609: 54: 17-2019-8-2914: 12: 31.
The goal is achieved: and judging whether the 6 pieces of real-time firework alarm data of the equipment 2019-9-9 belong to continuous alarms or not.
Constraint conditions are as follows: 1. the continuous alarm data comprises 25 fields of alarm self-increment ID, time, alarm content, image storage ID and the like; 2. the equipment image acquisition interval is 60 minutes; 3. threshold value R00.6, which is a common evaluation criterion for the correlation coefficient of pearson product moments, is a constant in the present model, and is not limited to the present embodiment.
The method for determining the visual smoke foreign matter continuous alarm of the power transmission line channel comprises the following steps:
a. preprocessing continuous alarm data and real-time alarm data, and only retaining the time attribute:
part of the continuous alarm sample data is as shown above;
real-time alarm data is as shown above;
b. constructing a two-dimensional array, wherein the column number of the two-dimensional array is 24h/0.5h ═ 48 of the sampling points per day, the behaviors 12 correspond to 1-12 months in sequence, so that a two-dimensional array data [48] [12] is constructed, and all values are initialized to 0;
c. traversing the alarm data of a, and assigning values to the number groups of b according to the assignment principle: firstly, determining a corresponding row position in the two-dimensional array according to the month to which each piece of data belongs, then determining a column position in the two-dimensional array by considering a sampling point corresponding to the time of each piece of data, adding 1 to the position value after determination, and obtaining characteristic data after traversing is completed;
[[0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,3,0,1,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[...],[...],[...],[...],[...],[...],[...],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,5,1,3,3,0,0,0,0,0,0],[...],[...],[...,0,1,0,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0]]
the assigned partial data is as described above. (ii) a
d. Processing the real-time alarm data to obtain a characteristic sequence [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 1,1,1,0,0,0,0,0,0, 0,0,0,0,0,0,0 ];
e. c, calculating a pearson product moment correlation coefficient by using the characteristic sequence obtained in the step d and the data of the month corresponding to the characteristic data obtained in the step c, namely 9-month data [0,0,0,0,0,0,0,0,0,0,0, 1,0,0, 3,0,1,0,0,0,0,0,1,0,1,0,1,0, 0,0,1,5,1,3,3,0,0,0,0,0,0, 0,0] to obtain a correlation coefficient R which is 0.6867;
f. the Pearson product moment correlation coefficient R obtained in the step e is 0.6867>R0And (4) 0.6, so the model judges that the real-time alarm data belongs to continuous alarm.
After the model triggers the continuous alarm, the continuous alarm is pushed to the maintainers, the image of the maintainers confirms that the continuous smoke and fire alarm is caused by the fire of the dry corn stalks of the power transmission lines, and after the emergency treatment, the accident that the power transmission lines are broken down and short-circuited due to solid particles is avoided.
Examples 2,
A visual inspection device with the device ID of 99000845000770 identifies foreign object alarms in an image shot by 2019-9-9, inquires historical foreign object alarm data of 2017-9-9-2019-9-8 of the visual inspection device, wherein 91 labeled continuous alarm sample data exist, and the time span is 2017-10-317: 24: 12-2019-8-2110: 17: 23.
The target is as follows: and judging whether the 7 pieces of real-time foreign matter alarm data of the equipment 2019-9-9 belong to continuous alarms or not.
In this embodiment, the same processing procedure as in embodiment 1 is adopted, and the pearson product moment correlation coefficient R is obtained as 0.6567, and it is determined that the alarm belongs to the foreign object type continuous alarm.
After the model triggers continuous warning in this patent, the maintainer has been given in the propelling movement, and through the image confirmation of maintainer, it is that the continuity foreign matter that a black plastic cloth arouses appears on the transmission line reports an emergency and asks for help or increased vigilance. After triggering continuous alarm, arranging patrol personnel to remove foreign matters by laser, and avoiding the accident of short circuit of the power transmission line caused by suspended matters.
The invention combines the embodiment to know that the invention carries out unsupervised learning based on a specific sample data feature extraction method and the calculation of the Pearson product moment correlation coefficient, realizes the automatic continuous alarm judgment of the real-time alarm data based on the unsupervised learning, and belongs to continuous alarm from the time distribution of 2 real-time alarm data and the confirmation of maintainers. The problem that continuous alarms cannot be effectively identified due to low occurrence frequency and obvious seasonal periodicity of smoke alarms and foreign body alarms is well solved, model support is provided for subsequent application scenes such as intelligent alarm level labeling, suspected false alarm of AI image identification models and identification of missed alarm samples, and the intelligent level of transmission line operation and detection is further improved.
Claims (6)
1. A method for determining continuous alarm of visual smoke and fire foreign matters of a power transmission line channel is characterized by comprising the following steps: the method comprises the following steps:
a. preprocessing image smoke and fire and foreign matter continuous alarm sample data of certain visual inspection equipment of the power transmission line and acquiring characteristic data;
b. b, processing smoke and fire and foreign matter real-time alarm data, and then performing Pearson product moment correlation coefficient calculation on the processed smoke and fire and foreign matter real-time alarm data and corresponding data in the characteristic data obtained in the step a to obtain a correlation coefficient R;
c. c, comparing R with a threshold value R by using the Pearson product moment correlation coefficient R obtained in the step b0Size, e.g. satisfies R>R0If the alarm data is a continuous alarm, otherwise, the alarm data is not a continuous alarm.
2. The method for determining the visual firework foreign matter type continuous alarm of the power transmission line channel according to claim 1, wherein the method comprises the following steps: the step a comprises the following detailed steps:
a 1: preprocessing continuous alarm sample data and real-time alarm data, and only reserving a time field;
a 2: constructing a two-dimensional array with all values of 0, wherein the number of columns is the number of sampling points per day, the behavior is 12 or the multiple N12 of 12, and the two-dimensional array sequentially corresponds to 1-N12 months, wherein N is a natural number;
a 3: traversing the continuous alarm sample data in the step a, and assigning values to the groups in the step a2, wherein the assignment principle is as follows: firstly, determining the corresponding row position in the two-dimensional array according to the month to which each piece of data belongs, secondly, determining the column position in the two-dimensional array by considering the sampling point corresponding to the time, adding 1 to the position value after determination, and obtaining the characteristic data after traversing is completed.
3. The method for determining the visual firework foreign matter type continuous alarm of the power transmission line channel according to claim 2, wherein the method comprises the following steps: the step b comprises the following detailed steps:
b 1: acquiring a characteristic sequence of real-time data according to the number of sampling points per day, wherein the acquisition method comprises the steps of constructing a full 0 value sequence with the same number of elements as the number of the sampling points per day, and adding 1 to the position value of each alarm data according to the sampling point corresponding to the time of each alarm data;
b 2: and c, carrying out Pearson product moment correlation coefficient calculation on the feature sequence obtained in the step b1 and the data of the month corresponding to the feature data obtained in the step a3 to obtain a correlation coefficient R:
in the formula (1), the set X is a feature sequence corresponding to the real-time data, and the set Y is a row sequence corresponding to the month of the real-time data in the feature data corresponding to the continuous alarm samples.
4. The method for determining the visual firework foreign matter type continuous alarm of the power transmission line channel according to claim 1, wherein the method comprises the following steps: the continuous alarm sample data in the step a refers to the smoke and fire alarm data and the foreign matter alarm data of the same equipment with the time span of 2 years.
5. The method for determining the visual firework foreign matter type continuous alarm of the power transmission line channel according to claim 1, wherein the method comprises the following steps: the above-mentionedThreshold value R0The general evaluation standard of the correlation coefficient of the Pearson product moment is that the corresponding meanings of all the value ranges are as follows: 0.8-1.0 strong correlation, 0.6-0.8 strong correlation, 0.4-0.6 moderate correlation, 0.2-0.4 weak correlation, 0.0-0.2 weak correlation or no correlation.
6. The method for determining the visual firework foreign matter type continuous alarm of the power transmission line channel according to claim 5, wherein the method comprises the following steps: the threshold value R of the step c0The value is 0.6.
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