CN111899448A - Method and system for filtering intelligent inspection information of traction substation - Google Patents
Method and system for filtering intelligent inspection information of traction substation Download PDFInfo
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- CN111899448A CN111899448A CN202010224688.7A CN202010224688A CN111899448A CN 111899448 A CN111899448 A CN 111899448A CN 202010224688 A CN202010224688 A CN 202010224688A CN 111899448 A CN111899448 A CN 111899448A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
- G08B13/19613—Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion
- G08B13/19615—Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion wherein said pattern is defined by the user
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
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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
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
Abstract
The invention relates to a method and a system for filtering intelligent patrol inspection information of a traction substation, wherein the method comprises the following steps: continuously receiving alarm information sent by a traction substation inspection system by adopting a TCP/IP protocol, wherein the alarm information comprises picture information of stranger intrusion alarm and/or text information of sensor abnormity alarm; generating corresponding characteristic vectors of the received alarm information according to the alarm types, and storing the characteristic vectors into a database; searching out the characteristic vectors of the received alarm message in a set time period in a database, performing Euclidean distance operation one by one to obtain a similarity result, and comparing the similarity result with a set threshold value; and if the similarity result is smaller than the set threshold value, carrying out alarm pushing on the user, otherwise, not carrying out alarm pushing on the user. The invention can filter repeated alarm information under the condition of ensuring that a user can timely obtain the current alarm information of the traction substation on the mobile phone, so as to reduce the repeated alarm amount and reduce the burden of the user.
Description
Technical Field
The invention relates to the technical field of substation fault inspection, in particular to a method and a system for filtering intelligent inspection information of a traction substation.
Background
With the continuous improvement of the automation degree of a traction substation (station), more and more instruments and meters and sensor equipment are installed. The monitoring of the field condition of the substation becomes an important problem which needs to be considered by the current substation management personnel, and if the data detected by the instrument is correct, and the situations such as illegal invasion, water immersion, even fire and the like occur, the on-duty personnel needs to be arranged for regular or irregular inspection. With the increase of the number of traction power substations along the railway, the demand on personnel is higher and higher, and the possible loss caused by the negligence of the personnel is increased. Meanwhile, the number of the transformer substations is large, the positions of the transformer substations are scattered, and even the transformer substations are located in remote positions, so that potential safety hazards are high.
Although the intelligent inspection system of the traction substation has stronger and stronger functions, for example, picture information of stranger intrusion alarm and character information of sensor abnormity alarm can be transmitted to a dispatching center of a section or a position through a network, and then the picture information and the character information are sent to the intelligent inspection mobile phone application of the traction substation by the dispatching center, so that a user can check the picture information and process the picture information and the character information in time through the intelligent inspection mobile phone application of the traction substation. But there is also an important problem: due to the excessively high similar alarm rate, the user experiences badly when using the smart phone of the traction substation, accurate information cannot be obtained at the first time, and the user is tired of dealing with the information. Therefore, a method for filtering intelligent routing inspection information of the traction substation is urgently needed to reduce repeated alarm amount and reduce user burden.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides the intelligent routing inspection information filtering method and system for the traction substation.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
on one hand, the embodiment of the invention provides a method for filtering intelligent patrol inspection information of a traction substation, which comprises the following steps:
continuously receiving alarm information sent by a traction substation inspection system by adopting a TCP/IP protocol, wherein the alarm information comprises picture information of stranger intrusion alarm and/or text information of sensor abnormity alarm;
generating corresponding characteristic vectors of the received alarm information according to the alarm types, and storing the characteristic vectors into a database;
searching out the characteristic vectors of the received alarm message in a set time period in a database, performing Euclidean distance operation one by one to obtain a similarity result, and comparing the similarity result with a set threshold value;
and if the similarity result is smaller than the set threshold value, carrying out alarm pushing on the user, otherwise, not carrying out alarm pushing on the user.
According to the embodiment of the invention, for the picture information of stranger intrusion alarm, a faceNet network model is adopted to extract the feature vector of the face.
According to the embodiment of the invention, for the character information of 'sensor abnormity alarm', a TF-IDF algorithm is adopted for calculating the alarm similarity to obtain the feature vector.
On the other hand, the embodiment of the invention provides a system for filtering intelligent patrol inspection information of a traction substation, which comprises the following steps:
an alarm information receiving module: continuously receiving alarm information sent by a traction substation inspection system by adopting a TCP/IP protocol, wherein the alarm information comprises picture information of stranger intrusion alarm and/or text information of sensor abnormity alarm;
a feature vector conversion module: generating corresponding characteristic vectors of the received alarm information according to the alarm types, and storing the characteristic vectors into a database;
a similarity calculation module: searching out the characteristic vectors of the received alarm message in a set time period in a database, and performing Euclidean distance operation one by one to obtain a similarity result;
the information filtering module: and comparing the similarity result with a set threshold, if the similarity result is smaller than the set threshold, carrying out alarm pushing on the user, otherwise, not carrying out alarm pushing on the user.
Compared with the prior art, the invention has the beneficial effects that: the invention adopts different methods to filter information aiming at two alarm types of stranger invasion and sensor abnormity. The method comprises the steps that in stranger intrusion alarm, a faceNet model is adopted, a face image transmitted from a dispatching center is mapped into a 128-dimensional feature vector, the vector is used as a feature, and a KNN model is adopted to filter a repeated face within 5 minutes; in the abnormal alarm of the sensor, a TF-IDF algorithm is adopted to filter the highly repeated alarm information, so that the aims of reducing the repeated alarm amount and reducing the burden of a user are fulfilled, but the user is not influenced to know the current alarm information of the traction substation in time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of the intelligent routing inspection information filtering method for the traction substation.
FIG. 2 is a diagram showing a triplet loss function in the example.
Fig. 3 is a block diagram of the intelligent routing inspection information filtering system of the traction substation in the embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment provides a method for filtering intelligent inspection information of a traction substation, which is applied to an intelligent inspection mobile phone of the traction substation. As shown in fig. 1, the method comprises the steps of:
step 1, continuously receiving alarm information sent by a traction substation inspection system by adopting a TCP/IP protocol, wherein the alarm information comprises picture information of stranger intrusion alarm and/or character information of sensor abnormity alarm. The picture information of stranger intrusion alarm and the character information of sensor abnormal alarm are two different alarm types respectively.
And 2, generating corresponding characteristic vectors of the received alarm information according to the alarm types, and storing the characteristic vectors into a database.
The method comprises the steps of extracting feature vectors of a human face by adopting a faceNet network model for picture information of stranger intrusion alarm, and then using the extracted feature vectors for human face comparison. And the FaceNet adopts a triplet loss function to train a convolutional neural network for extracting the human face characteristics. Triple loss function (Triplet loss) is generally used for fine-grained identification at an individual level, for example, identification of large classes such as a dog and a flower, and FaceNet uses it for identification of a human face by virtue of its classification idea, in order to map a human face image X into a d-dimensional euclidean space f (X) Rd. Within this vector space, it is desirable to guarantee the image of a single individual
And other images of the individual
Images of other individuals
The distance is far. FIG. 2 shows a schematic diagram of the triplex molecule. The purpose of triple-loss optimization is to enable the same person to pass the convolution spiritThe Euclidean distance of the feature vectors obtained through the network is as close as possible, and different people are as far as possible. The training purpose of the face feature extraction module is not only to extract the stable features of the face for recognition, but more importantly, to make the feature discrimination obtained after different faces pass through the network model higher, and the discrimination is measured by the distance of the Euclidean space of different people. The training data of the faceNet network model adopts a CASIA-WebFace Liqing face recognition data set, and the training set comprises 453453 images and the identity of the face of a person exceeding 10575 after face detection.
For the character information of 'sensor abnormity alarming', a TF-IDF algorithm is adopted for calculating the alarming similarity. TF-IDF is used to assess how important a word is to one of a set of documents or a corpus. The importance of a word increases in proportion to the number of times it appears in a document, but at the same time decreases in inverse proportion to the frequency with which it appears in the corpus. The main idea is as follows: if a word appears in an article with a high frequency TF and rarely appears in other articles, the word or phrase is considered to have a good classification capability and is suitable for classification. Wherein TF is Term Frequency (Term Frequency) and the calculation formula is
Wherein n isi，jThe number of times of the word appearing in the file, and the denominator is the sum of the number of times of all the words appearing in the file; the IDF is the Inverse document frequency (Inverse document frequency), and the IDF of a specific term can be obtained by dividing the total number of documents by the number of documents containing the term and taking the logarithm of the obtained quotient. If the documents containing the entry t are fewer and the IDF is larger, the entry has good category distinguishing capability. The formula is as follows,
the final TF-IDF feature vector is calculated as TFIDF.
And 3, searching the characteristic vectors of the received alarm message in a set time period (for example, five minutes) in the database, and performing Euclidean distance operation one by one to obtain a similarity result. And judging whether to push or not according to the set threshold value.
And 4, if the similarity result is smaller than the set threshold value, namely the similarity is smaller, which indicates that the alarm information is not the same alarm information, carrying out alarm pushing on the user.
And 5, if the similarity result is larger than or equal to the set threshold value, namely the similarity is larger, and the same alarm information is shown, not carrying out alarm pushing on the user.
As shown in fig. 3, an embodiment of the present invention further provides a system for filtering intelligent routing inspection information of a traction substation, including:
an alarm information receiving module: and continuously receiving alarm information sent by the inspection system of the traction substation by adopting a TCP/IP protocol, wherein the alarm information comprises picture information of stranger intrusion alarm and/or text information of sensor abnormity alarm.
A feature vector conversion module: and generating corresponding characteristic vectors of the received alarm information according to the alarm types, and storing the characteristic vectors into a database. For picture information of stranger intrusion alarm, the feature vector conversion module adopts a faceNet network model to extract feature vectors of human faces. For the character information of 'sensor abnormal alarm', the feature vector conversion module adopts TF-IDF algorithm for calculating alarm similarity to obtain feature vector.
A similarity calculation module: and searching the characteristic vectors of the received alarm message in a set time period in a database, and performing Euclidean distance operation one by one to obtain a similarity result.
The information filtering module: and comparing the similarity result with a set threshold, if the similarity result is smaller than the set threshold, carrying out alarm pushing on the user, otherwise, not carrying out alarm pushing on the user.
The system is corresponding to the method, and therefore, the related contents in the description of the method can be referred to when the system is not described here.
Along with the continuous improvement of the automation degree of the traction substation, more and more instruments and meters and sensor equipment are installed. The information collected by the equipment can be transmitted to a dispatching center of a section or a place through a network, stranger invasion and sensor abnormity alarming redundancy sent from the dispatching center to the intelligent inspection mobile phone application of the traction substation are high, and judgment and experience of a user are influenced. The invention adopts different methods to filter information aiming at two alarm types of stranger invasion and sensor abnormity. The method comprises the steps that in stranger intrusion alarm, a faceNet model is adopted, a face image transmitted from a dispatching center is mapped into a 128-dimensional feature vector, the vector is used as a feature, and a KNN model is adopted to filter a repeated face within 5 minutes; in the abnormal alarm of the sensor, a TF-IDF algorithm is adopted to filter the highly repeated alarm information, so that the aims of reducing the repeated alarm amount and reducing the burden of a user are fulfilled, but the user is not influenced to know the current alarm information of the traction substation in time.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. The method for filtering the intelligent patrol inspection information of the traction substation is characterized by comprising the following steps of:
continuously receiving alarm information sent by a traction substation inspection system by adopting a TCP/IP protocol, wherein the alarm information comprises picture information of stranger intrusion alarm and/or text information of sensor abnormity alarm;
generating corresponding characteristic vectors of the received alarm information according to the alarm types, and storing the characteristic vectors into a database;
searching out the characteristic vectors of the received alarm message in a set time period in a database, performing Euclidean distance operation one by one to obtain a similarity result, and comparing the similarity result with a set threshold value;
and if the similarity result is smaller than the set threshold value, carrying out alarm pushing on the user, otherwise, not carrying out alarm pushing on the user.
2. The intelligent routing inspection information filtering method for the traction substation according to claim 1, wherein feature vectors of human faces are extracted from picture information of strangers intrusion alarm by adopting a FaceNet network model.
3. The intelligent routing inspection information filtering method for the traction substation according to claim 1, wherein for the text information of "sensor abnormal alarm", a TF-IDF algorithm is adopted for alarm similarity calculation to obtain a feature vector.
4. The method for intelligent routing inspection information filtering of the traction substation according to claim 1, wherein the set time period is 5 minutes.
5. The utility model provides a system for information filter is patrolled and examined to traction substation intelligence which characterized in that includes:
an alarm information receiving module: continuously receiving alarm information sent by a traction substation inspection system by adopting a TCP/IP protocol, wherein the alarm information comprises picture information of stranger intrusion alarm and/or text information of sensor abnormity alarm;
a feature vector conversion module: generating corresponding characteristic vectors of the received alarm information according to the alarm types, and storing the characteristic vectors into a database;
a similarity calculation module: searching out the characteristic vectors of the received alarm message in a set time period in a database, and performing Euclidean distance operation one by one to obtain a similarity result;
the information filtering module: and comparing the similarity result with a set threshold, if the similarity result is smaller than the set threshold, carrying out alarm pushing on the user, otherwise, not carrying out alarm pushing on the user.
6. The system for filtering the intelligent routing inspection information of the traction substation according to claim 5, wherein for picture information of stranger intrusion alarm, the feature vector conversion module extracts feature vectors of human faces by adopting a FaceNet network model.
7. The system for filtering the intelligent routing inspection information of the traction substation according to claim 5, wherein for the text information of 'sensor abnormity alarming', the feature vector conversion module adopts a TF-IDF algorithm for calculating the alarm similarity to obtain a feature vector.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2023273665A1 (en) * | 2021-06-30 | 2023-01-05 | 武汉理工光科股份有限公司 | Repeated fire alarm determining method and apparatus, electronic device, and storage medium |
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