CN111884336B - Real-time monitoring system based on big data - Google Patents
Real-time monitoring system based on big data Download PDFInfo
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- CN111884336B CN111884336B CN202010674766.3A CN202010674766A CN111884336B CN 111884336 B CN111884336 B CN 111884336B CN 202010674766 A CN202010674766 A CN 202010674766A CN 111884336 B CN111884336 B CN 111884336B
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
- H02J13/00034—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving an electric power substation
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Abstract
The invention provides a transformer substation area real-time monitoring system based on big data, which is characterized by comprising the following components: the data acquisition module acquires related data based on a large data platform of the transformer substation; the data processing module is used for processing the equipment state data and the video data and sending the processed data to the LCD billboard; the real-time monitoring module monitors the working state of personnel of each transformer substation in real time; an anomaly reminder module capable of identifying individuals of the substation within a non-specified time period. The invention can carry out real-time monitoring based on the collected equipment state information and the worker state in the transformer substation, and can timely discover and process the equipment abnormality. Meanwhile, the attendance checking problem is solved through a face recognition algorithm, and the phenomena of absence, early retreat or overtime of part of staff are prevented.
Description
Technical Field
The invention relates to the technical field of monitoring in a big data environment, in particular to a real-time monitoring system and a real-time monitoring method based on big data, which are applied to monitoring of power supply equipment areas, especially transformer substations and power supply stations.
Background
With the increasing scale of the application of the internet of things, the complexity of the system is greatly enhanced, the control of the system is increasingly complex, the field information is increasingly huge and complex, and even the risk of information storm is generated, so that a manager of a transformer substation cannot quickly and accurately obtain effective information from the massive information on the field. Substation managers cannot make quick and correct decisions through rapid analysis. The current system data are various in types and large in information quantity, and if no special effective tool is provided to help a manager to carry out quick and effective statistics and analysis, a substation manager cannot quickly make correct judgment.
To address the above challenges, we have built a data processing platform for real-time monitoring of substations. The power supply equipment state information and the staff state can be monitored in real time based on the collected power supply equipment state information, and the power supply equipment abnormality can be timely found and processed. Meanwhile, the attendance checking problem is solved through a face recognition algorithm, the phenomena of absence, early retreat or overtime of part of employees are prevented, and particularly the accuracy of recognition is improved through the design of a pooling layer and an activation function.
Disclosure of Invention
In order to solve the above problems, the present invention provides a transformer substation area real-time monitoring system based on big data, which is characterized by comprising:
the data acquisition module acquires related data based on a large data platform of the transformer substation, wherein the related data comprises power supply equipment state data and video data, and is based on a common standard interface when the data is acquired;
the data processing module is used for processing the state data and the video data of the power supply equipment and sending the processed state data and the video data to the LCD billboard;
the real-time monitoring module provides a real-time billboard function through the LCD billboard, provides a substation manager with a real-time power supply equipment state checking function, and monitors the working state of personnel in each substation in real time;
the abnormal reminding module is used for monitoring the abnormal state of the power supply equipment in real time, carrying out framing reminding on the power supply equipment in the abnormal state by using a red rectangular frame, and giving abnormal parameters in the abnormal state and corresponding upper and lower limit threshold intervals thereof through an LCD (liquid crystal display) signboard; the anomaly reminder module can identify individuals who break into the substation within unspecified time periods.
Optionally, the monitoring system further includes: the video data comprises monitoring data of each transformer substation and monitoring data of a public interval in each transformer substation; the shooting range of the monitoring data of each transformer substation comprises power supply equipment and workers in the transformer substation, and when a plurality of cameras are arranged in one transformer substation, the data processing module can splice the same time sequence image frames from different cameras to form a panoramic image.
Optionally, the monitoring system further includes: carrying out face recognition on the video frames in the monitoring range, and matching the recognition result with the attendance list of the current day to obtain abnormal personnel information; the attendance list stores the working personnel of the transformer substation on the current day and the corresponding working time, and when the personnel which cannot be matched are found in the matching process, abnormal reminding is carried out.
Optionally, the monitoring system further includes: the monitoring range further includes a substation doorway.
Optionally, the monitoring system further includes: and the scheduling center linkage module is used for sending abnormal information to the engineering part if the system still cannot recover normally within a specified threshold time period after the system monitors and identifies the abnormality of the power supply equipment, and the scheduling center directly obtains the monitoring video permission of the corresponding abnormal power supply equipment.
The invention also provides a real-time monitoring method based on the regional big data of the transformer substation, which is characterized by comprising the following steps:
the data acquisition is completed through a data acquisition module, the data acquisition module acquires related data based on a large data platform of a transformer substation, the related data comprises power supply equipment state data and video data, and the data acquisition is based on a common standard interface;
the data processing module is used for processing the state data and the video data of the power supply equipment and sending the processed state data and the video data to the LCD billboard;
the monitoring of the power supply equipment and workers is completed through the real-time monitoring module, and the real-time billboard function is provided through the LCD billboard, so that the managers of the transformer substation can conveniently check the state of the power supply equipment in real time and supervise the working state of the workers in each transformer substation in real time;
monitoring the abnormal state of the power supply equipment in real time through an abnormal reminding module, carrying out frame selection reminding on the power supply equipment in the abnormal state by using a red rectangular frame, and giving abnormal parameters in the abnormal state and corresponding upper and lower limit threshold intervals through an LCD (liquid crystal display) board; the anomaly reminding module can identify individuals who break into the substation within unspecified time periods.
Optionally, the monitoring method further includes: the video data comprises monitoring data of each transformer substation and monitoring data of a common interval of the transformer substations; the shooting range of the monitoring data of each transformer substation comprises power supply equipment and transformer substation workers, and when a plurality of cameras are arranged in one transformer substation, the data processing module can splice the same time sequence image frames from different cameras to form a panoramic image.
Optionally, the monitoring method further includes: carrying out face recognition on the video frames in the monitoring range, and matching the recognition result with the attendance list of the current day to obtain abnormal personnel information; the attendance list stores the working personnel of the transformer substation on the current day and the corresponding working time, and when the personnel which cannot be matched are found in the matching process, abnormal reminding is carried out.
Optionally, the monitoring method further includes: the monitoring range further includes a substation doorway.
Optionally, the monitoring method further includes: and the scheduling center linkage module is used for sending abnormal information to the scheduling center if the power supply equipment cannot be normally recovered within a specified threshold time period after the system monitors and identifies the abnormality of the power supply equipment, and the scheduling center directly obtains the monitoring video permission of the corresponding abnormal power supply equipment.
Drawings
Fig. 1 shows a system configuration of the present invention.
Detailed Description
As shown in fig. 1, the present invention provides a transformer substation area real-time monitoring system based on big data, which is characterized by comprising:
the data acquisition module acquires related data based on the big data platform, wherein the related data comprises power supply equipment state data and video data, and is based on a common standard interface when the data is acquired;
the data processing module is used for processing the state data and the video data of the power supply equipment and sending the processed state data and the video data to the LCD billboard;
the real-time monitoring module provides a real-time billboard function through the LCD billboard, provides a substation manager with a real-time power supply equipment state checking function, and monitors the working state of personnel in each substation in real time;
the abnormal reminding module is used for monitoring the abnormal state of the power supply equipment in real time, carrying out framing reminding on the power supply equipment in the abnormal state by using a red rectangular frame, and giving abnormal parameters in the abnormal state and corresponding upper and lower limit threshold intervals thereof through an LCD (liquid crystal display) signboard; the anomaly reminding module can identify individuals who break into the substation within unspecified time periods.
Optionally, the monitoring system further includes: the video data comprises monitoring data of each transformer substation and monitoring data of a public interval in each transformer substation; the camera shooting range of the monitoring data of each transformer substation comprises power supply equipment and workers in the transformer substation, and when one transformer substation is provided with a plurality of cameras, the data processing module can splice the same time sequence image frames from different cameras to form a panoramic image.
Optionally, the monitoring system further includes: carrying out face recognition on the video frames in the monitoring range, and matching the recognition result with the attendance list of the current day to obtain abnormal personnel information; the attendance list stores the working personnel of the transformer substation on the current day and the corresponding working time, and when the personnel which cannot be matched are found in the matching process, abnormal reminding is carried out.
Optionally, the monitoring system further includes: the monitoring range further includes a substation doorway. Optionally, the monitoring system further includes: and the scheduling center linkage module is used for sending abnormal information to the scheduling center if the power supply equipment cannot be normally recovered within a specified threshold time period after the system monitors and identifies the abnormality of the power supply equipment, and the scheduling center directly obtains the monitoring video permission of the corresponding abnormal power supply equipment.
Optionally, the face recognition uses a deep learning model for recognition, where the deep learning model includes an input layer, a bidirectional long and short term memory network BiLSTM layer, a first convolution unit, a first pooling layer, a second convolution unit, a second pooling layer, a third convolution unit, a third pooling layer, and a full connection layer; the input layer is used for receiving an image frame containing a human face; the size of a convolution kernel adopted by the first convolution unit is 5 x 5, and a first activation function of the first convolution unit is marked as F1; the convolution kernel size of the second convolution unit is 2 x 2, and a second activation function of the second convolution unit is marked as F2; the convolution kernel of the third convolution unit is 1 × 1, and a third activation function of the third convolution unit is marked as F3; the characteristics of the human face obtained by the output of the full connection layer are compared with the data in the known human face database, and a human face recognition result is output; the pooling method of the first, the first and the third pooling layers is as follows:
the activation function F1 ═ F2 ═ F3 ═ F (), expressed as:
the loss function φ is as follows:
n represents the size of the sample data set, i takes values of 1-N, yiRepresents a sample xiA corresponding label; wyiRepresents a sample xiAt its label yiA b vector comprising byiAnd bj，byiRepresents a sample xiAt its label yiDeviation of (a) from (b)jRepresents the deviation at output node j; a is the prediction output of the neural network model;
the loss function is used for training the network model and converging model parameters.
Optionally, the system performs global state monitoring, integrates streaming media monitoring signals, realizes real-time monitoring, provides a real-time billboard function, and displays production information on the LCD billboard in real time by creating a display panel, a chart, a configuration diagram, a work order, and the like.
Optionally, the system integrates a GIS map, can directly interface map systems such as hundredths, Tencent and the like, and performs data interface integration with the GIS system.
Optionally, the system provides a three-dimensional engine, and can build and import a three-dimensional model by itself, and add, dynamically display, alarm, data presentation, and the like functions on the model.
Optionally, the system supports configuration diagram functionality. The user can add objects such as pictures, characters, geometric shapes, user-defined graphs and the like on the configuration diagram interface by simple operations such as mouse clicking, dragging and the like to make a picture desired by the user. And editing and high-level setting are carried out on the added objects. For example: locating coordinates, setting multi-states, adding hyperlinks, setting background colors, shades, transparency, hierarchies, copying, deleting, revoking and the like. The built-in gallery of the platform comprises most of on-site power supply equipment and accessory pictures, and meets the basic requirements of making the configuration diagram. By binding a data source to the object and adding the multi-state attribute, a plurality of state numbers, the range value, the font color, the background color, the flashing state and the like of each state can be set for the object, so that the running state of the power supply equipment can be displayed in real time. By adding the hyperlink to the object and setting the address and content description of the link, the corresponding module can be clicked to inquire the corresponding power supply equipment state information.
Optionally, the system further provides a power supply device management function, and in order to better organize data, the scheme provides a set of power supply device modeling tools by using an object-oriented idea. A user can perform operations such as abstract arrangement, instantiation and the like on the assets of the power supply equipment through the client. The main function of the data management module is to store data, so as to facilitate the access of users and help the users to perform system analysis. The system integrates a plurality of information isolated islands, and uniformly displays the power supply equipment assets or the process flow of an enterprise. Through asset modeling, the assets of the power supply equipment can be quickly organized, and various operation and maintenance analysis works such as standard alignment, operation efficiency analysis, fault analysis and state-based analysis of the similar power supply equipment can be performed. For example:
real-time status
Maintenance entry-query
Summary of service record information
Historical alarm log queries, and summary reports
Alarm information statistics, which provides support for analyzing the primary reason and the secondary reason of the alarm of the power supply equipment, thereby facilitating the user to make a targeted maintenance plan
Optionally, the system further provides an alarm management module, and the platform is also internally provided with an alarm function. The user can realize the alarm monitoring of any data by adding the data source and setting the alarm condition in a self-defined way. The method supports the addition of data in any form, and can check alarm historical information statistics through screening conditions such as time, name, type, grade and the like.
Optionally, the system further provides a data analysis function module, and the system supports processing the original data in various manners and then presents the analyzed data result. Such as:
accumulation of
Variance (c)
Maximum value
Minimum value
Mean value
Linear relation
The user can sequence, stack and accumulate the acquired data, and present the data in various different ways, and can further process the data by combining a report and utilizing the built-in analysis function of the online Excel built in the platform.
Optionally, the system further provides a data report function module, and strong data statistics, classification and the like can be realized through flexible report use skills, so that the user is helped to analyze and explain important information. Data points are added and the table and data are formatted. For example: the method comprises the steps of working book size, cell format, data screening and sorting, selection of historical values, sampling values, filing values, snapshot values and the like, setting of time points for corresponding values and the like.
The invention also provides a transformer substation area real-time monitoring method based on the big data, which is characterized by comprising the following steps:
the data acquisition is completed through a data acquisition module, the data acquisition module acquires related data based on a big data platform, the related data comprises power supply equipment state data and video data, and the data acquisition module is based on a common standard interface;
the data processing module is used for processing the state data and the video data of the power supply equipment and sending the processed state data and the video data to the LCD billboard;
the monitoring of the power supply equipment and workers is completed through the real-time monitoring module, and the real-time billboard function is provided through the LCD billboard, so that the managers of the transformer substation can conveniently check the state of the power supply equipment in real time and supervise the working state of the workers in each transformer substation in real time;
monitoring the abnormal state of the power supply equipment in real time through an abnormal reminding module, carrying out frame selection reminding on the power supply equipment in the abnormal state by using a red rectangular frame, and giving abnormal parameters in the abnormal state and corresponding upper and lower limit threshold intervals through an LCD (liquid crystal display) board; the anomaly reminder module can identify individuals who break into a particular substation within an unspecified period of time.
Optionally, the monitoring method further includes: the video data comprise monitoring data of each power supply equipment workshop and monitoring data of a public interval in the transformer substation; the camera shooting range of the monitoring data of each power supply equipment workshop comprises the power supply equipment and workers in the workshops, and when one power supply equipment workshop is provided with a plurality of cameras, the data processing module can splice the same time sequence image frames from different cameras to form a panoramic image.
Optionally, the monitoring method further includes: carrying out face recognition on the video frames in the monitoring range, and matching the recognition result with the attendance list of the current day to obtain abnormal personnel information; the attendance list is stored with attendance staff of each workshop on the same day and corresponding working time, and abnormal reminding is carried out when staff which cannot be matched is found during matching.
Optionally, the monitoring method further includes: the monitoring range further comprises a doorway of a substation area.
Optionally, the monitoring method further includes: and the engineering part linkage module is used for sending abnormal information to the engineering part if the power supply equipment cannot be normally recovered within a specified threshold time period after the system monitors and identifies the abnormality of the power supply equipment, and the engineering part directly obtains the monitoring video permission of the corresponding abnormal power supply equipment.
Optionally, the face recognition uses a deep learning model for recognition, where the deep learning model includes an input layer, a bidirectional long and short term memory network BiLSTM layer, a first convolution unit, a first pooling layer, a second convolution unit, a second pooling layer, a third convolution unit, a third pooling layer, and a full connection layer; the input layer is used for receiving an image frame containing a human face; the size of a convolution kernel adopted by the first convolution unit is 5 x 5, and a first activation function of the first convolution unit is marked as F1; the convolution kernel size of the second convolution unit is 2 x 2, and a second activation function of the second convolution unit is marked as F2; the convolution kernel of the third convolution unit is 1 × 1, and a third activation function of the third convolution unit is marked as F3; the characteristics of the human face obtained by the output of the full connection layer are compared with the data in the known human face database, and a human face recognition result is output; the pooling method of the first, the first and the third pooling layers is as follows:
the activation function F1 ═ F2 ═ F3 ═ F (), expressed as:
the loss function φ is as follows:
n represents the size of the sample data set, i takes values of 1-N, yiRepresents a sample xiA corresponding label; wyiRepresents a sample xiAt its label yiA b vector comprising byiAnd bj，byiRepresents a sample xiAt its label yiDeviation of (a) from (b)jRepresents the deviation at output node j; a is the prediction output of the neural network model;
the loss function is used for training the network model and converging model parameters.
Optionally, the system performs global state monitoring, integrates streaming media monitoring signals, realizes real-time monitoring, provides a real-time billboard function, and displays production information on the LCD billboard in real time by creating a display panel, a chart, a configuration diagram, a work order, and the like.
Optionally, the system integrates a GIS map, can directly interface map systems such as hundredths, Tencent and the like, and performs data interface integration with the GIS system.
Optionally, the system provides a three-dimensional engine, and can build and import a three-dimensional model by itself, and add, dynamically display, alarm, data presentation, and the like functions on the model.
Optionally, the system supports configuration diagram functionality. The user can add objects such as pictures, characters, geometric shapes, user-defined graphs and the like on the configuration diagram interface by simple operations such as mouse clicking, dragging and the like to make a picture desired by the user. And editing and high-level setting are carried out on the added objects. For example: locating coordinates, setting multi-states, adding hyperlinks, setting background colors, shades, transparency, hierarchies, copying, deleting, revoking and the like. The built-in gallery of the platform comprises most of on-site power supply equipment and accessory pictures, and meets the basic requirements of making the configuration diagram. By binding a data source to the object and adding the multi-state attribute, a plurality of state numbers, the range value, the font color, the background color, the flashing state and the like of each state can be set for the object, so that the running state of the power supply equipment can be displayed in real time. By adding the hyperlink to the object and setting the address and content description of the link, the corresponding module can be clicked to inquire the corresponding power supply equipment state information.
Optionally, the system further provides a power supply device management function, and in order to better organize data, the scheme provides a set of power supply device modeling tools by using an object-oriented idea. A user can perform operations such as abstract arrangement, instantiation and the like on the assets of the power supply equipment through the client. The main function of the data management module is to store data, so as to facilitate the access of users and help the users to perform system analysis. The system integrates a plurality of information isolated islands, and uniformly displays the power supply equipment assets or the process flow of an enterprise. Through asset modeling, the assets of the power supply equipment can be quickly organized, and various operation and maintenance analysis works such as standard alignment, operation efficiency analysis, fault analysis and state-based analysis of the similar power supply equipment can be performed. For example:
real-time status
Maintenance entry-query
Summary of service record information
Historical alarm log queries, and summary reports
Alarm information statistics, which provides support for analyzing the primary reason and the secondary reason of the alarm of the power supply equipment, thereby facilitating the user to make a targeted maintenance plan
Optionally, the system further provides an alarm management module, and the platform is also internally provided with an alarm function. The user can realize the alarm monitoring of any data by adding the data source and setting the alarm condition in a self-defined way. The method supports the addition of data in any form, and can check alarm historical information statistics through screening conditions such as time, name, type, grade and the like.
Optionally, the system further provides a data analysis function module, and the system supports processing the original data in various manners and then presents the analyzed data result. Such as:
accumulation of
Variance (c)
Maximum value
Minimum value
Mean value
Linear relation
The user can sequence, stack and accumulate the acquired data, and present the data in various different ways, and can further process the data by combining a report and utilizing the built-in analysis function of the online Excel built in the platform.
Optionally, the system further provides a data report function module, and strong data statistics, classification and the like can be realized through flexible report use skills, so that the user is helped to analyze and explain important information. Data points are added and the table and data are formatted. For example: the method comprises the steps of working book size, cell format, data screening and sorting, selection of historical values, sampling values, filing values, snapshot values and the like, setting of time points for corresponding values and the like.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a volatile computer readable storage medium or a non-volatile computer readable storage medium.
The embodiment of the present disclosure further provides an electronic power supply device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured as the above method. The electronic power supply device may be provided as a terminal, server or other form of power supply device.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible power supply device that may hold and store instructions for use by the instruction execution power supply device. The computer readable storage medium may be, for example, but not limited to, an electrical storage power supply, a magnetic storage power supply, an optical storage power supply, an electromagnetic storage power supply, a semiconductor storage power supply, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanically encoded power supply device, a raised structure such as a punch card or indentation having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to various computing/processing power supply devices, or to an external computer or external storage power supply device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing power supply device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing power supply device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other power generation devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other power providing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other power providing devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other power providing devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (8)
1. A transformer substation area real-time monitoring system based on big data is characterized by comprising:
the data acquisition module acquires related data based on a large data platform of the transformer substation, wherein the related data comprises power supply equipment state data and video data, and is based on a standard interface when the data is acquired;
the data processing module is used for processing the state data and the video data of the power supply equipment and sending the processed state data and the video data to the LCD billboard;
the real-time monitoring module provides a real-time billboard function through the LCD billboard, provides a substation manager with a real-time power supply equipment state checking function, and monitors the working state of personnel in each substation in real time;
the abnormal reminding module is used for monitoring the abnormal state of the power supply equipment in real time, carrying out framing reminding on the power supply equipment in the abnormal state by using a red rectangular frame, and giving abnormal parameters in the abnormal state and corresponding upper and lower limit threshold intervals thereof through an LCD (liquid crystal display) signboard; the abnormity reminding module can identify individuals who break into the transformer substation in an unspecified time period;
carrying out face recognition on the video frames in the monitoring range, and matching the recognition result with the attendance list of the current day to obtain abnormal personnel information; the attendance list is stored with the on-duty personnel of each transformer substation on the current day and the corresponding working time, and when the personnel which cannot be matched are found in the matching process, abnormal reminding is carried out;
the face recognition uses a deep learning model for recognition, and the deep learning model comprises an input layer, a bidirectional long-short term memory network (BilSTM) layer, a first convolution unit, a first pooling layer, a second convolution unit, a second pooling layer, a third convolution unit, a third pooling layer and a full-connection layer; the input layer is used for receiving an image frame containing a human face; the size of a convolution kernel adopted by the first convolution unit is 5 x 5, and a first activation function of the first convolution unit is marked as F1; the convolution kernel size of the second convolution unit is 2 x 2, and a second activation function of the second convolution unit is marked as F2; the convolution kernel of the third convolution unit is 1 × 1, and a third activation function of the third convolution unit is marked as F3; the characteristics of the human face obtained by the output of the full connection layer are compared with the data in the known human face database, and a human face recognition result is output; pooling methods of the first, and third pooling layersThe following were used:
wherein x iseRepresents the output of the current layer, ueRepresenting the input of an activation function, f () representing an activation function, weRepresents the weight of the current layer, phi represents the loss function, xe-1Represents the output of the previous layer, beRepresents a bias, δ represents a constant;
the activation function F1 ═ F2 ═ F3 ═ F (), expressed as:
the loss function φ is as follows:
n represents the size of the sample data set, i takes values of 1-N, yiRepresents a sample xiA corresponding label; wyiRepresents a sample xiAt its label yiA b vector comprising byiAnd bj，byiRepresents a sample xiAt its label yiDeviation of (a) from (b)jRepresents the deviation at output node j; a is the prediction output of the neural network model;
the loss function is used for training the network model and converging model parameters.
2. The monitoring system of claim 1, further comprising: the video data comprises monitoring data of each transformer substation and monitoring data of a public interval in each transformer substation; the shooting range of the monitoring data of each transformer substation comprises power supply equipment and workers in the transformer substation, and when a plurality of cameras are arranged in one transformer substation, the data processing module can splice the same time sequence image frames from different cameras to form a panoramic image.
3. The monitoring system of claim 2, further comprising: the monitoring range further includes a substation doorway.
4. The monitoring system of claim 1, further comprising: and the scheduling center linkage module is used for sending abnormal information to the scheduling center if the power supply equipment cannot be normally recovered within a specified threshold time period after the system monitors and identifies the abnormality of the power supply equipment, and the scheduling center directly obtains the monitoring video permission of the corresponding abnormal power supply equipment.
5. A transformer substation area real-time monitoring method based on big data is characterized by comprising the following steps:
the data acquisition is completed through a data acquisition module, the data acquisition module acquires related data based on a large data platform of the transformer substation, the related data comprises power supply equipment state data and video data, and the data acquisition is based on a standard interface;
the data processing module is used for processing the state data and the video data of the power supply equipment and sending the processed state data and the video data to the LCD billboard;
the monitoring of the power supply equipment and workers is completed through the real-time monitoring module, the real-time billboard function is provided through the LCD billboard, the state of the power supply equipment is checked by a substation manager in real time, and the working state of the workers in each substation is monitored in real time;
monitoring the abnormal state of the power supply equipment in real time through an abnormal reminding module, carrying out frame selection reminding on the power supply equipment in the abnormal state by using a red rectangular frame, and giving abnormal parameters in the abnormal state and corresponding upper and lower limit threshold intervals through an LCD (liquid crystal display) board; the abnormity reminding module can identify individuals who break into the transformer substation in an unspecified time period;
carrying out face recognition on the video frames in the monitoring range, and matching the recognition result with the attendance list of the current day to obtain abnormal personnel information; the attendance list is stored with the on-duty personnel of each transformer substation on the current day and the corresponding working time, and when the personnel which cannot be matched are found in the matching process, abnormal reminding is carried out;
the face recognition uses a deep learning model for recognition, and the deep learning model comprises an input layer, a bidirectional long-short term memory network (BilSTM) layer, a first convolution unit, a first pooling layer, a second convolution unit, a second pooling layer, a third convolution unit, a third pooling layer and a full-connection layer; the input layer is used for receiving an image frame containing a human face; the size of a convolution kernel adopted by the first convolution unit is 5 x 5, and a first activation function of the first convolution unit is marked as F1; the convolution kernel size of the second convolution unit is 2 x 2, and a second activation function of the second convolution unit is marked as F2; the convolution kernel of the third convolution unit is 1 × 1, and a third activation function of the third convolution unit is marked as F3; the characteristics of the human face obtained by the output of the full connection layer are compared with the data in the known human face database, and a human face recognition result is output; the pooling method of the first, the first and the third pooling layers is as follows:
wherein x iseRepresents the output of the current layer, ueRepresenting the input of an activation function, f () representing an activation function, weRepresents the weight of the current layer, phi represents the loss function, xe-1Represents the output of the previous layer, beRepresents a bias, δ represents a constant;
the activation function F1 ═ F2 ═ F3 ═ F (), expressed as:
the loss function φ is as follows:
n represents the size of the sample data set, i takes values of 1-N, yiRepresents a sample xiA corresponding label; wyiRepresents a sample xiAt its label yiA b vector comprising byiAnd bj，byiRepresents a sample xiAt its label yiDeviation of (a) from (b)jRepresents the deviation at output node j; a is the prediction output of the neural network model;
the loss function is used for training the network model and converging model parameters.
6. The monitoring method of claim 5, further comprising: the video data comprises monitoring data of each transformer substation and monitoring data of a public interval in each transformer substation; the shooting range of the monitoring data of each transformer substation comprises power supply equipment and workers in the transformer substation, and when a plurality of cameras are arranged in one transformer substation, the data processing module can splice the same time sequence image frames from different cameras to form a panoramic image.
7. The monitoring method of claim 6, further comprising: the monitoring range further includes a substation doorway.
8. The monitoring method of claim 5, further comprising: and the scheduling center linkage module is used for sending abnormal information to the scheduling center if the power supply equipment cannot be normally recovered within a specified threshold time period after the system monitors and identifies the abnormality of the power supply equipment, and the scheduling center directly obtains the monitoring video permission of the corresponding abnormal power supply equipment.
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