CN116702042A - Data characteristic processing method and equipment for medium-low voltage distribution system equipment - Google Patents
Data characteristic processing method and equipment for medium-low voltage distribution system equipment Download PDFInfo
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Abstract
The application discloses a data characteristic processing method and equipment for medium-low voltage distribution system equipment. The method comprises the following steps: acquiring power grid business data from a data collection sub-platform, wherein the power grid business data comprises an identity of the data collection sub-platform; performing feature classification on the power grid service data according to the identity to obtain at least two different types of sub-power grid service data of the power grid operation data; and correcting the service data of each sub-grid, and storing the corrected sub-grid service data into a category storage space associated with the category of the sub-grid service data and the identity. The embodiment of the application can improve the processing efficiency of the power grid business data.
Description
Technical Field
The application belongs to the technical field of data interaction performance optimization, and particularly relates to a data characteristic processing method and device for equipment of a medium-low voltage distribution system.
Background
The power enterprises are mainly characterized by dense technology and assets, and in the current operation of the power enterprises, information technology is widely applied in the enterprises, so that more data contents can be generated, and the quantity of real-time data in the operation process is continuously increased. For power enterprises, the current problem is how to collect and store data, and more critical is how to analyze and utilize the data, so that the data provides effective references for the development of the power enterprises, thereby supporting the self business development and future strategic decisions of the power enterprises. Therefore, the work of analyzing and processing big data is particularly critical in the current power big data development background aiming at the development needs of power enterprises. In recent years, the use of internet technology in the power field has become more and more widespread. Along with the improvement of the informatization degree of the company, the information quantity generated in different business processes is very huge, so that the data interaction performance optimization method and device become research hotspots.
However, the existing data interaction performance optimization method and device still have the problems that data access is mostly full-disk access, and the access process is stored, so that the running amount of a stored program is huge, and the optimization rate of the data interaction performance is low.
The information disclosed in the background section of the application is only for enhancement of understanding of the general background of the application and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The application provides a method and equipment for processing data characteristics of equipment of a medium-low voltage distribution system, which are used for improving the processing efficiency of power grid business data.
According to one aspect of the application, a method for processing data characteristics of equipment of a medium-low voltage power distribution system is provided, which comprises the following steps:
acquiring power grid business data from a data collection sub-platform, wherein the power grid business data comprises an identity of the data collection sub-platform;
performing feature classification on the power grid service data according to the identity to obtain at least two different types of sub-power grid service data of the power grid operation data;
and correcting the service data of each sub-grid, and storing the corrected sub-grid service data into a category storage space associated with the category of the sub-grid service data and the identity.
Further, the classifying the power grid service data according to the identity, and obtaining at least two sub-power grid service data of different categories of the power grid operation data includes:
determining at least two category characteristics to be classified of the power grid business data according to the identity;
and classifying the power grid service data according to the class characteristics to be classified to obtain at least two different classes of sub-power grid service data.
Further, the identity mark comprises an IP address field and domain name information of the data collection sub-platform.
Further, the correcting the service data of each sub-grid includes:
acquiring target normal data of the corresponding class of the sub-grid business data from a normal data information base;
if the sub-grid business data are determined to be abnormal according to the target normal data, determining that the sub-grid business data are not corrected;
and if the sub-grid business data are determined to be normal according to the target normal data, determining that the sub-grid business data are corrected to pass.
Further, after the determining that the correction of the sub-grid business data is not passed, the method further includes:
the data collection sub-platform is remotely monitored, and secondary correction is carried out according to the remote monitoring result;
and if the data collection sub-platform has the problem of parameter abnormality according to the secondary correction result, carrying out parameter adjustment on the data collection sub-platform.
Further, after storing the corrected sub-grid service data in the category storage space associated with the sub-grid service data category and the identity, the method further includes:
acquiring a query request of power grid business data from a foreground system, wherein the query request comprises a query identity and a query data category of a data collection sub-platform;
determining a target category storage space corresponding to the query request according to the query identity and the query data category;
and generating a query result according to the power grid business data in the target class storage space, and feeding back the query result to the foreground system.
Further, the method further comprises:
acquiring an operation monitoring image of a data collection sub-platform;
acquiring a historical operation image of the data collection sub-platform from a historical operation image library, and determining the appearance damage rate of the data collection sub-platform according to the historical operation image and the operation monitoring image;
and if the appearance damage rate is higher than the normal work appearance damage rate, pushing the operation alarm information of the data collection sub-platform to the staff.
According to another aspect of the present application, there is provided a data feature processing apparatus for a medium-low voltage power distribution system, including:
the service data acquisition module is used for acquiring power grid service data from the data collection sub-platform, wherein the power grid service data comprises an identity of the data collection sub-platform;
the service data classification module is used for carrying out feature classification on the power grid service data according to the identity mark to obtain at least two different types of sub-power grid service data of the power grid operation data;
the service data storage module is used for correcting the service data of each sub-power grid and storing the corrected sub-power grid service data into a category storage space associated with the category and the identity of the sub-power grid service data
According to another aspect of the present application, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method for processing grid business data according to any one of the embodiments of the present application.
According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for processing grid service data according to any embodiment of the present application when executed.
According to the embodiment of the application, the power grid business data are subjected to characteristic definition and classification, so that the power grid business data are subjected to fixed-point storage according to the data source and the data category, fixed-point data query is performed through the required data characteristic information when data interaction is performed, excessive storage steps in the data query process are reduced, and the optimization rate of the data interaction performance is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
fig. 1 is a flowchart of a method for processing data characteristics of a medium-low voltage power distribution system device according to an embodiment of the present application;
fig. 2 is a flowchart of a method for processing data characteristics of a medium-low voltage power distribution system device according to another embodiment of the present application;
fig. 3 is a schematic structural diagram of a data feature processing device of a medium-low voltage distribution system according to another embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device implementing an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, not all embodiments. Based on the embodiments in this disclosure, all other embodiments that a person of ordinary skill in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein.
It should be understood that, in various embodiments of the present disclosure, the size of the sequence number of each process does not mean that the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
It should be understood that in this disclosure, "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in this disclosure, "plurality" means two or more. "and/or" is merely an association relationship describing an association object, and means that three relationships may exist, for example, and/or B may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "comprising A, B and C", "comprising A, B, C" means that all three of A, B, C comprise, "comprising A, B or C" means that one of the three comprises A, B, C, and "comprising A, B and/or C" means that any 1 or any 2 or 3 of the three comprises A, B, C.
It should be understood that in this disclosure, "B corresponding to a", "a corresponding to B", or "B corresponding to a" means that B is associated with a from which B may be determined. Determining B from a does not mean determining B from a alone, but may also determine B from a and/or other information. The matching of A and B is that the similarity of A and B is larger than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection" depending on the context.
The technical scheme of the present disclosure is described in detail below with specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Fig. 1 is a flowchart of a method for processing data characteristics of a medium-low voltage power distribution system device according to an embodiment of the present application, where the method may be implemented by a processing device for processing power grid service data, where the device may be implemented in hardware and/or software, and the device may be configured in an electronic device with corresponding data processing capabilities, for example, a total processing background system, where the power grid service data is interacted through a power grid service data interaction platform formed by a total processing background and a plurality of data collection sub-platforms, so as to implement efficient storage and query of the power grid service data. As shown in fig. 1, the method includes:
s110, acquiring power grid business data from the data collection sub-platform, wherein the power grid business data comprises an identity of the data collection sub-platform.
The power grid business data interaction platform is connected with a plurality of data collection sub-platforms in a topological structure through a general processing background. Each data collection sub-platform contains monitoring devices, and monitoring devices comprises high definition digtal camera, fixed column, infrared remote control pole, and high definition digtal camera is connected with the fixed column, and the fixed column is connected with the infrared remote control pole to constitute the perception layer by RFID label, sensor, camera, two-dimensional code label, reader-writer, recognizer, GPS, sensing network and sensing gateway. The sensing layer is used for acquiring information data, the sensing gateway is used for receiving control commands, the overall processing background is used for uniformly controlling the data collection sub-platforms through the control module, meanwhile, professional personnel establish a local area network to form a network layer, the established local area network is encrypted, and the overall processing background and the data collection sub-platforms are connected in the same local area network through secret keys.
Specifically, the data collecting sub-platform acquires data and processes the acquired data. During data acquisition, the data acquisition sub-platform acquires power grid data through a sensing layer, wherein the acquired data comprises bus voltage, line voltage, current, power states, tap positions of transformers, states of circuit breakers, isolating switches and other devices on the lines, alarm device states, total active power and event sequences, the data acquired by the sensing layer are power grid business data, and the acquired power grid data are sent to the data acquisition sub-platform background. The method comprises the steps that after data are received by a data collection sub-platform background, the data are compared with data in a data collection sub-platform repository, the same data are removed through comparison results, different data are stored in the data collection sub-platform database, meanwhile, the data collection sub-platform background copies newly added power grid data of the repository, packages the copied data, compresses the packaged data to obtain a data compression packet, the data compression comprises an identity of the data collection sub-platform, the data collection sub-platform encrypts the obtained data compression packet, and the encrypted data compression packet is sent to a total processing background through an established local area network.
And S120, classifying the characteristics of the power grid service data according to the identity mark to obtain at least two different types of sub-power grid service data of the power grid operation data.
Specifically, the overall processing background decrypts the received data compression packet pair, and after decryption is completed, the overall processing background firstly acquires the data in the compression packet and classifies the acquired data. And when the classification is finished according to the classification rule information, extracting the characteristics of the power grid data information contained in the data compression to obtain at least two different types of sub-power grid business data.
S130, correcting the service data of each sub-grid, and storing the corrected sub-grid service data into a category storage space associated with the category of the sub-grid service data and the identity.
Specifically, the sub-grid service data may have an abnormality, and the sub-grid service data is checked to determine whether the sub-grid service data has an abnormality. If the sub-grid business data are abnormal, failing to pass the check; if the sub-grid business data is not abnormal, the sub-grid business data is checked. And dividing independent storage spaces for the sub-grid business data of each category in each data collection sub-platform, and storing the corrected sub-grid business data into the category storage spaces associated with the data collection sub-platform and the category to which the corrected sub-grid business data belong.
According to the embodiment of the application, the power grid business data are subjected to characteristic definition and classification, so that the power grid business data are subjected to fixed-point storage according to the data source and the data category, fixed-point data query is performed through the required data characteristic information when data interaction is performed, excessive storage steps in the data query process are reduced, and the optimization rate of the data interaction performance is improved.
Fig. 2 is a flowchart of a data feature processing method of a medium-low voltage distribution system device according to another embodiment of the present application, where the embodiment is optimized and improved based on the foregoing embodiment. As shown in fig. 2, the method includes:
s210, acquiring power grid business data from the data collection sub-platform, wherein the power grid business data comprises an identity of the data collection sub-platform.
S220, determining at least two category characteristics to be classified of the power grid business data according to the identity; and classifying the power grid service data according to the class characteristics to be classified to obtain at least two different classes of sub-power grid service data.
Specifically, a professional defines characteristics according to the power grid operation data at each data collection sub-platform in advance, and specifies category characteristics of at least two categories to be classified corresponding to the data collection sub-platform. The overall processing background acquires at least two category characteristics to be classified corresponding to the data collection sub-platform through the identity mark contained in the data compression, and divides the power grid business data into two sub-power grid business data of different categories according to the characteristic definition distinction among different predetermined categories.
Optionally, the identity comprises an IP address field and domain name information of the data collecting sub-platform.
Specifically, the data collection sub-platform takes the IP address field and the domain name information as the identity of the data collection sub-platform, and adds the IP address field and the domain name information into a data compression packet of a total data processing background, so that the accurate positioning of the data collection sub-platform to which the power grid business data belong is realized.
S230, acquiring target normal data of the sub-grid business data corresponding type from a normal data information base; if the sub-grid business data are determined to be abnormal according to the target normal data, determining that the sub-grid business data are not corrected; and if the sub-grid business data are determined to be normal according to the target normal data, determining that the sub-grid business data are corrected to pass.
Specifically, professional personnel calculate the service data displayed by the normal use range of the power grid to obtain the normal data range of the corresponding category characteristics, and store the normal data range in a normal data information base. And comparing the acquired data with the target normal data range existing in the corresponding feature classification during the calibration, judging through a comparison result, and processing through a judgment result. If the comparison result shows that the obtained corresponding characteristic data of the sub-grid business data are in the range of the corresponding target normal data, judging that the data are correct, correcting and passing, and covering the original data through the obtained data, uploading the corrected sub-grid business data to a cloud storage server, wherein the cloud storage server and a total processing background have the same classification mode, and if the judgment result is that the data are wrong, the covering of the original data is withdrawn; otherwise, if the comparison result shows that the corresponding characteristic data is not in the range of the corresponding target normal data, judging that the data is wrong, and correcting the data is not passed.
Optionally, after the determining that the correction of the sub-grid service data is not passed, the method further includes:
the data collection sub-platform is remotely monitored, and secondary correction is carried out according to the remote monitoring result;
and if the data collection sub-platform has the problem of parameter abnormality according to the secondary correction result, carrying out parameter adjustment on the data collection sub-platform.
Specifically, the remote monitoring is that a professional sends a remote monitoring command through a control module of a general processing background, the issued remote monitoring command is transmitted by adopting an established local area network, the data collecting sub-platform receives the command through a sensing gateway and processes the received command, the data collecting sub-platform firstly decodes the received command during processing, command content is obtained through decoding, an infrared remote control rod is controlled through the obtained command content, a high-definition video camera is controlled through the infrared remote control rod to record a primary power grid information data acquisition process, the data collecting sub-platform background sends the generated video content to the general processing background after the primary power grid information data acquisition process is completed, the general processing background automatically analyzes the video content after receiving the video, and sends the analysis result to a display desktop, the professional carries out secondary analysis on the analysis result through the display desktop, judges whether the error is a parameter problem or not through the secondary analysis result, and processes the judgment result. If the secondary analysis result shows that the data errors are parameter problems, carrying out remote parameter adjustment on the error data collection sub-platform by a professional through a local area network; if the secondary analysis result shows that the data error is a non-parameter problem, extracting the identity of the data collecting sub-platform by a professional, and dispatching the professional to perform the on-site inspection of the corresponding data collecting sub-platform according to the identity of the data collecting sub-platform.
S240, storing the corrected sub-grid business data into a category storage space associated with the sub-grid business data category and the identity.
S250, acquiring a query request of power grid business data from a foreground system, wherein the query request comprises a query identity and a query data category of a data collection sub-platform; determining a target category storage space corresponding to the query request according to the query identity and the query data category; and generating a query result according to the power grid business data in the target class storage space, and feeding back the query result to the foreground system.
Specifically, the overall processing background performs data interaction by controlling all data collecting sub-platforms, wherein the overall processing foreground system transmits a query request of grid business data when performing data interaction, the foreground transmits the query request, wherein the request content needs to comprise a data type to be queried and an identity of the collecting sub-platform where the data is located, the foreground transmits the query request as an input parameter to a server where the overall processing background is located through a URL interface address, wherein the URL interface is a predefined function, the URL interface comprises an interface address, the input parameter, a return parameter and the data, the server where the overall processing background analyzes the type of the data needed by the foreground and the data collecting sub-platform where the data is located according to the input parameter, the overall processing background automatically performs fixed-point query in a database through an analysis result after the analysis is completed, namely, a target type storage space where the data to be queried is located is determined according to the query identity and the query data type, the data queried in the target type storage space is returned to the foreground in a query result form, and the page display is performed on the received data by the foreground.
S260, acquiring an operation monitoring image of the data collection sub-platform; acquiring a historical operation image of the data collection sub-platform from a historical operation image library, and determining the appearance damage rate of the data collection sub-platform according to the historical operation image and the operation monitoring image; and if the appearance damage rate is higher than the normal work appearance damage rate, pushing the operation alarm information of the data collection sub-platform to the staff.
Specifically, the data collection sub-platform is monitored in real time by the high-definition camera, and equipment operated by the data collection sub-platform is photographed in real time by the high-definition camera during real-time monitoring, so that an operation monitoring image is obtained. And automatically comparing the photographing result with the historical operation image of the data collection sub-platform in the historical operation image library. The historical operating image may be an image of the first time the device previously stored in the data collection sub-platform is operating. And comparing the damage rate of the appearance of the computing equipment by the two, judging by the damage rate data, and processing by the judging result. The damage rate data is larger than the damage rate (for example, 60%) of the normal working appearance, the equipment performance is judged to be abnormal, the damage rate data is not larger than the damage rate of the normal working appearance, the equipment performance is judged to be normal, the alarm equipment is controlled by the data collection sub-platform to carry out alarm processing when the equipment performance is judged to be abnormal, alarm information is sent to a manager through the alarm equipment when the alarm processing is carried out, the alarm information comprises the name and the model of the damaged equipment, the manager changes the equipment through alarm information content after receiving the alarm information, and the equipment performance is judged to be normal when the equipment performance is judged to be not processed.
According to the embodiment of the application, the acquired data are checked, and the abnormal detection is carried out on the running state of the data collection sub-platform, so that the error rate of the data is reduced, and the user experience during data interaction is enhanced.
Fig. 3 is a schematic structural diagram of a data feature processing device of a medium-low voltage distribution system according to another embodiment of the present application. As shown in fig. 3, the apparatus includes:
a service data obtaining module 310, configured to obtain power grid service data from the data collecting sub-platform, where the power grid service data includes an identity of the data collecting sub-platform;
the service data classification module 320 is configured to perform feature classification on the power grid service data according to the identity identifier, so as to obtain at least two different types of sub-power grid service data of the power grid operation data;
the service data storage module 330 is configured to correct each sub-grid service data, and store the sub-grid service data that passes the correction in a category storage space associated with the sub-grid service data category and the identity.
The processing device for the power grid business data provided by the embodiment of the application can execute the processing method for the power grid business data provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method
Optionally, the service data classification module 320 module includes:
the category characteristic acquisition unit is used for determining at least two category characteristics to be classified of the power grid business data according to the identity;
and the service data classification unit is used for classifying the power grid service data according to the class characteristics to be classified to obtain at least two different classes of sub-power grid service data.
Optionally, the identity comprises an IP address field and domain name information of the data collecting sub-platform.
Optionally, the service data storage module 330 includes:
the normal data acquisition unit is used for acquiring target normal data of the category corresponding to the sub-grid business data from a normal data information base;
the first data correction unit is used for determining that the sub-grid business data is not corrected if the sub-grid business data is determined to be abnormal according to the target normal data;
and the second data correction unit is used for determining that the sub-grid business data is corrected and passed if the sub-grid business data is determined to be normal according to the target normal data.
Optionally, the apparatus further includes:
the business data secondary correction module is used for carrying out remote monitoring on the data collection sub-platform and carrying out secondary correction according to the remote monitoring result; and if the data collection sub-platform has the problem of parameter abnormality according to the secondary correction result, carrying out parameter adjustment on the data collection sub-platform.
Optionally, the apparatus further includes:
the query request acquisition module is used for acquiring a query request of power grid business data from a foreground system, wherein the query request comprises a query identity identifier and a query data category of the data collection sub-platform;
the storage space determining module is used for determining a target category storage space corresponding to the query request according to the query identity and the query data category;
and the query result feedback module is used for generating a query result according to the power grid business data in the target class storage space and feeding back the query result to the foreground system.
Optionally, the apparatus further includes:
the monitoring image acquisition module is used for acquiring an operation monitoring image of the data collection sub-platform;
the damage rate determining module is used for acquiring a historical operation image of the data collection sub-platform from a historical operation image library and determining the appearance damage rate of the data collection sub-platform according to the historical operation image and the operation monitoring image;
and the alarm information pushing module is used for pushing the operation alarm information of the data collection sub-platform to the staff if the appearance damage rate is higher than the normal work appearance damage rate.
The further explained processing device for the power grid business data can also execute the processing method for the power grid business data provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.
Fig. 4 shows a schematic diagram of an electronic device 40 that may be used to implement an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 4, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM) 42, a Random Access Memory (RAM) 43, etc., in which the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 42 or the computer program loaded from the storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 may also be stored. The processor 41, the ROM 42 and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to bus 44.
Various components in electronic device 40 are connected to I/O interface 45, including: an input unit 46 such as a keyboard, a mouse, etc.; an output unit 47 such as various types of displays, speakers, and the like; a storage unit 48 such as a magnetic disk, an optical disk, or the like; and a communication unit 49 such as a network card, modem, wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 41 may be various general and/or special purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 41 performs the various methods and processes described above, such as the processing of grid business data.
In some embodiments, the method of processing grid business data may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the above-described method of processing grid business data may be performed. Alternatively, in other embodiments, the processor 41 may be configured to perform the processing method of the grid traffic data in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present application, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present application are achieved, and the present application is not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (10)
1. The data characteristic processing method for the medium-low voltage power distribution system equipment is characterized by comprising the following steps of:
acquiring power grid business data from a data collection sub-platform, wherein the power grid business data comprises an identity of the data collection sub-platform;
performing feature classification on the power grid service data according to the identity to obtain at least two different types of sub-power grid service data of the power grid operation data;
and correcting the service data of each sub-grid, and storing the corrected sub-grid service data into a category storage space associated with the category of the sub-grid service data and the identity.
2. The method of claim 1, wherein the classifying the grid operation data according to the identity to obtain at least two different categories of sub-grid operation data of the grid operation data comprises:
determining at least two category characteristics to be classified of the power grid business data according to the identity;
and classifying the power grid service data according to the class characteristics to be classified to obtain at least two different classes of sub-power grid service data.
3. The method according to any of claims 1-2, wherein the identity comprises an IP address field and domain name information of the data collection sub-platform.
4. The method of claim 1, wherein correcting each sub-grid business data comprises:
acquiring target normal data of the corresponding class of the sub-grid business data from a normal data information base;
if the sub-grid business data are determined to be abnormal according to the target normal data, determining that the sub-grid business data are not corrected;
and if the sub-grid business data are determined to be normal according to the target normal data, determining that the sub-grid business data are corrected to pass.
5. The method of claim 1, wherein after the determining that the sub-grid business data correction is not passed, further comprising:
the data collection sub-platform is remotely monitored, and secondary correction is carried out according to the remote monitoring result;
and if the data collection sub-platform has the problem of parameter abnormality according to the secondary correction result, carrying out parameter adjustment on the data collection sub-platform.
6. The method of claim 1, wherein after storing the corrected sub-grid business data in the category storage space associated with the sub-grid business data category and the identity, further comprising:
acquiring a query request of power grid business data from a foreground system, wherein the query request comprises a query identity and a query data category of a data collection sub-platform;
determining a target category storage space corresponding to the query request according to the query identity and the query data category;
and generating a query result according to the power grid business data in the target class storage space, and feeding back the query result to the foreground system.
7. The method according to claim 1, wherein the method further comprises:
acquiring an operation monitoring image of a data collection sub-platform;
acquiring a historical operation image of the data collection sub-platform from a historical operation image library, and determining the appearance damage rate of the data collection sub-platform according to the historical operation image and the operation monitoring image;
and if the appearance damage rate is higher than the normal work appearance damage rate, pushing the operation alarm information of the data collection sub-platform to the staff.
8. A medium-low voltage distribution system equipment data characteristic processing device, the device comprising:
the service data acquisition module is used for acquiring power grid service data from the data collection sub-platform, wherein the power grid service data comprises an identity of the data collection sub-platform;
the service data classification module is used for carrying out feature classification on the power grid service data according to the identity mark to obtain at least two different types of sub-power grid service data of the power grid operation data;
and the service data storage module is used for correcting the service data of each sub-power grid and storing the corrected sub-power grid service data into a category storage space associated with the category of the service data of the sub-power grid and the identity mark.
9. An apparatus, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 7.
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|---|---|---|---|
| CN202310746173.7A CN116702042A (en) | 2023-06-21 | 2023-06-21 | Data characteristic processing method and equipment for medium-low voltage distribution system equipment |
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| CN202310746173.7A CN116702042A (en) | 2023-06-21 | 2023-06-21 | Data characteristic processing method and equipment for medium-low voltage distribution system equipment |
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