CN111245103A - Display and storage system of power grid transformer nameplate based on neural computing rod - Google Patents
Display and storage system of power grid transformer nameplate based on neural computing rod Download PDFInfo
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09F—DISPLAYING; ADVERTISING; SIGNS; LABELS OR NAME-PLATES; SEALS
- G09F9/00—Indicating arrangements for variable information in which the information is built-up on a support by selection or combination of individual elements
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
- Y04S40/126—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment using wireless data transmission
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Abstract
The invention discloses a display and storage system of a power grid transformer nameplate based on a neural calculation rod, which comprises an external configuration unit, a display unit and a storage unit, wherein the external configuration unit comprises the neural calculation rod and a power supply module; the internal configuration unit comprises a database storage module and an information display module; the external configuration unit and the internal configuration unit are based on a movable operation end and serve as control terminals, and the external configuration unit and the internal configuration unit are connected with the movable operation end; the tablet personal computer is used as a movable operation terminal and an available standby battery, and the online and offline data synchronization is completed through the Wifi function of the terminal, so that the repeatability of the work is reduced, and the accuracy is improved; the neural computing stick is used for participating in system operation, the recognition rate and the system linearity performance are improved, so that in operation, complicated power grid distribution transformation nameplate data are stored, the speed of the query process is increased, the stored data are quickly sorted according to the requirements of operators, the query and the display are facilitated, and the time cost is saved.
Description
Technical Field
The invention relates to the field of data information display and storage, in particular to a display and storage system of a grid transformer nameplate based on a neural computing bar.
Background
At present, operators in the power industry need to store records and realize quick query on a large number of power grid distribution transformation nameplate information, the traditional working scheme is that the inconvenience of recording work tasks and querying the power grid distribution transformation nameplate information exists in an off-line state in an on-line mode processing mode, the inconvenience of field implementation of the operators is caused by the special working environment, the real-time storage of a large number of data and the inaccurate and quick display of the data required by the operators exist in the prior art, and the high linear performance of the system is improved by adopting a display and storage system of the power grid distribution transformation nameplate based on a neural computing rod. And technical support is provided for subsequent maintenance and query of the data information of the power grid distribution transformation nameplate.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments, and in this section as well as in the abstract and the title of the invention of this application some simplifications or omissions may be made to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made keeping in mind the above problems occurring in the prior art and/or the problems occurring in the prior art.
Therefore, the technical problems to be solved by the invention are that the prior art has the defects that a large amount of data can be stored in real time, the data required by an operator cannot be accurately and quickly displayed, the identification system is inaccurate in identification, and the display, storage and working efficiency are influenced.
In order to solve the technical problems, the invention provides the following technical scheme: a display and storage system of a power grid transformer nameplate based on a neural calculation rod comprises an external configuration unit, a display unit and a storage unit, wherein the external configuration unit comprises the neural calculation rod and a power supply module; the internal configuration unit comprises a database storage module and an information display module; the external configuration unit and the internal configuration unit are based on a movable operation end and serve as control terminals, and the external configuration unit and the internal configuration unit are connected with the movable operation end.
As a preferred embodiment of the display and storage system for the neural computation rod-based grid transformer nameplate of the present invention, wherein: the movable operation end comprises a plurality of USB interfaces, a WiFi transmission module, a power supply and a central processing unit; the central processing unit is respectively connected with the USB interface, the WiFi transmission module and the power supply.
As a preferred embodiment of the display and storage system for the neural computation rod-based grid transformer nameplate of the present invention, wherein: the nerve calculation stick is connected with the movable operation end through the USB interface, and the power supply module is connected with the movable operation end through the USB interface.
As a preferred embodiment of the display and storage system for the neural computation rod-based grid transformer nameplate of the present invention, wherein: the database storage module is installed at the movable operation end, and the database of the database storage module is MySQL8.0 version.
As a preferred embodiment of the display and storage system for the neural computation rod-based grid transformer nameplate of the present invention, wherein: the WiFi transmission module is in wireless connection with the database storage module, and the WiFi transmission module is in wireless connection with the information display module.
As a preferred embodiment of the display and storage system for the neural computation rod-based grid transformer nameplate of the present invention, wherein: the movable operation end is connected with the database storage module, and the movable operation end is connected with the information display module.
As a preferred embodiment of the display and storage system for the neural computation rod-based grid transformer nameplate of the present invention, wherein: the mobile operation terminal is connected with the WiFi transmission module in a wireless mode.
As a preferred embodiment of the display and storage system for the neural computation rod-based grid transformer nameplate of the present invention, wherein: the movable operation end further comprises an operation panel, and the WiFi transmission module, the database storage module and the information display module are controlled by the operation panel.
As a preferred embodiment of the display and storage system for the neural computation rod-based grid transformer nameplate of the present invention, wherein: the portable operation end is a tablet computer, the WiFi transmission module is an 88W8782 chip, the central processing unit is an A12 Bionic processor, and the nerve computer rod is of a Myriad X type.
In order to solve the technical problems, the invention also provides the following technical scheme: wherein: the use method of the display and storage system of the grid transformer nameplate based on the neural computing stick comprises the following steps,
the nerve calculation rod is connected with the movable operation end and is used for identifying and storing the nameplate;
a movable operation end acquires a user query request;
screening a display template corresponding to the user query request from a display library stored in the server to obtain a target empty template;
calling corresponding storage system information according to the target empty template to obtain target information;
the information display module displays the acquired target value.
The invention has the beneficial effects that:
1. the tablet personal computer serves as a movable operation terminal and an available standby battery, the portability and the cruising ability of the working tool are improved, the display function and the storage function are installed in a terminal system, online and offline data synchronization is completed through a terminal Wifi function, and the repeatability of work is reduced, so that the accuracy is improved;
2. the neural computing stick is used for participating in system operation, the linear performance of the system is improved, so that in operation, complicated power grid distribution transformation nameplate data are stored, the speed of the query process is increased, the stored data are accelerated and sorted according to the requirements of operators, the query and the display are facilitated, and the time cost is saved;
3. when the neural computing stick is used for participating in the YOLO3+ CRNN algorithm, under the condition that training samples are the same, the accuracy of recognition results is far higher than that of other two algorithms.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic connection diagram of a display and storage system of a grid transformer nameplate based on a neural computing stick according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the connection of the movable operation end in the display and storage system of the neural computing rod-based grid transformer nameplate according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a display and storage system of a grid transformer nameplate based on a neural computing bar according to an embodiment of the present invention
Fig. 4 is a schematic flow chart of displaying and storing in a display and storage system of a grid transformer nameplate based on a neural computing stick according to an embodiment of the present invention;
fig. 5 is a flow chart illustrating identification of a neural computation stick in a display and storage system of a grid transformer nameplate based on the neural computation stick according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Next, the present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially according to the general scale for convenience of illustration when describing the embodiments of the present invention, and the drawings are only examples, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1 to 5, the embodiment provides a display and storage system of a power grid transformer nameplate based on a neural computation rod, which includes an external configuration unit 100, including a neural computation rod 101 and a power supply module 102, where the power supply module 102 continuously supplies power or indirectly supplies power to an operating device, the power supply module 102 is preferably a portable mobile power supply so as to provide electric quantity at any time, and the neural computation rod 101 is based on edge computation, can accelerate the computation process in the computation process, solve the problem of slow computation performance and low efficiency of a memory of the operating device, and can be used for recording and updating real-time information of the nameplate to realize storage, retrieval and summarization of information; the internal configuration unit 200 comprises a database storage module 201 and an information display module 202; the database storage module 201 stores data, the information display module 202 is used for displaying, and the database storage module 201 is preferably a TLC NAND flash memory, which is one of solid NAND flash memories, and the data storage capacity of the TLC NAND flash memory is three times that of an SLC memory and 1.5 times that of an MLC memory. Further, the external configuration unit 100 and the internal configuration unit 200 are based on a mobile operation terminal 300 as a control terminal, the external configuration unit 100 and the internal configuration unit 200 are both connected with the mobile operation terminal 300, preferably, the mobile operation terminal 300 is a tablet computer, and the mobile operation terminal 300 includes a plurality of USB interfaces 301, a WiFi transmission module 302, a power supply 303, and a central processing unit 304; the central processor 304 is connected to the USB interface 301, the WiFi transmission module 302, and the power supply 303 respectively. The WiFi transmission module 302 is preferably an 88W8782 chip, the central processing unit 304 is preferably an A12 Bionic processor, and the neuro-computing stick 101 is preferably of Myriad X type; the checked nameplate information is uploaded to the server 400 through the WiFi transmission module 302 to be processed, so that the information can be stored.
Specifically, the nerve computing stick 101 is connected with the movable operation terminal 300 through the USB interface 301, and the power supply module 102 is connected with the movable operation terminal 300 through the USB interface 301 to supply power to the power supply 303. The database storage module 201 is installed at the movable operation terminal 300, and the database of the database storage module 201 is the version of MySQL8.0; the mobile operation terminal 300 is connected with the database storage module 201, and the mobile operation terminal 300 is connected with the information display module 202. The mobile terminal further comprises a server 400, wherein the server 400 is wirelessly connected with the mobile operation terminal 300 through the WiFi transmission module 302, and the information display module 202 displays a screen of the mobile operation terminal 300.
The display and storage method of the embodiment comprises the following steps:
s100, connecting a nerve calculation rod 101 with a movable operation end 300, and identifying and storing a nameplate;
s200, the movable operation terminal 300 acquires a user query request;
s300, screening a display template corresponding to the user query request from a display library stored in the server 400 to obtain a target empty template;
s400, calling corresponding storage system information according to the target empty template to obtain target information;
s500, storing the target information in a target empty template to obtain an obtained target value;
s600, the information display module 202 displays the acquired target value.
Further, the neural computation rod 101 is identified by the following method:
s101, collecting nameplate information by using a camera;
s102, positioning an effective information area in the nameplate, and cutting the area outside the effective information area;
s103, performing image enhancement on the image obtained in the step S102 by adopting a multi-scale Retinex algorithm;
s104, performing horizontal inclination correction and perspective deformation correction on the nameplate image subjected to image enhancement;
s105, positioning the text information in the corrected image nameplate to enable the related text information
Forming a vocabulary entry, and positioning all vocabulary entries in the image nameplate;
s106, identifying the positioned entry, identifying text information in the nameplate and displaying the text information;
and S107, uploading the identified nameplate information to a server for storage.
Preferably, the nameplate is identified in S100, and the image is detected and identified by using a combination of the YOLO3 algorithm and the CRNN algorithm.
YOLO sets shortcut links (shortcut connections) between some layers by using a dark net-53 skeleton network for reference, further adopts 3 feature maps with different scales to detect objects, divides an image into a plurality of cells on the aspect of detection efficiency, and sets a priori frame on different scales. Due to the adoption of the pure convolution neural network, the feature map has position invariance, and the features of the prior frame can be directly calculated from the final feature map. And performing regression training after the characteristic diagram is obtained so as to obtain an efficient classification model. The detection target is not a regular square or rectangle. YOLO sets 3 prior frames for each downsampling scale, downsamples three times, and clusters 9 prior frames in total. The size of the prior box can be obtained by adopting K-means clustering, and the 9 prior boxes in the COCO data set are as follows: (10x13), (16x30), (33x23), (30x61), (62x45), (59x119), (116x90), (156x198), (373x 326).
In assignment, larger prior boxes (116x90), (156x198), (373x326) are applied on the smallest 13 x13 signature (with the largest receptive field), suitable for detecting larger objects. Medium boxes (30x61), (62x45), (59x119) were applied on the medium 26 × 26 signature (medium receptive field), suitable for detecting medium sized objects. Smaller a priori boxes (10x13), (16x30), (33x23) are applied on the larger 52 x 52 signature (smaller receptive field), suitable for detecting smaller objects.
yolo divides the input image into S × S cells, followed by output in units of cells:
1) if the center of an object falls on a cell, then the cell is responsible for predicting the object.
2) Each cell needs to predict B bbox values (x, y, w, h, c), while a confidence score (confidence scores) is predicted for each bbox value, i.e. each cell needs to predict B x (4+1) values.
3) Therefore, the output dimension of the final network is:
S*S*(B*5+C)
here, although each cell is responsible for predicting one kind of object, each cell can predict a plurality of bbox values.
The meaning of each Bbox is as follows:
x, y is the offset of the center of bbox relative to the cell, representing the offset of the center relative to the cell, and is calculated as follows:
w, h is the ratio of bbox to the whole picture, the predicted width and height of bbox is (w, h) shows that bbox is the ratio of bbox to the whole picture, and the calculation formula is as follows:
confidence, which is composed of two parts, namely whether a target exists in the grid or not and the accuracy of the bbox. The confidence is defined as:
if there are objects in the grid, PTWhen (object) is 1, the confidence is equal to IoU. If there is no object in the cell, PTWhen (object) is 0, the confidence is 0.
Conditional probability of class C, the conditional probability being defined as PT(classiI object) indicating the probability that the cell has an object and belongs to the i-th class. The probability of each cell predicting the final output is defined as:
template matching is one of the methods for finding a specific target in an image, and the principle of this method is very simple, and it is possible to traverse every possible position in the image, compare every place with the template to see if "similar", and when the similarity is high enough, consider that we have found our target.
Further, S102 can adopt a Hough transform straight line detection method to cut the outline region of the nameplate to be recognized, and adopts perspective transform to correct the cut outline region of the nameplate to be recognized to obtain the image of the nameplate region to be recognized.
The Retinex algorithm in S103 comprises the following steps: firstly, carrying out multi-scale convolution filtering operation on each color channel of an acquired image in a surrounding function respectively; secondly, performing linear weighted summation on the processed image under multiple scales; and then obtaining an enhanced image after calculation through the following formula:
wherein i is the channel of RGB color, i is the convolution operator, N is the scale, wjIs the weight corresponding to the enhancement result in the j-th scale, Si(x, y) represents the original image, G is a gaussian surround function:
delta is a scale parameter, and x and y respectively represent the coordinates of pixel points in the image.
Further, S104, performing expansion operation on the image, performing edge detection by using a sobel operator to obtain edge points, performing hough transformation on the edge points, finding a rotation angle, performing horizontal inclination correction by using the rotation angle, and extracting and marking feature points of the nameplate image by using a Harris operator; and recording the positions of Harris angular points closest to the four boundaries of the image after positioning processing, and performing perspective deformation correction by taking the four angular points as reference points. S105, decomposing the image into a plurality of different connected domains by using a color clustering algorithm, and verifying the information in the connected domains according to the aspect ratio and the area ratio of the text information, so as to screen out the entry text information. S106, performing character segmentation on each entry text message, segmenting different characters, performing normalization processing on each character, normalizing the character to 25 x 50, processing an image by using a Rosenfeld skeleton thinning algorithm, and obtaining each character characteristic value based on stroke slope cumulative characteristic extraction, inflection point amplitude cumulative characteristic extraction, character outline depth characteristic extraction and character jumping point statistics; training the character characteristic value by using a BP neural network classifier algorithm to obtain the classification characteristic of each character, repeating the steps to identify and display the character information in each entry; preferably, a LeNet-5 convolutional neural network is adopted to recognize characters in a nameplate region image to be detected, the LeNet-5 convolutional neural network adopts nameplate characters commonly used by substation equipment as training data to train, in the invention, the characters on the nameplate are mostly fixed codes in a power application system unit, all characters and phrases contained in non-daily application are used, and because a character set on the nameplate is relatively fixed, in order to ensure the recognition rate, the characters in the collected nameplate sample are directly used as the training data of the LeNet-5 convolutional neural network to train.
In this embodiment, the image is subjected to linear weighted summation in multiple scales to ensure that the enhanced image has the advantages of different scales, and generally, three scales of high, medium, and low are taken. When the value of the variable delta is large, the image focuses on color preservation, and detailed information is easy to ignore, and when the value of the delta is small, the detailed information is highlighted, but color distortion is easy to cause.
In this embodiment, the neural computation rod performs image preprocessing based on a RETINEX algorithm, and detects and identifies images by combining a YOLO3 algorithm and a CRNN algorithm, and in the comparison process of the identification algorithm results, samples selected from collected data are divided into training set samples and verification set samples, and the selection of the samples needs to satisfy representativeness, and meanwhile, no erroneous samples exist, so that the problem that invalid and erroneous data participate in the training process, which causes the range fluctuation of the accuracy of the device results. Training SVM, BPNN, YOLO3+ CRNN algorithms by using the selected training set as training learning samples, identifying and detecting the algorithms by using the samples of the verification set after the training is finished, recording relevant data of identification results, and finally calculating the average values of the accuracy of the training set, the accuracy of the verification set and the training time of each algorithm, wherein the average values are shown in table 1:
TABLE 1
| The accuracy of the training set% | The correct rate of the verification set% | Training time/s | |
| SVM | 92.62 | 91.35 | 893.1531 |
| BPNN | 93.50 | 89.33 | 36.8747 |
| YOLO3+CRNN | 98.20 | 96.50 | 1.4533 |
From the above table, it can be seen that the recognition result accuracy based on the neuro-computing stick YOLO3+ CRNN algorithm is much higher than that of the other two algorithms under the condition that the training samples are the same. And the average training time is 615 times that of the SVM algorithm and 25 times that of the BPNN algorithm. The YOLO3+ CRNN algorithm can better process the image recognition problem from visual data, the neural calculation stick-based algorithm further researches the power grid transformer nameplate recognition technology, and the accuracy of the algorithm is improved.
Further, the movable operation terminal 300 is configured to obtain a query request of a user, and send the query request to the server 400 through the WiFi transmission module 302, in this process, the neural computation stick 101 is based on edge computation, and can accelerate the computation process in the computation process.
The principle of the system of the embodiment is as follows: mainly explained is that the RETINEX algorithm based on the neural computing stick 101 carries out image preprocessing, and the YOLO3 algorithm and the CRNN algorithm are combined to carry out detection and identification on the image; the nerve computing stick 101 is connected with the movable operation end 300 through the USB interface 301, and the movable operation end 300 is used for building a structure through internal software of equipment and executing functions of an internal module; the WiFi transmission module 302 is called through a software program to achieve data uploading and downloading between terminal data and the server 400, data synchronization is completed, the mobile operation terminal 300 can selectively synchronize when the data synchronization is completed by calling the WiFi transmission module 302, only data related to workers are processed, and the situation that the operation of the system is affected due to overlarge data amount is avoided; the manager introduces the nameplate information into the server 400 from the mobile operation terminal 300, or directly inputs the nameplate information into the nameplate information system manually, sends an instruction through the nameplate information system, stores the professional information of the nameplate through the wireless local area network of the WiFi transmission module 302, and can display the professional information of the nameplate in the information display module 202 of the mobile operation terminal 300.
Example 2
Referring to fig. 3, a second embodiment of the present invention is based on the previous embodiment, and is different from the previous embodiment in that: the WiFi transmission module 302 is in wireless connection with the database storage module 201, and the WiFi transmission module 302 is in wireless connection with the information display module 202. The mobile operation terminal 300 further comprises an operation panel 305, and the operation panel 305 controls the WiFi transmission module 302, the database storage module 201, and the information display module 202. Specifically, the display and storage system of the grid transformer nameplate based on the neural calculation rod comprises an external configuration unit 100, a neural calculation rod 101 and a power supply module 102, wherein the power supply module 102 is used for continuously supplying power or indirectly supplying power to operating equipment; the internal configuration unit 200 comprises a database storage module 201 and an information display module 202; the database storage module 201 stores data, and the information display module 202 is used for displaying. Further, the external configuration unit 100 and the internal configuration unit 200 are based on a mobile operation terminal 300 as a control terminal, the external configuration unit 100 and the internal configuration unit 200 are both connected with the mobile operation terminal 300, preferably, the mobile operation terminal 300 is a tablet computer, and the mobile operation terminal 300 includes a plurality of USB interfaces 301, a WiFi transmission module 302, a power supply 303, and a central processing unit 304; the central processor 304 is connected to the USB interface 301, the WiFi transmission module 302, and the power supply 303 respectively. The WiFi transmission module 302 is preferably a 88W8782 model chip, the central processor 304 is preferably an a12 Bionic processor, and the neuro-computing stick 101 is preferably a Myriad X model.
The principle of the embodiment is as follows: mainly explained is that the RETINEX algorithm based on the neural computing stick 101 carries out image preprocessing, and the YOLO3 algorithm and the CRNN algorithm are combined to carry out detection and identification on the image; the nerve computing stick 101 is connected with the movable operation end 300 through the USB interface 301, and the movable operation end 300 is used for building a structure through internal software of equipment and executing functions of an internal module; the WiFi transmission module 302 is called through a software program to achieve data uploading and downloading between terminal data and the server 400, data synchronization is completed, the mobile operation terminal 300 can selectively synchronize when the data synchronization is completed by calling the WiFi transmission module 302, only data related to workers are processed, and the situation that the operation of the system is affected due to overlarge data amount is avoided; the operation panel 305 performs database operation through a software program to complete data calling and storage, the operation panel 305 performs data calculation through the software program, the data acquired from the database storage module 201 is connected with the nerve calculation stick 101 through the USB interface 301, calculation performance is improved, finally, information display is performed on the power grid distribution and transformation nameplate data, query and display of the data are achieved, the operation panel 305 performs corresponding processing (adding, deleting and modifying) on the displayed power grid distribution and transformation nameplate data, the operation panel is connected with the nerve calculation stick 101 through the USB interface 301 to participate in data calculation processing, and calculation time is shortened.
It is important to note that the construction and arrangement of the present application as shown in the various exemplary embodiments is illustrative only. Although only a few embodiments have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters (e.g., temperatures, pressures, etc.), mounting arrangements, use of materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter recited in this application. For example, elements shown as integrally formed may be constructed of multiple parts or elements, the position of elements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of this invention. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. In the claims, any means-plus-function clause is intended to cover the structures described herein as performing the recited function and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present inventions. Therefore, the present invention is not limited to a particular embodiment, but extends to various modifications that nevertheless fall within the scope of the appended claims.
Moreover, in an effort to provide a concise description of the exemplary embodiments, all features of an actual implementation may not be described (i.e., those unrelated to the presently contemplated best mode of carrying out the invention, or those unrelated to enabling the invention).
It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure, without undue experimentation.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (9)
1. The utility model provides a display and storage system of electric wire netting transformer data plate based on neural stick of calculating which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the external configuration unit (100) comprises a nerve calculation stick (101) and a power supply module (102);
the internal configuration unit (200) comprises a database storage module (201) and an information display module (202);
the external configuration unit (100) and the internal configuration unit (200) are based on a movable operation terminal (300) as a control terminal, and the external configuration unit (100) and the internal configuration unit (200) are both connected with the movable operation terminal (300).
2. The nerve computing stick based grid transformer nameplate display and storage system of claim 1 further comprising: the movable operation end (300) comprises a plurality of USB interfaces (301), a WiFi transmission module (302), a power supply (303) and a central processing unit (304); the central processing unit (304) is respectively connected with the USB interface (301), the WiFi transmission module (302) and the power supply (303).
3. The nerve computing stick based grid transformer nameplate display and storage system of claim 2 further comprising: the nerve computation rod (101) is connected with the movable operation end (300) through the USB interface (301), and the power supply module (102) is connected with the movable operation end (300) through the USB interface (301).
4. The nerve computing stick based grid transformer nameplate display and storage system of claim 3 further comprising: the database storage module (201) is installed at the movable operation end (300), and the database of the database storage module (201) is MySQL8.0.
5. The nerve computing stick based grid transformer nameplate display and storage system of claim 4 further comprising: the WiFi transmission module (302) is in wireless connection with the database storage module (201), and the WiFi transmission module (302) is in wireless connection with the information display module (202).
6. The nerve computing stick based grid transformer nameplate display and storage system of claim 5 further comprising: the movable operation end (300) is connected with the database storage module (201), and the movable operation end (300) is connected with the information display module (202).
7. The nerve computing stick based grid transformer nameplate display and storage system of claim 6 further comprising: the mobile operation terminal further comprises a server (400), wherein the server (400) is in wireless connection with the mobile operation terminal (300) through the WiFi transmission module (302).
8. The display and storage system of a grid transformer nameplate based on a neural computation rod as claimed in any one of claims 1 to 7, wherein: the movable operation end (300) further comprises an operation panel (305), and the operation panel (305) controls the WiFi transmission module (302), the database storage module (201) and the information display module (202).
9. The nerve computing stick based grid transformer nameplate display and storage system of claim 8 further comprising: the mobile operation end (300) is a tablet computer, the WiFi transmission module (302) is an 88W8782 chip, the central processing unit (304) is an A12 Bionic processor, and the nerve computer stick (101) is of a Myriad X type.
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