CN115984720B - Heat supply pipe network management system based on big data technology - Google Patents
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Abstract
The invention belongs to the field of management, and discloses a heating network management system based on big data technology, which comprises an unmanned plane and a management center; the unmanned aerial vehicle is used for obtaining an image to be judged firstly, then obtaining a set tgu of linear pixel points, then calculating image parameters according to tgu, and finally judging whether the image to be judged needs to be acquired again or not based on the image parameters; the management center is used for managing the heat supply pipe network based on the final image. The invention can ensure the quality of the obtained final image in the acquisition step, and avoid the need of re-acquiring the final image of the heating pipe network, thereby influencing the management efficiency of the heating pipe network.
Description
Technical Field
The invention relates to the field of management, in particular to a heating network management system based on big data technology.
Background
A heating network is a piping system for transporting and distributing a heating medium from a central urban heating source to heating users. The heat supply pipe network consists of a heat transfer trunk line, a heat distribution trunk line, branch lines and the like, wherein the heat transfer trunk line is led out from a heat source and is not connected with the branch lines in general; the heat distribution rail is connected from the heat transmission rail or directly from the heat source and supplies heat to the user via the heat distribution rail.
The laying mode of the heat supply pipe network comprises underground laying and overground laying. In order to improve the efficiency of managing the heating network laid on the ground, in the prior art, a system for acquiring data of the heating network laid on the ground by using an unmanned plane, thereby realizing management of the heating network, has been presented.
The existing system for managing the heating pipe network laid on the ground based on the unmanned aerial vehicle directly transmits the shot image to the management center in the shooting process, and the computer of the management center analyzes the image, so that the heating pipe network laid on the ground is managed.
However, the unmanned aerial vehicle is easily affected by external factors in the shooting process, the shot image is directly transmitted to the management center, and the situation that the low-quality image is transmitted to the management center is obviously easy to occur, so that the image of the heating pipe network needs to be acquired again, and the management efficiency of the heating pipe network is affected.
Disclosure of Invention
The invention aims to disclose a heating network management system based on a big data technology, which solves the problem that how to easily transmit low-quality images to a management center in the process of managing a heating network laid on the ground based on an unmanned plane, and the efficiency of managing the heating network is affected.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a heating network management system based on big data technology comprises an unmanned plane and a management center;
the unmanned aerial vehicle is used for obtaining a final image of the heating network in the following manner and transmitting the obtained final image to the management center:
s1, shooting a heat supply pipe network to obtain an image to be judged;
s2, calculating an image to be judged by using a straight line detection algorithm to obtain a set tgu of straight line pixel points;
calculating image parameters based on tgu:
where igdx denotes an image parameter, α, β denote auxiliary coefficients, α+β=1, ntgu denotes the number of pixel points contained in tgu, flr i Gradient value flr representing horizontal direction of pixel point i in tgu r Gradient values in the horizontal direction of the pixel points on the right side of the pixel point i are represented, aveflr represents a reference value of a set average gradient difference, asu represents a set of pixel points which do not belong to the set tgu in 8 adjacent to the pixel point in the set tgu, upn and top represent the minimum value and the maximum value of the pixel values of the pixel points in the set asu, respectivelyLarge value, num j Representing the total number of pixel points with the pixel value j in the set asu, nasu representing the total number of pixel points in the set asu, sh representing a preset calculated information amount reference value,
s3, judging whether the image parameters are larger than a set image parameter threshold, if so, taking the image to be judged as a final image, and transmitting the obtained final image to a management center; if not, executing S1;
the management center is used for managing the heat supply pipe network based on the final image.
Optionally, the management center comprises a communication module, a storage module, an operation module and a management module;
the communication module is used for communicating with the unmanned aerial vehicle and receiving a final image sent by the unmanned aerial vehicle;
the storage module is used for storing the final image received by the communication module;
the operation module is used for carrying out image recognition processing on the final image to obtain a recognition result;
the management module is used for managing the heat supply pipe network based on the identification result.
Optionally, the operation module comprises a construction unit, a training unit and an image recognition unit;
the construction unit is used for constructing an identification model for carrying out image identification on the final image;
the training unit is used for training the recognition model constructed by the construction unit by adopting a big data technology to obtain a trained recognition model;
the image recognition unit is used for inputting the final image into the trained recognition model for calculation, and a recognition result is obtained.
Optionally, the identification model is a convolutional neural network model.
Optionally, the identification result includes a name of the equipment failure present in the final image and a location of each equipment failure.
Optionally, the inputting the final image into the trained recognition model for calculation to obtain a recognition result includes:
performing pre-processing on the final image to obtain a pre-processed image;
and inputting the pre-processed image into the trained recognition model for calculation to obtain a recognition result.
Optionally, the storage module is further configured to store a data set for training the recognition model constructed by the construction unit.
Optionally, the management module comprises a prompt unit and a maintenance unit;
the prompting unit is used for prompting management personnel based on the recognition result;
the maintenance unit is used for generating a maintenance work order based on the identification result.
Optionally, the maintenance unit includes a user subunit, a generation subunit and a management subunit;
the generating subunit is used for generating a maintenance work order based on the identification result and sending the maintenance work order to the user subunit;
the user sub-unit is used for storing the maintenance work order sent by the generating sub-unit;
the management subunit is used for managing the maintenance work orders.
Optionally, the managing the maintenance work order includes querying the maintenance work order, counting the maintenance work order, modifying the maintenance work order, and deleting the maintenance work order.
According to the heat supply pipe network management system, in the process of acquiring the final image of the heat supply pipe network through the unmanned aerial vehicle, the image to be judged is firstly obtained, then the image parameters of the image to be judged are calculated, and then whether the image to be judged can be used as the final image is judged based on the image parameters. Therefore, the invention can ensure the quality of the obtained final image in the acquisition step, and avoid the need of re-acquiring the final image of the heating pipe network, thereby influencing the management efficiency of the heating pipe network.
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The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
Fig. 1 is a diagram of an embodiment of a heating network management system based on big data technology according to the present invention.
Fig. 2 is a diagram of another embodiment of a heating network management system based on big data technology according to the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
The invention provides a heating network management system based on big data technology, which is shown in an embodiment of FIG. 1 and comprises an unmanned plane and a management center;
the unmanned aerial vehicle is used for obtaining a final image of the heating network in the following manner and transmitting the obtained final image to the management center:
s1, shooting a heat supply pipe network to obtain an image to be judged;
s2, calculating an image to be judged by using a straight line detection algorithm to obtain a set tgu of straight line pixel points;
calculating image parameters based on tgu:
where igdx denotes an image parameter, α, β denote auxiliary coefficients, α+β=1, ntgu denotes the number of pixel points contained in tgu, flr i Gradient value flr representing horizontal direction of pixel point i in tgu r Gradient values in the horizontal direction of the pixel points on the right side of the pixel point i are represented, aveflr represents a reference value of a set average gradient difference, asu represents a set of pixel points which do not belong to the set tgu among 8 neighbors of the pixel points in the set tgu, upn and top represent minimum and maximum values of the pixel points in the set asu, respectively, and numj tableShowing the total number of pixel points with the pixel value j in the set asu, nasu represents the total number of pixel points in the set asu, sh represents a preset calculated information quantity reference value,
s3, judging whether the image parameters are larger than a set image parameter threshold, if so, taking the image to be judged as a final image, and transmitting the obtained final image to a management center; if not, executing S1;
the management center is used for managing the heat supply pipe network based on the final image.
According to the heat supply pipe network management system, in the process of acquiring the final image of the heat supply pipe network through the unmanned aerial vehicle, the image to be judged is firstly obtained, then the image parameters of the image to be judged are calculated, and then whether the image to be judged can be used as the final image is judged based on the image parameters. Therefore, the invention can ensure the quality of the obtained final image in the acquisition step, and avoid the need of re-acquiring the final image of the heating pipe network, thereby influencing the management efficiency of the heating pipe network.
In the process of calculating the image parameters, the invention adopts the steps of firstly carrying out straight line detection to obtain a set tgu of the pixel points belonging to the straight line, and then calculating according to tgu to obtain the image parameters. This arrangement can avoid calculating the image parameters based on all the pixels, improve the calculation efficiency of the image parameters, and utilize the straight line characteristics of the heat supply pipeline, so that most of the pixels in the set tgu are the pixels of the heat supply pipeline, which enables the image parameters to be ready for representing the quality condition of the image to be judged even if the image parameters are not calculated based on all the pixels.
The image parameters are calculated by integrating the gradient value of the pixel points in the horizontal direction and the information content of the pixel points around the heat supply pipeline. Because the gradient of the pixel point of the heat supply pipeline area in the horizontal direction can be very small, under the condition that the right side of the formula plus sign is unchanged, the left side part can be adaptively changed along with the gradient change of the heat supply pipeline in the horizontal direction, specifically, the smaller the average value of the gradient difference is, the larger the value of the left side part is, and at the moment, the clearer the pixel point of the heat supply pipeline part is. On the right side of the plus sign, the smaller the right numerical value is along with the increase of the information content, the accuracy of quality judgment of the image of the vertically distributed heat supply pipeline can be improved by the arrangement mode on the right side. As it is not affected by direction. The larger the information content is, the better the quality of the image to be judged is.
Optionally, as shown in fig. 2, the management center includes a communication module, a storage module, an operation module and a management module;
the communication module is used for communicating with the unmanned aerial vehicle and receiving a final image sent by the unmanned aerial vehicle;
the storage module is used for storing the final image received by the communication module;
the operation module is used for carrying out image recognition processing on the final image to obtain a recognition result;
the management module is used for managing the heat supply pipe network based on the identification result.
Optionally, the communication module may communicate with the unmanned aerial vehicle through a 4G network, a 5G network, and the like, so as to receive a final image sent by the unmanned aerial vehicle.
In addition to the 2.4G communication capability, the unmanned aerial vehicle is generally provided with a communication mode through a cellular network, so that automatic return and remote control are avoided under the condition that a signal of a remote controller cannot be received.
Optionally, the operation module comprises a construction unit, a training unit and an image recognition unit;
the construction unit is used for constructing an identification model for carrying out image identification on the final image;
the training unit is used for training the recognition model constructed by the construction unit by adopting a big data technology to obtain a trained recognition model;
the image recognition unit is used for inputting the final image into the trained recognition model for calculation, and a recognition result is obtained.
Optionally, training is performed by big data technology, mainly by distributing training tasks to different computing nodes to perform synchronous computation, so that training efficiency is improved.
Optionally, the identification model is a convolutional neural network model.
The convolutional neural network comprises an input layer, an implicit layer and an output layer, wherein the input layer is mainly used for carrying out standardized processing on input data; while the hidden layers include convolution layers, pooling layers, full connection layers, etc. The output layer is typically connected to the full connection layer, outputting the names of the device faults identified in the final image and the location of each device fault.
When the convolutional neural network is trained, the convolutional neural network is realized through the following steps:
firstly, determining initial parameter values of a convolutional neural network model;
secondly, inputting the data set and the known correct labels into a convolutional neural network model for training;
step three, the model repeatedly verifies, trains data, continuously adjusts parameter values until finding out proper parameters, and enables the model to output as many correct results as possible;
and fourthly, after training is completed, finding out parameters of the optimal model, and taking the convolutional neural network model under the parameters as the trained convolutional neural network model.
Optionally, the identification result includes a name of the equipment failure present in the final image and a location of each equipment failure.
Specifically, equipment failure may include water leakage, cracks, rust, and the like. The position of the equipment fault can be obtained from the image attribute information of the image to be judged.
Optionally, the inputting the final image into the trained recognition model for calculation to obtain a recognition result includes:
performing pre-processing on the final image to obtain a pre-processed image;
and inputting the pre-processed image into the trained recognition model for calculation to obtain a recognition result.
Optionally, preprocessing is performed on the final image to obtain a preprocessed image, including:
performing edge detection on the final image to obtain a first image;
the final image is processed as follows to obtain a second image:
sedig(x,y)＝mid(nei(x,y))
wherein sedig represents the second image, sedig (x, y) represents the pixel value of the pixel point of the coordinate (x, y) in sedig, nei (x, y) represents the set of pixel points in the 8-neighbor of the pixel point of the coordinate (x, y) in the final image, mid represents the intermediate value of the variable in brackets;
conducting guided filtering processing on the first image to obtain coefficient a of a linear equation in the first image k，fir And b k,fir ；
Conducting guided filtering processing on the second image to obtain coefficient a of a linear equation in the second image k，scd And b k,scd ；
The fusion coefficient was calculated using the following formula:
a k,final ＝λ×a k,fir +(1-λ)a k,scd
b k,final ＝λ×b k,fir +(1-λ)b k,scd
wherein λ represents an adaptive scaling factor;
the final image is filtered using the following formula to obtain a filtered image:
flitG v ＝b k,final ×finalI v +b k,final ，v∈nbr v
wherein nbr v Representing a set of pixel points v within a filter window centered around a pixel point k in a final image finall v Representing the pixel value of pixel point v in final image finall, flitG v Representing pixel values of pixel point v in the filtered image flitG;
and performing image segmentation on the filtered image to obtain a pre-processing image.
When the preprocessing is performed, the coefficients of the linear equations of the first image and the second image are acquired, then the coefficients of the linear equations are acquired based on the first image and the second image respectively, and then the fusion coefficients are obtained according to the self-adaptive proportionality coefficients, so that the coefficients of the linear equations of the two different images can be utilized to perform filtering processing on the final image, and a filtering result is obtained. Compared with the existing filtering mode, the method and the device have the advantages that the corresponding linear equation coefficients are obtained through additionally calculating the first image and the second image, the edge highlighting processing is performed through edge detection, so that the coefficients of the linear equation in the first image can bring more edge detail information to the fusion coefficient, and the coefficients of the linear equation in the second image realize the intermediate value processing, so that partial noise is removed, noise information in the fusion coefficient can be reduced, and therefore edge details in a filtering result can be improved and noise can be removed more effectively.
Optionally, only the pixel points of the heating pipe network area are reserved in the pre-processing image.
The pre-processed image may be obtained by an aliquoting algorithm such as a watershed algorithm.
Optionally, the first image is subjected to a guided filtering process to obtain coefficients a of a linear equation in the first image k，fir And b k,fir Comprising:
a is calculated using the following function k，fir ：
Where fc denotes the variance of the pixel values in the final image finall, zs denotes the set calculation parameters, nfzs denotes the number of pixel points in the filter window w centered around pixel point k, and fig d Representing the pixel value of pixel d in the first image fir, finall d Representing the pixel value of pixel d in finalI, avef represents the average value of the pixel values in final image finalI, fd w Representing an average value of pixel values in the filter window w;
calculating b using the following function k，fir ：
b k，fir ＝fd w -a k，fir ×avef。
Alternatively, a linear equationCoefficient a of (2) k，scd And b k,scd And a) k，fir And b k,fir The calculation method is the same as that of the first image, and the first image is replaced by the second image, which is not described herein.
Optionally, the calculation function of the adaptive scaling factor is:
wherein fc is fir Representing the amount of information in the first image, fc scd Representing the amount of information in the second image.
The adaptive scaling factor is related to the amount of information, and the larger the amount of information is, the higher the quality of the image is represented, so that the weight given to the coefficient of the linear equation obtained by the image is larger, thereby realizing the adaptive change of the scaling factor.
Optionally, the storage module is further configured to store a data set for training the recognition model constructed by the construction unit.
Specifically, when the data set is used for training, the data set is divided into a training set and a testing set according to the set proportion.
Optionally, the management module comprises a prompt unit and a maintenance unit;
the prompting unit is used for prompting management personnel based on the recognition result;
the maintenance unit is used for generating a maintenance work order based on the identification result.
Specifically, if the identification result contains equipment faults, reporting names of the equipment faults and positions where the faults occur to a manager; if the identification result does not contain equipment faults, no prompt is sent to the manager.
The maintenance work order generally contains information such as maintenance low points, maintenance time limit, maintenance personnel and the like.
Optionally, the maintenance unit includes a user subunit, a generation subunit and a management subunit;
the generating subunit is used for generating a maintenance work order based on the identification result and sending the maintenance work order to the user subunit;
the user sub-unit is used for storing the maintenance work order sent by the generating sub-unit;
the management subunit is used for managing the maintenance work orders.
The user subunit can be an intelligent terminal carried by a maintenance worker, the maintenance worker can quickly know a heat supply pipeline to be maintained through the intelligent terminal, and data such as video and photos in the maintenance process can be put on, so that the management layer can conveniently check.
Optionally, the managing the maintenance work order includes querying the maintenance work order, counting the maintenance work order, modifying the maintenance work order, and deleting the maintenance work order.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (7)
1. A heat supply pipe network management system based on big data technology is characterized by comprising an unmanned plane and a management center;
the unmanned aerial vehicle is used for obtaining a final image of the heating network in the following manner and transmitting the obtained final image to the management center:
s1, shooting a heat supply pipe network to obtain an image to be judged;
s2, calculating an image to be judged by using a straight line detection algorithm to obtain a set tgu of straight line pixel points;
calculating image parameters based on tgu:
where igdx denotes an image parameter, α, β denote auxiliary coefficients, α+β=1, ntgu denotes the number of pixel points contained in tgu, flr i Gradient value flr representing horizontal direction of pixel point i in tgu r Gradient values in the horizontal direction of the pixel point on the right side of the pixel point i are represented, aveflr represents a reference value of a set average gradient difference, asu represents a set of pixel points which do not belong to the set tgu among 8 neighbors of the pixel points in the set tgu, upn and top represent minimum and maximum values of the pixel points in the set asu, num, respectively j Representing the total number of pixel points with the pixel value j in the set asu, nasu representing the total number of pixel points in the set asu, sh representing a preset calculated information amount reference value,
s3, judging whether the image parameters are larger than a set image parameter threshold, if so, taking the image to be judged as a final image, and transmitting the obtained final image to a management center; if not, executing S1;
the management center is used for managing the heat supply pipe network based on the final image;
the management center comprises a communication module, a storage module, an operation module and a management module;
the communication module is used for communicating with the unmanned aerial vehicle and receiving a final image sent by the unmanned aerial vehicle;
the storage module is used for storing the final image received by the communication module;
the operation module is used for carrying out image recognition processing on the final image to obtain a recognition result;
the management module is used for managing the heat supply pipe network based on the identification result;
the operation module comprises a construction unit, a training unit and an image recognition unit;
the construction unit is used for constructing an identification model for carrying out image identification on the final image;
the training unit is used for training the recognition model constructed by the construction unit by adopting a big data technology to obtain a trained recognition model;
the image recognition unit is used for inputting the final image into the trained recognition model for calculation to obtain a recognition result;
inputting the final image into the trained recognition model for calculation to obtain a recognition result, wherein the method comprises the following steps of:
performing pre-processing on the final image to obtain a pre-processed image;
inputting the pre-processing image into the trained recognition model for calculation to obtain a recognition result;
pre-processing the final image to obtain a pre-processed image, including:
performing edge detection on the final image to obtain a first image;
the final image is processed as follows to obtain a second image:
sedig(x,y)＝mid(nei(x,y))
wherein sedig represents the second image, sedig (x, y) represents the pixel value of the pixel point of the coordinate (x, y) in sedig, nei (x, y) represents the set of pixel points in the 8-neighbor of the pixel point of the coordinate (x, y) in the final image, mid represents the intermediate value of the variable in brackets;
conducting guided filtering processing on the first image to obtain coefficient a of a linear equation in the first image k，fir And b k,fir ；
Conducting guided filtering processing on the second image to obtain coefficient a of a linear equation in the second image k，scd And b k,scd ；
The fusion coefficient was calculated using the following formula:
a k,final ＝λ×a k,fir +(1-λ)a k,scd
b k,final ＝λ×b k,fir +(1-λ)b k,scd
wherein λ represents an adaptive scaling factor;
the final image is filtered using the following formula to obtain a filtered image:
flitG v ＝b k,final ×finalI v +b k,final ，v∈nbr v
wherein nbr v Representing a set of pixel points v within a filter window centered around a pixel point k in a final image finall v Representing the pixel value of pixel point v in final image finall, flitG v Representing pixel values of pixel point v in the filtered image flitG;
and performing image segmentation on the filtered image to obtain a pre-processing image.
2. A heating network management system based on big data technology according to claim 1, wherein the identification model is a convolutional neural network model.
3. A heating network management system based on big data technology according to claim 1, characterized in that the identification result comprises the name of the equipment fault present in the final image and the location of each equipment fault.
4. A heating network management system based on big data technology according to claim 1, wherein the storage module is further configured to store a data set for training the recognition model constructed by the construction unit.
5. A heating network management system based on big data technology according to claim 1, wherein the management module comprises a prompt unit and a maintenance unit;
the prompting unit is used for prompting management personnel based on the recognition result;
the maintenance unit is used for generating a maintenance work order based on the identification result.
6. A heating network management system based on big data technology according to claim 5, characterized in that the maintenance unit comprises a user subunit, a generation subunit and a management subunit;
the generating subunit is used for generating a maintenance work order based on the identification result and sending the maintenance work order to the user subunit;
the user sub-unit is used for storing the maintenance work order sent by the generating sub-unit;
the management subunit is used for managing the maintenance work orders.
7. The big data technology based heating network management system of claim 6, wherein the managing the maintenance worksheet includes querying the maintenance worksheet, counting the maintenance worksheet, modifying the maintenance worksheet, and deleting the maintenance worksheet.
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| CN112019739A (en) * | 2020-08-03 | 2020-12-01 | RealMe重庆移动通信有限公司 | Shooting control method and device, electronic equipment and storage medium |
| CN114331932A (en) * | 2021-12-06 | 2022-04-12 | 腾讯科技（深圳）有限公司 | Target image generation method and device, computing equipment and computer storage medium |
| CN114941807A (en) * | 2022-05-19 | 2022-08-26 | 普晟智链(山东)安全科技有限责任公司 | Unmanned aerial vehicle-based rapid monitoring and positioning method for leakage of thermal pipeline |
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