CN117710882A - Power transmission line online monitoring method based on cloud and mist cooperative calculation - Google Patents
Power transmission line online monitoring method based on cloud and mist cooperative calculation Download PDFInfo
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Abstract
An on-line monitoring method for a power transmission line based on cloud and fog cooperative calculation belongs to the field of line monitoring. The system comprises an image sensor module, a front-end processor and a background processor; completing target sample information acquisition through an image sensor, generating image pixel data corresponding to a target sample, and generating a pixel tone vector from the acquired data through a front-end processor, wherein the pixel tone vector is used as characteristic quantity data of the target; transmitting the data to a background processor through a communication module; performing pattern recognition on the target feature quantity data through a pattern recognition model in the cloud platform, and outputting a recognition result; judging whether the target threatens the power transmission line or not, and further taking effective precautionary measures to ensure the operation safety of the power transmission line. The method reduces the data volume of data communication and the hardware requirement of the front-end system while ensuring the system identification precision, reduces the power consumption of hardware equipment, keeps long-time stable operation, and provides safety guarantee for stable operation of the circuit. The method is suitable for the field of online monitoring of the power transmission line.
Description
Technical Field
The utility model belongs to the field of online monitoring, and particularly relates to an online monitoring method for an overhead power transmission line.
Background
Long distance transmission line voltage levels are typically 110 kv and above, and line lengths are also up to tens to hundreds of kilometers, so overhead line modes are commonly used.
The overhead line mode power transmission line is more susceptible to interference from environmental factors including extreme weather factors, tall tree factors, man-made illegal construction factors, etc., wherein man-made large mechanical impacts account for nearly 80% of power transmission line accidents. Therefore, high-standard monitoring and operation of the transmission line are very necessary.
However, the existing reliable power transmission line monitoring means are still manual inspection lines, the consumption of labor cost is large, and meanwhile real-time monitoring of the power transmission line cannot be guaranteed.
In summary, the operation and maintenance of the power transmission line are urgently needed to be realized by means of a reliable automatic online monitoring system, so that the conventional manual inspection is replaced, and the automatic identification and early warning of foreign object targets around the power transmission line are realized.
The utility model patent with the authorized bulletin date of 2019.08.09 and the authorized bulletin number of CN 209231449U discloses a power distribution network line disconnection fault positioning device, which comprises a monitoring device and a cloud server, wherein the monitoring device comprises a high-voltage electrified display device which is fixed on a pole tower; the high-voltage live display device comprises a live sensor and a central control unit, wherein the live sensor is electrically connected with an overhead line on the tower through an insulated wire and is used for detecting an electric quantity signal of the overhead line; the central control unit is electrically connected with the electric charge sensor and is used for receiving the electric quantity signal detected by the electric charge sensor; the central control unit is also in communication connection with the cloud server and is used for transmitting the electric quantity signals to the cloud server. By adopting the technical scheme, the line operation and maintenance manager can obtain the line disconnection fault information of the power distribution network by accessing the cloud server, so that the fault point can be rapidly positioned, and efficient fault investigation can be performed. In the technical scheme, the on-site central control unit directly processes the detected data on site and is also in communication connection with the cloud server, and transmits the on-site electric quantity signals to the cloud server, so that the on-site central control unit bears more detection data comparison, calculation and storage functions, has higher requirements on hardware resources of the front end, increases the data transmission quantity with the cloud server intangibly, increases the power consumption of the front end device, and is unfavorable for the service life of the on-site monitoring device; and the technical scheme does not fully exert the function of the back-end software resource, and is unfavorable for fully exerting the working efficiency of the whole set of monitoring system.
The patent of the utility model, which is entitled to bulletin day 2021.01.01 and entitled to bulletin number CN 110783834B, discloses a power distribution network overhead line real-time monitoring device, which comprises a base, a cylindrical box is arranged at the opening of the center position of the base along the up-down direction, an annular plate is rotatably connected with the bottom end of the outer wall of the cylindrical box through a bearing, a PLC (programmable logic controller) is arranged at the front side of the top end of the annular plate, an arc-shaped block is arranged at the left side of the top end of the annular plate, a moving mechanism is arranged at the bottom end of the rectangular plate, an electric cradle head is arranged at the left side of the bottom end of the moving mechanism, a monitoring camera is arranged at the moving end of the electric cradle head, and an infrared temperature sensor is arranged at the front side of the monitoring camera. This distribution network overhead line real-time monitoring equipment has increased monitoring facilities and has monitored the field of vision, prevents to have the monitoring dead angle to accessible monitoring facilities carries out the multi-angle monitoring, need not to install a plurality of monitoring facilities, has reduced the staff and has examined the degree of difficulty, easy operation, and prevent to cause unnecessary extravagant, the practicality is strong. The technical scheme has the advantages that the acquisition problem of the field image is considered, but the functions of data processing, image recognition and the like are still processed and completed on the spot, the hardware requirement of the front end of the field is higher, the long-term stable operation of the field monitoring device is not facilitated, the consideration of the cost of a large amount of data processing and data transmission is not carried out, the function of the rear end software resource is not fully exerted, and the work efficiency of the whole set of monitoring system is not facilitated.
In the on-line monitoring process of the overhead transmission line, how to realize rapid monitoring/identification of a moving target/foreign object (such as a moving vehicle, a large construction vehicle or a large crane) with low cost and low energy consumption, including improving the accuracy of the pixel extraction of the moving target/foreign object, is one of the keys in the actual transmission line monitoring work.
Disclosure of Invention
The utility model aims to provide an online monitoring method for a power transmission line based on cloud and mist cooperative calculation. The cloud and fog collaborative computing mode is used, front-end hardware resources and rear-end software resources are reasonably utilized, and a large amount of computing work is put in a rear-end cloud platform, so that the hardware requirements of a front-end system of a power transmission line are obviously reduced while the system identification precision is ensured, the data volume of data communication is reduced, the power consumption of hardware equipment is reduced, the system can also keep stable operation for a long time in a field environment, and safety guarantee is provided for the stable operation of the power transmission line continuously.
The technical scheme of the utility model is as follows: the utility model provides a transmission line on-line monitoring method based on cloud and fog collaborative calculation, including on overhead transmission line's scene, real-time acquisition overhead transmission line around target sample/target data, characterized by:
1) Dividing an on-line monitoring system of a power transmission line into two parts;
the power transmission line field part comprises an image sensor module and a front-end processor, and the front-end processor is called a fog calculation module;
the background part comprises a background processor, and the background processor is called a 'cloud computing module';
storing a pattern recognition model of the moving object/foreign object in the background processor;
2) Firstly, completing information acquisition of a target sample through an image sensor, generating image pixel data corresponding to the target sample, processing the acquired data through a fog calculation process in a front-end processor, generating a pixel tone vector as characteristic quantity data of the target, and transmitting the pixel tone vector to a background processor through a communication module;
3) The characteristic quantity data of the target are transmitted to a rear-end cloud platform, the characteristic quantity data of the target are subjected to pattern recognition through a pattern recognition model trained in the cloud platform, and a recognition result is output;
4) The output identification result is used for helping staff to judge whether the target threatens the power transmission line or not, and further effective precautionary measures are adopted to ensure the operation safety of the power transmission line.
Specifically, performing preliminary data processing in a front-end processor on site, screening available data and performing data transmission; after receiving the front-end data, the background system performs pattern matching of the target data by utilizing a database and an algorithm model established in the cloud platform, so as to output the identification result of the data and provide reliable research and judgment reference for staff.
Specifically, based on an image sensor installed on the power transmission line site, pixel data of a target sample is collected, and after 'fog calculation' processing, corresponding pixel tone vectors are generated and used as characteristic quantity data of the target, so that high-precision pattern recognition is performed.
Further, the high-precision pattern recognition process is as follows:
the image sensor stores pixel data of a target object in real time, wherein the pixels are in RGB format, namely, three primary colors of red, green and blue, and the value of each color is 0-255;
the tone data S of the pixel point can be obtained by the following tone data conversion formula:
V＝max(R,G,B)，Q＝min(R,G,B)
the tone data S of all the pixels of the target object constitute feature quantity data of the target:
T＝[S 1 ,S 2 ,...,S n ]
wherein T is the target pixel tone vector, and n is the number of pixels of the target image.
Specifically, the performing pattern recognition on the target feature data at least includes: after the target feature vector T is obtained, a mode recognition algorithm is utilized to realize target recognition.
Further, the target recognition is realized by using a pattern recognition algorithm, which specifically comprises the following steps:
(1) Identifying a result;
(2) A pattern recognition algorithm;
(3) And (5) a database.
Further, the identification result includes: the recognition result of the target is threat sample similarity R, namely the similarity between the target characteristic quantity and a target sample with threat to the power transmission line in the database, and the target is used for representing the probability of threat to the power transmission line by the target;
setting a required threshold value, and judging that the target is a threat target when the threat sample similarity is higher than the threshold value; when the threat sample similarity is below this threshold, the target is determined to be a non-threat target. .
Furthermore, the pattern recognition algorithm adopts a BP neural network model, and the model structure is divided into three layers: an input layer, an hidden layer, and an output layer; the input layer is T vector, the hidden layer is generated by database training, and the output layer is threat sample similarity R.
Further, the database is composed of vectors T of a plurality of target objects collected on site and judging information of whether the targets have threat or not; the database is collected in advance in an experimental stage and is used for training a neural network model.
According to the power transmission line online monitoring method disclosed by the technical scheme of the utility model, a cloud and mist cooperative computing mode is used, front-end hardware resources and rear-end software resources are reasonably utilized, and a large amount of computing work is put in a rear-end cloud platform, so that the hardware requirements on a power transmission line front-end system are obviously reduced while the system identification precision is ensured, the data volume of data communication is reduced, the power consumption of hardware equipment is reduced, and the system can be kept to stably operate for a long time in a field environment, so that the safety guarantee is continuously provided for the stable operation of the power transmission line.
Compared with the prior art, the utility model has the advantages that:
1. according to the technical scheme, the cloud computing method and the fog computing method are combined, front-end hardware resources and rear-end software resources are reasonably utilized, a large amount of computing work is put in a rear-end cloud platform by using a cloud-fog cooperative computing mode, so that the hardware requirements of a front-end system of a power transmission line are obviously reduced while the system identification precision is ensured, the data volume of data communication is reduced, the power consumption of hardware equipment is reduced, the system can also keep stable running for a long time in a field environment, and safety guarantee is continuously provided for the stable running of the power transmission line;
2. according to the technical scheme, foreign object target data around the power transmission line is collected in real time through an image sensor installed on the power transmission line on site, preliminary data processing (called fog calculation) is carried out in a front-end processor on site, available data are screened out, and data transmission is carried out; after receiving the front-end data, the background system performs pattern matching (called 'cloud computing') on the data by utilizing a database and an algorithm model established in the cloud platform, so as to output the identification result of the data and provide reliable research and judgment reference for staff;
3. by adopting the technical scheme of the utility model, the highest target recognition accuracy can reach 95.97 percent, the higher target recognition accuracy can be ensured, and more reliable threat target judgment information can be provided for staff; meanwhile, the cloud and fog collaborative calculation mode adopted by the method reduces the front-end and back-end data transmission quantity by 99.87%, obviously improves the data processing speed, reduces the power consumption of a front-end processing system by 27.1%, and verifies the effectiveness and practicability of the utility model.
Drawings
FIG. 1 is a schematic diagram of the system topology of the present utility model;
FIG. 2 is a schematic diagram of a neural network model of the present utility model;
FIG. 3 is a schematic representation of the results of a threat sample similarity experiment of the present utility model.
Detailed Description
The utility model is further described below with reference to the drawings and examples.
The main idea of the technical scheme of the utility model is as follows: the method comprises the steps of collecting foreign object target data around a power transmission line in real time through an image sensor installed on the power transmission line, performing preliminary data processing (called fog calculation) in a front-end processor on the site, screening available data and performing data transmission. After receiving the front-end data, the background system performs pattern matching (called 'cloud computing') on the data by utilizing a database and an algorithm model established in the cloud platform, so as to output the identification result of the data and provide reliable research and judgment reference for staff.
In fig. 1, the power transmission line online monitoring system in the technical scheme mainly comprises two parts: the transmission line field part mainly comprises an image sensor module and a front-end processor (called as a 'fog calculating module'), and the background part comprises a background processor (called as a 'cloud calculating module').
In the running process of the system, firstly, information acquisition of a target sample is completed through an image sensor, image pixel data corresponding to the target is generated, then, the data is processed through a fog calculation process in a front-end processor, a pixel tone vector is generated and is used as characteristic quantity data of the target, and then, the characteristic quantity data is transmitted to a background processor through a communication module.
The target characteristic data are transmitted to a back-end cloud platform, and the target characteristic data are subjected to pattern recognition through a pattern recognition model trained in the cloud platform, so that the recognition result can be output.
The output result can help staff judge whether the target threatens the power transmission line or not, so that effective countermeasure is adopted, and the operation safety of the power transmission line is ensured.
1. Extracting target feature quantity:
according to the technical scheme, based on the image sensor installed on the power transmission line on site, the pixel data of the target sample are collected, the corresponding pixel tone vector is generated after fog calculation processing and is used as the characteristic quantity data of the target, so that the image sensor has strong uniqueness and is convenient for high-precision pattern recognition; the specific process is as follows:
the image sensor stores pixel data of a target object in real time, pixels are in RGB format (namely, three primary colors of red, green and blue are formed, each color value is 0-255), and tone data S of the pixel point can be obtained through the following tone data conversion formula (1):
V＝max(R,G,B)，Q＝min(R,G,B)
the tone data S of all the pixels of the target object constitute feature quantity data of the target:
T＝[S 1 ,S 2 ,...,S n ](2)
wherein T is the target pixel tone vector, and n is the number of pixels of the target image.
2. Target feature pattern recognition:
after the target feature vector T is obtained, a mode recognition algorithm is utilized to realize target recognition.
The target recognition realized by using the pattern recognition algorithm specifically comprises the following aspects:
(1) Identifying a result;
the recognition result of the target is threat sample similarity R (unit is%), namely the similarity between the target characteristic quantity and a target sample threatening the power transmission line in the database, and the target characteristic quantity is used for representing the probability of threat of the target to the power transmission line.
(2) A pattern recognition algorithm;
the mode recognition algorithm of the technical scheme selects a BP (Back Propagation) neural network model, and the model structure is divided into three layers: input layer, hidden layer and output layer.
The input layer, i.e. the T vector, the hidden layer is generated by database training, the output layer is threat sample similarity R, and the specific structure of the threat sample similarity R is shown in fig. 2.
(3) Database:
the database is composed of vectors T of a plurality of target objects collected in the field and judgment information of whether the targets have threat or not.
The database is collected in advance in the experimental stage and is used for training the neural network model.
3. And (3) test verification:
in order to verify the effectiveness of the online monitoring method of the power transmission line based on cloud and mist cooperative calculation, relevant experimental tests are carried out on the power transmission line on site.
A total of 1500 samples were used in the experiment, including the threat sample, no threat sample, in random proportions.
As described above, the pattern recognition model of the present utility model outputs threat sample similarity, and then a threshold value needs to be set in the experiment: and when the similarity is higher than the threshold value, judging the target as a threat target, and when the similarity is lower than the threshold value, judging the target as a threat-free target. The results of the specific experiments are shown in fig. 3.
As can be seen from the experimental results in conjunction with fig. 3, the accuracy of target identification in the technical scheme can reach 95.97%, and the corresponding threshold is set to 86%.
When the threshold value is increased or decreased, the recognition accuracy is correspondingly decreased. By the method, when the threshold value is reasonably selected, higher target identification accuracy can be ensured, and reliable threat target judgment information can be provided for staff.
Meanwhile, the data in the experimental process show that the cloud and fog collaborative calculation mode adopted by the technical scheme of the utility model reduces the front end data transmission quantity and the rear end data transmission quantity by 99.87%, obviously improves the data processing speed, reduces the power consumption of a front end processing system by 27.1%, and verifies the effectiveness and the practicability of the technical scheme of the utility model.
In summary, the technical scheme of the utility model uses the cloud and fog cooperative computing mode to place a large amount of computing work in the back-end cloud platform, thereby obviously reducing the hardware requirement of the front-end system of the power transmission line while guaranteeing the system identification precision, simultaneously reducing the data volume of data communication and reducing the power consumption of hardware equipment, so that the system can keep stable operation for a long time in a field environment, and further, the safety guarantee is continuously provided for the stable operation of the power transmission line.
The utility model can be widely applied to the field of online monitoring of overhead transmission lines.
Claims (10)
1. The utility model provides a transmission line on-line monitoring method based on cloud and fog collaborative calculation, includes on the scene of overhead transmission line, real-time acquisition overhead transmission line around target sample/target data, characterized by:
1) Dividing an on-line monitoring system of a power transmission line into two parts;
the power transmission line field part comprises an image sensor module and a front-end processor, and the front-end processor is called a fog calculation module;
the background part comprises a background processor, and the background processor is called a 'cloud computing module';
storing a pattern recognition model of the moving object/foreign object in the background processor;
2) Firstly, completing information acquisition of a target sample through an image sensor, generating image pixel data corresponding to the target sample, processing the acquired data through a fog calculation process in a front-end processor, generating a pixel tone vector as characteristic quantity data of the target, and transmitting the pixel tone vector to a background processor through a communication module;
3) The characteristic quantity data of the target are transmitted to a rear-end cloud platform, the characteristic quantity data of the target are subjected to pattern recognition through a pattern recognition model trained in the cloud platform, and a recognition result is output;
4) The output identification result is used for helping staff to judge whether the target threatens the power transmission line or not, and further effective precautionary measures are adopted to ensure the operation safety of the power transmission line.
2. The online monitoring method of the power transmission line based on cloud and fog cooperative computing according to claim 1, which is characterized in that preliminary data processing is carried out in a front-end processor of a site, available data are screened out, and data transmission is carried out; after receiving the front-end data, the background system performs pattern matching of the target data by utilizing a database and an algorithm model established in the cloud platform, so as to output the identification result of the data and provide reliable research and judgment reference for staff.
3. The power transmission line online monitoring method based on cloud and fog cooperative computing according to claim 1, wherein the method is characterized in that based on an image sensor installed on the power transmission line on site, pixel data of a target sample are collected, corresponding pixel tone vectors are generated after 'fog computing' processing and serve as characteristic quantity data of the target, and high-precision pattern recognition is performed.
4. The online monitoring method for the power transmission line based on cloud and fog cooperative computing according to claim 3, wherein the high-precision pattern recognition process is as follows:
the image sensor stores pixel data of a target object in real time, wherein the pixels are in RGB format, namely, three primary colors of red, green and blue, and the value of each color is 0-255;
the tone data S of the pixel point can be obtained by the following tone data conversion formula:
V＝max(R,G,B)，Q＝min(R,G,B)
the tone data S of all the pixels of the target object constitute feature quantity data of the target:
T＝[S 1 ,S 2 ,...,S n ]
wherein T is the target pixel tone vector, and n is the number of pixels of the target image.
5. The online monitoring method of the power transmission line based on cloud and fog cooperative computing according to claim 1, wherein the pattern recognition of the target feature quantity data at least comprises the following steps: after the target feature vector T is obtained, a mode recognition algorithm is utilized to realize target recognition.
6. The online monitoring method for the power transmission line based on cloud and fog cooperative computing according to claim 5, wherein the target recognition is realized by using a pattern recognition algorithm, and the method specifically comprises the following steps:
(1) Identifying a result;
(2) A pattern recognition algorithm;
(3) And (5) a database.
7. The online monitoring method for the power transmission line based on cloud and fog cooperative computing according to claim 6, wherein the identification result comprises:
the recognition result of the target is threat sample similarity R, namely the similarity between the target characteristic quantity and a target sample with threat to the power transmission line in the database, and the target is used for representing the probability of threat to the power transmission line by the target;
setting a required threshold value, and judging that the target is a threat target when the threat sample similarity is higher than the threshold value; when the threat sample similarity is below this threshold, the target is determined to be a non-threat target. .
8. The online monitoring method of the power transmission line based on cloud and fog cooperative computing according to claim 6, wherein the pattern recognition algorithm adopts a BP neural network model, and the model structure is divided into three layers: an input layer, an hidden layer, and an output layer;
the input layer is T vector, the hidden layer is generated by database training, and the output layer is threat sample similarity R.
9. The online monitoring method for the power transmission line based on cloud and fog cooperative computing according to claim 5, wherein the database is composed of vectors T of a plurality of target objects collected on site and judgment information of whether the targets have threat or not;
the database is collected in advance in an experimental stage and is used for training a neural network model.
10. The power transmission line online monitoring method based on cloud and fog cooperative computing according to claim 1 is characterized in that the power transmission line online monitoring method uses a cloud and fog cooperative computing mode, reasonably utilizes front-end hardware resources and rear-end software resources, and places a large amount of computing work in a rear-end cloud platform, so that the hardware requirements on a power transmission line front-end system are remarkably reduced while the system identification precision is ensured, the data volume of data communication is reduced, the power consumption of hardware equipment is reduced, the system can also keep stable operation for a long time in a field environment, and safety guarantee is provided for the stable operation of the power transmission line continuously.
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