Power grid deicing device and method based on environment detection and monitoring

Abstract

The invention relates to the technical field of power grid deicing, and discloses a power grid deicing device and method based on environment detection and monitoring, and the device comprises a mounting part which sleeves an overhead cable between two power transmission line towers through a configured moving through cavity, so that the device can freely move in the direction of the overhead cable; therefore, monitoring and deicing operations at different positions are realized. The environment monitoring part judges whether icing occurs or not by acquiring environment information (such as temperature, humidity and wind speed) around the overhead cable, extracts character behavior characteristics in combination with image information, and further judges whether suspicious persons exist or not. And if it is judged that the overhead cable is iced, the mounting part can deice by oscillating the overhead cable, and the oscillation amplitude is automatically adjusted according to the environment information monitored in real time, so that the optimal deicing effect is achieved. According to the invention, the environment change can be intelligently monitored, the working strategy can be dynamically adjusted according to the real-time data, and the deicing efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid deicing, and in particular to a power grid deicing device and method based on environmental detection and monitoring. Background Art

[0002] With the continuous development of the power industry, the security and reliability of the power grid play a vital role in ensuring energy supply.

[0003] However, the traditional way of monitoring power grids relies on manual inspections, which not only requires a large amount of manpower and material resources, but also has low monitoring efficiency, and is prone to missed inspections or misjudgments, leading to power grid failures and safety hazards. This traditional model is particularly vulnerable when responding to emergencies, especially under complex environmental conditions, where manual inspections are difficult to ensure timeliness and accuracy, creating potential risks for power grid operation. At the same time, the environment around the power grid is complex and changeable, including natural factors such as extreme weather and severe climate, as well as human factors such as arson, which may affect the normal operation of power grid equipment and increase the difficulty and risk of power grid maintenance. Secondly, ice and snow weather will pose a more severe challenge to power grid operation. Ice and snow accumulate on overhead cables, which may not only cause line breakage and equipment damage, but also increase power grid load and affect the stability of power transmission. Traditional de-icing operations usually rely on manual and mechanical equipment, which is time-consuming and labor-intensive, and the timeliness and efficiency of de-icing operations are difficult to guarantee, further exacerbating the risk of power grid failures.

[0004] Therefore, there is an urgent need to invent a technology for breaking the ice on power grids to solve the problems of traditional power grid monitoring and de-icing operations that rely on manual inspections and mechanical equipment, low monitoring efficiency, poor timeliness, false positives and missed detections, and low operating efficiency. Especially in complex environments and extreme weather conditions, the safe and reliable operation of the power grid cannot be effectively guaranteed. Summary of the invention

[0005] In view of this, the present invention proposes a power grid de-icing device and method based on environmental detection and monitoring, aiming to solve the problems of traditional power grid monitoring and de-icing operations relying on manual inspections and mechanical equipment, low monitoring efficiency, poor timeliness, false positives and missed detections, low operating efficiency, etc., especially in complex environments and extreme weather conditions, and unable to effectively guarantee the safe and reliable operation of the power grid.

[0006] The present invention proposes a power grid deicing device based on environmental detection and monitoring, comprising: Mounting Department and Environmental Monitoring Department; A movable through cavity is arranged on the top of the mounting part, and the movable through cavity is sleeved on the overhead cable between two transmission line towers, so that the mounting part can move along the setting direction of the overhead cable; The environmental monitoring unit is arranged at the middle and lower part of the mounting unit, and is configured to obtain environmental information around the overhead cable, and determine whether the overhead cable is frozen according to the environmental information, and the environmental monitoring unit is also configured to obtain image information around the overhead cable, and extract human behavior features in the image information; the environmental monitoring unit is also configured to determine whether to send warning information according to the relationship between the human behavior features and the human warning behavior features configured by the environmental monitoring unit; Wherein, when the environmental monitoring unit determines that the overhead cable is frozen, the mounting unit is further configured to oscillate the overhead cable and adjust the oscillation amplitude according to environmental information of the overhead cable.

[0007] Furthermore, the mounting portion includes: The mounting modules are provided with two groups, and the two groups of mounting modules are arranged opposite to each other in the vertical direction of the overhead cable, and a movable groove is provided on one side of the upper part of the mounting module adjacent to the overhead cable, so that when the two mounting modules are connected, the movable through cavity is formed between the two movable grooves, wherein a placement groove is provided on one side of the lower part of the mounting module adjacent to the overhead cable; A moving module is arranged inside the moving groove, so that when the two mounting modules are connected, the moving module is connected to the overhead cable, and the moving module is configured to drive the mounting module to move along the setting direction of the overhead cable; A vibration module is disposed inside the moving groove, and the vibration module is configured to vibrate toward the overhead cable.

[0008] Furthermore, the environmental monitoring unit includes: A wide-angle image acquisition module, wherein the mounting module has installation slots on two adjacent sides of the movable slot, the wide-angle image acquisition module is arranged inside the installation slots, and the wide-angle image acquisition module is configured to obtain image information around the overhead cable; A temperature detection sensor is disposed inside the placement slot, and the temperature detection sensor is configured to detect the real-time temperature of the overhead cable; A humidity detection sensor and a wind speed detection sensor are respectively arranged inside the placement groove, the humidity detection sensor is configured to detect the humidity of the surrounding environment of the overhead cable, and the wind speed detection sensor is configured to detect the wind speed of the surrounding environment of the overhead cable; A power supply is arranged inside the placement slot, the power supply is connected to the placement slot, and the power supply is configured to provide electrical energy to the moving module, the vibration module, the wide-angle image acquisition module, the temperature detection sensor, the humidity detection sensor and the wind speed detection sensor based on connecting wires.

[0009] Furthermore, the environmental monitoring unit also includes: A collection module, electrically connected to the wide-angle image collection module, the temperature detection sensor, the humidity detection sensor and the wind speed detection sensor, respectively, and configured to collect image information around the overhead cable, the real-time temperature of the overhead cable, the surrounding environmental humidity and the surrounding environmental wind speed; An early warning module is electrically connected to the acquisition module, and is configured to determine whether there is a suspicious person around the overhead cable based on the image information around the overhead cable, and determine whether to send the early warning information based on the relationship between the person behavior characteristics of the suspicious person and the person warning behavior characteristics; a central control module, electrically connected to the acquisition module and the vibration module, the central control module being configured to determine whether the overhead cable is frozen based on the real-time temperature of the overhead cable, the surrounding environmental humidity and the surrounding environmental wind speed, and to adjust the vibration amplitude of the vibration module when it is determined that the overhead cable is frozen; The communication module is electrically connected to the warning module. When the warning module determines to issue a warning message, the communication module is configured to create a warning message package based on the image information around the overhead cable and the behavioral characteristics of the suspicious person, and send it to the pan-tilt head.

[0010] Furthermore, when the early warning module determines whether there is a suspicious person around the overhead cable based on the image information around the overhead cable, it includes: The early warning module is configured to perform image filtering processing on the image information, and perform differential processing on the image information processed by image filtering to extract suspicious objects; The early warning module is configured to classify and locate the suspicious object based on a deep learning algorithm, and determine whether it is a suspicious person according to the classification result and the positioning result, wherein: If the suspicious object is classified as a person and the location of the suspicious object is within the preset warning range, the warning module determines that the suspicious object is a suspicious person and obtains the facial feature image and character behavior features of the suspicious person in the image information; If the suspicious object is classified as a person, and the location of the suspicious object is outside the preset warning range, the module determines that the suspicious object is not a suspicious person.

[0011] Furthermore, when the early warning module determines whether to send the early warning information based on the relationship between the suspicious person's behavior characteristics and the person's early warning behavior characteristics, it includes: The early warning module is further configured to obtain the person's behavioral characteristics within a preset time period of the suspicious person, and substitute the person's behavioral characteristics within the preset time period into the behavioral characteristic model pre-established by the early warning module to obtain the person's early warning behavioral characteristics; The early warning module is further configured to determine the similarity between the character behavior characteristics of the suspicious person and the character early warning behavior characteristics based on the Euclidean distance: ; Among them, S is the similarity, n is the dimension of the behavior feature vector, bi is the i-th dimension of the character's behavior feature, and Bi is the i-th dimension of the character's warning behavior feature; The early warning module is further configured to determine whether to send early warning information according to the relationship between the similarity and a preset similarity configured by the early warning module: When the similarity is greater than or equal to the preset similarity, the warning module determines to send the warning information; When the similarity is less than the preset similarity, the warning module determines not to send the warning information.

[0012] Furthermore, the behavior feature model pre-established by the early warning module includes: The warning module is also configured to obtain movement data and posture change data of each dangerous person and establish a warning behavior association formula; The early warning module is configured to extract the posture features and action features of the dangerous person in the early warning behavior association formula based on a convolutional neural network; The warning module is also configured to classify the posture features and action features of the dangerous person in each warning behavior association formula based on random forest: The early warning module is also configured to establish the behavior feature model based on the classified posture features and action features.

[0013] Furthermore, when the central control module determines whether the overhead cable is frozen based on the real-time temperature of the overhead cable, the surrounding environmental humidity and the surrounding environmental wind speed, the central control module includes: The central control module is further configured to determine an environmental similarity score for the occurrence of icing of the overhead cable according to the real-time temperature of the overhead cable, the surrounding environmental humidity and the surrounding environmental wind speed: ; Wherein, H is the environmental similarity score, t is the real-time temperature of the overhead cable, T is the preset freezing temperature of the overhead cable, g is the ambient humidity of the overhead cable, G is the preset freezing humidity of the overhead cable, j is the ambient wind speed of the overhead cable, J is the preset freezing wind speed of the overhead cable, σt is the similarity coefficient between the real-time temperature and the preset freezing temperature, σg is the similarity coefficient between the ambient humidity and the preset freezing humidity, σj is the similarity coefficient between the ambient wind speed and the preset freezing wind speed, z1, z2 and z3 are weight coefficients, and z1, z2 and z3 are not 0; The central control module is further configured to determine whether the overhead cable is frozen according to a relationship between the environmental similarity score and a preset environmental similarity score configured in the central control module: When the environmental similarity score is greater than or equal to the preset environmental similarity score, the central control module determines that the overhead cable is frozen; When the environmental similarity score is less than the preset environmental similarity score, the central control module determines that the overhead cable is not frozen.

[0014] Furthermore, when the central control module determines that the overhead cable is frozen, the central control module adjusts the vibration amplitude of the vibration module, including: The central control module is further configured to determine an adjustment coefficient according to a similarity score difference between the environment similarity score and the preset environment similarity score, and according to a relationship between the similarity score difference and the first preset similarity score difference and the second preset similarity score difference configured as the central control module, and adjust the vibration amplitude of the vibration module according to the adjustment coefficient: When the similarity score difference is less than the first preset similarity score difference, the central control module determines that the adjustment coefficient is M1; When the similarity score difference is greater than or equal to the first preset similarity score difference, and the similarity score difference is less than the second preset similarity score difference, the central control module determines that the adjustment coefficient is M2; When the similarity score difference is greater than or equal to the second preset similarity score difference, the central control module determines that the adjustment coefficient is M3; The first preset similarity score difference is smaller than the second preset similarity score difference, and M1<1<M2<M3<1.5.

[0015] Compared with the prior art, the beneficial effect of the present invention is that: through the environmental monitoring unit, the environmental information (such as temperature, humidity, wind speed, etc.) around the overhead cable can be obtained in real time, and based on this information, it is accurately determined whether the overhead cable is frozen. Traditional power grid deicing operations usually rely on manual inspections and mechanical equipment, and it is difficult to monitor environmental changes in real time, which leads to delays or misjudgments in deicing operations. Through real-time environmental monitoring and automated feedback mechanisms, the present device can greatly improve the timeliness and accuracy of deicing operations, and reduce the tediousness of manual inspections and the risk of misjudgment. Secondly, the environmental monitoring unit can also obtain image information around the overhead cable and extract human behavior features from it. This function is not only used to determine the operating status of power grid equipment, but also can monitor the safety hazards around the power grid in real time by comparing with the preset human behavior feature model. In particular, when there are security threats such as arson in the power grid operation area, the device can issue an early warning through behavior recognition technology, discover potential safety risks in advance, and prevent damage to power grid equipment caused by human interference. In addition, the combination of the mounting unit and the environmental monitoring unit enables the device to automatically move along the setting direction of the overhead cable and oscillate the overhead cable for deicing. Based on the environmental data acquired in real time, the mounting unit can dynamically adjust the oscillation amplitude to ensure the optimal de-icing effect. Compared with traditional manual de-icing, automated oscillation de-icing can not only improve work efficiency, but also avoid damage to power grid equipment due to improper operation. This intelligent de-icing process can continue to operate under extreme weather conditions, ensuring that power grid equipment is not affected by ice and snow accumulation, thereby improving the stability and reliability of the power grid. Finally, through intelligent monitoring and de-icing technology, the need for manual intervention is greatly reduced, labor costs are reduced, and work can be continued and stable for a long time.

[0016] On the other hand, the present application also provides a power grid deicing method based on environmental detection and monitoring, comprising: Acquiring environmental information around the overhead cable, and determining whether the overhead cable is frozen according to the environmental information; Acquire image information around the overhead cable, and extract human behavior features from the image information; Determining whether to send warning information according to the relationship between the character behavior characteristics and the character warning behavior characteristics configured by the environment monitoring unit; When it is determined that the overhead cable is frozen, the overhead cable is oscillated, and the oscillation amplitude is adjusted according to environmental information of the overhead cable.

[0017] It can be understood that the power grid deicing device and method based on environmental detection and monitoring in the above-mentioned embodiments have the same beneficial effects, which will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A schematic diagram of the structure of a power grid deicing device based on environmental detection and monitoring provided by an embodiment of the present invention; Figure 2 A structural block diagram of an environmental monitoring unit provided by an embodiment of the present invention; Figure 3 A schematic diagram of a flow chart of a power grid deicing method based on environmental detection and monitoring provided by an embodiment of the present invention; Among them, 100, mounting part; 110, mounting module; 111, moving slot; 112, installation slot; 210, wide-angle image acquisition module; 240, wind speed detection sensor. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] like Figure 1-Figure 2 As shown, in some embodiments of the present application, this embodiment provides a power grid deicing device based on environmental detection and monitoring, including: a mounting part 100 and an environmental monitoring part.

[0021] Specifically, a movable cavity is arranged at the top of the mounting part 100, and the movable cavity is sleeved on the overhead cable between two transmission line towers so that the mounting part 100 can move along the setting direction of the overhead cable. The environmental monitoring part is arranged at the middle and lower part of the mounting part 100, and the environmental monitoring part is arranged to obtain environmental information around the overhead cable, and determine whether the overhead cable is frozen according to the environmental information. The environmental monitoring part is also arranged to obtain image information around the overhead cable, and extract the behavior characteristics of the characters in the image information. The environmental monitoring part is also arranged to determine whether to send the warning information according to the relationship between the behavior characteristics of the characters and the warning behavior characteristics of the characters configured by the environmental monitoring part. Among them, when the environmental monitoring part determines that the overhead cable is frozen, the mounting part 100 is also arranged to oscillate the overhead cable, and adjust the oscillation amplitude according to the environmental information of the overhead cable.

[0022] Specifically, the mounting part 100 includes: two groups of mounting modules 110, and the two groups of mounting modules 110 are arranged relatively along the vertical direction of the overhead cable, and a moving groove 111 is provided on the side of the upper part of the mounting module 110 adjacent to the overhead cable, so that when the two mounting modules 110 are connected, a moving cavity is formed between the two moving grooves 111, wherein a placement groove is provided on the side of the lower part of the mounting module 110 adjacent to the overhead cable. The moving module is arranged inside the moving groove 111, so that when the two mounting modules 110 are connected, the moving module is connected to the overhead cable, and the moving module is configured to drive the mounting module 110 to move along the setting direction of the overhead cable. The vibration module is arranged inside the moving groove 111, and the vibration module is configured to oscillate toward the overhead cable.

[0023] Specifically, the environmental monitoring unit includes: a wide-angle image acquisition module 210, a mounting slot 112 is provided on both adjacent sides of the mobile slot 111 provided on the mounting module 110, the wide-angle image acquisition module 210 is arranged inside the mounting slot 112, and the wide-angle image acquisition module 210 is configured to obtain image information around the overhead cable. A temperature detection sensor is arranged inside the placement slot, and the temperature detection sensor is configured to detect the real-time temperature of the overhead cable. A humidity detection sensor and a wind speed detection sensor 240 are respectively arranged inside the placement slot, the humidity detection sensor is configured to detect the ambient humidity around the overhead cable, and the wind speed detection sensor 240 is configured to detect the ambient wind speed around the overhead cable. A power supply is arranged inside the placement slot, the power supply is connected to the placement slot, and the power supply is configured to provide power to the mobile module, the vibration module, the wide-angle image acquisition module 210, the temperature detection sensor, the humidity detection sensor, and the wind speed detection sensor 240 based on the connecting wire.

[0024] Specifically, the environmental monitoring unit also includes: a collection module electrically connected to the wide-angle image collection module 210, a temperature detection sensor, a humidity detection sensor, and a wind speed detection sensor 240, respectively, and the collection module is configured to collect image information around the overhead cable, the real-time temperature of the overhead cable, the surrounding environmental humidity, and the surrounding environmental wind speed. The early warning module is electrically connected to the collection module, and the early warning module is configured to determine whether there is a suspicious person around the overhead cable based on the image information around the overhead cable, and determine whether to send an early warning message based on the relationship between the character behavior characteristics of the suspicious person and the character early warning behavior characteristics. The central control module is electrically connected to the collection module and the vibration module, and the central control module is configured to determine whether the overhead cable is frozen based on the real-time temperature of the overhead cable, the surrounding environmental humidity, and the surrounding environmental wind speed, and adjust the vibration amplitude of the vibration module when it is determined that the overhead cable is frozen. The communication module is electrically connected to the early warning module, and the communication module is configured to create an early warning information package based on the image information around the overhead cable and the character behavior characteristics of the suspicious person when the early warning module determines to send an early warning message, and send it to the pan-tilt head.

[0025] It is understandable that the mounting part 100 is set on the overhead cable between the two transmission line towers through the mobile through-cavity, so that the mounting part 100 can move freely along the setting direction of the overhead cable. This design enables the device to automatically inspect at different positions and effectively process according to the environmental information and icing state of the overhead cable without manual intervention, thereby improving the working efficiency and monitoring coverage. Secondly, the environmental monitoring unit realizes comprehensive monitoring of the surrounding environment of the overhead cable through a series of sensors and image acquisition modules. Specifically, the wide-angle image acquisition module 210 can obtain image information around the overhead cable, the temperature detection sensor monitors the temperature of the overhead cable in real time, and the humidity detection sensor and the wind speed detection sensor 240 detect the surrounding humidity and wind speed respectively. These environmental data can help the system determine whether there is icing and provide a basis for subsequent deicing measures. Through these sensors, the system can perceive environmental changes in real time, thereby improving the accuracy and timeliness of icing monitoring. Then, the environmental monitoring unit also transmits image information and environmental data to the central control module and the early warning module through the acquisition module. The central control module is responsible for comprehensively analyzing real-time environmental data, determining whether ice has occurred, and adjusting the vibration amplitude of the vibration module according to the severity of ice to ensure the efficiency of de-icing operations. The vibration module can oscillate the overhead cables according to the instructions of the central control module to help remove ice and snow accumulation and avoid equipment damage or power grid failure caused by ice and snow accumulation. In addition, the early warning module is connected to the acquisition module to analyze and identify whether there are suspicious persons around based on the behavioral characteristics of the characters in the image information. By comparing with the preset behavioral characteristic model, the system can determine in real time whether the character's behavior conforms to the dangerous behavior pattern, thereby triggering the early warning function. When suspicious behavior is detected, the early warning module will connect with the communication module, establish an early warning information package, and send it to the pan-tilt to ensure the safety of the power grid. Finally, as the core connection component in the system, the communication module is responsible for transmitting the early warning information through the pan-tilt in real time to ensure that the information can reach the relevant personnel in time. The design of this module ensures the efficient coordination and information transmission of the system, and improves the automation level of power grid de-icing operations and safety monitoring.

[0026] It can be seen that, through the design of the mounting part 100 and the environmental monitoring part, the device can move freely along the setting direction of the overhead cable, and realize dynamic monitoring and deicing operations of power grids at different locations. The mobile through cavity configured on the top of the mounting part 100 can be effectively set on the overhead cable between the two transmission line towers, so that the device can move freely in the power grid area as needed, thereby improving the inspection range and operation efficiency, and avoiding the inefficiency and high cost problems of traditional manual inspections. Secondly, the environmental monitoring part can comprehensively monitor the real-time changes of the environment around the power grid, including the temperature, humidity, wind speed and other factors of the overhead cables, by configuring a variety of sensors, so as to accurately determine whether there is icing. The coordinated use of temperature, humidity and wind speed sensors makes environmental monitoring more accurate and comprehensive, thereby providing sufficient data support for icing judgment. The data collected in real time by these sensors helps to discover potential icing problems in advance, avoid power outages or equipment damage caused by ice and snow accumulation, and significantly improve the operational safety of the power grid. Third, the device integrates a vibration module. When the environmental monitoring unit detects icing, the mounting unit 100 can automatically start the vibration module to oscillate the overhead cables. This design effectively solves the problem of traditional deicing operations that require a large amount of manpower and mechanical equipment. The vibration module automatically adjusts the oscillation amplitude according to the feedback of real-time environmental information to ensure the optimization of the deicing effect, reduce the potential damage to the overhead cables caused by excessive oscillation, and improve the efficiency and accuracy of the deicing operation. In addition, the environmental monitoring unit is also equipped with a wide-angle image acquisition module 210, which can obtain image information around the overhead cables in real time and determine whether there are suspicious persons around by analyzing the behavioral characteristics of the characters. The early warning module compares the image information with the preset behavioral characteristic model, can trigger an early warning in time when suspicious behavior is found, and send the early warning information to the pan-tilt through the communication module. This not only improves the safety of the power grid deicing operation, but also effectively avoids the potential threat to power grid equipment caused by arson and other behaviors, and provides better protection for the long-term safe operation of power facilities. Finally, the design of this device effectively combines intelligent monitoring and automatic control functions, and can monitor and analyze the operating status and surrounding environment of the power grid in real time. Through the integrated central control module, it can automatically determine whether the overhead cables are frozen and adjust the oscillation amplitude according to environmental data to ensure the timeliness and accuracy of de-icing operations. While improving the de-icing efficiency of the power grid, the device also greatly reduces manual intervention, improves the intelligence and automation level of the power grid, reduces reliance on manual inspections, reduces the risk of power grid failures, and improves the overall reliability and safety of the power grid.

[0027] Specifically, when the early warning module determines whether there are suspicious persons around the overhead cables based on the image information around the overhead cables, it includes: the early warning module is configured to perform image filtering processing on the image information, and perform differential processing on the image information processed by the image filtering to extract suspicious objects. The early warning module is configured to classify and locate suspicious objects based on the deep learning algorithm, and determine whether they are suspicious persons based on the classification results and the positioning results, wherein: if the suspicious object is classified as a person, and the location of the suspicious object is within the preset early warning range, the early warning module determines that the suspicious object is a suspicious person, and obtains the facial feature image and character behavior features of the suspicious person in the image information. If the suspicious object is classified as a person, and the location of the suspicious object is outside the preset early warning range, the module determines that the suspicious object is not a suspicious person.

[0028] Specifically, when the early warning module determines whether to send an early warning message based on the relationship between the suspicious person's behavior characteristics and the person's early warning behavior characteristics, the early warning module is further configured to obtain the person's early warning behavior characteristics based on the obtained person's behavior characteristics within a preset time period, and substitute the person's behavior characteristics within the preset time period into the behavior characteristic model pre-established by the early warning module. The early warning module is also configured to determine the similarity between the suspicious person's behavior characteristics and the person's early warning behavior characteristics based on the Euclidean distance: .

[0029] Wherein, S is the similarity, n is the dimension of the behavior feature vector, bi is the i-th dimension of the character behavior feature, and Bi is the i-th dimension of the character warning behavior feature. The warning module is also configured to determine whether to send a warning message based on the relationship between the similarity and the preset similarity configured by the warning module: when the similarity is greater than or equal to the preset similarity, the warning module determines to send a warning message. When the similarity is less than the preset similarity, the warning module determines not to send a warning message.

[0030] Specifically, when the behavior feature model pre-established by the early warning module includes: the early warning module is also configured to obtain the movement data and posture change data of each dangerous person, and establish a warning behavior association. The early warning module is configured to extract the posture features and action features of the dangerous person in the warning behavior association based on a convolutional neural network. The early warning module is also configured to classify the posture features and action features of the dangerous person in each warning behavior association based on a random forest: the early warning module is also configured to establish a behavior feature model based on the classified posture features and action features.

[0031] It can be understood that the early warning module filters and performs differential processing on the image information to extract suspicious objects around the overhead cables. The purpose of this process is to enhance the clarity and contrast of the image, remove noise, and identify potential suspicious targets through differential processing. Through this preprocessing, the system can more accurately distinguish objects or people that do not conform to normal behavior patterns from the environment, providing accurate basic data for subsequent analysis. Next, the early warning module uses a deep learning algorithm to classify and locate the extracted suspicious objects. Specifically, the early warning module uses deep learning technology to classify objects in the image and determine whether they are "people". If it is identified as a "person", the system will further determine whether the location information of the object is within the preset early warning range. Only when the location meets the requirements will the object be further analyzed for behavior. This technology can significantly improve the accuracy of the early warning system, avoid false alarms, and ensure that the system will only initiate an early warning when a potential threat occurs. The early warning module further uses the behavioral characteristics of suspicious persons to determine whether they are potential security threats. The system first analyzes the behavior of suspicious persons within a preset period of time to extract their action characteristics. Then, these behavioral features are substituted into the pre-established behavioral feature model and compared with the preset "warning behavioral features". Through Euclidean distance calculation, the system can determine the similarity between the behavior of the suspicious person and the warning behavioral features. If the similarity exceeds the preset threshold, the system will judge the person as a potential threat and trigger an early warning. In order to improve the accuracy of behavioral analysis, the early warning module uses a convolutional neural network (CNN) to extract the posture and action features of dangerous people during the model establishment process. Convolutional neural networks can automatically learn and extract important features in images, especially the posture and action of people, further improving the system's recognition ability in complex scenes. This technology ensures that the system can efficiently and accurately extract key features related to dangerous behaviors from a large amount of video data, providing strong support for subsequent behavioral judgments. Finally, the early warning module classifies the extracted posture and action features through the random forest algorithm, further enhancing the robustness of the behavioral feature model. Random forest is an integrated learning method that can process high-dimensional data and avoid overfitting, ensuring that the system can maintain high accuracy when facing diverse behavioral patterns. This process provides technical support for the automation and intelligence of the early warning system, enabling the system to not only identify potential threat personnel, but also continuously optimize its own recognition accuracy through learning, thereby improving the safety and efficiency of power grid de-icing operations.

[0032] It can be seen that the early warning module can effectively extract suspicious objects through image filtering and differential processing. This preprocessing step can significantly improve image clarity, reduce background noise, and make subsequent target detection more accurate. In addition, through differential processing of images, potential suspicious targets can be identified more quickly, especially in dynamic environments, which can capture changes around overhead cables in real time. Using deep learning algorithms, the early warning module can classify and locate the extracted suspicious objects and determine whether they are "people". This technology improves the accuracy of recognition and can accurately distinguish between people and objects in complex environments, thereby reducing false alarms. For example, when the detected target is identified as a "person" and is located in a preset warning area, the behavioral characteristics of the object are further analyzed. This makes it possible not only to identify people with potential threats, but also to accurately locate and track them to ensure the effectiveness of the alarm. Further behavioral analysis, based on the Euclidean distance calculation of similarity, can compare the behavioral characteristics of suspicious persons with the warning behavioral characteristics. This method can effectively reduce the risk of false alarms. When the behavior of a suspicious person is highly similar to the preset dangerous behavioral characteristics, an early warning will be automatically issued. Through this analysis based on behavioral similarity, the early warning system can avoid interference from irrelevant personnel and focus on targets that may pose a threat, thereby improving the accuracy of the early warning. In order to improve the ability to identify dangerous behaviors, the early warning module uses a convolutional neural network (CNN) to extract the posture and action features of dangerous people. Convolutional neural networks can automatically learn and extract key actions and posture features of people from a large amount of data. This technology ensures that complex behavior patterns can be accurately identified and analyzed, especially in dynamic monitoring scenarios. Combined with posture and action features, the early warning module can accurately identify potential dangerous behaviors in complex environments and avoid misjudgments due to external interference. Finally, the posture and action features of dangerous people are classified by the random forest algorithm, further enhancing the ability to identify abnormal behaviors. Random forests can process high-dimensional data and effectively avoid overfitting, thereby improving the stability and generalization ability of the behavior recognition model.

[0033] Specifically, the central control module determines whether the overhead cable is frozen based on the real-time temperature of the overhead cable, the surrounding environmental humidity and the surrounding environmental wind speed, including: The central control module is also configured to determine the environmental similarity score of the overhead cable freezing based on the real-time temperature of the overhead cable, the surrounding environmental humidity and the surrounding environmental wind speed: .

[0034] Wherein, H is the environmental similarity score, t is the real-time temperature of the overhead cable, T is the preset freezing temperature of the overhead cable, g is the ambient humidity of the overhead cable, G is the preset freezing humidity of the overhead cable, j is the ambient wind speed of the overhead cable, J is the preset freezing wind speed of the overhead cable, σt is the similarity coefficient between the real-time temperature and the preset freezing temperature, σg is the similarity coefficient between the ambient humidity and the preset freezing humidity, σj is the similarity coefficient between the ambient wind speed and the preset freezing wind speed, z1, z2 and z3 are weight coefficients, and z1, z2 and z3 are not 0. The central control module is also configured to determine whether the overhead cable is frozen based on the relationship between the environmental similarity score and the preset environmental similarity score configured by the central control module: when the environmental similarity score is greater than or equal to the preset environmental similarity score, the central control module determines that the overhead cable is frozen. When the environmental similarity score is less than the preset environmental similarity score, the central control module determines that the overhead cable is not frozen.

[0035] Specifically, when the central control module determines that the overhead cable is frozen, it adjusts the vibration amplitude of the vibration module, including: the central control module is also configured to determine the adjustment coefficient according to the similarity score difference between the environmental similarity score and the preset environmental similarity score, and according to the relationship between the similarity score difference and the central control module is configured as the first preset similarity score difference and the second preset similarity score difference, and adjust the vibration amplitude of the vibration module according to the adjustment coefficient: when the similarity score difference is less than the first preset similarity score difference, the central control module determines the adjustment coefficient to be M1. When the similarity score difference is greater than or equal to the first preset similarity score difference, and the similarity score difference is less than the second preset similarity score difference, the central control module determines the adjustment coefficient to be M2. When the similarity score difference is greater than or equal to the second preset similarity score difference, the central control module determines the adjustment coefficient to be M3. Among them, the first preset similarity score difference is less than the second preset similarity score difference, and M1<1<M2<M3<1.5.

[0036] It is understandable that the central control module determines the match between the current environment and the preset icing conditions by calculating an environmental similarity score. The score is obtained by comparing the real-time data of the overhead cable with the preset icing conditions, combining the similarity coefficient and the weight coefficient to obtain a comprehensive score. This method can judge whether there is icing based on the combined influence of multiple environmental factors, which is more accurate and reliable than the judgment method of a single parameter. The calculation of the environmental similarity score takes into account three important meteorological factors: temperature, humidity and wind speed. Each factor has a corresponding similarity coefficient and weight, indicating the degree of their influence on the icing risk. Through this weighted calculation, the icing risk under different environmental conditions can be quantitatively evaluated. In actual application, the environmental similarity score is compared with the preset environmental similarity score to determine whether the icing alarm is triggered. This method can realize intelligent judgment and avoid the misjudgment caused by relying solely on a single indicator such as temperature or humidity. Once it is determined that the overhead cable is frozen, the central control module will also adjust the vibration amplitude of the vibration module according to the difference between the environmental similarity score and the preset score. This adjustment mechanism ensures that the oscillation intensity can be dynamically adjusted according to the severity of icing, thereby improving the deicing effect. By dividing the similarity score difference into multiple preset intervals and setting different adjustment coefficients, the vibration amplitude can be flexibly adjusted according to the actual situation. In this way, the de-icing effect can be effectively optimized according to the real-time monitored environmental changes, ensuring that the de-icing process is both efficient and safe.

[0037] In the above embodiment, the environmental monitoring unit can obtain environmental information (such as temperature, humidity, wind speed, etc.) around the overhead cable in real time, and accurately judge whether the overhead cable is frozen based on this information. Traditional power grid deicing operations usually rely on manual inspections and mechanical equipment, and it is difficult to monitor environmental changes in real time, which leads to delays or misjudgments in deicing operations. Through real-time environmental monitoring and automated feedback mechanisms, the device can greatly improve the timeliness and accuracy of deicing operations, and reduce the tediousness of manual inspections and the risk of misjudgment. Secondly, the environmental monitoring unit can also obtain image information around the overhead cable and extract human behavior features from it. This function is not only used to judge the operating status of power grid equipment, but also can monitor the safety hazards around the power grid in real time by comparing with the preset human behavior feature model. In particular, when there are security threats such as arson in the power grid operation area, the device can issue an early warning through behavior recognition technology, discover potential safety risks in advance, and thus prevent damage to power grid equipment caused by human interference. In addition, the combination of the mounting unit 100 and the environmental monitoring unit enables the device to automatically move along the setting direction of the overhead cable and oscillate the overhead cable to de-ice. According to the environmental data acquired in real time, the mounting part 100 can dynamically adjust the oscillation amplitude to ensure the optimization of the deicing effect. Compared with traditional manual deicing, automated oscillation deicing can not only improve the work efficiency, but also avoid damage to the power grid equipment due to improper operation. This intelligent deicing process can continue to operate under extreme weather conditions, ensuring that the power grid equipment is not affected by the accumulation of ice and snow, thereby improving the stability and reliability of the power grid. Finally, through intelligent monitoring and deicing technology, the need for manual intervention is greatly reduced, the labor cost is reduced, and it can work continuously and stably for a long time.

[0038] In another preferred embodiment based on the above embodiment, Figure 3 As shown, this embodiment provides a power grid deicing method based on environmental detection and monitoring, including: Step S100: Acquire environmental information around the overhead cable, and determine whether the overhead cable is frozen according to the environmental information.

[0039] Step S200: Acquire image information around the overhead cable and extract human behavior features from the image information.

[0040] Step S300: Determine whether to send warning information based on the relationship between the character behavior characteristics and the character warning behavior characteristics configured by the environment monitoring unit.

[0041] Step S400: When it is determined that the overhead cable is frozen, the overhead cable is oscillated, and the oscillation amplitude is adjusted according to environmental information of the overhead cable.

[0042] It can be understood that the power grid deicing device and method based on environmental detection and monitoring in the above-mentioned embodiments have the same beneficial effects, which will not be described in detail.

[0043] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0044] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0045] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A power grid deicing device based on environmental detection and monitoring, characterized in that: include: Mounting Department and Environmental Monitoring Department; A movable through cavity is arranged on the top of the mounting part, and the movable through cavity is sleeved on the overhead cable between two transmission line towers, so that the mounting part can move along the setting direction of the overhead cable; The environment monitoring unit is arranged at the middle and lower part of the mounting unit, and is configured to obtain environment information around the overhead cable, and determine whether the overhead cable is frozen according to the environment information, and the environment monitoring unit is also configured to obtain image information around the overhead cable, and extract human behavior features in the image information; The environment monitoring unit is further configured to determine whether to send warning information according to the relationship between the character behavior characteristics and the character warning behavior characteristics configured by the environment monitoring unit; Wherein, when the environmental monitoring unit determines that the overhead cable is frozen, the mounting unit is further configured to oscillate the overhead cable and adjust the oscillation amplitude according to environmental information of the overhead cable.

2. The power grid deicing device based on environmental detection and monitoring according to claim 1, characterized in that: The mounting portion comprises: The mounting modules are provided with two groups, and the two groups of mounting modules are arranged opposite to each other in the vertical direction of the overhead cable, and a movable groove is provided on one side of the upper part of the mounting module adjacent to the overhead cable, so that when the two mounting modules are connected, the movable through cavity is formed between the two movable grooves, wherein a placement groove is provided on one side of the lower part of the mounting module adjacent to the overhead cable; A moving module is arranged inside the moving groove, so that when the two mounting modules are connected, the moving module is connected to the overhead cable, and the moving module is configured to drive the mounting module to move along the setting direction of the overhead cable; A vibration module is disposed inside the moving groove, and the vibration module is configured to vibrate toward the overhead cable.

3. The power grid deicing device based on environmental detection and monitoring as claimed in claim 2, characterized in that: The environmental monitoring department includes: A wide-angle image acquisition module, wherein the mounting module has installation slots on two adjacent sides of the movable slot, the wide-angle image acquisition module is arranged inside the installation slots, and the wide-angle image acquisition module is configured to obtain image information around the overhead cable; A temperature detection sensor is disposed inside the placement slot, and the temperature detection sensor is configured to detect the real-time temperature of the overhead cable; A humidity detection sensor and a wind speed detection sensor are respectively arranged inside the placement groove, the humidity detection sensor is configured to detect the humidity of the surrounding environment of the overhead cable, and the wind speed detection sensor is configured to detect the wind speed of the surrounding environment of the overhead cable; A power supply is arranged inside the placement slot, the power supply is connected to the placement slot, and the power supply is configured to provide electrical energy to the moving module, the vibration module, the wide-angle image acquisition module, the temperature detection sensor, the humidity detection sensor and the wind speed detection sensor based on connecting wires.

4. The power grid deicing device based on environmental detection and monitoring as claimed in claim 3, characterized in that: The environmental monitoring department also includes: A collection module, electrically connected to the wide-angle image collection module, the temperature detection sensor, the humidity detection sensor and the wind speed detection sensor, respectively, and configured to collect image information around the overhead cable, the real-time temperature of the overhead cable, the surrounding environmental humidity and the surrounding environmental wind speed; An early warning module is electrically connected to the acquisition module, and is configured to determine whether there is a suspicious person around the overhead cable based on the image information around the overhead cable, and determine whether to send the early warning information based on the relationship between the person behavior characteristics of the suspicious person and the person warning behavior characteristics; a central control module, electrically connected to the acquisition module and the vibration module, the central control module being configured to determine whether the overhead cable is frozen based on the real-time temperature of the overhead cable, the surrounding environmental humidity and the surrounding environmental wind speed, and to adjust the vibration amplitude of the vibration module when it is determined that the overhead cable is frozen; The communication module is electrically connected to the warning module. When the warning module determines to issue a warning message, the communication module is configured to create a warning message package based on the image information around the overhead cable and the behavioral characteristics of the suspicious person, and send it to the pan-tilt head.

5. The power grid deicing device based on environmental detection and monitoring as claimed in claim 4, characterized in that: When the early warning module determines whether there is a suspicious person around the overhead cable based on the image information around the overhead cable, it includes: The early warning module is configured to perform image filtering processing on the image information, and perform differential processing on the image information processed by image filtering to extract suspicious objects; The early warning module is configured to classify and locate the suspicious object based on a deep learning algorithm, and determine whether it is a suspicious person according to the classification result and the positioning result, wherein: If the suspicious object is classified as a person and the location of the suspicious object is within the preset warning range, the warning module determines that the suspicious object is a suspicious person and obtains the facial feature image and character behavior features of the suspicious person in the image information; If the suspicious object is classified as a person, and the location of the suspicious object is outside the preset warning range, the module determines that the suspicious object is not a suspicious person.

6. The power grid deicing device based on environmental detection and monitoring as claimed in claim 5, characterized in that: The early warning module determines whether to send the early warning information based on the relationship between the suspicious person's behavior characteristics and the person's early warning behavior characteristics, including: The early warning module is further configured to obtain the person's behavioral characteristics within a preset time period of the suspicious person, and substitute the person's behavioral characteristics within the preset time period into the behavioral characteristic model pre-established by the early warning module to obtain the person's early warning behavioral characteristics; The early warning module is further configured to determine the similarity between the character behavior characteristics of the suspicious person and the character early warning behavior characteristics based on the Euclidean distance: ； Among them, S is the similarity, n is the dimension of the behavior feature vector, bi is the i-th dimension of the character's behavior feature, and Bi is the i-th dimension of the character's warning behavior feature; The early warning module is further configured to determine whether to send early warning information according to the relationship between the similarity and a preset similarity configured in the early warning module: When the similarity is greater than or equal to the preset similarity, the warning module determines to send the warning information; When the similarity is less than the preset similarity, the warning module determines not to send the warning information.

7. The power grid deicing device based on environmental detection and monitoring as claimed in claim 6, characterized in that: The behavior feature model pre-established by the early warning module includes: The warning module is also configured to obtain movement data and posture change data of each dangerous person and establish a warning behavior association formula; The early warning module is configured to extract the posture features and action features of the dangerous person in the early warning behavior association formula based on a convolutional neural network; The warning module is also configured to classify the posture features and action features of the dangerous person in each warning behavior association formula based on random forest: The early warning module is also configured to establish the behavior feature model based on the classified posture features and action features.

8. The power grid deicing device based on environmental detection and monitoring as claimed in claim 6, characterized in that: The central control module determines whether the overhead cable is frozen based on the real-time temperature of the overhead cable, the surrounding environmental humidity and the surrounding environmental wind speed, including: The central control module is further configured to determine an environmental similarity score for the occurrence of icing of the overhead cable according to the real-time temperature of the overhead cable, the surrounding environmental humidity and the surrounding environmental wind speed: ； Wherein, H is the environmental similarity score, t is the real-time temperature of the overhead cable, T is the preset freezing temperature of the overhead cable, g is the ambient humidity of the overhead cable, G is the preset freezing humidity of the overhead cable, j is the ambient wind speed of the overhead cable, J is the preset freezing wind speed of the overhead cable, σt is the similarity coefficient between the real-time temperature and the preset freezing temperature, σg is the similarity coefficient between the ambient humidity and the preset freezing humidity, σj is the similarity coefficient between the ambient wind speed and the preset freezing wind speed, z1, z2 and z3 are weight coefficients, and z1, z2 and z3 are not 0; The central control module is further configured to determine whether the overhead cable is frozen according to a relationship between the environmental similarity score and a preset environmental similarity score configured in the central control module: When the environmental similarity score is greater than or equal to the preset environmental similarity score, the central control module determines that the overhead cable is frozen; When the environmental similarity score is less than the preset environmental similarity score, the central control module determines that the overhead cable is not frozen.

9. The power grid deicing device based on environmental detection and monitoring as claimed in claim 8, characterized in that: When the central control module determines that the overhead cable is frozen, adjusting the vibration amplitude of the vibration module includes: The central control module is further configured to determine an adjustment coefficient according to a similarity score difference between the environment similarity score and the preset environment similarity score, and according to a relationship between the similarity score difference and the first preset similarity score difference and the second preset similarity score difference configured as the central control module, and adjust the vibration amplitude of the vibration module according to the adjustment coefficient: When the similarity score difference is less than the first preset similarity score difference, the central control module determines that the adjustment coefficient is M1; When the similarity score difference is greater than or equal to the first preset similarity score difference, and the similarity score difference is less than the second preset similarity score difference, the central control module determines that the adjustment coefficient is M2; When the similarity score difference is greater than or equal to the second preset similarity score difference, the central control module determines that the adjustment coefficient is M3; The first preset similarity score difference is smaller than the second preset similarity score difference, and M1<1<M2<M3<1.

5.

10. A method for deicing a power grid based on environmental detection and monitoring, applicable to a deicing device for a power grid based on environmental detection and monitoring as claimed in any one of claims 1 to 9, characterized in that: include: Acquiring environmental information around the overhead cable, and determining whether the overhead cable is frozen according to the environmental information; Acquire image information around the overhead cable, and extract human behavior features from the image information; Determining whether to send warning information according to the relationship between the character behavior characteristics and the character warning behavior characteristics configured by the environment monitoring unit; When it is determined that the overhead cable is frozen, the overhead cable is oscillated, and the oscillation amplitude is adjusted according to environmental information of the overhead cable.

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