Ultrasonic damage-expelling equipment control method applied to electric power facilities

Abstract

The invention relates to the technical field of electric power facility protection, and discloses an ultrasonic damage expelling equipment control method applied to electric power facilities. The method comprises the following steps: acquiring environmental parameter data in real time, constructing a multi-dimensional pest feature recognition system and outputting a standardized signal; establishing an ultrasonic parameter database based on a frequency characteristic conversion technology, and generating a control instruction through characteristic matching; constructing a historical pest expelling case information base; establishing a multi-factor weight distribution model, calculating a priority score and generating a task sequence; an optimal control scheme is solved by adopting an improved genetic optimization algorithm, and the targets are to maximize the pest expelling range, minimize the energy consumption and shorten the response time; and outputting a control result through the interaction platform and tracking and feeding back. According to the method, accurate identification, dynamic parameter matching and multi-target optimization control of the pest activities are realized, and the efficiency and adaptability of power facility pest expelling are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power facility protection, in particular to a control method for ultrasonic repellent equipment applied to power facilities. Background Art

[0002] During power system operation, the infestation of power facilities by various pests (such as birds, rodents, and insects) has always been a significant issue affecting the safe and stable operation of power systems. These pests can cause serious consequences such as power line short circuits, equipment failures, and line tripping. These can not only result in significant economic losses but can also trigger widespread power outages, disrupting the normal functioning of society.

[0003] Currently, various methods are available for repelling harmful particles from power facilities, including physical repellent, chemical control, and ultrasonic repellent. Ultrasonic repellent technology, due to its non-contact, pollution-free nature and minimal impact on humans and the environment, has become an increasingly important tool for protecting power facilities. However, existing control methods for ultrasonic repellent equipment have numerous shortcomings.

[0004] When it comes to pest identification, traditional methods often rely on a single sensor to collect data, making it difficult to fully and accurately capture pest activity characteristics. They are also easily affected by background noise, resulting in low pest identification accuracy. Furthermore, the lack of a comprehensive, multi-dimensional pest feature recognition system prevents effective differentiation between pests of different types and activity states, hindering the effectiveness of subsequent pest control measures.

[0005] Existing technologies for selecting ultrasonic parameters are mostly based on experience, with fixed parameters set without a systematic ultrasonic parameter database. This makes it difficult to dynamically adjust parameters based on specific pest types, power facility types, and environmental conditions, resulting in unstable pest control results. Furthermore, when faced with multiple pest control tasks, there is a lack of a scientific prioritization mechanism, which prevents the rational allocation of resources based on factors such as facility importance and pest hazard level, potentially preventing critical facilities from receiving timely protection.

[0006] Existing control methods often focus on a single objective (such as maximizing pest control coverage) when optimizing equipment control schemes, while ignoring factors such as equipment energy consumption and response time. This results in poor overall control effectiveness. Furthermore, they lack effective utilization of historical pest control case studies, making it impossible to learn from past experience and optimize control strategies, making it difficult to continuously improve the operating efficiency of pest control equipment.

[0007] The continuous development and increasing intelligence of power systems are placing higher demands on the accuracy, adaptability, and efficiency of ultrasonic repellent control methods. Therefore, developing an ultrasonic repellent control method that comprehensively considers multiple factors and achieves intelligent control has become a pressing issue in the field of power facility protection. Summary of the Invention

[0008] The object of the present invention is to provide a control method for ultrasonic pest control equipment applied to power facilities to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides a control method for ultrasonic pest control equipment applied to power facilities, the method comprising: Real-time collection of environmental parameter data around power facilities, building a multi-dimensional pest feature recognition system, and outputting standardized pest activity signals through the signal processing module; An ultrasonic parameter database is established based on frequency characteristic conversion technology. When a need for repelling damage is detected, the target parameters are located through a feature matching algorithm and equipment control instructions are generated. Build a historical disaster prevention case information database to dynamically record the use of different power facility types, environmental conditions, and ultrasonic parameters; A multi-factor weight allocation model was established. By combining the importance of facilities, the level of hazard, the degree of environmental interference, and the urgency of tasks, the Delphi method was used to assign weights to each indicator. A control model was constructed using a weighted summation method to calculate the control priority score, thereby generating an equipment control priority sequence. An improved genetic optimization algorithm is used to solve the optimal equipment control scheme, with the optimization goals set in the direction of maximizing the pest control coverage, minimizing the equipment energy consumption and shortening the response time; The final equipment control results are output through the interactive control platform, and the actual pest control effect feedback is continuously tracked.

[0010] Preferably, the real-time collection of environmental parameter data around power facilities, the construction of a multi-dimensional pest feature recognition system, and the output of standardized pest activity signals through a signal processing module are specifically: The array-type ultrasonic sensor acquires the signal data of harmful activities around the power facilities. The signal acquisition terminal receives the original signal from the sensor and performs noise suppression, time domain analysis, and frequency domain conversion preprocessing to remove environmental background clutter. Based on the biological characteristics of pest activities and ultrasonic reflection laws, parameters closely related to pest characteristics are selected as candidate features based on correlation analysis; The candidate features are divided into frequency features, amplitude features, and time-frequency features, and a multi-level feature system is constructed. The overall pest recognition degree is used as the top-level feature, and the top-level feature is decomposed downward into several first-level features, and each first-level feature is further subdivided into several second-level features. Based on the multi-level feature system, the wavelet transform model is used to complete the signal processing module training; Correlate the location information of the power facilities with the harmful features of the pre-processed signal to establish a location-feature mapping relationship; Signal analysis software is selected as the pest control platform, and areas are marked on the interface according to the actual layout of the power facilities. Different symbols are used for visual display based on the differences in pest characteristics. Areas with large characteristic differences are marked with eye-catching symbols, and areas with small characteristic differences are marked with conventional symbols.

[0011] Preferably, the ultrasonic parameter database is established based on the frequency characteristic conversion technology, specifically: Collect sensitive frequency data for common types of hazards and power facilities, including standard parameters of base frequency values, frequency range values, and intensity thresholds; Obtain physical attribute information of parameters, including device model, emission angle, and effective distance parameters; Parameters are abstracted into data nodes, and the frequency relationship between parameters is abstracted into data association, and a node-association data structure is constructed; Establish database architecture, including data table definition, field settings and data relationship establishment, storage parameter information, attribute information and association relationships; Import the preprocessed data into the database of the signal analysis software to establish an ultrasonic parameter database.

[0012] Preferably, the method of locating target parameters and generating device control instructions by using a feature matching algorithm is as follows: Based on the ultrasonic parameter database, each parameter node is regarded as a sample point in the data set, and the frequency correlation between parameters is regarded as the similarity between samples; Taking the pest characteristics of the area to be driven as the query point, the fuzzy matching algorithm is used to calculate the characteristic distance to each sample point. When the sample point with the highest matching degree is found, the parameter information of the sample point is recorded. Analyze the calculated characteristic distance, combine it with the power facility type and environmental interference information, and obtain the matching range of the target parameters; Extract the fundamental frequency value, frequency range value, and intensity threshold parameter of the target parameter from the ultrasonic parameter database to generate the device control parameters; The control parameters of the target parameters, facility type, and environmental conditions are combined into a label and visualized on the signal analysis software interface.

[0013] Preferably, the historical pest control case information database is constructed to dynamically record the usage records of different power facility types, environmental conditions, and ultrasonic parameters, specifically: Obtain the main dimensions for building the information database, including usage records of different power facility types, environmental conditions, and ultrasonic parameters; At the same time, specific information fields are planned for each dimension, including facility model, ambient temperature and humidity, frequency of use, actual pest control effect, and user feedback and evaluation; By connecting the power monitoring and management system with the user feedback platform, the usage parameters, effect feedback, and equipment operation information of historical damage prevention cases are continuously updated; Build a historical damage case information database based on main dimensions and data fields.

[0014] Preferably, the multi-factor weight distribution model is established by combining the importance of facilities, the level of harmful substances, the degree of environmental interference and the urgency of tasks, using the Delphi method to assign weights to each indicator, and using a weighted summation method to construct a control model to calculate the control priority score, thereby generating an equipment control priority sequence, specifically: According to the importance of facilities and the level of hazards, the control importance level is divided into high importance levels for critical facilities or high-hazard hazards and low importance levels for non-critical facilities or low-hazard hazards. The degree of environmental interference is divided into strong interference environment, medium interference environment and weak interference environment; The task urgency factors are divided into urgent tasks, routine tasks, and delayed tasks; Based on the Delphi method, weights are assigned to control importance level, environmental interference level and task urgency factors; The control model is constructed by weighted summation. Each indicator value is multiplied by the corresponding weight and then added together to obtain the control priority score of each device control task. According to the control priority scores calculated by the control model, all device control tasks are sorted. Tasks with higher scores have higher control priorities, thus generating a device control priority sequence.

[0015] Preferably, the improved genetic optimization algorithm is used to solve the optimal equipment control solution, and the optimization objectives are set in the direction of maximizing the pest control coverage, minimizing the equipment energy consumption and shortening the response time, specifically: Randomly generate a set of candidate solutions as the initial population; Calculate the fitness value of each candidate solution according to the optimization goal; Select the optimal solution as the parent individual according to the fitness value, and update the crossover probability and mutation probability of other individuals; By simulating the gene recombination and mutation behavior of the population, the candidate solution is continuously updated iteratively until the termination condition is met; A set of optimal solutions is selected from the final population as the optimal equipment control scheme.

[0016] Preferably, the control priority score is specifically: The control priority score is determined by the control importance level weight, environmental interference level weight, task urgency weight and equipment status weight. The control importance level weight corresponds to the influence of facility importance and harmful substance hazard level, the environmental interference level weight corresponds to the influence of environmental conditions, the task urgency weight corresponds to the urgency of the hazard control demand, and the equipment status weight corresponds to the influence of the current operating status of the equipment. Each weight is multiplied by the corresponding level score and then added up to obtain the final control priority score.

[0017] Preferably, the optimization goal is specifically: The optimization objectives include maximizing the pest control coverage, minimizing equipment energy consumption, and shortening the response time. The pest control coverage objective is achieved by calculating the overlap between the candidate solution and the pest activity area. The equipment energy consumption objective is achieved by limiting the equipment power and operating time range of the candidate solution. The response time objective is achieved by reducing the number of algorithm iterations and instruction transmission time. The final optimization objective is the comprehensive optimal of the three sub-objectives.

[0018] Preferably, the power monitoring and management system is connected to the user feedback platform data to continuously update the usage parameters, effect feedback, and equipment operation information of historical de-harm cases, specifically: Establish a data interface protocol and define the data transmission format between the power monitoring and management system and the user feedback platform, including structured usage parameter fields and semi-structured feedback evaluation fields; Set a scheduled task to synchronize data from 11:00 AM to 1:00 PM daily to obtain new de-infestation case data within the previous 24 hours. Verify the validity of synchronized data and eliminate invalid data that is missing facility models or does not record actual pest control effects; The verified data is classified and stored according to the facility type and environmental conditions, and the usage records in the historical decontamination case information database are updated.

[0019] Compared with the prior art, the present invention has the following beneficial effects: By collecting real-time environmental parameter data around power facilities, a multi-dimensional pest feature recognition system was constructed. This system, combined with array ultrasonic sensors to acquire pest activity signals, performs pre-processing such as noise suppression and time-domain analysis. This effectively removes interference from environmental background clutter and improves the accuracy of pest activity signals. Furthermore, candidate features are categorized into frequency, amplitude, and time-frequency features, creating a multi-level feature system. The signal processing module is trained using a wavelet transform model, enhancing the ability to identify different pest characteristics and making pest identification more accurate, providing a reliable basis for subsequent pest control.

[0020] By building an ultrasonic parameter database based on frequency characteristic conversion technology, the system collects information such as the sensitive frequency data of common pests and power facilities. When pest control needs are detected, a feature matching algorithm can quickly locate the target parameters and generate appropriate equipment control instructions. This overcomes the drawbacks of traditional methods that rely on experience to set fixed parameters, enabling dynamic adjustment of ultrasonic parameters, improving the adaptability of pest control equipment to different scenarios, and ensuring the stability of pest control results.

[0021] A historical pest control case database was constructed to dynamically record usage records under different circumstances. This database is continuously updated through integration with the power monitoring and management system and user feedback platform, enabling control methods to continuously learn from historical data and optimize control strategies. Furthermore, a multi-factor weight allocation model was established, combining factors such as facility importance and pest hazard level. The Delphi method was used to assign weights and calculate control priority scores, generating a device control priority sequence. This ensures that when multiple pest control tasks occur simultaneously, important and urgent tasks are prioritized, effectively allocating resources and improving overall pest control efficiency.

[0022] An improved genetic optimization algorithm was used to solve the optimal equipment control solution, with the optimization goals of maximizing pest control coverage, minimizing equipment energy consumption, and shortening response time. Through multi-objective optimization, the overall optimal equipment control solution was achieved. This not only improved the coverage of pest control equipment, ensuring more areas are effectively protected, but also reduced equipment energy consumption and energy waste. It also shortened response time, enabling timely removal of pests and further improving the safety of power facilities.

[0023] By outputting control results and continuously tracking feedback through an interactive control platform, a closed-loop control process is formed, facilitating timely problem detection and adjustment of control strategies, continuously improving pest control effectiveness. In summary, this control method has significantly improved pest identification accuracy, parameter adaptability, resource allocation rationality, and control scheme optimization. It can provide more reliable and efficient protection for power facilities and ensure the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the control method of ultrasonic pest control equipment applied to power facilities according to the present invention; Figure 2 Flowchart of pest feature recognition and signal processing; Figure 3 Flowchart constructed for ultrasound parameter database; Figure 4 Flowcharts generated for feature matching and device control instructions; Figure 5 Flowchart for building and updating the historical extermination case information database. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] See also Figure 1-Figure 5 The present invention provides a control method for ultrasonic pest control equipment applied to power facilities, and the specific implementation steps are as follows: Collect environmental parameter data around power facilities in real time, build a multi-dimensional pest feature recognition system, and output standardized pest activity signals through the signal processing module.

[0027] An ultrasonic parameter database is established based on frequency characteristic conversion technology. When a destructive need is detected, the target parameters are located through a feature matching algorithm and equipment control instructions are generated.

[0028] Build a historical disaster prevention case information database to dynamically record the usage records of different power facility types, environmental conditions, and ultrasonic parameters.

[0029] A multi-factor weight allocation model is established. By combining the importance of facilities, the level of harmful substances, the degree of environmental interference and the urgency of tasks, the Delphi method is used to assign weights to each indicator. A control model is constructed using a weighted summation method to calculate the control priority score, thereby generating an equipment control priority sequence.

[0030] An improved genetic optimization algorithm is used to solve the optimal equipment control scheme, and the optimization objectives are set in the direction of maximizing the pest control coverage, minimizing the equipment energy consumption and shortening the response time.

[0031] The final equipment control results are output through the interactive control platform, and the actual pest control effect feedback is continuously tracked.

[0032] Example 1: In this embodiment, arrayed ultrasonic sensors are used to acquire data on pest activity signals around power facilities, while collecting real-time environmental parameter data around them, building a multi-dimensional pest signature recognition system, and outputting standardized pest activity signals through a signal processing module. These arrayed ultrasonic sensors are deployed around the power facilities, collecting signals from the surrounding environment from multiple angles and positions to ensure comprehensiveness and accuracy. A signal acquisition terminal is connected to these sensors to receive the raw signals from them.

[0033] After the raw signal is transmitted to the signal acquisition terminal, it undergoes a series of preprocessing operations. Preprocessing includes noise suppression, time domain analysis, and frequency domain conversion. The purpose of noise suppression is to remove environmental background clutter. In the actual environment surrounding power facilities, various interfering signals exist, such as the operating noise of other equipment and sounds from the natural environment. These noises can interfere with the identification of harmful activity signals, so they must be suppressed using appropriate algorithms and techniques. Time domain analysis can help understand the signal's characteristics in the time dimension, such as how the signal's amplitude changes over time. Frequency domain conversion converts the time domain signal to the frequency domain to better analyze the signal's frequency components.

[0034] After preprocessing, correlation analysis is performed based on the biological characteristics of pest activity and ultrasonic reflection patterns to identify parameters closely related to pest characteristics as candidate features. Different pests have different biological characteristics, and the ultrasonic signals they generate during activity also have different characteristics. Furthermore, the reflection patterns of ultrasonic waves when encountering pests are also related to the pest's nature. Correlation analysis can identify parameters that are highly correlated with pest characteristics, allowing these parameters to more accurately reflect the presence and activity of pests.

[0035] Candidate features are classified into frequency, amplitude, and time-frequency features, and a multi-level feature system is constructed. In this multi-level feature system, overall pest recognition is used as the top-level feature, which is then broken down into several first-level features, each of which is further broken down into several second-level features. This hierarchical structure provides a more systematic and comprehensive description of pest characteristics, resulting in more accurate and detailed pest identification.

[0036] Based on the established multi-level feature system, a wavelet transform model is used to train the signal processing module. The wavelet transform model has excellent time-frequency localization properties and can perform multi-resolution signal analysis, making it suitable for processing signals with complex time-frequency characteristics, such as pest activity signals. Through training, the signal processing module can better identify and process pest activity signals.

[0037] The location information of power facilities is associated with the pest characteristics of the preprocessed signal to establish a location-feature mapping relationship. Power facilities have specific locations in the real environment, and pest activity may be related to the location of power facilities. Establishing this mapping relationship can clearly identify pest characteristics at different locations, providing more accurate positioning information for subsequent pest control operations.

[0038] Signal analysis software was selected as the pest control platform, and areas were labeled on the interface according to the actual layout of the power facility. Different symbols were used for visual display based on the characteristics of the pests. Areas with significant differences in characteristics were marked with eye-catching symbols, while areas with less significant differences were marked with standard symbols. This visual display allows operators to intuitively understand the pest characteristics of different areas around the power facility, facilitating appropriate decision-making and operations.

[0039] Example 2: In this embodiment, establishing an ultrasonic parameter database based on frequency characteristic conversion technology requires collecting sensitive frequency data for common pest types and power facility types. Common pest types here include a variety of organisms that may pose a threat to power facilities, such as birds and rodents. For each pest, standard parameters such as the base frequency value, frequency range, and intensity threshold are obtained. Furthermore, sensitive frequency data applicable to different types of power facilities, such as substations and transmission lines, must also be collected during the pest control process. This data forms the foundation for building the database.

[0040] After collecting sensitive frequency data, it's also necessary to obtain information on the physical properties of the parameters, including device model, emission angle, and effective distance. Different device models may have different performance and parameters. The emission angle determines the coverage of the ultrasound, while the effective distance affects the range of the repellent effect. This physical property information is crucial for the accurate use of ultrasonic equipment.

[0041] The collected parameters are abstracted into data nodes, and the frequency relationships between parameters are abstracted into data associations, thus constructing a node-association data structure. Each parameter is treated as an independent data node, and nodes are associated with each other through similar frequency relationships. This structure clearly reflects the relationship between different parameters, facilitating subsequent queries and matching.

[0042] Establishing the database architecture involves defining data tables, setting fields, and establishing data relationships. When defining data tables, categorize them according to parameter type and attributes. For example, you can create a table for pest types, a table for power facility types, and a table for ultrasonic parameters. The fields in each data table must accurately reflect the specific parameter information. For example, the pest type table includes fields such as the pest name and category, while the ultrasonic parameter table includes fields such as the base frequency value, frequency range value, intensity threshold, and device model. Furthermore, relationships between data must be clearly defined, such as the correspondence between pest types and ultrasonic parameters, to ensure the consistency and integrity of the data in the database.

[0043] After the database architecture is established, the preprocessed data is imported into the signal analysis software database to establish the ultrasonic parameter database. The preprocessing process includes cleaning, organizing, and verifying the collected data, removing invalid and erroneous data, and ensuring that the data imported into the database is accurate and reliable.

[0044] When a repellent requirement is detected, a feature matching algorithm is used to locate the target parameters and generate device control instructions. First, based on an established ultrasonic parameter database, each parameter node is considered a sample point in the dataset, and the frequency correlation between parameters is considered the similarity between samples. This creates a dataset containing multiple sample points and their similarity relationships.

[0045] Using the pest signature of the target area as a query point, a fuzzy matching algorithm is used to calculate the characteristic distance from this query point to each sample point. Fuzzy matching algorithms are capable of handling feature matching problems involving uncertainty and ambiguity, making them suitable for matching pest signatures with ultrasonic parameters. When the sample point with the highest matching score is found while traversing all sample points, its parameter information is recorded and becomes the initial target parameter match.

[0046] The calculated characteristic distances are analyzed and combined with information about the power facility type and environmental interference to determine the matching range of the target parameters. The characteristic distance reflects the degree of similarity between the query point and the sample points, and by analyzing the characteristic distances, the possible range of the target parameters can be determined. Furthermore, different power facility types and environmental interference levels can influence the selection of ultrasonic parameters, so these factors need to be comprehensively considered to further narrow the matching range and ensure the accuracy of the target parameters.

[0047] The target parameters, such as the base frequency, frequency range, and intensity threshold, are extracted from the ultrasonic parameter database to generate device control parameters. These device control parameters are the key to controlling the operation of the ultrasonic device and determine the frequency, intensity, and other operating conditions of the ultrasonic device.

[0048] The target parameter's control parameters, facility type, and environmental conditions are combined into a single label and visualized on the signal analysis software interface. This visualization allows operators to intuitively understand the equipment control parameters, corresponding facility type, and environmental conditions required for the current pest control task, enabling them to quickly and accurately perform equipment control operations.

[0049] Example 3: In this embodiment, when constructing a historical decontamination case database and dynamically recording the usage records of different power facility types, environmental conditions, and ultrasonic parameters, the primary dimensions for constructing the database are obtained. These dimensions include the usage records of different power facility types, environmental conditions, and ultrasonic parameters. Power facility types include substations, transmission lines, and distribution equipment, while environmental conditions include factors such as temperature, humidity, wind speed, and sunlight. Ultrasonic parameter usage records include information such as the ultrasonic frequency, intensity, and emission time used.

[0050] Specific information fields need to be planned for each dimension, including facility model, ambient temperature and humidity, frequency of use, actual pest control effectiveness, and user feedback. The facility model identifies the specific electrical equipment; different models may differ in pest control requirements and effectiveness. Ambient temperature and humidity are important environmental parameters that can affect pest activity and ultrasonic wave propagation. Frequency of use records the operating frequency of the ultrasonic equipment, which is directly related to pest control effectiveness. Actual pest control effectiveness describes changes in pest activity after the pest control operation. User feedback collects the subjective evaluation of the pest control effectiveness by operators or other relevant personnel.

[0051] By connecting the power monitoring and management system with the user feedback platform, data from historical disaster prevention cases, including usage parameters, effectiveness feedback, and equipment operation information, is continuously updated. This process first requires establishing a data interface protocol to define the data transmission format between the power monitoring and management system and the user feedback platform. This includes structured usage parameter fields and semi-structured feedback evaluation fields. Structured usage parameter fields, such as facility model, ambient temperature and humidity, and frequency of use, have clear formats and specifications; semi-structured feedback evaluation fields are more flexible and are used to record users' subjective evaluations.

[0052] Set up a scheduled task to synchronize data between 11:00 AM and 1:00 PM daily to obtain new anti-malware case data from the previous 24 hours. Setting up a scheduled task ensures timely and regular data updates, allowing you to promptly incorporate the latest anti-malware case data into the database.

[0053] After acquiring synchronized data, it is necessary to perform validity checks on the synchronized data to eliminate invalid data that is missing facility models or does not record actual pest control effects. Validation is to ensure the accuracy and completeness of the data in the information database. Data missing key information will affect subsequent analysis and application.

[0054] Verified data is categorized and stored by facility type and environmental conditions, updating the usage records in the historical decontamination case database. Categorized storage facilitates data management and query, allowing users to quickly find relevant decontamination case data based on facility type and environmental conditions.

[0055] In order to more clearly express the structure of data storage, the following formula is introduced: in, Represents a record in the historical extermination case information database; Indicates the type of power facilities, including substations, transmission lines, etc.; Indicates environmental conditions, including parameters such as temperature and humidity; Indicates ultrasonic parameters, such as frequency, intensity, etc. Indicates the actual pest-repelling effect; Indicates user feedback rating.

[0056] Through the above steps, a historical damage control case database is constructed based on key dimensions and data fields. This database dynamically records the usage of different power facility types, environmental conditions, and ultrasonic parameters, providing a reference and basis for subsequent damage control work. In the process of building this database, every link is closely linked. From the determination of dimensions and fields to data docking, synchronization, verification, and storage, all are carried out in strict accordance with the prescribed procedures to ensure that the data in the database is accurate, complete, and usable. By establishing such a historical damage control case database, past damage control experience can be summarized and analyzed, providing support for optimizing the control methods of ultrasonic damage control equipment and improving the efficiency and effectiveness of damage control work.

[0057] Example 4: In this embodiment, when establishing a multi-factor weight allocation model and generating a device control priority sequence, it is necessary to first use the Delphi method to assign weights to each indicator based on the facility's importance, the level of harmful hazard, the degree of environmental interference, and the urgency of the task. Then, a control model is constructed through weighted summation to calculate the priority score. For example, a regional power grid contains various types of power facilities, including substations, high-voltage transmission lines, and general distribution equipment. These facilities also face various pest threats, such as bird nesting and rodents gnawing on cables.

[0058] When categorizing control importance levels, critical facilities or high-hazard pests should be assigned a high priority level based on their importance and the level of hazard. For example, a substation, as a core node for regional power supply, has a direct impact on the stability of the entire region's power supply. If the substation faces a threat from nesting birds such as hawks, which could cause equipment short circuits and pose a high hazard level, the pest control requirement should be assigned a high priority level. On the other hand, ordinary power distribution equipment, which is relatively less important, should face minor interference from pests such as squirrels, which are unlikely to cause serious damage in the short term, so the pest control requirement should be assigned a low priority level.

[0059] Environmental interference levels are categorized as strong, medium, and weak. For example, areas near power transmission lines on highways, where noise and vibration from vehicles are generated, constitute a strong interference environment. Areas near power facilities in suburban farmland experience relatively little environmental interference, constituting a medium interference environment. And isolated power facilities in remote mountainous areas experience minimal human activity and low levels of environmental interference, constituting a weak interference environment.

[0060] Task urgency factors are categorized as emergency tasks, routine tasks, and delayed tasks. When harmful activity is detected on a transmission line, causing abnormal heating of equipment and potentially triggering a trip, the task is considered an emergency task. If only minor signs of harmful activity are detected during routine inspections, but haven't significantly impacted equipment operations, this is considered a routine task. If harmful activity is in its early stages and poses a minimal threat to equipment, the task can be postponed, thus becoming a delayed task.

[0061] To assign weights to control importance, environmental interference, and mission urgency based on the Delphi method, a team of experts, including power equipment operation and maintenance specialists and ultrasonic extermination engineers, was assembled. A questionnaire was distributed to these experts, inviting them to rate the importance of each indicator. Based on their experience and expertise, the experts assessed the impact of different indicators on the extermination decision-making process. After multiple rounds of feedback and adjustments, the weights for each indicator were determined. Assume that, based on the Delphi method, the control importance was weighted at 0.4, the environmental interference at 0.3, the mission urgency at 0.2, and the equipment status at 0.1. (The equipment status weight corresponds to the impact of the equipment's current operating status, such as whether the equipment is undergoing maintenance or whether the operating power is within the normal range.)

[0062] A weighted summation approach is used to construct the control model. Each indicator value is multiplied by its corresponding weight and then added together to obtain the control priority score for each equipment control task. For example, consider a substation disaster prevention task. This substation is a critical facility with a control importance score of 90 (out of 100). It is located in a moderate interference environment near an industrial area, with an environmental interference score of 70. Current hazardous activity has caused partial discharge in the equipment, making it an emergency task with an urgency factor score of 95, and a good equipment condition score of 85. The control priority score for this task is calculated as: 90 × 0.4 + 70 × 0.3 + 95 × 0.2 + 85 × 0.1 = 36 + 21 + 19 + 8.5 = 84.5.

[0063] Another example is a task to exterminate common power distribution equipment. This equipment is a non-critical facility with a control importance score of 60. The environment is a suburban environment with low interference, with an environmental interference score of 80. The pest activity is a small number of rodents discovered during routine inspections. The task urgency score is 60, and the equipment status is normal. The control priority score is calculated as: 60 × 0.4 + 80 × 0.3 + 60 × 0.2 + 90 × 0.1 = 24 + 24 + 12 + 9 = 69 points.

[0064] All equipment control tasks are ranked according to the control priority scores calculated by the control model. Tasks with higher scores are assigned higher control priorities, thus generating an equipment control priority sequence. In the above example, the substation extermination task scored 84.5 points, while the general distribution equipment extermination task scored 69 points. Therefore, the substation extermination task has a higher priority than the general distribution equipment task, and the substation's extermination needs should be prioritized during resource allocation and scheduling.

[0065] Throughout the implementation process, the impact of various factors on pest control tasks must be comprehensively considered. Through scientific weight assignment and score calculation, the priority sequence must ensure that it reflects the actual importance and urgency of the tasks. For example, if a task, despite being in a low-interference environment, represents an urgent pest threat to a critical facility, its high score for control importance and mission urgency will place it higher in the priority sequence. On the other hand, if a task, despite being in a high-interference environment, represents a routine task for a non-critical facility, its overall score may be lower, placing it lower in priority. This multi-factor weight assignment model and priority calculation method can provide a rational basis for decision-making in the control of ultrasonic pest control equipment, enabling more optimized allocation and utilization of equipment resources, and improving the overall efficiency and targeted effectiveness of pest control efforts.

[0066] Example 5: In this embodiment, when solving the optimal device control scheme using an improved genetic optimization algorithm, the optimization target direction is to maximize the pest control coverage, minimize the device energy consumption, and shorten the response time. A group of candidate solutions is randomly generated as the initial population. The candidate solutions here can be understood as different combinations of ultrasonic device control schemes. For example, for an area containing multiple power facilities, each candidate solution in the initial population may correspond to a different number of devices turned on, device transmission frequency settings, device working time arrangements, etc. Assuming that there are 5 ultrasonic pest control devices in the area, each device has different frequency adjustment gears and working time setting options, then a candidate solution may be a combination such as device 1 using frequency A and working for 2 hours, device 2 using frequency B and working for 3 hours, and the initial population then contains multiple similar randomly generated combinations.

[0067] The fitness value of each candidate solution is calculated based on the optimization objectives. These objectives include maximizing pest control coverage, minimizing device energy consumption, and shortening response time. The pest control coverage objective is achieved by calculating the overlap between the candidate solution and the pest activity area. For example, preliminary monitoring indicates that pest activity primarily occurs in a specific area around power facilities. The transmission angle and effective range of the ultrasonic device in each candidate solution determine its coverage. Calculating the overlap percentage between this coverage area and the pest activity area provides the candidate solution's performance in terms of pest control coverage. The device energy consumption objective is achieved by constraining the device power and operating time ranges of the candidate solution. Each device has a different power and operating time, resulting in a correspondingly different energy consumption. By summing the energy consumption of all devices in each candidate solution, the energy consumption value is calculated; a smaller value indicates lower energy consumption. The response time objective is achieved by reducing the number of algorithm iterations and instruction transmission time. The fitness calculation considers whether the control instructions corresponding to the candidate solution can be quickly generated and transmitted. While this factor primarily serves as an assessment of the solution's simplicity and efficiency in the initial calculation, it impacts the overall fitness value.

[0068] The optimal solution is selected as the parent individual based on the fitness value, and the crossover probability and mutation probability of other individuals are updated. Candidate solutions with high fitness values, that is, solutions that are closer to maximizing pest coverage, minimizing energy consumption and shortening response time, have a higher probability of being selected as parents. For example, among the 100 candidate solutions in the initial population, the top 20 solutions in fitness will be given priority as parents. The update of crossover probability and mutation probability is based on the fitness of the parent individuals. Generally, the higher the fitness of the parent, the corresponding crossover probability and mutation probability will be adjusted to values ​​that are more conducive to producing excellent offspring. For example, the crossover probability may be increased from 0.6 to 0.8, and the mutation probability may be adjusted from 0.01 to 0.02 to increase the diversity and optimization potential of new solutions.

[0069] By simulating the genetic recombination and mutation behavior of a population, candidate solutions are continuously iterated and updated until the termination condition is met. Genetic recombination is a crossover operation. For example, two parent candidate solutions are selected and some of their control parameters are swapped to generate a new child candidate solution. Suppose that parent 1 is device 1 operating at frequency A for 2 hours and device 2 at frequency B for 3 hours; and parent 2 is device 1 operating at frequency C for 4 hours and device 2 at frequency A for 2 hours. After crossover, the resulting child 1 may be device 1 operating at frequency A for 4 hours and device 2 operating at frequency B for 2 hours. Mutation involves randomly changing individual parameters of a candidate solution, such as changing a device's operating frequency from A to B or changing its operating time from 2 hours to 3 hours. This increases population diversity and prevents the algorithm from falling into a local optimum. During the iteration process, each time a new offspring population is generated, the fitness value of each solution is recalculated, and the selection, crossover, and mutation operations are repeated until the preset termination conditions are met, such as the number of iterations reaches 1000, or the fitness value of the optimal solution does not improve significantly in 50 consecutive iterations.

[0070] A set of optimal solutions is selected from the final population as the optimal device control solution. After multiple rounds of iterative optimization, the candidate solution with the highest fitness value is selected as the optimal solution. For example, after iterations, a candidate solution that achieves 90% overlap with the pest activity area in terms of pest control coverage, has the lowest device energy consumption of 1500 watt-hours (of all solutions), and has a short control command generation and transmission time, and the highest overall fitness value, is selected as the optimal device control solution.

[0071] The interactive control platform outputs the final device control results and continuously tracks actual pest control effectiveness. The interactive control platform intuitively displays the optimal solution's device control parameters, such as each device's operating frequency, emission intensity, and operating time, to the operator, allowing them to control the device according to the platform's prompts. Simultaneously, the platform collects real-time device operation data and pest activity monitoring data to track actual pest control effectiveness. For example, it records whether pest activity around power facilities has decreased after implementing the solution and whether the device's energy consumption is consistent with expectations. While this feedback data is not used to make hypothetical effectiveness claims, it will serve as a reference for subsequent optimization algorithms and solutions, helping to continuously improve the control methods of ultrasonic pest control equipment.

[0072] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0073] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A control method for ultrasonic pest control equipment applied to power facilities, characterized in that: The following steps are involved: Real-time collection of environmental parameter data around power facilities, building a multi-dimensional pest feature recognition system, and outputting standardized pest activity signals through the signal processing module; An ultrasonic parameter database is established based on frequency characteristic conversion technology. When a need for repelling damage is detected, the target parameters are located through a feature matching algorithm and equipment control instructions are generated. Build a historical disaster prevention case information database to dynamically record the use of different power facility types, environmental conditions, and ultrasonic parameters; A multi-factor weight allocation model was established. By combining the importance of facilities, the level of hazard, the degree of environmental interference, and the urgency of tasks, the Delphi method was used to assign weights to each indicator. A control model was constructed using a weighted summation method to calculate the control priority score, thereby generating an equipment control priority sequence. An improved genetic optimization algorithm is used to solve the optimal equipment control scheme, with the optimization goals set in the direction of maximizing the pest control coverage, minimizing the equipment energy consumption and shortening the response time; The final equipment control results are output through the interactive control platform, and the actual pest control effect feedback is continuously tracked.

2. The ultrasonic pest control device control method for electric power facilities according to claim 1, characterized in that: The real-time collection of environmental parameter data around power facilities, the construction of a multi-dimensional pest feature recognition system, and the output of standardized pest activity signals through a signal processing module are specifically as follows: The array-type ultrasonic sensor acquires the signal data of harmful activities around the power facilities. The signal acquisition terminal receives the original signal from the sensor and performs noise suppression, time domain analysis, and frequency domain conversion preprocessing to remove environmental background clutter. Based on the biological characteristics of pest activities and ultrasonic reflection laws, parameters closely related to pest characteristics are selected as candidate features based on correlation analysis; The candidate features are divided into frequency features, amplitude features, and time-frequency features, and a multi-level feature system is constructed. The overall pest recognition degree is used as the top-level feature, and the top-level feature is decomposed downward into several first-level features, and each first-level feature is further subdivided into several second-level features. Based on the multi-level feature system, the wavelet transform model is used to complete the signal processing module training; Correlate the location information of the power facilities with the harmful features of the pre-processed signal to establish a location-feature mapping relationship; Signal analysis software is selected as the pest control platform, and areas are marked on the interface according to the actual layout of the power facilities. Different symbols are used for visual display based on the differences in pest characteristics. Areas with large characteristic differences are marked with eye-catching symbols, and areas with small characteristic differences are marked with conventional symbols.

3. The method for controlling ultrasonic pest control equipment for electric power facilities according to claim 2, wherein: The ultrasonic parameter database is established based on the frequency characteristic conversion technology, specifically: Collect sensitive frequency data for common types of hazards and power facilities, including standard parameters of base frequency values, frequency range values, and intensity thresholds; Obtain physical attribute information of parameters, including device model, emission angle, and effective distance parameters; Parameters are abstracted into data nodes, and the frequency relationship between parameters is abstracted into data association, and a node-association data structure is constructed; Establish database architecture, including data table definition, field settings and data relationship establishment, storage parameter information, attribute information and association relationships; Import the preprocessed data into the database of the signal analysis software to establish an ultrasonic parameter database.

4. The method for controlling ultrasonic pest control equipment for electric power facilities according to claim 3, wherein: The method of locating target parameters and generating device control instructions through feature matching algorithm is specifically as follows: Based on the ultrasonic parameter database, each parameter node is regarded as a sample point in the data set, and the frequency correlation between parameters is regarded as the similarity between samples; Taking the pest characteristics of the area to be driven as the query point, the fuzzy matching algorithm is used to calculate the characteristic distance to each sample point. When the sample point with the highest matching degree is found, the parameter information of the sample point is recorded. Analyze the calculated characteristic distance, combine it with the power facility type and environmental interference information, and obtain the matching range of the target parameters; Extract the fundamental frequency value, frequency range value, and intensity threshold parameter of the target parameter from the ultrasonic parameter database to generate the device control parameters; The control parameters of the target parameters, facility type, and environmental conditions are combined into a label and visualized on the signal analysis software interface.

5. The method for controlling ultrasonic pest control equipment for electric power facilities according to claim 4, wherein: The historical damage control case information database is constructed to dynamically record the use records of different power facility types, environmental conditions, and ultrasonic parameters, specifically: Obtain the main dimensions for building the information database, including usage records of different power facility types, environmental conditions, and ultrasonic parameters; At the same time, specific information fields are planned for each dimension, including facility model, ambient temperature and humidity, frequency of use, actual pest control effect, and user feedback and evaluation; By connecting the power monitoring and management system with the user feedback platform, the usage parameters, effect feedback, and equipment operation information of historical damage prevention cases are continuously updated; Build a historical damage case information database based on main dimensions and data fields.

6. The method for controlling ultrasonic pest control equipment for electric power facilities according to claim 5, characterized in that: The multi-factor weight distribution model is established by combining the importance of facilities, the level of harmful substances, the degree of environmental interference and the urgency of tasks. The Delphi method is used to assign weights to each indicator. The control model is constructed by weighted summation to calculate the control priority score, thereby generating the equipment control priority sequence, specifically: According to the importance of facilities and the level of hazards, the control importance level is divided into high importance levels for critical facilities or high-hazard hazards and low importance levels for non-critical facilities or low-hazard hazards. The degree of environmental interference is divided into strong interference environment, medium interference environment and weak interference environment; The task urgency factors are divided into urgent tasks, routine tasks, and delayed tasks; Based on the Delphi method, weights are assigned to control importance level, environmental interference level and task urgency factors; The control model is constructed by weighted summation. Each indicator value is multiplied by the corresponding weight and then added together to obtain the control priority score of each device control task. According to the control priority scores calculated by the control model, all device control tasks are sorted. Tasks with higher scores have higher control priorities, thus generating a device control priority sequence.

7. The method for controlling ultrasonic pest control equipment for electric power facilities according to claim 6, wherein: The improved genetic optimization algorithm is used to solve the optimal equipment control solution, setting the optimization goals in the direction of maximizing the pest control coverage, minimizing the equipment energy consumption and shortening the response time, specifically: Randomly generate a set of candidate solutions as the initial population; Calculate the fitness value of each candidate solution according to the optimization goal; Select the optimal solution as the parent individual according to the fitness value, and update the crossover probability and mutation probability of other individuals; By simulating the gene recombination and mutation behavior of the population, the candidate solution is continuously updated iteratively until the termination condition is met; A set of optimal solutions is selected from the final population as the optimal equipment control scheme.

8. The method for controlling ultrasonic pest control equipment for electric power facilities according to claim 7, wherein: The control priority score is specifically: The control priority score is determined by the control importance level weight, environmental interference level weight, task urgency weight and equipment status weight. The control importance level weight corresponds to the influence of facility importance and harmful substance hazard level, the environmental interference level weight corresponds to the influence of environmental conditions, the task urgency weight corresponds to the urgency of the hazard control demand, and the equipment status weight corresponds to the influence of the current operating status of the equipment. Each weight is multiplied by the corresponding level score and then added up to obtain the final control priority score.

9. The method for controlling ultrasonic pest control equipment for electric power facilities according to claim 8, characterized in that: The optimization objectives are specifically: The optimization objectives include maximizing the pest control coverage, minimizing equipment energy consumption, and shortening the response time. The pest control coverage objective is achieved by calculating the overlap between the candidate solution and the pest activity area. The equipment energy consumption objective is achieved by limiting the equipment power and operating time range of the candidate solution. The response time objective is achieved by reducing the number of algorithm iterations and instruction transmission time. The final optimization objective is the comprehensive optimal of the three sub-objectives.

10. The method for controlling ultrasonic pest control equipment for electric power facilities according to claim 5, characterized in that: The power monitoring and management system is connected to the user feedback platform data to continuously update the usage parameters, effect feedback, and equipment operation information of historical de-harm cases, specifically: Establish a data interface protocol and define the data transmission format between the power monitoring and management system and the user feedback platform, including structured usage parameter fields and semi-structured feedback evaluation fields; Set a scheduled task to synchronize data from 11:00 AM to 1:00 PM daily to obtain new de-infestation case data within the previous 24 hours. Verify the validity of synchronized data and eliminate invalid data that is missing facility models or does not record actual pest control effects; The verified data is classified and stored according to the facility type and environmental conditions, and the usage records in the historical decontamination case information database are updated.

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