A control method of a power line inspection robot based on dynamic event triggering

Abstract

The application provides a control method of a power line inspection robot based on a dynamic event trigger, and relates to the technical field of power line inspection, and comprises the following steps: receiving a first early warning signal comprising a first predicted starting moment of a dynamic event, determining a mapping factor corresponding to the dynamic event and an observation window period; when entering the observation window period, real-time monitoring data of each mapping factor is obtained to obtain a mapping factor set, and the event occurrence probability of the dynamic event at the first predicted starting moment is determined according to the mapping factor set; if the event occurrence probability is less than a first preset threshold, the inspection robot is controlled to maintain a default inspection mode; if the event occurrence probability is greater than or equal to the first preset threshold, the inspection robot is controlled to switch from the default inspection mode to an event response mode corresponding to the dynamic event at the first predicted starting moment. The application can reduce unnecessary mode switching, thereby reducing energy consumption and ensuring the stability of the inspection system.

Description

Technical Field

[0001] The present invention relates to the technical field of power line inspection, and in particular to a control method of a power line inspection robot based on dynamic event triggering. Background Art

[0002] In today's power system, the safe and stable operation of power lines is crucial. To ensure the normal operation of power lines, inspection robots are widely used in power line inspection tasks. However, existing power line inspection robots often have some problems when facing dynamic events.

[0003] Current inspection robots rely mainly on weather forecasts or alerts from weather stations about dynamic meteorological events to respond to impending dynamic events, such as switching to inspection mode when dynamic events occur. However, the accuracy and reliability of warning signals such as weather forecasts are still unclear. For example, the meteorological system may issue warnings of possible strong winds, heavy rains, etc. due to changes in the local microclimate, but in reality these dynamic events may not actually occur.

[0004] In this situation, inspection robots frequently switch between different control modes based on warning signals. Each mode switch requires the robot to mobilize different sensors, perform different actions, and adjust operating parameters. In practice, due to the uncertainty and frequency of warning signals, the robot makes multiple unnecessary mode switches in a short period of time, resulting in excessive energy consumption. This not only increases inspection costs but also affects the robot's endurance and work efficiency. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, the present invention aims to provide a control method for a power line inspection robot based on dynamic event triggering, comprising the following steps:

[0006] receiving a first warning signal of a dynamic event, determining at least one mapping factor corresponding to the dynamic event, and determining an observation window period corresponding to the dynamic event; the mapping factor being a variable reflected on or around the power line before the dynamic event occurs; the first warning signal including a first predicted start time of the dynamic event; and the observation window period being before the first predicted start time;

[0007] When entering the observation window period, real-time monitoring data of each mapping factor is acquired in real time at intervals of a first preset time length to obtain a mapping factor set; the mapping factor set is composed of real-time monitoring sequences corresponding to multiple mapping factors;

[0008] Determining, based on the mapping factor set, the probability of occurrence of the dynamic event at the first prediction start time;

[0009] If the probability of the event occurring is less than the first preset threshold, the patrol robot is controlled to maintain the default patrol mode; if the probability of the event occurring is greater than or equal to the first preset threshold, the patrol robot is controlled to switch from the default patrol mode to the event response mode corresponding to the dynamic event at the start time of the first prediction.

[0010] According to the technical solution provided by the present invention, determining at least one mapping factor corresponding to the dynamic event specifically includes the following steps:

[0011] Retrieving and traversing a historical database, the historical database including multiple characteristic factors of the power line and a historical monitoring sequence composed of multiple historical monitoring data corresponding to each characteristic factor in a time sequence; wherein each of the historical monitoring sequences is marked with a historical start time of at least one historical event, and the historical monitoring data in the historical monitoring sequence that is within a second preset time period before the historical start time is marked as first reference monitoring data;

[0012] The characteristic factor of each of the first reference monitoring data that is in continuous change within the second preset time period is used as the mapping factor corresponding to the dynamic event with the same type as the historical event.

[0013] According to the technical solution provided by the present invention, for the mapping factor, the plurality of first reference monitoring data corresponding thereto constitute a first reference monitoring sequence of the mapping factor;

[0014] The determining, based on the mapping factor set, the probability of occurrence of the dynamic event at the first prediction start time specifically comprises the following steps:

[0015] respectively calculating a first distance of each of the mapping factors, where the first distance is a Euclidean distance between the real-time monitoring sequence and the first reference monitoring sequence;

[0016] The first distances of the mapping factors are weighted and averaged to obtain the event occurrence probability.

[0017] According to the technical solution provided by the present invention, determining the probability of occurrence of the dynamic event at the first prediction start time based on the mapping factor set specifically includes the following steps:

[0018] Finding, in the historical database, multiple reference sets based on the mapping factor set, each of the reference sets including a second reference monitoring sequence corresponding to each of the mapping factors; wherein a Euclidean distance between the real-time monitoring sequence and the second reference monitoring sequence for the same mapping factor in the reference set and the mapping factor set is less than or equal to a second preset threshold;

[0019] A reference set in the history database that is marked with the historical start time within a third preset time period after the reference set and whose historical events corresponding to the historical start time are of the same type as the dynamic event is used as a trusted set;

[0020] The ratio of the number of the credible set to the number of the reference set is used as the probability of the event occurrence.

[0021] According to the technical solution provided by the present invention, determining the observation window period corresponding to the dynamic event specifically includes the following steps:

[0022] The observation window period corresponding to the dynamic event is obtained based on the time leading interval and the duration range of the trusted set relative to the corresponding historical start time.

[0023] According to the technical solution provided by the present invention, after retrieving and traversing the historical database, the following steps are further included:

[0024] Determine whether the historical database contains the historical event of the same type as the dynamic event;

[0025] The method specifically includes the following steps: using the characteristic factor of each of the first reference monitoring data that is in a state of continuous change within the second preset time period as the mapping factor corresponding to the dynamic event of the same type as the historical event:

[0026] If the historical event with the same type as the dynamic event is matched in the historical database, the characteristic factor of each first reference monitoring data that is in continuous change within the second preset time period is used as the mapping factor corresponding to the dynamic event with the same type as the historical event.

[0027] According to the technical solution provided by the present invention, after determining whether the historical database contains a historical event of the same type as the dynamic event, the following steps are further included:

[0028] If the historical event of the same type as the dynamic event is not matched in the historical database, then based on principal component analysis, the plurality of characteristic factors in the historical database are converted into a plurality of principal components, each of which is composed of a combination of its corresponding plurality of characteristic factors and the weight of each characteristic factor, and the number of the principal components is less than the number of the characteristic factors;

[0029] Relevant components of the dynamic event are obtained, and the principal component that matches the relevant components is used as the mapping factor corresponding to the dynamic event.

[0030] According to the technical solution provided by the present invention, the historical database is also marked with the second predicted starting time of each historical event;

[0031] The method further comprises the following steps:

[0032] When the dynamic event occurs, the actual start time of the dynamic event is recorded, and the dynamic event is used as a historical event to update the historical database; wherein the actual start time of the dynamic event is used as the historical start time, and the first predicted start time is used as the second predicted start time.

[0033] According to the technical solution provided by the present invention, after receiving the first warning signal of the dynamic event, the following steps are also included:

[0034] Retrieving and traversing the historical database to determine whether the historical database contains the historical event of the same type as the dynamic event;

[0035] If there is at least one historical event of the same type as the dynamic event, the historical event of the same type as the dynamic event is taken as a matching event;

[0036] Determining the observation window period corresponding to the dynamic event specifically includes the following steps:

[0037] If the deviation between the historical start time and the second predicted start time of all the matching events is less than or equal to a third preset threshold, the observation window period corresponding to the dynamic event is determined according to the first predicted start time.

[0038] According to the technical solution provided by the present invention, after taking the historical event of the same type as the dynamic event as the matching event, the following steps are further included:

[0039] If there are at least N matching events whose deviations between the historical start time and the second predicted start time are greater than the third preset threshold, the N matching events are regarded as abnormal matching events, and the matching events other than the N abnormal matching events are regarded as normal matching events;

[0040] Obtaining a first adjustment amount according to the second predicted start time of the normal matching event, and obtaining a second adjustment amount according to the second predicted start time of the abnormal matching event;

[0041] The first adjustment amount matching weight is calculated based on the ratio of the normal matching events to all the matching events, and the second adjustment amount matching weight is calculated based on the ratio of the abnormal matching events to all the matching events, so as to obtain a total adjustment amount;

[0042] Before determining the observation window period corresponding to the dynamic event, the following steps are also included:

[0043] The first predicted start time is updated with the total adjustment amount.

[0044] Compared with the prior art, the beneficial effect of the present invention is that the present invention uses the mapping factor of the dynamic event to judge the probability of the dynamic event actually occurring during the observation window period, and makes a more accurate judgment and reasonable response to the warning signal, instead of switching the mode at the predicted moment of the warning signal after obtaining the warning signal, thereby reducing unnecessary mode switching and thus reducing energy consumption. At the same time, it avoids the time waste and system instability caused by frequent mode switching, allowing the inspection robot to perform inspection tasks more attentively and improve work efficiency. In addition, a more stable working mode also helps to reduce equipment wear and failure risks. A reasonable control mode switching strategy can improve the inspection robot's ability to respond to dynamic events, ensure stable operation under normal conditions, and enhance the reliability of the entire power line inspection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flowchart of the steps of a control method for a power line inspection robot based on dynamic event triggering provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0047] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention 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.

[0048] Example 1

[0049] As mentioned in the background technology, in order to solve the problems in the prior art, the present invention proposes a control method for a power line inspection robot based on dynamic event triggering, please refer to Figure 1 As shown, the following steps are included:

[0050] S1. Receive a first warning signal for a dynamic event, determine at least one mapping factor corresponding to the dynamic event, and determine an observation window period corresponding to the dynamic event; the mapping factor is a variable reflected on or around the power line before the dynamic event occurs; the first warning signal includes a first predicted start time of the dynamic event; and the observation window period is before the first predicted start time.

[0051] Specifically, dynamic events can be meteorological (strong winds, heavy rain, lightning, snowstorms, etc.), human-related (construction activities, vandalism, etc.), or biological (bird activity, large animal activity). For each dynamic event, variables that may be reflected on or around power lines before they occur are mapped as factors. For example, for a strong wind dynamic event, mapping factors may include wind speed, wind direction, power line sway amplitude, sway frequency, etc.; for a human-induced construction event, mapping factors may include construction noise intensity, etc. For example, if the first warning signal for a meteorological dynamic event is issued by a weather station, the first warning signal also includes the dynamic event type, such as strong wind weather, and the first predicted start time is the onset time of the strong wind weather.

[0052] Specifically, the observation window period is before the first predicted start time of the dynamic event, so that there is enough time for monitoring and judgment. For example, for a strong wind event, the observation window period can be set to 1 hour before the predicted strong wind arrives.

[0053] Specifically, the entire inspection system establishes connections with external data sources, such as meteorological departments and construction monitoring systems, to promptly receive the first warning signal of dynamic events. The first warning signal should include the first predicted start time of the dynamic event and possible other relevant information, such as the event type and expected intensity.

[0054] In a preferred embodiment, determining at least one mapping factor corresponding to the dynamic event specifically includes the following steps:

[0055] Retrieving and traversing a historical database, the historical database including multiple characteristic factors of the power line and a historical monitoring sequence composed of multiple historical monitoring data corresponding to each characteristic factor in a time sequence; wherein each of the historical monitoring sequences is marked with a historical start time of at least one historical event, and the historical monitoring data in the historical monitoring sequence that is within a second preset time period before the historical start time is marked as first reference monitoring data;

[0056] Specifically, the historical database is derived from data recorded during the power line's historical operation. Characteristic factors include, but are not limited to, meteorological factors such as wind speed, wind direction, rainfall, humidity, and temperature; factors related to the power line's own state such as line tension, current, and voltage; and factors related to the surrounding environment such as the surrounding magnetic field strength. Each historical monitoring sequence is also labeled with the corresponding historical event type.

[0057] The characteristic factor of each of the first reference monitoring data that is in continuous change within the second preset time period is used as the mapping factor corresponding to the dynamic event with the same type as the historical event.

[0058] For example, if within 1 hour before the historical start time of the strong wind event, the monitoring data of the characteristic factor of wind speed continues to increase and the monitoring data of the characteristic factor of wind direction also changes continuously, then the wind speed and wind direction are determined as the mapping factors of the strong wind dynamic event.

[0059] In a preferred embodiment, after retrieving and traversing the historical database, the following steps are further included:

[0060] Determine whether the historical database contains the historical event of the same type as the dynamic event;

[0061] The method specifically includes the following steps: using the characteristic factor of each of the first reference monitoring data that is in a state of continuous change within the second preset time period as the mapping factor corresponding to the dynamic event of the same type as the historical event:

[0062] If the historical event with the same type as the dynamic event is matched in the historical database, the characteristic factor of each first reference monitoring data that is in continuous change within the second preset time period is used as the mapping factor corresponding to the dynamic event with the same type as the historical event.

[0063] Specifically, the historical event type of the corresponding marked historical event in each historical monitoring sequence is used to determine whether there are historical events of the same type as the dynamic event in the historical database. Since the types of these historical events are the same as the current dynamic event, their characteristic change patterns before the event are of reference value. For example, for a dynamic event such as a strong wind, if there are strong wind event records in the historical database, characteristic factors such as wind speed and wind direction that continue to change within the second preset time period before the strong wind arrives (such as 1 hour before the strong wind arrives) can be found based on these records. This method of determining mapping factors based on historical events of the same type can more accurately capture key variables related to the current dynamic event and avoid blindly selecting characteristic factors that may be irrelevant.

[0064] S2. When entering the observation window period, obtain real-time monitoring data of each mapping factor at intervals of a first preset time to obtain a mapping factor set; the mapping factor set is composed of real-time monitoring sequences corresponding to multiple mapping factors;

[0065] Specifically, when the observation window approaches, the inspection robot begins preparing to monitor the mapping factors in real time. During the observation window, at intervals of a first preset duration (e.g., 5 minutes), the inspection robot uses its sensors to acquire real-time monitoring data for each mapping factor and compiles this data into a mapping factor set. A mapping factor set consists of multiple real-time monitoring sequences corresponding to each mapping factor. Each real-time monitoring sequence records the monitoring values ​​of the mapping factor at different time points. The first preset duration is the same as the interval between two adjacent characteristic factors in the historical monitoring sequence.

[0066] S3. Determine, based on the mapping factor set, the probability of occurrence of the dynamic event at the first prediction start time;

[0067] In a preferred embodiment, for the mapping factor, the plurality of first reference monitoring data corresponding thereto constitute a first reference monitoring sequence of the mapping factor;

[0068] The determining, based on the mapping factor set, the probability of occurrence of the dynamic event at the first prediction start time specifically comprises the following steps:

[0069] respectively calculating a first distance of each of the mapping factors, where the first distance is a Euclidean distance between the real-time monitoring sequence and the first reference monitoring sequence;

[0070] For example, for strong wind events, the monitoring data of wind speed, wind direction and other mapping factors within 1 hour before the start of the historical strong wind event respectively constitute their respective first reference monitoring sequences; during the observation window period, the inspection robot obtains real-time monitoring data of wind speed and wind direction every 5 minutes to form their respective real-time monitoring sequences. For the wind speed mapping factor, the real-time monitoring sequence is [V1, V2, V3,..., Vn], and the first reference monitoring sequence is [W1, W2, W3,..., Wn]. Then the first distance of the wind speed mapping factor is .

[0071] The first distances of the mapping factors are weighted and averaged to obtain the event occurrence probability.

[0072] Specifically, a weight is assigned to each mapping factor based on its impact on the dynamic event. Mapping factors with greater impact can be assigned higher weights, while mapping factors with lesser impact can be assigned lower weights. Weights can be determined through expert experience, historical data analysis, or machine learning algorithms. For example, for a strong wind event, the wind speed mapping factor will be given a higher weight, while the wind direction mapping factor will be given a lower weight.

[0073] Specifically, the probability of an event occurring can be calculated using the following formula: , where P is the probability of an event occurring, is the weight of the i-th mapping factor, is the first distance of the i-th mapping factor, and m is the number of mapping factors.

[0074] In a preferred embodiment, the determination of the probability of occurrence of the dynamic event at the first prediction start time based on the mapping factor set can be obtained in another manner, specifically comprising the following steps:

[0075] Finding, in the historical database, multiple reference sets based on the mapping factor set, each of the reference sets including a second reference monitoring sequence corresponding to each of the mapping factors; wherein a Euclidean distance between the real-time monitoring sequence and the second reference monitoring sequence for the same mapping factor in the reference set and the mapping factor set is less than or equal to a second preset threshold;

[0076] Specifically, if the distance between the historical monitoring sequence in the historical database and the real-time monitoring sequence of a mapping factor of the current dynamic event meets the conditions, it is included in the corresponding reference set.

[0077] The reference sets in the historical database that are marked with the historical start time within a third preset time period after each reference set and whose historical events corresponding to the historical start time are of the same type as the dynamic event are used as the trusted sets;

[0078] For example, if the current dynamic event is a strong wind event, within a period of time (a third preset time length) after the time point corresponding to a certain reference set, if the historical start time of the strong wind event appears in the historical database, then this reference set becomes a trusted set, and the historical database is traversed to find all trusted sets.

[0079] The ratio of the number of the credible set to the number of the reference set is used as the probability of the event occurrence.

[0080] In a preferred embodiment, determining the observation window period corresponding to the dynamic event specifically includes the following steps:

[0081] The observation window period corresponding to the dynamic event is obtained based on the time leading interval and the duration range of the trusted set relative to the corresponding historical start time.

[0082] Specifically, the time lead interval refers to the period from the time point corresponding to the trusted set to the start time of the historical event. For example, if the starting time point in the time series corresponding to the trusted set is T1, and the start time of the historical event corresponding to the trusted set is T2, then the time lead interval is [T1, T2]. For the duration range, if the durations of the time series corresponding to several trusted sets are t1, t2, t3, ..., tn, respectively, then the lower limit of the duration range can be the minimum value of these durations, min (t1, t2, t3, ..., tn), and the upper limit can be the maximum value, max (t1, t2, t3, ..., tn).

[0083] S4. If the probability of the event occurring is less than the first preset threshold, the inspection robot is controlled to maintain the default inspection mode; if the probability of the event occurring is greater than or equal to the first preset threshold, the inspection robot is controlled to switch from the default inspection mode to the event response mode corresponding to the dynamic event at the start time of the first prediction.

[0084] For example, if the calculated probability of a strong wind event is lower than a first preset threshold, it indicates that the strong wind is unlikely to occur at the first prediction start time, and the external signal source may be a false alarm. The inspection robot continues to operate according to the pre-set default inspection mode. The default inspection mode typically performs routine inspections of power lines along a specific route and frequency. For example, it moves along the power lines at a steady speed, monitoring basic parameters such as appearance, temperature, and current, while recording and uploading the data to the control center. When the probability of an event is greater than or equal to the first preset threshold, it indicates that the current dynamic event is likely to occur and the warning signal from the external signal source is credible. For example, if the calculated probability of a rainstorm event reaches or exceeds the first preset threshold, it means that there is a high probability of rainstorms occurring at the first prediction start time. Upon receiving the judgment result of the event probability, the inspection robot's control system immediately begins preparing to switch inspection modes. Each inspection robot's event response mode for dynamic events is different, and the event response mode will not be detailed here. The event response mode includes notifying the robot's various sensors and actuators to prepare for corresponding adjustments.

[0085] For example, the event response mode for heavy rain events includes notifying waterproof sensors to strengthen monitoring, reducing movement speed, strengthening monitoring of power line insulation performance, checking whether the pole tower foundation is affected by rain erosion, etc., so as to better deal with safety risks under severe weather conditions.

[0086] In a preferred embodiment, after determining whether the historical database contains a historical event of the same type as the dynamic event, the method further includes the following steps:

[0087] If the historical event of the same type as the dynamic event is not matched in the historical database, then based on principal component analysis, the plurality of characteristic factors in the historical database are converted into a plurality of principal components, each of which is composed of a combination of its corresponding plurality of characteristic factors and the weight of each characteristic factor, and the number of the principal components is less than the number of the characteristic factors;

[0088] Relevant components of the dynamic event are obtained, and the principal component that matches the relevant components is used as the mapping factor corresponding to the dynamic event.

[0089] Specifically, the covariance matrix between the characteristic factors is calculated, and then the covariance matrix is ​​subjected to eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors. The eigenvalues ​​are arranged in descending order, and the eigenvectors corresponding to the first few larger eigenvalues ​​are selected to form the basis vectors of the principal components. Each principal component is composed of a combination of its corresponding multiple characteristic factors and the weight of each characteristic factor, and the number of principal components is less than the number of original characteristic factors. For example, if the dynamic event is an unknown meteorological phenomenon affecting the power lines, the relevant components can be determined by some key information in the weather forecast, or the relevant components related to the dynamic event type in the first warning signal can be obtained, and then at least one principal component is matched as the mapping factor of the dynamic event type according to the relevant components corresponding to the dynamic event type.

[0090] For example, data for all characteristic factors, including wind speed, wind direction, rainfall, humidity, power line tension, current, and voltage, are extracted from a historical database. After preprocessing, the covariance matrix is ​​calculated and eigenvalue decomposition is performed. Assume that three principal components are obtained. The first principal component is primarily composed of wind speed, wind direction, and rainfall, with weights w11, w12, and w13, respectively; the second principal component is primarily composed of power line tension, current, and voltage, with weights w21, w22, and w23, respectively; and the third principal component is primarily composed of humidity and other characteristic factors, with weights w31, w32, and so on. By analyzing weather forecasts and current electricity consumption, it is determined that the relevant components for this dynamic event type include specific meteorological conditions (such as strong winds and dry air) and increased current caused by peak electricity demand. Comparing the characteristics of the relevant components of the dynamic event with the principal components, it is found that the strong wind-related characteristic factor in the first principal component and the humidity-related characteristic factor in the third principal component are related to the meteorological component of the dynamic event, while the current-related characteristic factor in the second principal component is related to the peak electricity demand component. Therefore, these three principal components are used as mapping factors of the current dynamic events.

[0091] This embodiment provides a flexible solution for determining mapping factors. It does not rely on a specific type of historical experience. Instead, it is based on the intrinsic structure of the data itself (through principal component analysis) and analysis of components related to dynamic events. By combining multiple characteristic factors and their weights, the potential relationship between the characteristic factors is explored to obtain the mapping factors.

[0092] In a preferred embodiment, the historical database is further marked with the second predicted starting time of each historical event;

[0093] The method further comprises the following steps:

[0094] When the dynamic event occurs, the actual start time of the dynamic event is recorded, and the dynamic event is used as a historical event to update the historical database; wherein the actual start time of the dynamic event is used as the historical start time, and the first predicted start time is used as the second predicted start time.

[0095] Specifically, if a strong wind event actually occurred in the past, the system recorded the time as 10:00 AM. This time is the actual start time of the strong wind event. At the same time, the start time predicted when the strong wind warning signal was received, such as 9:50 AM, is used as the second predicted start time. At this point, the dynamic event has completed and can be updated as a new historical event in the historical database.

[0096] In a preferred embodiment, after receiving the first warning signal of the dynamic event, the method further includes the following steps:

[0097] Retrieving and traversing the historical database to determine whether the historical database contains the historical event of the same type as the dynamic event;

[0098] If there is at least one historical event of the same type as the dynamic event, the historical event of the same type as the dynamic event is taken as a matching event;

[0099] Determining the observation window period corresponding to the dynamic event specifically includes the following steps:

[0100] If the deviation between the historical start time and the second predicted start time of all the matching events is less than or equal to a third preset threshold, the observation window period corresponding to the dynamic event is determined according to the first predicted start time.

[0101] Specifically, if the deviation between the historical start time of all matching events and the second predicted start time is less than or equal to the third preset threshold, it means that the predicted start time of the historical event is close to the actual start time, which means that the predicted start time is relatively accurate. The observation window period can be determined based on the first predicted start time of the current dynamic event.

[0102] Exemplarily, the third preset threshold is set to 10 minutes. If the time difference between the historical start time and the second predicted start time in past rainstorm events is within 10 minutes, then the observation window period can be determined based on the first predicted start time (for example, 11 a.m.) of the dynamic event of the current warning rainstorm, for example, 10:30 a.m. to 11 a.m. is the observation window period.

[0103] In a preferred embodiment, after taking the historical event of the same type as the dynamic event as a matching event, the method further includes the following steps:

[0104] If there are at least N matching events whose deviations between the historical start time and the second predicted start time are greater than the third preset threshold, the N matching events are regarded as abnormal matching events, and the matching events other than the N abnormal matching events are regarded as normal matching events;

[0105] For example, let N = 3. If there are 5 historical rainstorm events in the historical database as matching events, and the deviation between the historical start time of 3 of these events and the second predicted start time is greater than 10 minutes, then these 3 events are abnormal matching events, and the other 2 are normal matching events.

[0106] Obtaining a first adjustment amount according to the second predicted start time of the normal matching event, and obtaining a second adjustment amount according to the second predicted start time of the abnormal matching event;

[0107] Specifically, the average time difference between the second predicted start time and the historical start time of the normal matching event can be calculated as the first adjustment amount. Similarly, the second adjustment amount can be obtained.

[0108] For example, if the average time difference of normal matching events is 5 minutes ahead, the first adjustment amount is -5 minutes (assuming that ahead is negative and delayed is positive); if the average time difference of abnormal matching events is 8 minutes behind, the second adjustment amount is +8 minutes.

[0109] The first adjustment amount matching weight is calculated based on the ratio of the normal matching events to all the matching events, and the second adjustment amount matching weight is calculated based on the ratio of the abnormal matching events to all the matching events, so as to obtain a total adjustment amount;

[0110] For example, if there are five matching events in the historical database, two of which are normal matching events and three of which are abnormal matching events, then the ratio of normal matching events is 2 / 5 and the ratio of abnormal matching events is 3 / 5. In other words, the total adjustment amount = (2 / 5) × (-5) + (3 / 5) × 8 = (-2) + (24 / 5) = -2 + 4.8 = 2.8 minutes.

[0111] Before determining the observation window period corresponding to the dynamic event, the following steps are also included:

[0112] The first predicted start time is updated with the total adjustment amount.

[0113] For example, the first predicted start time included in the first warning signal is 11 a.m. According to the above, the total adjustment amount is 2.8 minutes in advance, so the updated first predicted start time is 10:57.2 a.m., and the observation window period is determined based on the updated first predicted start time (10:57.2 a.m.).

[0114] By adjusting the first prediction start time, this embodiment can more reasonably determine the range of the observation window period, so that the inspection robot can start monitoring the mapping factors at a more appropriate time, without wasting resources too early or missing key information too late, thereby better responding to dynamic events and improving the overall efficiency and performance of the system.

[0115] Furthermore, the total adjustment amount can be obtained by using abnormal matching events in the following manner: after treating the N matching events as abnormal matching events and treating the matching events other than the N abnormal matching events as normal matching events, the following steps are further included:

[0116] Analyze the inducing factors that cause a large deviation between the predicted start time and the actual start time in the abnormal matching events, wherein the inducing factors are factors that are common to all the abnormal matching events and are not present in the normal matching events;

[0117] Determine whether the dynamic event has the same inducing factor; if it has the same inducing factor, use the average value of the deviation between the historical start time of the abnormal matching event and the second predicted start time as the total adjustment amount.

[0118] For example, by comparing the surrounding environment and circumstances corresponding to the occurrence time of abnormal matching events, it is discovered that they all share a common trait: large-scale road construction occurred nearby before these abnormal matching events occurred, while this was not the case in normal matching events. Therefore, the presence of large-scale road construction nearby is determined to be a triggering factor. If large-scale road construction is also currently occurring, it indicates that the first predicted start time included in the first warning signal is likely to be inaccurate due to the large-scale road construction nearby. In this case, the average of the deviations between the historical start time of all previous abnormal matching events and the second predicted start time is used as the total adjustment amount to mitigate the problem of poor prediction accuracy caused by the triggering factor.

[0119] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. The above is only a preferred implementation method of the present invention. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of the present invention.

Claims

1. A control method for a power line inspection robot based on dynamic event triggering, characterized in that: The following steps are involved: receiving a first warning signal of a dynamic event, determining at least one mapping factor corresponding to the dynamic event, and determining an observation window period corresponding to the dynamic event; The mapping factor is a variable reflected on or around the power line before the dynamic event occurs; the first warning signal includes a first predicted start time of the dynamic event; and the observation window period is before the first predicted start time; When entering the observation window period, real-time monitoring data of each mapping factor is acquired in real time at intervals of a first preset time length to obtain a mapping factor set; The mapping factor set is composed of a plurality of real-time monitoring sequences corresponding to the mapping factors; Determining, based on the mapping factor set, the probability of occurrence of the dynamic event at the first prediction start time; If the probability of the event occurring is less than a first preset threshold, the inspection robot is controlled to maintain the default inspection mode; if the probability of the event occurring is greater than or equal to the first preset threshold, the inspection robot is controlled to switch from the default inspection mode to the event response mode corresponding to the dynamic event at the start time of the first prediction; The determining, based on the mapping factor set, the probability of occurrence of the dynamic event at the first prediction start time specifically comprises the following steps: Finding, in a historical database, multiple reference sets based on the mapping factor set, each of the reference sets including a second reference monitoring sequence corresponding to each of the mapping factors; wherein a Euclidean distance between the real-time monitoring sequence and the second reference monitoring sequence for the same mapping factor in the reference set and the mapping factor set is less than or equal to a second preset threshold; A reference set in the history database that is marked with a historical start time within a third preset time period after the reference set and whose historical events corresponding to the historical start time are of the same type as the dynamic event is used as a trusted set; The ratio of the number of the credible set to the number of the reference set is used as the probability of the event occurrence.

2. The control method of the power line inspection robot based on dynamic event triggering according to claim 1, characterized in that: The determining of at least one mapping factor corresponding to the dynamic event specifically includes the following steps: Retrieving and traversing a historical database, the historical database including multiple characteristic factors of the power line and a historical monitoring sequence composed of multiple historical monitoring data corresponding to each characteristic factor in a time sequence; wherein each of the historical monitoring sequences is marked with a historical start time of at least one historical event, and the historical monitoring data in the historical monitoring sequence that is within a second preset time period before the historical start time is marked as first reference monitoring data; The characteristic factor of each of the first reference monitoring data that is in continuous change within the second preset time period is used as the mapping factor corresponding to the dynamic event with the same type as the historical event.

3. The control method of the power line inspection robot based on dynamic event triggering according to claim 2, characterized in that: After retrieving and traversing the historical database, the following steps are also included: Determine whether the historical database contains the historical event of the same type as the dynamic event; The method specifically includes the following steps: using the characteristic factor of each of the first reference monitoring data that is in a state of continuous change within the second preset time period as the mapping factor corresponding to the dynamic event of the same type as the historical event: If the historical event with the same type as the dynamic event is matched in the historical database, the characteristic factor of each first reference monitoring data that is in continuous change within the second preset time period is used as the mapping factor corresponding to the dynamic event with the same type as the historical event.

4. The control method of the power line inspection robot based on dynamic event triggering according to claim 3 is characterized in that: After determining whether the historical database contains a historical event of the same type as the dynamic event, the following steps are further included: If the historical event of the same type as the dynamic event is not matched in the historical database, then based on principal component analysis, the plurality of characteristic factors in the historical database are converted into a plurality of principal components, each of which is composed of a combination of its corresponding plurality of characteristic factors and the weight of each characteristic factor, and the number of the principal components is less than the number of the characteristic factors; Relevant components of the dynamic event are obtained, and the principal component that matches the relevant components is used as the mapping factor corresponding to the dynamic event.

5. The control method of the power line inspection robot based on dynamic event triggering according to claim 2, characterized in that: The historical database also contains a second predicted start time for each historical event; The method further comprises the following steps: When the dynamic event occurs, the actual start time of the dynamic event is recorded, and the dynamic event is used as a historical event to update the historical database; wherein the actual start time of the dynamic event is used as the historical start time, and the first predicted start time is used as the second predicted start time.

6. The control method of the power line inspection robot based on dynamic event triggering according to claim 5, characterized in that: After receiving the first warning signal of the dynamic event, the method further comprises the following steps: Retrieving and traversing the historical database to determine whether the historical database contains the historical event of the same type as the dynamic event; If there is at least one historical event of the same type as the dynamic event, the historical event of the same type as the dynamic event is taken as a matching event; Determining the observation window period corresponding to the dynamic event specifically includes the following steps: If the deviation between the historical start time and the second predicted start time of all the matching events is less than or equal to a third preset threshold, the observation window period corresponding to the dynamic event is determined according to the first predicted start time.

7. The control method of the power line inspection robot based on dynamic event triggering according to claim 6, characterized in that: After the historical event of the same type as the dynamic event is used as a matching event, the following steps are also included: If there are at least N matching events whose deviations between the historical start time and the second predicted start time are greater than the third preset threshold, the N matching events are regarded as abnormal matching events, and the matching events other than the N abnormal matching events are regarded as normal matching events; Obtaining a first adjustment amount according to the second predicted start time of the normal matching event, and obtaining a second adjustment amount according to the second predicted start time of the abnormal matching event; The first adjustment amount matching weight is calculated based on the ratio of the normal matching events to all the matching events, and the second adjustment amount matching weight is calculated based on the ratio of the abnormal matching events to all the matching events, so as to obtain a total adjustment amount; Before determining the observation window period corresponding to the dynamic event, the following steps are also included: The first predicted start time is updated with the total adjustment amount.

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