Bus duct detection equipment motion control system and method

Abstract

The invention discloses a bus duct detection equipment motion control system and method, and belongs to the technical field of power distribution equipment, and the system comprises a monitoring configuration module which is used for obtaining a multi-dimensional sensing array; the fault framing module is used for obtaining a fault detection interval; the equipment matching module is used for obtaining a plurality of pieces of real-time detection equipment; the strategy optimization module is used for obtaining a target multi-mode diagnosis path of the target detection equipment; and the fault identification module is used for outputting a real-time fault node and a real-time fault type. The technical problems of inaccurate fault area positioning and low detection efficiency caused by incomplete coverage of a single detection mode due to a complex bus duct structure are solved, the positioning accuracy of the fault area is effectively improved through multi-sensor data fusion, dynamic path planning and intelligent equipment switching, and the detection efficiency is improved. The technical effect of efficient and accurate holographic fault identification is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution equipment, and in particular to a motion control system and method for bus duct detection equipment. Background Art

[0002] With the rapid development of the power system, bus duct, as an important power distribution equipment, its operating status monitoring and fault detection are particularly important. However, existing bus duct detection technologies often rely on manual inspections or single sensor detection methods, which have certain limitations in practical applications. First, traditional detection methods are difficult to accurately locate the fault area in real time, and have poor adaptability to obstacles or spatial layouts in complex environments, and cannot intelligently adjust the detection path of the equipment according to actual conditions. Secondly, the monitoring method of a single sensor often cannot fully reflect the fault information of the bus duct, resulting in the risk of missed detection or false detection. Therefore, there is an urgent need for an efficient and intelligent detection device that can achieve accurate positioning and intelligent detection of bus duct faults through multi-sensor data fusion, real-time path planning, and equipment motion control. Summary of the Invention

[0003] This application provides a motion control system and method for bus duct detection equipment, aiming to solve the technical problem that due to the complex structure of the bus duct, a single detection method has incomplete coverage, resulting in inaccurate fault area positioning and low detection efficiency. Through multi-sensor data fusion, dynamic path planning and intelligent equipment switching, the positioning accuracy of the fault area is effectively improved, and the technical effect of efficient and accurate fault holographic identification is achieved.

[0004] In a first aspect disclosed in the present application, a bus duct detection equipment motion control system is provided, which includes: a monitoring configuration module for configuring multi-dimensional monitoring coverage of a bus distribution network according to a monitoring scenario to obtain a multi-dimensional sensor array; a fault framing module for a patrol control center to preliminarily frame a fault range based on a multi-dimensional monitoring timing array transmitted back by the multi-dimensional sensor array to obtain a fault detection interval, wherein the fault detection interval is identified by real-time characteristic parameters; a device matching module for matching hot standby detection equipment according to the fault detection interval and real-time characteristic parameters to obtain multiple real-time detection equipment; a strategy optimization module for optimizing a movement strategy according to the fault detection interval and multiple real-time device positions of multiple real-time detection equipment to screen a target multi-modal diagnostic path for a target detection equipment; a fault identification module for, after controlling the target detection equipment to move to the fault detection interval using the target multi-modal diagnostic path, driving the target detection equipment to perform fault holographic identification in the fault detection interval according to the real-time characteristic parameters to output a real-time fault node and a real-time fault type.

[0005] Another aspect disclosed in the present application provides a method for controlling the motion of bus duct detection equipment, which includes: configuring multi-dimensional monitoring coverage of the bus distribution network according to the monitoring scenario to obtain a multi-dimensional sensor array; the inspection control center preliminarily frames the fault range based on the multi-dimensional monitoring timing array transmitted back by the multi-dimensional sensor array to obtain a fault detection interval, wherein the fault detection interval is identified by real-time characteristic parameters; hot standby detection equipment is matched according to the fault detection interval and the real-time characteristic parameters to obtain multiple real-time detection equipment; mobile strategy optimization is performed according to the fault detection interval and multiple real-time device positions of multiple real-time detection equipment to screen and obtain a target multi-modal diagnostic path for the target detection equipment; after controlling the target detection equipment to move to the fault detection interval using the target multi-modal diagnostic path, the target detection equipment is driven to perform fault holographic identification in the fault detection interval according to the real-time characteristic parameters to output a real-time fault node and a real-time fault type.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] Due to the adoption of a monitoring configuration module to configure multi-dimensional monitoring coverage of the bus distribution network according to the monitoring scenario, a multi-dimensional sensor array is obtained; based on the fault framing module, the inspection control center performs a preliminary framing of the fault range based on the multi-dimensional monitoring timing array sent back by the multi-dimensional sensor array to obtain a fault detection interval, wherein the fault detection interval is identified by real-time characteristic parameters; based on the device matching module, hot standby detection devices are matched according to the fault detection interval and real-time characteristic parameters to obtain multiple real-time detection devices; based on the strategy optimization module, a mobile strategy is optimized according to the fault detection interval and multiple real-time device positions of multiple real-time detection devices to screen and obtain a target multi-modal diagnostic path for the target detection device; based on the fault identification module, after controlling the target detection device to move to the fault detection interval by adopting the target multi-modal diagnostic path, the target detection device is driven to perform fault holographic identification in the fault detection interval according to the real-time characteristic parameters to output a real-time fault node and a real-time fault type. It solves the technical problem of incomplete coverage of a single detection method due to the complex structure of the bus duct, resulting in inaccurate fault area positioning and low detection efficiency. It achieves the technical effect of effectively improving the positioning accuracy of the fault area and realizing efficient and accurate fault holographic identification through multi-sensor data fusion, dynamic path planning and intelligent equipment switching.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A structural schematic diagram of a bus duct detection equipment motion control system is provided for an embodiment of the present application.

[0010] Figure 2 A flow chart of a bus duct detection equipment motion control method is provided for an embodiment of the present application.

[0011] Explanation of the accompanying symbols: monitoring configuration module 11, fault framing module 12, device matching module 13, strategy optimization module 14, fault identification module 15. DETAILED DESCRIPTION

[0012] This application provides a motion control system and method for bus duct detection equipment to solve the technical problem that due to the complex structure of the bus duct, a single detection method has incomplete coverage, resulting in inaccurate fault area positioning and low detection efficiency. Through multi-sensor data fusion, dynamic path planning and intelligent equipment switching, the positioning accuracy of the fault area is effectively improved, and the technical effect of efficient and accurate fault holographic identification is achieved.

[0013] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0014] Example 1, as Figure 1 As shown, an embodiment of the present application provides a bus duct detection equipment motion control system, the system comprising:

[0015] The monitoring configuration module 11 is used to configure multi-dimensional monitoring coverage of the bus distribution network according to the monitoring scenario to obtain a multi-dimensional sensor array.

[0016] Specifically, in the monitoring configuration module 11, the system terminal first performs comprehensive monitoring coverage of the bus distribution network according to the requirements of the monitoring scenario. This process involves configuring multiple different types of sensors according to different detection requirements and environmental conditions (such as straight sections, elbows, shafts or high-voltage enclosed spaces) to form a multi-dimensional sensor array. Specifically, the sensor array will combine different sensor types, such as infrared thermal imagers, ultrasonic sensors, partial discharge detectors, current transformers, etc., to comprehensively monitor the various operating parameters and states of the bus duct. Each sensor is deployed at different locations in the bus distribution network according to its specific function, and can monitor the bus duct in multiple dimensions. Among them, the infrared thermal imager is mainly responsible for monitoring temperature anomalies, the ultrasonic sensor is used to detect mechanical vibrations and internal defects, the partial discharge detector captures insulation degradation signals, and the current transformer monitors load current fluctuations in real time. Through such a comprehensive configuration, it can ensure that no possible fault signals or abnormal conditions are missed during the monitoring process, and at the same time provide accurate data support for subsequent fault location and diagnosis. Ultimately, this data will form a highly integrated multi-dimensional sensor array that can achieve accurate monitoring and data collection in any area of ​​the bus duct.

[0017] The fault framing module 12 is used for the inspection control center to preliminarily frame the fault range based on the multi-dimensional monitoring time series array returned by the multi-dimensional sensor array, and obtain the fault detection interval, wherein the fault detection interval is identified by real-time characteristic parameters.

[0018] Specifically, in the fault framing module 12, the inspection control center receives monitoring data from the multi-dimensional sensor array. These data are transmitted back in a time series manner and include various monitoring information of the bus duct at different time points. The control center processes and analyzes these returned data. First, it comprehensively evaluates the characteristic parameters collected at each moment, and then determines the potential fault area. Specifically, the control center uses the time series information in the multi-dimensional monitoring data to identify faults from two aspects: signal range and data trend. In terms of signal range, the signal value of the sensor will be detected to determine whether it has signal values ​​that are not within the standard range in multiple consecutive return cycles (which can be set according to actual needs, such as two return cycles). For example, for infrared sensors, their output values ​​should be within a standard temperature range. If the returned data exceeds the standard range in consecutive cycles, it means that the monitoring area corresponding to the multi-dimensional sensor array may have a fault. In terms of data trends, the returned data is checked for fluctuations that are significantly different from normal operating conditions. Under normal circumstances, the data should show a certain trend change (such as temperature gradually changing at the allowable rate of change). If the data shows an abnormal increase in temperature, a sudden change in vibration frequency, or a sharp increase in current load within multiple consecutive return cycles, that is, the rate of change exceeds the allowable rate of change, it indicates that the monitoring area corresponding to the multi-dimensional sensor array may have a fault. Through data comparison and trend analysis, the area where the fault may occur is preliminarily identified, and the monitoring range of the sensor corresponding to the abnormal data in the multi-dimensional sensor array is used to preliminarily frame the fault range, thereby determining the fault detection interval. Each fault detection interval is marked with real-time characteristic parameters, that is, it is calibrated using key parameters such as abnormal temperature, vibration, and current. These characteristic parameters facilitate subsequent more accurate diagnosis and detection, ensuring that busbar detection equipment (such as magnetic-multi-legged composite robots) can locate the specific location of the fault in the shortest possible time and provide basic data support for subsequent accurate detection and maintenance.

[0019] The device matching module 13 is used to match hot standby detection devices according to the fault detection interval and real-time characteristic parameters to obtain multiple real-time detection devices.

[0020] Specifically, in the device matching module 13, the inspection control center will match the hot standby detection equipment based on the fault detection interval and real-time characteristic parameters determined in the previous step. Specifically, the control center will input the real-time characteristic parameter identifier of the fault detection interval into the pre-built fault parameter association topology for reverse deduction, determine the possible fault types within the fault detection interval, and then determine the functional characteristics that the appropriate detection equipment needs to have based on these possible fault types. Subsequently, with the fault detection interval as a constraint, the equipment that can complete the fault detection interval detection within the response time is screened from all bus duct detection equipment. The bus duct detection equipment is a composite robot that combines magnetic attraction and multi-legged gripping functions, specifically used for accurate fault detection and diagnosis of bus ducts. The design goal of this equipment is to be able to move flexibly through magnetic attraction or climbing in a complex power distribution environment, and automatically complete the bus duct inspection task. Different bus duct detection equipment has different detection functions and moving speeds due to the different sensors they carry. Afterwards, the functions of the screened bus duct detection equipment are matched with the functional characteristics required by the previous analysis to form hot standby detection equipment (equipment that is on standby and can be dispatched to the fault area at any time, maintaining real-time availability and requiring no warm-up or startup time), thereby obtaining multiple real-time detection devices to provide sufficient equipment resources for subsequent fault detection and analysis.

[0021] Furthermore, the device matching module 13 includes:

[0022] The first demand reverse inference unit is used to reversely infer the detection demand based on the real-time characteristic parameters, and match the functional characteristics of the detection equipment based on the reverse inference results; the first equipment matching unit is used to match hot standby detection equipment in the bus distribution network based on the functional characteristics of the detection equipment and the fault detection interval to obtain multiple real-time detection equipment.

[0023] In a preferred embodiment, the first demand reverse inference unit extracts real-time characteristic parameters from the fault detection interval. These data include characteristic parameters such as temperature, vibration, current, and thermal imaging. The system terminal analyzes these real-time characteristic parameters based on the fault parameter association topology to identify the possible fault types that may occur in the current bus duct. Subsequently, based on the correspondence between the fault type and the fault detection requirement, these possible fault types are reversely matched to determine the functional characteristics of the detection equipment. These functional characteristics of the detection equipment reflect the capabilities that the bus duct detection equipment should have. For example, if the temperature sensor detects an abnormal increase in temperature, it may indicate an overheating fault. Based on this fault characteristic, it is reversely inferred that a bus duct detection device with infrared detection function is required to further diagnose the specific fault in the temperature abnormality area. Afterwards, the first device matching unit matches the hot standby detection equipment from the two aspects of response and function by jointly processing the functional characteristics of the detection equipment and the fault detection interval, ensuring that the selected detection equipment is suitable for the current monitoring range and detection requirements. Finally, the first device matching unit selects multiple real-time detection devices based on the above matching rules. These devices do not require preheating or startup time and can be dispatched to the fault area for detection at any time, ensuring efficient and comprehensive detection of the fault area and providing guarantees for subsequent fault location and diagnosis.

[0024] Furthermore, the first demand reverse estimation unit includes:

[0025] A historical record calling unit is used to locally call the historical detection records of the bus distribution network; a historical record clustering unit is used to cluster the historical detection records based on the characteristic parameter-fault type mapping characteristics to obtain multiple sample fault type sets of multiple sample characteristic parameters; an associated topology construction unit is used to construct a fault parameter associated topology based on the multiple sample characteristic parameters and multiple sample fault type sets according to the fault coincidence relationship of the multiple sample fault type sets; a fault reverse inference unit is used to input the real-time characteristic parameters into the fault parameter associated topology to perform fault type reverse inference to obtain K associated fault types; a second demand reverse inference unit is used to perform detection demand reverse inference based on the K associated fault types to obtain K types of sample equipment functions, wherein the K types of sample equipment functions constitute the detection equipment function characteristics.

[0026] In a preferred embodiment, the system terminal retrieves the historical detection data of the bus distribution network from the local database or storage system through the historical record calling unit. These historical data include previous fault detection records, operating parameters (such as temperature, current, vibration, etc.) and fault types and other information. These historical data help provide reference and background for the current detection task. Subsequently, the historical record clustering unit maps the characteristic parameters (such as temperature, vibration, current, etc.) in the historical detection data with the corresponding fault type, and then based on these mapping relationships, aggregates the characteristic parameters belonging to the same fault type. For example, if certain characteristic parameters are associated with the same fault type in multiple historical records, these records will be clustered into a sample set. Through clustering, multiple sample fault type sets are generated, each fault type set contains sample characteristic parameters related to the type, and these sample sets can help the system terminal understand the relationship between different characteristic parameters and different fault types. The association topology construction unit then uses the fault overlap relationships within the sample fault type set (e.g., whether different fault types occur under similar characteristic parameter conditions, such as whether increased current may lead to overheating or overload faults) to construct a fault parameter association topology. This topology reflects the inherent connections and mutual influences between different fault types and characteristic parameters. Topology construction can be implemented using a graph model, where each node represents a characteristic parameter (such as temperature, current, or vibration) and a fault type (such as overload or poor contact), and edges represent the associations between them. For each pair of associated characteristic parameters and fault types, if they exhibit similar patterns across multiple fault type sets, an edge is created between them. The weight of the edge can be set based on the degree of overlap between them. For example, if current and overload faults frequently occur together, a larger weight (the ratio of the number of simultaneous current and overload faults to the number of overload faults) is assigned to the edge between them, indicating a strong association between them. Then, the fault reverse inference unit inputs the real-time characteristic parameters (such as real-time temperature, vibration, current, etc.) into the fault parameter association topology that has been constructed, and performs reverse reasoning through the guidance of edges, thereby deducing the possible K associated fault types based on the position of the real-time characteristic parameters in the topology. After obtaining the K associated fault types, the second demand reverse inference unit will reversely infer the corresponding detection requirements based on these fault types. Each fault type may require a specific detection method, such as ultrasonic detection, infrared thermal imaging, etc. During the reverse inference process, the system terminal determines the most appropriate detection method for each fault type based on the characteristic requirements of each fault type and combined with industry knowledge, including the detection equipment functions required for each fault type during detection. For example, if a certain fault type requires the detection of temperature changes, a busbar detection device with infrared thermal imaging function will be selected; if the vibration of the mechanical structure needs to be detected, a busbar detection device with an ultrasonic sensor may be selected.In this way, the system terminal obtains K sample device functions based on the requirements of K fault types and combines these sample device functional characteristics into the final detection device functional characteristics. Through these steps, based on historical detection records and real-time data, it can intelligently reverse-infer the fault type and its corresponding detection requirements, and then match the appropriate detection device to each fault type, ensuring the accuracy and efficiency of fault detection.

[0027] Furthermore, the first device matching unit includes:

[0028] A detection device interaction unit is used to interactively obtain the standard inspection speed of the bus duct detection device; a response scale calculation unit is used to calculate the fault detection response scale based on the fault detection response window of the bus distribution network and the standard inspection speed; a device selection unit is used to select hot standby detection devices based on the fault detection response scale with the fault detection interval as the starting point to obtain multiple alternative detection devices; a second device matching unit is used to traverse the multiple device function descriptions of the multiple alternative detection devices by adopting the detection device functional characteristics to perform hot standby detection device matching to obtain the multiple real-time detection devices.

[0029] In a preferred embodiment, the detection device interaction unit obtains the standard inspection speed of the equipment by interacting with the bus duct detection equipment. This speed is usually determined by the technical parameters and design requirements of the equipment, and reflects the movement speed of the equipment when performing the inspection task. After obtaining the standard inspection speed, the working efficiency and movement capacity of the equipment during the inspection process can be determined. Subsequently, the response scale calculation unit calculates based on the fault detection response window of the bus distribution network and the standard inspection speed of the equipment. The fault detection response window refers to a pre-set time range used to determine how quickly the equipment needs to respond and perform detection when a fault occurs. Based on the standard inspection speed and the fault detection response window, the response scale calculation unit multiplies the standard inspection speed with the fault detection response window to calculate a fault detection response scale. This scale reflects the detection range that the bus duct detection equipment can cover within the specified time, determines the working area and movement path of the equipment, and the calculation of the response scale helps to ensure that the detection equipment can complete the fault detection task in the specified area in a timely and effective manner. Afterwards, the device selection unit will select all hot standby detection devices based on the fault detection response scale calculated above, starting from the fault detection interval. In this process, the selection unit will use the fault detection interval as the starting point to find devices that can reach and complete the detection of the interval within the response scale. By comparing the distance from the fault detection interval to the device location and the fault detection response scale, all hot standby detection devices that can meet the requirements are screened out as multiple alternative detection devices. Then, based on the multiple alternative detection devices obtained by the selection, the second device matching unit traverses the functional description of each device and determines the functional characteristics of each device. For example, some devices may have infrared thermal imaging, and other devices may have ultrasonic detection. The system terminal compares the functional characteristics of multiple alternative detection devices with the functional characteristics of the detection device to select devices that meet the current detection requirements. These devices will be matched as multiple real-time detection devices and are ready to be put into the fault detection process to effectively perform the detection task of the fault interval, thereby improving the accuracy and automation of fault diagnosis.

[0030] The strategy optimization module 14 is configured to optimize the movement strategy according to the fault detection interval and multiple real-time device positions of multiple real-time detection devices, so as to screen and obtain a target multimodal diagnosis path of a target detection device.

[0031] Specifically, in the strategy optimization module 14, the system terminal first performs global motion planning based on the previously determined fault detection interval and the current position of multiple real-time detection devices (which can be obtained through preset position information). The goal is to generate multiple feasible diagnostic paths so that the device can cover the fault detection interval and avoid known obstacles at the same time. After obtaining multiple fault diagnosis paths, the system terminal will evaluate each path. This evaluation process not only considers the energy consumption and time required for the path, but also needs to evaluate the feasibility of the path. For example, whether the device can flexibly switch the motion mode (magnetic mode and multi-legged mode) on the path to adapt to different environments. After the evaluation, the most suitable target multimodal diagnostic path will be screened out. This path can not only ensure that the device covers all detection requirements within the fault detection interval, but also flexibly switch the motion mode to adapt to the complexity of the bus duct environment, thereby achieving accurate diagnosis of bus duct faults.

[0032] Furthermore, the strategy optimization module 14 includes:

[0033] A diagnostic path fitting unit is used to perform diagnostic path fitting in the bus distribution network according to the fault detection interval and multiple real-time device positions of multiple real-time detection devices to obtain multiple fault diagnostic paths; a first mobile strategy evaluation unit is used to screen and obtain a target multimodal diagnostic path of the target detection device by performing mobile strategy evaluation on the multiple fault diagnostic paths.

[0034] In a preferred embodiment, the diagnostic path fitting unit first considers the spatial layout of the fault detection interval. The fault detection interval is an area determined based on the actual situation of the bus duct and combined with sensor data. This area includes not only horizontal segments but also vertical bus duct segments. During the diagnostic path fitting process, the starting point (such as the initial position of the device) is first defined and expressed as three-dimensional coordinates. The target point (such as the center position of the fault detection area) is defined and expressed as three-dimensional coordinates. Then, a tree is established at the starting point. The nodes of the tree represent the position of the device in three-dimensional space. When the tree is initialized, the root node of the tree is the starting point. Subsequently, the three-dimensional workspace of the bus duct is defined, including all possible areas, which are divided into obstacle areas (such as equipment, brackets, walls, etc.) and free space (walkable area). The sampling range and step size are then set. For example, points within a certain range are randomly sampled, and the sampling points are ensured to be within the walkable area of ​​the bus duct, including vertical and horizontal segments. The range of motion in the z direction needs to be considered. At the same time, the tree expansion step size is set, that is, the maximum distance from the current node to the new node of the tree. In three-dimensional space, the step size affects the tree expansion speed and path accuracy. After the environmental parameters are set, a point is randomly sampled in three-dimensional space. This point can be a completely random location, or a target attraction strategy (i.e., the sample point is closer to the target point) can be used. This strategy helps accelerate expansion toward the target point and improves fitting efficiency. In three-dimensional path planning, the coordinates of the sample point include x, y, and z. The change in the z coordinate is particularly crucial for path planning on vertical segments. To expand the tree, the system terminal needs to find the node in the tree closest to the sample point. The tree is expanded by generating new nodes along the direction from this node to the sample point, with a maximum step size. If the generated new node exceeds the distance from the sample point to the nearest node, the node is expanded to the sample point. In three-dimensional space, the expansion process requires special consideration of changes in the z direction to ensure smooth transitions between vertical segments. Furthermore, collision detection is required during expansion to ensure that the path avoids collisions with obstacles. This is especially important for vertical segment path planning, ensuring stability during vertical motion. Once the tree is expanded to the vicinity of the target point, backtracking begins. This process traces back through the parent nodes in the tree until it returns to the starting point, resulting in the path from the starting point to the target point—the fault diagnosis path. After obtaining a fault diagnosis path, the system starts again from the starting point and fits the remaining nodes until every node is fit. This results in multiple possible fault diagnosis paths. After obtaining multiple possible fault diagnosis paths, the first mobility strategy evaluation unit begins evaluating these paths, focusing on each path's energy consumption, switching, and time consumption. By evaluating multiple fault diagnosis paths, the system terminal selects the optimal path as the target multimodal diagnostic path. This path meets the requirements for efficient and accurate detection while taking into account the equipment's motion mode switching in the vertical and horizontal busbar sections.For example, when the device needs to transition from a horizontal section to a vertical section, it switches to magnetic mode. After completing the inspection in the vertical section, it switches back to multi-leg mode to continue moving in the horizontal section. This process ensures that the device can flexibly handle the transition between vertical and horizontal sections in complex bus duct structures, thereby efficiently performing inspection tasks and improving the accuracy of fault identification.

[0035] Furthermore, the first mobile strategy evaluation unit includes:

[0036] A diagnostic path decomposition unit is used to decompose the first fault diagnostic path according to the path vector switching to obtain M local diagnostic paths; a second mobile strategy evaluation unit is used to perform a phased mobile strategy evaluation on the M local diagnostic paths based on the path space characteristics to obtain a first mobile strategy coefficient; a third mobile strategy evaluation unit is used to perform a mobile strategy evaluation on the multiple fault diagnostic paths in this way and obtain multiple mobile strategy coefficients; a diagnostic path screening unit is used to screen the target multimodal diagnostic path from the multiple fault diagnostic paths according to the multiple mobile strategy coefficients, and locate the target detection device according to the path device correspondence.

[0037] In a preferred embodiment, the diagnostic path decomposition unit switches the path vector of the first fault diagnostic path. Path vector switching refers to decomposing the entire fault diagnostic path according to certain rules, mainly based on the direction change of the path, the spatial layout and the movement mode of the equipment. For example, if the first fault diagnostic path transitions from the horizontal segment to the vertical segment, the path will be cut into two local paths, one for the horizontal segment and the other for the vertical segment, or if the direction of the first fault diagnostic path changes, it will be cut at the inflection point. By path decomposition, M local diagnostic paths are generated. Each local path represents a stage or a specific motion trajectory. For example, there may be a path specifically for the movement of the equipment on the horizontal segment of the bus duct, and another path specifically for the movement of the equipment in the vertical segment. Each local path has a clear goal to ensure that the equipment successfully completes the fault diagnosis task. The second mobility strategy evaluation unit then periodically evaluates each local diagnostic path based on its spatial characteristics, which primarily include path shape and device motion. By analyzing energy consumption, switching, and time characteristics, and then weighting and integrating the analysis results using pre-set evaluation rules, the unit generates a first mobility strategy coefficient for the first fault diagnostic path. This coefficient measures the path's adaptability and execution difficulty. After the second mobility strategy evaluation unit evaluates the local paths, the third mobility strategy evaluation unit performs a mobility strategy evaluation on all diagnostic paths, assessing their adaptability to overall device movement. During this phase, each diagnostic path is assigned a mobility strategy coefficient, and the strategy coefficients for multiple paths are aggregated. This process comprehensively considers the complexity of each path and selects the most appropriate motion strategy for the device, ensuring both efficiency and accuracy. The diagnostic path screening unit then selects the optimal diagnostic path (i.e., the one with the largest mobility strategy coefficient) based on these multiple mobility strategy coefficients. This path serves as the target multimodal diagnostic path, ensuring the fastest possible detection time and minimizing device motion risks. After selecting the target multimodal diagnostic paths, the system terminal determines the final target detection device based on the corresponding relationship between the paths and devices. Each path is matched with a specific detection device, ensuring that the device can adapt to changes in the path and space requirements. This method enables each busbar detection device to perform fault diagnosis based on the most suitable path, allowing it to successfully complete the detection task.

[0038] Furthermore, the second mobile strategy evaluation unit includes:

[0039] A motion pattern matching unit is used to perform motion pattern matching on the M local diagnostic paths to obtain M local motion patterns; a motion switching analysis unit is used to perform motion switching analysis on the M local motion patterns to output M motion pattern features; a control energy consumption analysis unit is used to load the M local diagnostic paths and M motion pattern features into a motion energy consumption prediction model to perform control energy consumption analysis to obtain M local energy consumption features, and then obtain a first energy consumption feature by adding the M local energy consumption features; a switching statistics unit is used to perform motion control switching statistics based on the M local motion patterns to obtain a first switching feature; a time-consuming feature calculation unit is used to calculate a first time-consuming feature based on the M local diagnostic paths and M motion pattern features; a feature weighting unit is used to weightedly fuse the first energy consumption feature, the first switching feature and the first time-consuming feature based on a preset evaluation rule as the first mobile strategy coefficient.

[0040] In a preferred embodiment, after the diagnostic path is decomposed, the motion pattern matching unit performs motion pattern matching analysis on each local diagnostic path. The goal of motion pattern matching is to determine the motion mode that the device should adopt based on the spatial characteristics of each local path (such as the shape and direction of the path). For example, if the path is a horizontal section, the device can use a multi-legged mode to walk; if the path is a vertical section, the device may need to switch to a magnetic mode to ensure stable attachment to the bus duct surface. Through the matching process, the system terminal assigns a suitable motion mode to each local diagnostic path, and obtains M local motion modes. These modes describe the specific motion modes that the device should adopt on different local paths. Subsequently, the motion switching analysis unit analyzes the M local motion patterns to determine the possible impact of switching from one mode to another. In this process, the M local motion patterns are matched with the sample motion pattern to obtain the motion speed of each local motion pattern, and these motion speeds are used as M motion pattern features, corresponding one-to-one with the M local motion patterns. Afterwards, the M local diagnostic paths and M motion pattern features are loaded into the motion energy consumption prediction model for analysis. Before loading, the local diagnostic paths and motion pattern features are standardized, that is, the coordinate points and running speeds are scaled to a fixed range (such as [0,1]) through min-max scaling. The energy consumption prediction model controls the energy consumption analysis of the received local diagnostic paths and motion pattern features based on the learned mapping relationship, thereby obtaining M local energy consumption features. This motion energy consumption prediction model can be constructed based on a fully connected neural network, or based on a convolutional neural network, random forest regression, etc. Taking the fully connected neural network as an example, the system terminal will input the sample diagnostic path and sample running speed into the network, and iterative training will be carried out through steps such as forward propagation, loss calculation, backpropagation, and parameter optimization. When the iteration end conditions (such as the maximum number of iterations or loss function convergence) are reached, the current network will be tested using test data and evaluated based on indicators such as the accuracy obtained from the test. When the evaluation result is greater than or equal to the preset value, the current network will be saved as a motion energy consumption prediction model. Otherwise, hyperparameters such as the learning rate and the number of training batches will be adjusted to further improve the network's prediction performance. Based on model analysis, the system terminal will calculate the energy consumption characteristics of each local path. Each energy consumption characteristic reflects the energy consumption of the device when executing the path. By accumulating these energy consumption characteristics, a first energy consumption characteristic representing the first fault diagnosis path can be obtained. Then, the switching statistics unit will count the number of times the device switches from one mode to another based on M local motion modes, and use the counted number as the first switching characteristic to describe the switching frequency and complexity of the device when executing on the path.Furthermore, the time characteristic calculation unit calculates the time required for the device to complete each local path based on the M local diagnostic paths and their corresponding motion pattern characteristics by dividing the local diagnostic path by the motion pattern characteristic. This time is used as the time characteristic. All time characteristics are accumulated to obtain a first time characteristic, which represents the total time required for the device to complete all local diagnostic paths. Finally, the feature weighting unit performs a weighted fusion of the first energy consumption characteristic, the first switching characteristic, and the first time characteristic based on preset evaluation rules. The evaluation rules include assigning different weights to different characteristics based on different task requirements. For example, if energy conservation is a priority, the energy consumption characteristic may be given a higher weight, while if efficiency is more important, the time characteristic may be given a higher weight. This weighted fusion results in a first mobility strategy coefficient, which reflects the device's overall performance in executing the task, including energy efficiency, switching complexity, and the time required to complete the task. The goal is to use this coefficient to evaluate the device's overall mobility strategy, ensuring the efficiency and stability of path execution while avoiding unnecessary path switching or energy waste.

[0041] Furthermore, the motion pattern characteristics include a sample motion pattern and a sample motion speed.

[0042] In a preferred embodiment, the motion pattern features include sample motion patterns and sample motion speeds. Sample motion patterns serve as reference data for comparison with local motion patterns, providing some predefined motion patterns, such as magnetic and multi-legged patterns. Sample motion speeds are parameters associated with each sample motion pattern, describing the device's motion speed within that specific motion pattern. These speed values ​​help the system terminal more accurately assess the device's performance and motion efficiency along each local diagnostic path.

[0043] The fault identification module 15 is used to drive the target detection device to perform fault holographic identification in the fault detection interval according to the real-time characteristic parameters after controlling the target detection device to move to the fault detection interval using the target multimodal diagnostic path, and output the real-time fault node and real-time fault type.

[0044] Specifically, in the fault identification module 15, under the control of the target multimodal diagnostic path, the target detection device will move according to the pre-planned path until it reaches the specified fault detection interval. When the device reaches the fault detection interval, it will operate based on real-time characteristic parameters (such as data collected by sensors such as temperature, vibration, and current). These real-time characteristic parameters provide key data about the current operating status of the bus duct, helping the device to determine whether there is an abnormality. Specifically, the target detection device will start the fault holographic identification process, which means that the device not only simply detects the fault, but also comprehensively identifies and analyzes the type and location of the fault. The fault holographic identification process usually involves the combined use of multimodal sensors (such as ultrasonic, infrared thermal imagers, etc.), which captures data from multiple angles and levels to provide comprehensive information on the occurrence of the fault. During this process, the target detection device outputs the real-time fault node (i.e., the specific location where the fault occurs) and the real-time fault type (i.e., the specific nature of the fault, such as overload, short circuit, poor contact, etc.) to the system terminal. The system terminal will make judgments based on this information and take appropriate follow-up measures, such as alarms and maintenance scheduling, for subsequent decision-making, ensuring that sufficient information support can be provided for subsequent maintenance and optimization.

[0045] Furthermore, the fault identification module includes:

[0046] A detection mode activation unit is used to activate a multimodal joint detection mode according to the functions of the K sample devices, wherein the multimodal joint detection mode includes K single-modal detection modes; a first fault identification unit is used to adopt the multimodal joint detection mode to drive the target detection device to perform fault holographic identification in the fault detection interval.

[0047] In a preferred embodiment, the detection mode activation unit activates the corresponding multimodal joint detection mode based on K sample device functions. Here, the K sample device functions are based on the previous demand inversion and device matching process. These device functions may include infrared thermal imaging, ultrasonic detection, vibration monitoring, etc., and each device function is suitable for different types of fault detection. The multimodal joint detection mode is achieved by combining multiple single-modal detection modes, each of which represents an independent detection technology. For example, in a fault diagnosis task, an infrared thermal imaging mode may be used to identify temperature anomalies, and an ultrasonic detection mode may also be used to identify mechanical faults such as poor contact. By combining these single-modal modes, the system terminal ensures multi-angle and all-round detection of faults. After activating the multimodal joint detection mode, the first fault identification unit drives the target detection device to perform holographic identification. Holographic identification refers to the comprehensive diagnosis and analysis of the fault area by combining multiple detection methods and information. During this process, the target detection device will perform different detection tasks within the fault detection interval and use the activated single-modal detection mode to conduct a detailed analysis of the device status. For example, the device may first use the ultrasonic mode to locate the fault, and then use the infrared mode to collect data in the location area. This data is then input into the fault type discriminator for analysis. The fault type discriminator is constructed in a similar manner to the above. The device will perform effective diagnosis within the fault detection interval based on the multimodal joint detection mode. Multimodal detection can simultaneously capture different types of fault signals (such as temperature, pressure, vibration, etc.), thereby providing a more comprehensive and accurate fault identification result. Through this joint detection, the risk of missed detection and misjudgment can be reduced, and the accuracy of overall fault diagnosis can be improved.

[0048] In summary, the bus duct detection equipment motion control system provided by the embodiments of the present application has the following technical effects:

[0049] A monitoring configuration module 11 is used to configure multi-dimensional monitoring coverage of the bus distribution network according to the monitoring scenario to obtain a multi-dimensional sensor array; a fault framing module 12 is used for the inspection control center to preliminarily frame the fault range according to the multi-dimensional monitoring timing array sent back by the multi-dimensional sensor array to obtain a fault detection interval, wherein the fault detection interval is identified by real-time characteristic parameters; a device matching module 13 is used to match hot standby detection devices according to the fault detection interval and real-time characteristic parameters to obtain multiple real-time detection devices; a strategy optimization module 14 is used to optimize the movement strategy according to the fault detection interval and multiple real-time device positions of multiple real-time detection devices to screen and obtain a target multi-modal diagnostic path for the target detection device; a fault identification module 15 is used to drive the target detection device to perform fault holographic identification in the fault detection interval according to the real-time characteristic parameters after controlling the target detection device to move to the fault detection interval using the target multi-modal diagnostic path, and output a real-time fault node and real-time fault type. Through the above steps, the technical problem of incomplete coverage of a single detection method due to the complex structure of the bus duct, resulting in inaccurate fault area positioning and low detection efficiency, was solved. The technical effect of effectively improving the positioning accuracy of the fault area and realizing efficient and accurate fault holographic identification was achieved through multi-sensor data fusion, dynamic path planning and intelligent equipment switching.

[0050] Embodiment 2 is based on the same inventive concept as the motion control system of the bus duct detection equipment in the above embodiment. Figure 2 As shown, an embodiment of the present application provides a motion control method for a bus duct detection device, the method comprising:

[0051] According to the monitoring scenario, a multi-dimensional monitoring coverage configuration is performed on the bus distribution network to obtain a multi-dimensional sensor array; the inspection control center preliminarily frames the fault range based on the multi-dimensional monitoring timing array sent back by the multi-dimensional sensor array to obtain a fault detection interval, wherein the fault detection interval is identified by real-time characteristic parameters; hot standby detection equipment is matched according to the fault detection interval and real-time characteristic parameters to obtain multiple real-time detection equipment; a mobile strategy is optimized according to the fault detection interval and multiple real-time device positions of multiple real-time detection equipment to screen and obtain a target multi-modal diagnostic path for the target detection equipment; after the target detection equipment is controlled to move to the fault detection interval by adopting the target multi-modal diagnostic path, the target detection equipment is driven to perform fault holographic identification in the fault detection interval according to the real-time characteristic parameters to output a real-time fault node and a real-time fault type.

[0052] Furthermore, the method includes:

[0053] The detection demand is reversed according to the real-time characteristic parameters, and the functional characteristics of the detection equipment are matched according to the reversed results; according to the functional characteristics of the detection equipment and the fault detection interval, hot standby detection equipment is matched in the bus distribution network to obtain multiple real-time detection devices.

[0054] Furthermore, the method includes:

[0055] A history record calling unit is used to locally call the historical detection records of the bus distribution network; based on the characteristic parameter-fault type mapping characteristics, the historical detection records are clustered to obtain multiple sample fault type sets of multiple sample characteristic parameters; according to the fault coincidence relationship of the multiple sample fault type sets, a fault parameter association topology is constructed based on the multiple sample characteristic parameters and the multiple sample fault type sets; the real-time characteristic parameters are input into the fault parameter association topology to reversely infer the fault type to obtain K associated fault types; according to the K associated fault types, the detection requirements are reversed to obtain K sample equipment functions, wherein the K sample equipment functions constitute the functional characteristics of the detection equipment.

[0056] Furthermore, the method includes:

[0057] A multimodal joint detection mode is activated according to the K sample device functions, wherein the multimodal joint detection mode includes K single-modal detection modes; and the multimodal joint detection mode is used to drive the target detection device to perform fault holographic identification in the fault detection interval.

[0058] Furthermore, the method includes:

[0059] According to the fault detection interval and multiple real-time device positions of multiple real-time detection devices, diagnostic path fitting is performed in the bus distribution network to obtain multiple fault diagnostic paths; and by performing mobile strategy evaluation on the multiple fault diagnostic paths, a target multimodal diagnostic path of the target detection device is screened.

[0060] Furthermore, the method includes:

[0061] The first fault diagnosis path is decomposed according to the path vector switching to obtain M local diagnostic paths; based on the path space characteristics, the M local diagnostic paths are evaluated in stages for movement strategies to obtain a first movement strategy coefficient; and similarly, the movement strategies of the multiple fault diagnosis paths are evaluated to obtain multiple movement strategy coefficients; the target multimodal diagnostic path is obtained by screening the multiple fault diagnosis paths according to the multiple movement strategy coefficients, and the target detection device is located according to the path device correspondence.

[0062] Furthermore, the method includes:

[0063] Perform motion pattern matching on the M local diagnostic paths to obtain M local motion patterns; perform motion switching analysis on the M local motion patterns to output M motion pattern features; after loading the M local diagnostic paths and the M motion pattern features into a motion energy consumption prediction model to perform control energy consumption analysis to obtain M local energy consumption features, obtain a first energy consumption feature by adding the M local energy consumption features; perform motion control switching statistics based on the M local motion patterns to obtain a first switching feature; calculate a first time-consuming feature based on the M local diagnostic paths and the M motion pattern features; based on a preset evaluation rule, weightedly fuse the first energy consumption feature, the first switching feature and the first time-consuming feature as the first mobile strategy coefficient.

[0064] Furthermore, the method includes:

[0065] The motion pattern features include a sample motion pattern and a sample motion speed.

[0066] Furthermore, the method includes:

[0067] Interactively obtain the standard inspection speed of the bus duct detection equipment; calculate the fault detection response scale based on the fault detection response window of the bus distribution network and the standard inspection speed; take the fault detection interval as the starting point, select the hot standby detection equipment according to the fault detection response scale to obtain multiple alternative detection equipment; traverse the multiple device function descriptions of the multiple alternative detection equipment by using the functional characteristics of the detection equipment to match the hot standby detection equipment and obtain the multiple real-time detection equipment.

[0068] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0069] Furthermore, the terms "first" or "second" as described above may not only represent an order relationship but may also represent a specific concept and / or refer to the selectability of multiple elements individually or collectively. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, if such modifications and variations fall within the scope of this application and its equivalents, this application is intended to include such modifications and variations.

Claims

1. The bus duct detection equipment motion control system is characterized by: The system comprises: A monitoring configuration module is used to configure multi-dimensional monitoring coverage of the bus distribution network according to the monitoring scenario to obtain a multi-dimensional sensor array; A fault framing module is used by the inspection control center to preliminarily frame the fault range based on the multi-dimensional monitoring time series array transmitted back by the multi-dimensional sensing array, and obtain a fault detection interval, wherein the fault detection interval is identified by real-time characteristic parameters; A device matching module, configured to match hot standby detection devices according to the fault detection interval and real-time characteristic parameters to obtain multiple real-time detection devices; a strategy optimization module, configured to optimize a mobile strategy based on the fault detection interval and multiple real-time device positions of multiple real-time detection devices, so as to screen and obtain a target multimodal diagnostic path for a target detection device; A fault identification module is used to drive the target detection device to perform fault holographic identification in the fault detection interval according to the real-time characteristic parameters after controlling the target detection device to move to the fault detection interval using the target multimodal diagnostic path, and output a real-time fault node and a real-time fault type.

2. The bus duct detection equipment motion control system according to claim 1, characterized in that: The device matching module includes: A first demand inverse inference unit is configured to inversely infer detection requirements based on the real-time characteristic parameters, and match detection equipment functional characteristics based on the inverse inference results; The first device matching unit is used to match hot standby detection devices in the bus distribution network according to the functional characteristics and fault detection interval of the detection devices to obtain multiple real-time detection devices.

3. The bus duct detection equipment motion control system according to claim 2, characterized in that: The first demand reverse estimation unit includes: A history record calling unit, used for locally calling the historical detection records of the bus distribution network; A historical record clustering unit is used to cluster the historical detection records based on feature parameter-fault type mapping features to obtain multiple sample fault type sets of multiple sample feature parameters; a correlation topology construction unit, configured to construct a fault parameter correlation topology according to the fault coincidence relationship of the plurality of sample fault type sets, the plurality of sample characteristic parameters and the plurality of sample fault type sets; A fault inverse inference unit, configured to input the real-time characteristic parameters into the fault parameter association topology to perform fault type inverse inference, and obtain K associated fault types; The second demand inverse deduction unit is configured to perform detection demand inverse deduction based on the K associated fault types to obtain K types of sample device functions, wherein the K types of sample device functions constitute the functional characteristics of the detection device.

4. The bus duct detection equipment motion control system according to claim 3, characterized in that: The fault identification module includes: a detection mode activation unit, configured to activate a multimodal joint detection mode according to the K sample device functions, wherein the multimodal joint detection mode includes K single-modal detection modes; The first fault identification unit is configured to drive the target detection device to perform fault holographic identification in the fault detection interval by adopting the multimodal joint detection mode.

5. The bus duct detection equipment motion control system according to claim 1, characterized in that: The strategy optimization module includes: a diagnostic path fitting unit, configured to perform diagnostic path fitting on the bus distribution network according to the fault detection interval and multiple real-time device positions of multiple real-time detection devices, to obtain multiple fault diagnostic paths; The first movement strategy evaluation unit is configured to perform movement strategy evaluation on the multiple fault diagnosis paths to screen and obtain a target multimodal diagnosis path of the target detection device.

6. The bus duct detection equipment motion control system according to claim 5, characterized in that: The first mobile strategy evaluation unit includes: a diagnostic path decomposition unit, configured to decompose the first fault diagnostic path according to the path vector switching to obtain M local diagnostic paths; a second movement strategy evaluation unit, configured to perform a phased movement strategy evaluation on the M local diagnostic paths based on the path space characteristics to obtain a first movement strategy coefficient; a third movement strategy evaluation unit, configured to perform movement strategy evaluation on the plurality of fault diagnosis paths in a similar manner to obtain a plurality of movement strategy coefficients; The diagnostic path screening unit is configured to screen the target multimodal diagnostic path from the multiple fault diagnostic paths according to the multiple movement strategy coefficients, and locate the target detection device according to the path-device correspondence relationship.

7. The bus duct detection equipment motion control system according to claim 6, characterized in that: The second mobile strategy evaluation unit includes: a motion pattern matching unit, configured to perform motion pattern matching on the M local diagnostic paths to obtain M local motion patterns; a motion switching analysis unit, configured to perform motion switching analysis on the M local motion patterns and output M motion pattern features; a control energy consumption analysis unit, configured to load the M local diagnostic paths and the M motion pattern features into a motion energy consumption prediction model to perform control energy consumption analysis, obtain M local energy consumption features, and then obtain a first energy consumption feature by summing the M local energy consumption features; a switching statistics unit, configured to perform motion control switching statistics according to the M local motion patterns to obtain a first switching feature; a time-consuming feature calculation unit, configured to calculate a first time-consuming feature based on the M local diagnostic paths and the M motion pattern features; A feature weighting unit is used to weight and fuse the first energy consumption feature, the first switching feature and the first time consumption feature based on a preset evaluation rule as the first movement strategy coefficient.

8. The bus duct detection equipment motion control system according to claim 7, characterized in that: The motion pattern features include a sample motion pattern and a sample motion speed.

9. The bus duct detection equipment motion control system according to claim 2, characterized in that: The first device matching unit includes: Detection equipment interaction unit, used to interactively obtain the standard inspection speed of bus duct detection equipment; a response scale calculation unit, configured to calculate a fault detection response scale based on the fault detection response window of the bus distribution network and the standard inspection speed; a device selection unit, configured to select a hot standby detection device based on the fault detection response scale and taking the fault detection interval as a starting point, to obtain a plurality of candidate detection devices; The second device matching unit is configured to traverse multiple device function descriptions of the multiple candidate detection devices by using the detection device function characteristics to perform hot standby detection device matching to obtain the multiple real-time detection devices.

10. A motion control method for bus duct detection equipment, characterized in that: The method is performed by the bus duct detection equipment motion control system according to any one of claims 1 to 9, comprising: Perform multi-dimensional monitoring coverage configuration on the bus distribution network according to the monitoring scenario to obtain a multi-dimensional sensor array; The inspection control center preliminarily defines the fault range based on the multi-dimensional monitoring time series array transmitted back by the multi-dimensional sensor array, and obtains a fault detection interval, wherein the fault detection interval is identified by a real-time characteristic parameter; Matching hot standby detection devices according to the fault detection interval and real-time characteristic parameters to obtain multiple real-time detection devices; Optimizing a movement strategy based on the fault detection interval and multiple real-time device positions of multiple real-time detection devices to screen and obtain a target multimodal diagnostic path for a target detection device; After the target multimodal diagnostic path is used to control the target detection device to move to the fault detection interval, the target detection device is driven to perform fault holographic identification in the fault detection interval according to the real-time characteristic parameters, and the real-time fault node and real-time fault type are output.

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