Secondary comprehensive intelligent hunting system applying multi-scene cable positioning

Abstract

The invention discloses a secondary comprehensive intelligent hunting system applying multi-scene cable positioning, and relates to the technical field of electrical variable measurement, and the system comprises a signal identification processing module which is used for generating and modulating signals, adapting to cable characteristics of different scenes, and establishing signal transmission links with target cables of different scenes through adapting to various interfaces; and the sensing classification positioning module is used for sensing signals sent by the cables in real time, searching the positions of the cables according to electromagnetic field changes of the cables, processing data of a plurality of signal sources at the same time, identifying state information and potential faults of each cable through signal processing analysis, and generating sensing diagnosis information. According to the invention, cables can be efficiently and accurately positioned and fault diagnosis can be carried out in various complex application scenarios, and stable signal links can be established with different types of cables through flexible adaptation and multi-interface support, so that effective work in various environments can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of measuring electrical variables, and in particular to a secondary integrated intelligent wire tracing system for multi-scenario cable positioning. Background Art

[0002] As an efficient and fast cable detection tool, a wire tracer is widely used in the construction and daily maintenance of network cables, communication cables, and various metal lines. Especially in scenarios where it is necessary to quickly find the target cable from a large number of wire harnesses, it plays an indispensable and important role.

[0003] The advantage of the wire tracer is that it does not require damaging the insulation layer of the cable and can sense signals by detecting different parts of the cable outer skin surface. Therefore, it is particularly suitable for complex cable wiring environments, such as cable bundles, under carpets, inside decorative walls, or on ceilings. Through this principle, the wire tracer can accurately locate the target cable and its possible break point positions, greatly improving the efficiency and accuracy of line maintenance and detection.

[0004] However, in the actual applications of secondary equipment maintenance and line connection confirmation, the existing wire tracers face certain technical and application scenario challenges. Most of the wire tracers on the market currently have relatively simple circuits and single functions, and are usually only applicable to specific scenarios, making it difficult to meet complex and variable work requirements. For example, many devices are large in size and have poor interactivity, resulting in inconvenient operation and low efficiency for users. In some specific scenarios, especially in the secondary safety measures scenario, when one side is a common active terminal, the existing wire tracers cannot effectively respond, and the existing technologies have not provided effective solutions. Due to multiple cables being connected in parallel to the common terminal on-site, in this situation, traditional wire tracing tools are difficult to accurately identify the target cable or its break point, and usually can only rely on manual trial-and-error methods for troubleshooting, which is not only time-consuming and laborious, but also may lead to serious safety accidents and economic losses if there are misoperations.

[0005] In addition, wire tracers commonly using traditional operation methods such as knobs on the market have problems of unsmooth interaction and poor user experience. Especially in high-voltage and complex electrical maintenance environments, the method of manually adjusting the knob is both laborious and inefficient, making it difficult to meet the requirements of rapid positioning.

[0006] Therefore, the existing wire tracers cannot fully meet the needs of complex scenarios such as secondary equipment maintenance. The single function and cumbersome operation make them show obvious deficiencies in some high-demand environments. Summary of the Invention

[0007] Based on this, it is necessary to provide a secondary integrated intelligent wire tracing system for multi-scenario cable positioning in view of the above technical problems.

[0008] The present invention provides a secondary comprehensive intelligent wire tracing system for multi-scenario wire positioning, which includes:

[0009] A signal recognition and processing module, which is used to generate a modulated signal, adapt to the characteristics of wires in different scenarios, and establish a signal transmission link with the target wires in different scenarios by adapting multiple interfaces;

[0010] An induction classification and positioning module, which is used to sense the signals emitted by wires in real time, find the positions of wires according to the electromagnetic field changes of the wires, and simultaneously process the data of multiple signal sources. Through signal processing and analysis, it identifies the status information and potential faults of each wire, and generates sensing and diagnostic information;

[0011] A scenario adaptation and switching module, which is used to provide multiple working modes that meet different application scenarios, and automatically adjusts the wire tracing strategy by identifying the current working environment and wire type;

[0012] A remote control and interaction module, which is used to provide a touch display screen and a communication interface, and is operated through the touch screen, voice control and intelligent terminal, and can be monitored and adjusted through a remote monitoring device;

[0013] A storage analysis and management module, which is used to store and analyze historical detection data, fault diagnosis data and wire position information in real time. Through retrospective analysis, it provides data support for later troubleshooting and maintenance;

[0014] A power management and maintenance module, which is responsible for intelligent power management, automatically detects the battery power and adjusts the power consumption to extend the working time of the system.

[0015] Further, the signal recognition and processing module includes: a signal generation unit, a signal modulation and distribution unit, and an interface adaptation unit;

[0016] Among them, the signal generation unit is used to generate different types of signals, adapt to different wire characteristics, and introduce an adaptive signal modulation technology to adjust the signal frequency and intensity according to the real-time detected environmental noise and signal attenuation;

[0017] The signal modulation and distribution unit is used to maintain the non-interference of signals between multiple wires through frequency distribution and signal modulation, and optimize the signal transmission path;

[0018] The interface adaptation unit is used to adopt an intelligent interface self-adaptation technology to automatically identify the type of connected wire, adjust the signal interface and adapter, and be compatible with wires in various scenarios.

[0019] Further, the induction classification and positioning module includes: a signal sensing unit, a classification and filtering unit, a wire positioning unit, a status recognition unit, and a diagnostic analysis unit;

[0020] Among them, the signal sensing unit is used to utilize a highly sensitive sensor to sense the electromagnetic signal emitted by the target cable in real time, convert it into a readable data format, and mark it as a sensed signal;

[0021] The classification and filtering unit is used to classify the various sensed signals obtained by sensing by using a classifier, identify the types of different signal sources, and synthesize the sensed signals from multiple signal sensing units through a signal fusion algorithm to form a multi-source fusion signal;

[0022] The cable positioning unit is used to calculate the position of the cable by measuring the electromagnetic signal strength and signal transmission time emitted by the cable, generate spatial coordinates, and combine the real-time positioning data to use a path inference algorithm to draw the route of the cable in real time;

[0023] The status recognition unit is used to analyze the working status of the cable in real time according to the multi-source fusion signal of a single cable from multiple signal sensing units and identify potential faults;

[0024] The diagnostic analysis unit is used to deeply process the multi-source fusion signal of the cable to generate a diagnostic report for each cable.

[0025] Furthermore, calculating the position of the cable by measuring the electromagnetic signal strength and signal transmission time emitted by the cable, using a three-dimensional positioning algorithm to generate spatial coordinates, and combining the real-time positioning data to use a path inference algorithm to draw the route of the cable in real time includes:

[0026] Utilizing a sensor to obtain the electromagnetic signal strength and signal transmission time emitted by the cable, calculating the distance of the cable through the signal strength and signal transmission time; and combining multi-dimensional data, using multiple sensors to measure the propagation time and signal strength of the electromagnetic signal, calculating the distances from multiple sensors to the cable, and using three-dimensional geometry to infer the coordinates of the cable to obtain the three-dimensional spatial coordinates of the cable;

[0027] Based on the three-dimensional spatial coordinates of the cable, combining real-time positioning technology, using the least squares method for path inference, according to the path inference result, drawing the route of the cable in three-dimensional space, and using different colors and marks to distinguish the routes of each cable.

[0028] Furthermore, the calculation formula for calculating the distance of the cable through the signal strength and signal transmission time is:

[0029] ;

[0030] In the formula, d represents the distance from the sensor to the cable; d 0 represents the reference distance; P r (d) represents the received signal strength; P t (d 0 ) represents the reference distance d0 The received signal strength at [location]; α represents the path loss coefficient.

[0031] Furthermore, based on the multi-source fusion signals of multiple signal sensing units for a single cable, the working state of the cable is analyzed in real time, and potential faults are identified, including:

[0032] Obtain the multi-source fusion signals corresponding to each cable, perform denoising processing using a filtering algorithm, and perform normalization processing on the denoised multi-source fusion signals;

[0033] Use the maximum mutual information coefficient to reduce the dimension of the multi-source features of the multi-source fusion signals. By calculating the maximum mutual information coefficient between various signal features within the multi-source fusion signals, select the features most relevant to the fault type as the input features;

[0034] Input the extracted input features into a deep belief network, perform training and learning using a preset training dataset, and optimize the hyperparameters of the deep belief network through an improved Archimedes optimization algorithm to obtain a trained and optimized deep belief network as the fault diagnosis model;

[0035] Input the multi-source fusion signals of the cable collected in real time into the fault diagnosis model, output the probability distribution of various fault types, and judge the potential faults existing in the cable.

[0036] Furthermore, optimizing the hyperparameters of the deep belief network through an improved Archimedes optimization algorithm to obtain a trained and optimized deep belief network as the fault diagnosis model includes:

[0037] Initialize the initial parameters of the Archimedes optimization algorithm and the hyperparameters of the deep belief network, set a search space for each hyperparameter, and the hyperparameters include the number of network layers, the number of neurons in each layer, and the learning rate;

[0038] Randomly generate multiple solutions, each solution corresponding to a set of hyperparameter combinations. Use the solutions to form an initial population, and the fitness of each solution is evaluated according to the performance of the deep belief network;

[0039] In each iteration process, use the global search mechanism and the local search mechanism to continuously update the solutions, optimize the hyperparameter combinations, and calculate the new fitness after each update;

[0040] Until the termination condition is met, stop the iteration and output the hyperparameter combination corresponding to the current optimal solution.

[0041] Furthermore, the calculation formula for fitness is:

[0042] ;

[0043] Wherein, Fitness(x) represents the fitness; Loss(x) represents the corresponding loss value;

[0044] The expression of the loss value is:

[0045] ;

[0046] Wherein, N represents the number of fault type labels; y i represents the true label; represents the predicted label.

[0047] Furthermore, the scenario adaptation switching module includes: an environment perception unit, a cable identification unit, a mode switching unit, a mode optimization unit, and a guidance assistance unit;

[0048] Among them, the environment perception unit is used to identify the current working environment and its characteristics;

[0049] The cable identification unit is used to identify different types of cables, select corresponding signal generation and processing modes according to different cable types, and analyze the electrical characteristics and material characteristics of the cables;

[0050] The mode switching unit is used to automatically select and execute the corresponding working mode according to the identified working environment and cable type;

[0051] The mode optimization unit is used to dynamically adjust the wire tracing strategy according to the actual working state of the cable, environmental changes, and identification results under each working mode. The wire tracing strategy includes transmission power, transmission frequency, and scanning speed;

[0052] The guidance assistance unit is used to provide a display interface and can manually select the mode switching function.

[0053] The beneficial effects of the present invention are as follows:

[0054] 1. The present invention can efficiently and accurately locate cables and perform fault diagnosis in a variety of complex application scenarios. By flexibly adapting and supporting multiple interfaces, a stable signal link can be established with different types of cables to ensure effective operation in various environments. It can intelligently adjust the working mode, automatically optimize the wire tracing strategy according to the environment and cable type, and improve the adaptability of the system in different application scenarios; and it supports touch screen, voice control, and remote monitoring, making the system operation more convenient, thus having significant innovations in improving wire tracing efficiency, fault diagnosis accuracy, operation convenience, and system reliability, and meeting the complex and changeable on-site requirements.

[0055] 2. By using a highly sensitive sensor to capture the electromagnetic signals emitted by the cables in real time, the accuracy and efficiency of signal perception are ensured. Through a classifier and a signal fusion algorithm, multi-source signals from different cables are effectively distinguished and synthesized, enhancing the reliability and accuracy of the signals. By measuring the signal strength and transmission time and applying a three-dimensional positioning algorithm, the position of the cable can be calculated in real time, generating accurate spatial coordinates. The cable routing can be plotted in real time, especially suitable for identifying the position and routing of cables in complex scenarios, enhancing the timeliness and accuracy of fault identification, and optimizing the maintenance and management of power systems and communication networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings described herein are used to provide a further understanding of the present invention and form a part of the present invention. The illustrative embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0057] Figure 1 is a schematic block diagram of a secondary comprehensive intelligent wire tracing system for multi-scenario cable positioning according to an embodiment of the present invention.

[0058] Reference numerals in the drawings: 1, signal recognition and processing module; 2, induction classification and positioning module; 3, scenario adaptation and switching module; 4, remote control and interaction module; 5, storage analysis and management module; 6, power management and maintenance module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] Please refer to Figure 1 , a secondary comprehensive intelligent wire tracing system for multi-scenario cable positioning, the system includes:

[0061] A signal recognition and processing module 1, configured to generate and modulate signals, adapt to the cable characteristics of different scenarios, and establish a signal transmission link with the target cables in different scenarios through adapting multiple interfaces.

[0062] In the description of the present invention, the signal recognition and processing module 1 includes: a signal generation unit, a signal modulation and distribution unit, and an interface adaptation unit.

[0063] The signal generation unit is configured to generate different types of signals (such as low frequency, high frequency, pulse, etc.), adapt to different cable characteristics (such as power lines, communication lines, control lines, etc.), and introduce an adaptive signal modulation technology to adjust the signal frequency and intensity according to the ambient noise and signal attenuation detected in real time.

[0064] A signal modulation and distribution unit, which is used to maintain non-interference between signals of multiple cables through frequency allocation and signal modulation, and optimize the signal transmission path.

[0065] An interface adaptation unit, which is used to adopt intelligent interface self-adaptive technology to automatically identify the type of connected cable, adjust the signal interface and adapter, and be compatible with cables in various scenarios. This unit can provide multiple interfaces, such as RJ45, RJ11, BNC interfaces, etc., to ensure that the device can adapt to different cable types and establish a stable signal transmission link.

[0066] An induction classification and positioning module 2, which is used to sense the signals emitted by cables in real time, find the positions of cables according to the electromagnetic field changes of the cables, and simultaneously process the data of multiple signal sources. Through signal processing and analysis, it identifies the status information and potential faults of each cable, and generates sensing and diagnostic information.

[0067] In the description of the present invention, the induction classification and positioning module 2 includes: a signal sensing unit, a classification and filtering unit, a cable positioning unit, a status recognition unit, and a diagnostic analysis unit.

[0068] The signal sensing unit is used to use a high-sensitivity sensor to sense the electromagnetic signals emitted by the target cable in real time, convert them into a readable data format, and mark them as sensed signals.

[0069] The classification and filtering unit is used to classify the multiple sensed signals obtained by sensing by using a classifier, identify the types of different signal sources, and synthesize the sensed signals from multiple signal sensing units through a signal fusion algorithm to form a multi-source fusion signal.

[0070] The cable positioning unit is used to calculate the position of the cable by measuring the electromagnetic signal intensity and signal transmission time of the cable, generate spatial coordinates, and combine the real-time positioning data to use the path inference algorithm to draw the trend of the cable in real time.

[0071] Specifically, high-sensitivity electromagnetic sensors, such as Hall effect sensors, magnetic field sensors, or superconducting quantum interference devices (SQUIDs), are used to sense the electromagnetic fields generated by the current in the cable. These sensors can accurately detect weak electromagnetic waves or magnetic field changes in complex environments, especially for cables buried underground or in walls, with high sensitivity.

[0072] For obstacles that are difficult for electromagnetic signals to penetrate, acoustic wave sensing technology (such as ultrasonic sensors) can be used to locate the cable by generating and sensing acoustic wave signals. This method is especially suitable for complex environments (such as underground pipelines, inside walls, etc.) and can still work effectively in a strong noise environment.

[0073] In the description of the present invention, by measuring the electromagnetic signal intensity and signal transmission time emitted by the cable, using a three-dimensional positioning algorithm to calculate the position of the cable, generating spatial coordinates, and combining real-time positioning data, using a dead reckoning algorithm to real-time draw the cable route includes:

[0074] S11. Obtain the electromagnetic signal intensity and signal transmission time emitted by the cable using a sensor, calculate the distance of the cable through the signal intensity and signal transmission time. And combine multi-dimensional data, measure the propagation time and signal intensity of the electromagnetic signal using multiple sensors, calculate the distances from multiple sensors to the cable, and use three-dimensional geometry to deduce the coordinates of the cable to obtain the three-dimensional spatial coordinates of the cable.

[0075] In the description of the present invention, the calculation formula for calculating the distance of the cable through the signal intensity and signal transmission time is:

[0076] ;

[0077] In the formula, d represents the distance from the sensor to the cable; d 0 represents the reference distance; P r (d) represents the received signal strength; P t (d 0 ) represents the received signal strength at the reference distance d 0 ; α represents the path loss coefficient.

[0078] S12. Based on the three-dimensional spatial coordinates of the cable, combine real-time positioning technology, use the least squares method for dead reckoning, according to the dead reckoning result, draw the cable route in three-dimensional space, and use different colors and marks to distinguish the routes of each cable.

[0079] A state recognition unit for real-time analyzing the working state of the cable and identifying potential faults according to the multi-source fusion signals of a single cable from multiple signal sensing units.

[0080] In the description of the present invention, real-time analyzing the working state of the cable and identifying potential faults according to the multi-source fusion signals of a single cable from multiple signal sensing units includes:

[0081] S21. Obtain the multi-source fusion signals corresponding to each cable, perform denoising processing using a filtering algorithm, and perform normalization processing on the denoised multi-source fusion signals.

[0082] S22. Use the maximum mutual information coefficient to reduce the dimension of the multi-source features of the multi-source fusion signals, select the features most relevant to the fault type by calculating the maximum mutual information coefficients between various signal features in the multi-source fusion signals as input features.

[0083] S23. Input the extracted input features into the deep belief network, perform training and learning using a preset training data set, and optimize the hyperparameters of the deep belief network through an improved Archimedes optimization algorithm to obtain a trained and optimized deep belief network as the fault diagnosis model.

[0084] In the description of the present invention, optimizing the hyperparameters of the deep belief network through an improved Archimedes optimization algorithm to obtain a trained and optimized deep belief network as the fault diagnosis model includes:

[0085] S231. Initialize the initial parameters of the Archimedes optimization algorithm and the hyperparameters of the deep belief network, set a search space for each hyperparameter, and the hyperparameters include the number of network layers, the number of neurons in each layer, and the learning rate.

[0086] S232. Randomly generate multiple solutions, each solution corresponding to a set of hyperparameter combinations, use the solutions to form an initial population, and evaluate the fitness of each solution according to the performance of the deep belief network.

[0087] S233. In each iteration process, continuously update the solutions using the global search mechanism and the local search mechanism, optimize the hyperparameter combinations, and calculate the new fitness after each update.

[0088] In the description of the present invention, the calculation formula for fitness is:

[0089] ;

[0090] In the formula, Fitness(x) represents the fitness. Loss(x) represents the corresponding loss value.

[0091] The expression for the loss value is:

[0092] ;

[0093] In the formula, N represents the number of fault type labels; y i represents the true label; represents the predicted label.

[0094] S234. Until the termination condition is met, stop the iteration and output the hyperparameter combination corresponding to the current optimal solution.

[0095] S24. Input the multi-source fusion signal of the cable collected in real time into the fault diagnosis model, output the probability distribution of various fault types, and judge the potential faults existing in the cable.

[0096] That is, classify the input features through the optimized DBN model, identify the state and potential fault types of the cable. The output results include the fault type (such as overload, short circuit, open circuit, aging, etc.) and the fault location.

[0097] A diagnostic analysis unit for deeply processing the multi-source fusion signals of cables and generating diagnostic reports for each cable.

[0098] A scenario adaptation and switching module 3 for providing multiple working modes that meet different application scenarios, and automatically adjusting the cable tracing strategy by identifying the current working environment and cable type.

[0099] In the description of the present invention, the scenario adaptation and switching module 3 includes: an environment perception unit, a cable identification unit, a mode switching unit, a mode optimization unit, and a guidance assistance unit.

[0100] An environment perception unit for identifying the current working environment and its characteristics.

[0101] A cable identification unit for identifying different types of cables, selecting corresponding signal generation and processing modes according to different cable types, and analyzing the electrical characteristics and material characteristics of the cables.

[0102] Specifically, it can automatically identify different types of cables, including power cables, communication cables, fiber optic cables, signal cables, etc., and select appropriate signal generation and processing modes according to different cable types. This unit can analyze the electrical characteristics of the cables (such as resistance, conductivity, etc.), material characteristics (such as copper wire, aluminum wire, etc.), and even identify the external characteristics of the cables through visual recognition technology.

[0103] A mode switching unit for automatically selecting and executing the corresponding working mode according to the identified working environment and cable type.

[0104] A mode optimization unit for dynamically adjusting the cable tracing strategy according to the actual working state of the cables, environmental changes, and identification results under each working mode. The cable tracing strategy includes transmission power, transmission frequency, and scanning speed.

[0105] Specifically, the common working modes can be divided into the following types, and the corresponding adjustment methods are used to ensure the efficient operation of the system:

[0106] 1. Standard mode

[0107] Applicable scenarios: Common environments with less signal interference and good cable conditions.

[0108] 2. Enhanced mode

[0109] Applicable scenarios: Environments with weak signals or a long distance from the signal source.

[0110] 3. Anti-interference mode

[0111] Applicable scenarios: Environments with a large amount of electromagnetic interference (such as near industrial equipment, power facilities, etc.).

[0112] 4. Deep Penetration Mode

[0113] Applicable Scenarios: Situations where the cable is buried underground or in a wall, requiring stronger signal penetration ability.

[0114] 5. Low Power Consumption Mode

[0115] Applicable Scenarios: When the device is in long-term operation or has limited battery power.

[0116] 6. High-Speed Scanning Mode

[0117] Applicable Scenarios: Emergency scenarios that require quick positioning or situations that need to comprehensively scan a large area.

[0118] The guiding and assisting unit is used to provide a display interface and can manually select the mode switching function.

[0119] The remote control interaction module 4 is used to provide a touch display screen and a communication interface, and can be operated through the touch screen, voice control, and intelligent terminal, and can be monitored and adjusted through a remote monitoring device.

[0120] In the description of the present invention, the remote control interaction module 4 includes: a touch display unit, a voice recognition and control unit, and a remote monitoring and adjustment unit.

[0121] The touch display unit is used to provide a touch display interface, display the cable position, signal strength, and fault information, and allow users to operate and select the corresponding function mode.

[0122] In actual application, the augmented reality (AR) technology or virtual reality (VR) technology can be combined to display the cable position, status, and path information in a three-dimensional graph, and users can interact and view through the touch screen, mouse, or voice command. Through the AR technology, the device can superimpose the virtual cable path and fault points onto the real scene to help technicians accurately locate in a complex environment.

[0123] The voice recognition and control unit is used to accurately identify complex voice commands and make responses in combination with natural language processing technology, and supports multi-language switching.

[0124] The remote monitoring and adjustment unit is used to allow remote devices to monitor, adjust, and control through a wireless network connection, reducing on-site manual operations.

[0125] The storage, analysis, and management module 5 is used to store and analyze historical detection data, fault diagnosis data, and cable position information in real time, and provide data support for later troubleshooting and maintenance through retrospective analysis.

[0126] In the description of the present invention, the module provides data storage and analysis functions, and can store all detection data, cable path diagrams, fault records and other data in real time on a local or cloud platform, supporting data backup and historical data query. Through big data analysis, the system can generate reports such as cable service life prediction, fault frequency analysis, and maintenance suggestions to help management personnel with long-term maintenance planning and decision-making.

[0127] The power management and maintenance module 6 is responsible for intelligent power management, automatically detecting the battery power and adjusting the power consumption to extend the system working time.

[0128] In the description of the present invention, this module is responsible for monitoring and optimizing the power supply of each component of the system to ensure that the device can operate stably in various working environments. It can automatically adjust the power consumption according to the on-site conditions to extend the service life of the device. In addition, for scenarios of long-term continuous use, it also has a low-power mode and an energy-saving mode to ensure the efficient operation of the system.

[0129] In summary, by means of the above technical solutions of the present invention, the present invention can efficiently and accurately locate cables and perform fault diagnosis in a variety of complex application scenarios. Through flexible adaptation and multi-interface support, it can establish a stable signal link with different types of cables to ensure effective operation in various environments. It can intelligently adjust the working mode, automatically optimize the cable tracing strategy according to the environment and cable type, and improve the adaptability of the system in different application scenarios; and it supports touch screen, voice control and remote monitoring, making the system operation more convenient, thus having significant innovations in improving cable tracing efficiency, fault diagnosis accuracy, operation convenience and system reliability, and meeting the complex and changeable on-site requirements.

[0130] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit and can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments, and their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

Claims

1. A secondary integrated intelligent cable-finding system for multi-scenario cable positioning, characterized in that: The system includes: The signal recognition and processing module is used to generate and modulate signals, adapt to the cable characteristics of different scenarios, and establish signal transmission links with target cables in different scenarios by adapting to multiple interfaces; The induction classification and positioning module is used to sense the signals sent by the cables in real time, find the cable locations according to the changes in the electromagnetic fields of the cables, and simultaneously process data from multiple signal sources. Through signal processing and analysis, it identifies the status information and potential faults of each cable and generates perception diagnosis information. The scene adaptation switching module is used to provide a variety of working modes that meet different application scenarios. It automatically adjusts the line-finding strategy by identifying the current working environment and cable type. Remote control interaction module, used to provide touch display screen and communication interface, operated through touch screen, voice control and intelligent terminal, and can be monitored and adjusted through remote monitoring equipment; The storage analysis management module is used to store and analyze historical detection data, fault diagnosis data and cable location information in real time, and provide data support for later troubleshooting and maintenance through retrospective analysis; The power management and maintenance module is responsible for intelligent power management, automatically detecting battery power and adjusting power consumption to extend system working time.

2. According to the secondary integrated intelligent cable locating system for multi-scenario cable positioning according to claim 1, it is characterized in that: The signal identification and processing module includes: a signal generation unit, a signal modulation and distribution unit and an interface adaptation unit; The signal generation unit is used to generate different types of signals, adapt according to different cable characteristics, and introduce adaptive signal modulation technology to adjust the signal frequency and strength according to the real-time detected environmental noise and signal attenuation; The signal modulation and distribution unit is used to maintain the signals between the multiple cables from interfering with each other and optimize the signal transmission path through frequency distribution and signal modulation; The interface adapter unit is used to adopt intelligent interface adaptive technology to automatically identify the type of connected cables, adjust the signal interface and the adapter, and be compatible with cables in various scenarios.

3. According to the secondary integrated intelligent cable locating system for multi-scenario cable positioning according to claim 1, it is characterized in that: The sensing classification and positioning module includes: a signal sensing unit, a classification and filtering unit, a cable positioning unit, a state recognition unit and a diagnosis and analysis unit; The signal sensing unit is used to sense the electromagnetic signal emitted by the target cable in real time using a sensor, and convert it into a readable data format, marked as a sensing signal; The classification and filtering unit is used to classify the multiple perception signals obtained by perception using a classifier, identify the types of different signal sources, and synthesize the perception signals from the multiple signal perception units through a signal fusion algorithm to form a multi-source fusion signal; The cable positioning unit is used to calculate the position of the cable by measuring the electromagnetic signal strength and signal transmission time emitted by the cable, using a three-dimensional positioning algorithm to generate spatial coordinates, and in combination with real-time positioning data, using a path calculation algorithm to draw the direction of the cable in real time; The state recognition unit is used to analyze the working state of the cable in real time and identify potential faults according to the multi-source fusion signals of a single cable from the multiple signal sensing units; The diagnostic analysis unit is used to perform in-depth processing on the multi-source fusion signal of the cable and generate a diagnostic report for each cable.

4. According to claim 3, a secondary integrated intelligent cable-finding system for multi-scenario cable positioning is characterized in that: The method of measuring the electromagnetic signal strength and signal transmission time emitted by the cable, calculating the position of the cable using a three-dimensional positioning algorithm, generating spatial coordinates, and combining the real-time positioning data to draw the direction of the cable in real time using a path calculation algorithm includes: The electromagnetic signal strength and signal transmission time emitted by the cable are obtained by using sensors, and the distance of the cable is calculated by the signal strength and signal transmission time. In combination with multi-dimensional data, multiple sensors are used to measure the propagation time and signal strength of the electromagnetic signal, and the distance from multiple sensors to the cable is calculated. The coordinates of the cable are deduced using three-dimensional geometry to obtain the three-dimensional spatial coordinates of the cable. Based on the three-dimensional spatial coordinates of the cable, combined with real-time positioning technology, the least squares method is used to calculate the path. According to the path calculation results, the direction of the cable is drawn in the three-dimensional space, and different colors and marks are used to distinguish the direction of each cable.

5. According to the secondary integrated intelligent cable-finding system for multi-scenario cable positioning according to claim 4, it is characterized in that: The calculation formula for calculating the distance of the cable by using the signal strength and the signal transmission time is: ； Where d is the distance from the sensor to the cable; d0 is the reference distance; P r (d) indicates the received signal strength; P t (d0) represents the received signal strength at the reference distance d0; α represents the path loss coefficient.

6. According to the secondary integrated intelligent cable-finding system for multi-scenario cable positioning according to claim 3, it is characterized in that: The real-time analysis of the working status of the cable and identification of potential faults based on the multi-source fusion signals of a single cable from the multiple signal sensing units includes: Obtain the multi-source fusion signal corresponding to each cable, perform denoising processing using a filtering algorithm, and perform normalization processing on the denoised multi-source fusion signal; The multi-source features of the multi-source fusion signal are reduced in dimension by using the maximum mutual information coefficient, and the features most relevant to the fault type are selected as input features by calculating the maximum mutual information coefficients between various signal features in the multi-source fusion signal; The extracted input features are input into the deep belief network, and the preset training data set is used for training and learning. The hyperparameters of the deep belief network are optimized by improving the Archimedean optimization algorithm to obtain the trained and optimized deep belief network as a fault diagnosis model. The multi-source fusion signal of the cable collected in real time is input into the fault diagnosis model, and the probability distribution of various fault types is output to determine the potential faults of the cable.

7. The secondary integrated intelligent cable-finding system for multi-scenario cable positioning according to claim 6, characterized in that: The hyperparameters of the deep belief network are optimized by improving the Archimedean optimization algorithm to obtain a trained and optimized deep belief network as a fault diagnosis model including: Initialize the initial parameters of the Archimedean optimization algorithm and the hyperparameters of the deep belief network, and set the search space for each hyperparameter, wherein the hyperparameters include the number of network layers, the number of neurons in each layer, and the learning rate; Randomly generate multiple solutions, each solution corresponds to a set of hyperparameter combinations, use the solutions to form an initial population, and evaluate the fitness of each solution based on the performance of the deep belief network; In each iteration, the global search mechanism and the local search mechanism are used to continuously update the solution, optimize the hyperparameter combination, and calculate the new fitness after each update; Until the termination condition is met, the iteration stops and the hyperparameter combination corresponding to the current optimal solution is output.

8. The secondary integrated intelligent cable-finding system for multi-scenario cable positioning according to claim 7, characterized in that: The calculation formula of the fitness is: ； In the formula, Fitness(x) represents the fitness; Loss(x) represents the corresponding loss value.

9. The secondary integrated intelligent cable-finding system for multi-scenario cable positioning according to claim 8, characterized in that: The expression of the loss value is: ； Where N represents the number of fault type labels; y i represents the true label; Represents the predicted label.

10. The secondary integrated intelligent cable-finding system for multi-scenario cable positioning according to claim 1, characterized in that: The scene adaptation switching module includes: an environment perception unit, a cable recognition unit, a mode switching unit, a mode optimization unit and a guidance assistance unit; Wherein, the environment sensing unit is used to identify the current working environment and its characteristics; The cable identification unit is used to identify different types of cables and select corresponding signal generation and processing modes according to different cable types to analyze the electrical characteristics and material characteristics of the cables; The mode switching unit is used to automatically select and execute a corresponding working mode according to the working environment and cable type at the identification location; The mode optimization unit is used to dynamically adjust the line-finding strategy according to the actual working state of the cable, environmental changes and identification results in each working mode, and the line-finding strategy includes transmission power, transmission frequency and scanning speed; The guidance auxiliary unit is used to provide a display interface and a mode switching function can be manually selected.

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