Cable operation state intelligent monitoring method and system

Abstract

The invention discloses a cable operation state intelligent monitoring method and system. The method comprises the following steps: acquiring cable operation data in real time by adopting a pre-calibrated sensor and transmitting the cable operation data to a main control center; and the main control center carries out centralized monitoring on the cable operation data, analyzes the cable operation state based on a neural network model, determines whether a fault occurs based on a threshold coefficient and plans a fault processing path. The cable operation state can be accurately monitored, the fault can be rapidly determined, the maintenance path can be planned, and the cable operation safety and the maintenance efficiency can be improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of cable operation state monitoring, and relates to a cable operation state intelligent monitoring method and system. BACKGROUND

[0002] In modern power transmission systems, as a key power transmission carrier, the stability of the operation state of the cable is directly related to the reliability and safety of power supply. With the rapid development of urban construction and the continuous growth of industrial electricity demand, the scale of cable laying is expanding, and the operation environment is becoming increasingly complex and diverse. Traditional cable monitoring methods have many limitations and cannot meet the current requirements for efficient and accurate cable monitoring.

[0003] On the one hand, the precision of traditional monitoring equipment is limited, and when measuring key operating parameters such as cable temperature, current, and voltage, the error is large, and the actual operating state of the cable cannot be accurately reflected in a timely manner. For example, ordinary temperature sensors are greatly affected by environmental factors, and in complex electromagnetic environments or high-temperature and high-humidity environments, the accuracy of the measurement data is greatly reduced, which can easily lead to misjudgment or missed judgment of cable temperature abnormalities, and cannot timely discover serious faults such as cable insulation aging and short circuit caused by excessive temperature.

[0004] On the other hand, the data transmission method is backward, and in the past, wired transmission or low-bandwidth wireless transmission technology was used, which has slow transmission rate and poor stability. When facing a large amount of cable operation data, the data transmission delay is serious, and even data loss may occur, so that the master control center cannot obtain real-time cable operation information, and it is difficult to make fault warning and processing decisions in a timely manner.

[0005] In addition, for the analysis and processing of cable faults, the traditional method mainly relies on manual experience, and lacks scientific and systematic data analysis and fault judgment mechanism. When a fault occurs, it is difficult to quickly and accurately determine the fault type and location, and it is also difficult to develop an efficient repair scheme, resulting in long repair time, long power supply interruption time, great inconvenience to social production and resident life, and serious economic losses. SUMMARY

[0006] To solve the problems in the prior art, the application provides a cable operation state intelligent monitoring method and system, which can accurately monitor the operation state of the cable, quickly determine the fault and plan the repair path, and improve the safety of the cable operation and the efficiency of the maintenance and repair.

[0007] The application adopts the following technical solutions.

[0008] The first aspect of the application provides a cable operation state intelligent monitoring method, comprising:

[0009] Step 1: Real-time acquisition of cable operation data by pre-calibrated sensors and transmission to the master control center;

[0010] Step 2: Centralized monitoring of cable operation data by the master control center and analysis of cable operation state based on neural network model and threshold criterion respectively.

[0011] Preferably, the sensors include fiber grating temperature sensors, Rogowski coil current sensors, and voltage sensors, wherein the fiber grating temperature sensors obtain the temperature of the cable based on the change in Bragg wavelength; the Rogowski coil current sensors obtain the current of the cable based on induced voltage.

[0012] Preferably, in step 1, 5G remote communication technology is used for data transmission, and transmission parameters are dynamically optimized according to a data transmission rate model to achieve fast and stable data transmission.

[0013] Preferably, in step 2, a neural network model based on deep learning is used to analyze the cable operation data, and model parameters are continuously adjusted to minimize the loss function, to improve the accuracy of cable operation state analysis.

[0014] Preferably, the threshold criterion in step 2 is:

[0015]

[0016] where F = 1 indicates a cable failure, x is the real-time monitored operation data, is the mean of historical normal operation data, σ is the standard deviation of historical normal operation data, and k is the threshold coefficient.

[0017] Preferably, the threshold coefficient is calculated as:

[0018] k = 1.96 × (1 - β)

[0019] where 0 < β < 1 is a risk adjustment factor obtained through historical data and in-depth analysis of the actual situation of cable operation.

[0020] Preferably, it further includes: when a cable failure is analyzed, a shortest path algorithm is used to plan a maintenance path, and the objective function of the shortest path algorithm is:

[0021]

[0022] where D(s, t) is the shortest path length from the starting point s to the failure point t, P is the path set from the starting point s to the failure point t, and w(e) is the weight of edge e in the path set P.

[0023] The second aspect of the present application provides a cable operation state intelligent monitoring system, comprising a high-precision sensor module, a remote communication module and a master control center.

[0024] The high-precision sensor module is distributed in the cable system and is used for acquiring cable operation data in real time by using a pre-calibrated sensor.

[0025] The remote communication module is connected with the high-precision sensor module and is used for transmitting the cable operation data acquired by the high-precision sensor module to the master control center.

[0026] The master control center is used for centralized monitoring of the cable operation data and analysis of the cable operation state based on a neural network model and a threshold criterion respectively.

[0027] The third aspect of the present application provides a terminal, comprising a processor and a storage medium; the storage medium is used for storing instructions; and the processor is used for operating according to the instructions to execute the steps of the method.

[0028] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program; and the program is executed by a processor to realize the steps of the method.

[0029] Compared with the prior art, the present application has at least the following beneficial effects:

[0030] 1. The temperature and current sensors of the present application can accurately collect cable temperature and current data by using the precise fiber Bragg grating temperature sensing and Rogowski coil current sensing measurement formula, and provide reliable data basis for subsequent analysis in combination with high-precision voltage measurement means; and the high-precision sensor module is calibrated before data sampling, and regular maintenance calibration ensures that the sensor measurement data is accurate and reliable.

[0031] 2. When the cable operation data is transmitted to the master control center, the 5G communication technology is selected, the parameters are optimized according to the data transmission rate formula, the operation data is quickly and stably transmitted to the master control center, data loss or delay is avoided, the real-time performance of the monitoring system is ensured, and the master control center can timely acquire the cable operation information.

[0032] 3. The master control center analyzes the cable operation state based on the neural network model and determines whether a fault occurs based on the threshold coefficient; the determination based on the threshold coefficient is used as an independent rule engine, the neural network output is cross-validated, and monitoring loopholes caused by single algorithm defects are avoided.

[0033] 4. The master control center uses the neural network based on deep learning to analyze the cable operation state; the model minimizes the loss function by continuously optimizing the parameters, significantly improves the accuracy of the analysis of the cable operation state, and timely discovers potential fault hazards.

[0034] 5. When the main control center determines whether a fault has occurred based on the threshold coefficient, it combines a large amount of historical data to determine the mean, standard deviation and threshold coefficient of the normal operation data. This allows it to quickly and accurately determine whether the cable operation data is abnormal. Once a fault occurs, the relevant processing mechanism is immediately triggered to reduce the duration and scope of the fault.

[0035] 6. The main control center uses the shortest path objective function to plan maintenance paths. It sets the edge weights by comprehensively considering factors such as path length, traffic conditions, and distribution of maintenance resources. It can quickly calculate the optimal path from the location of maintenance personnel or the storage point of maintenance equipment to the fault point, thereby improving maintenance efficiency, reducing maintenance costs, shortening cable fault repair time, and ensuring the stability of power supply.

[0036] 7. The main control center hardware adopts high-performance servers and large-capacity storage devices. The various units of the software system work together to ensure the stable operation of the system in all aspects, from data storage and analysis to fault handling, thereby reducing the system failure rate and extending the system's service life. Attached Figure Description

[0037] Figure 1 This is a flowchart of an intelligent monitoring method for cable operating status according to the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0039] Embodiment 1 of the present invention provides an intelligent monitoring method for cable operating status, such as... Figure 1 As shown, it includes:

[0040] Step 1: Use pre-calibrated sensors to acquire cable operation data in real time and transmit it to the main control center;

[0041] More preferably, the pre-calibrated sensors are distributed within the cable system to sample cable operating data in real time, the operating data including at least temperature, current, and voltage data;

[0042] (1) The temperature sensor adopts a fiber optic grating temperature sensor, and its temperature measurement formula is:

[0043]

[0044] Where T is the temperature of the cable, T0 is the initial temperature, n is the effective refractive index of the fiber core, and Δ εxxis the axial strain variation, λ B is the Bragg wavelength, and α is the thermal expansion coefficient of the optical fiber.

[0045] The reasoning process is:

[0046] Δλ B = 2n·Δ εxx ·λ B , the axial strain variation Δ εxx = α·ΔT, where α is the thermal expansion coefficient of the optical fiber and ΔT is the temperature variation;

[0047] Substitute Δ εxx = α·ΔT into Δλ B = 2n·Δ εxx ·λ B , and obtain Then The temperature T of the cable can be calculated.

[0048] (2) The current sensor adopts a Rogowski coil current sensor, and its current measurement formula is:

[0049]

[0050] where i(t) is the measured current (the current of the cable) at time t, N is the number of turns of the Rogowski coil, S is the cross-sectional area of the Rogowski coil, μ0 is the vacuum permeability, R is the integral resistance, and V(t) is the induced voltage of the Rogowski coil.

[0051] (3) The voltage measurement formula used by the voltage sensor is:

[0052]

[0053] where V1 is the primary voltage, V2 is the voltage measured by the secondary side, and N1 and N2 are the number of turns of the primary and secondary windings, respectively.

[0054] Before the data sampling step, a step of calibrating the high-precision sensor is further included. The calibration formula calculates and adjusts the calibration parameters according to different types of sensors using the temperature measurement formula or the current measurement formula.

[0055] The remote communication adopts 5G communication technology, and the data transmission rate satisfies the formula:

[0056]

[0057] where R is the data transmission efficiency, B is the channel bandwidth, S is the signal power, and N is the noise power, which is used to ensure that the operation data can be quickly and stably transmitted to the main control center.

[0058] Step 2: The master center centrally monitors the cable operation data, analyzes the cable operation state based on the neural network model, determines whether a fault has occurred based on the threshold coefficient, and plans a fault handling path.

[0059] The neural network algorithm based on deep learning is used to analyze the operation data, and the loss function formula of the constructed neural network model is:

[0060]

[0061] Where L(θ) is the loss function, m is the number of samples, h θ (x i ) is the predicted value of the i-th sample by the neural network model, x i is the input feature of the i-th sample, y i is the i-th sample, and θ is the parameter of the neural network model. By continuously adjusting the parameter θ to minimize the loss function, the accuracy of the cable operation state analysis is improved.

[0062] In specific implementation, the neural network model may miss or misjudge due to data bias (such as lack of a certain type of fault sample in the training set) or calculation error (such as hardware floating point precision problem). For example: the new type of insulation aging is not included in the training data, and the model may not be able to identify it, so the invention introduces threshold determination, which triggers an alarm when the real-time data deviates from the normal range. For example, by analyzing the joint fluctuation mode of cable temperature, current and voltage through the neural network model, it is found that when the temperature rises by 2℃ and the current harmonic component increases by 15%, the probability of short circuit within the next 24 hours is 85% (potential fault risk warning); when the real-time temperature suddenly exceeds the historical mean value + 3σ (such as exceeding 90℃), regardless of the neural network prediction probability, an emergency fault alarm is triggered (sudden overheating protection), so as to perform double verification; the threshold coefficient determination as an independent rule engine can cross verify the neural network output, avoiding monitoring loopholes caused by single algorithm defects.

[0063] Specifically, the threshold determination method is used to determine abnormal data, and the determination formula is:

[0064]

[0065] Where F is the fault identifier, x is the real-time monitoring data, is the mean value of the historical normal operation data, σ is the standard deviation of the historical normal operation data, and k is the threshold coefficient. When F = 1, it is determined that a fault has occurred.

[0066] k = 1.96 x (1-β)

[0067] Wherein, beta (0< beta <1) is a risk adjustment factor, beta is determined according to the severity of the failure consequences and the trade-off of false alarm cost, and is obtained through a large number of historical data and in-depth analysis of the actual situation of cable operation.

[0068] According to the fault type and the real-time operation state of the cable, a shortest path algorithm is used to plan a maintenance path, and the objective function of the shortest path algorithm is as follows:

[0069]

[0070] Wherein, D (s, t) is the shortest path length from the starting point s to the end point t, P is the path set from s to t, and w (e) is the weight of the edge e in the path P, and the optimal maintenance path is obtained by solving the objective function.

[0071] Embodiment 2 of the present application provides a kind of cable operation state intelligent monitoring system, comprising:

[0072] High-precision sensor module is distributed in cable system, for real-time sampling of cable operation data, the operation data at least includes temperature, current, voltage data;

[0073] Installation layout mode is: according to the structural characteristics of cable system, at the key parts of cable, such as cable joint, cable turning and the middle position of long distance cable, high-precision sensor module is reasonably distributed.These positions are prone to temperature anomalies, current and voltage fluctuations and other problems, which facilitate accurate data acquisition.

[0074] Temperature sensor installation and calibration:

[0075] Opt for fiber grating temperature sensor, ensure that it is in close contact with cable during installation, to accurately perceive cable temperature change, and its temperature measurement formula is:

[0076] Delta lambda B = 2n·Delta εxx ·lambda B

[0077] Wherein, delta lambda B It is the change amount of bragg wavelength, n is the effective refractive index of optical fiber core, delta εxx It is the axial strain change amount, lambda B It is the bragg wavelength;The real-time temperature of the cable is calculated by the change amount of bragg wavelength.

[0078] After installation, it is calibrated using known stable temperature environment (such as constant temperature oven). According to the temperature measurement formula, different temperature values are set in the constant temperature oven, and the corresponding bragg wavelength change amount delta lambda B , the actual temperature and the calculated temperature are compared, and the calibration coefficient is obtained, which is used to correct the subsequent measurement data.

[0079] Current sensor installation and calibration:

[0080] With the Rogowski coil current sensor, ensure the position of the Rogowski coil around the cable during installation, the number of turns N is fixed, and the integral resistor R is connected. The current measurement formula is:

[0081]

[0082] Where i(t) is the measured current, N is the number of turns of the Rogowski coil, S is the cross-sectional area of the Rogowski coil, μ0 is the vacuum permeability, R is the integral resistor, and V(t) is the induced voltage of the Rogowski coil.

[0083] During calibration, different current values are output by the standard current source, the induced voltage V(t) is measured according to the current measurement formula, the deviation between the measured current and the standard current is calculated, the calibration parameters are adjusted, and the current measurement accuracy is improved.

[0084] Remote communication module, connected with the high-precision sensor module, used for transmitting the sampling operation data to the main control center;

[0085] Equipment selection and parameter configuration:

[0086] Select a module that supports 5G communication technology, select a 5G network with appropriate frequency bands according to the signal strength, interference, etc. of the cable system environment. The data transmission rate satisfies the formula:

[0087]

[0088] Where R is the data transmission efficiency, B is the channel bandwidth, S is the signal power, and N is the noise power, to ensure that the operation data can be quickly and stably transmitted to the main control center.

[0089] Configure the channel bandwidth B and signal power S, optimize the parameter combination according to the data transmission rate formula, ensure that the data transmission rate meets the real-time requirements, and can stably transmit data.

[0090] Connection and debugging:

[0091] Connect the remote communication module and the high-precision sensor module through wired (such as shielded cable) or wireless (such as Bluetooth, Zigbee, etc. short-range wireless communication technology) mode, ensure stable connection.

[0092] During debugging, simulate sensor data acquisition, check if the data can be accurately and quickly transmitted to the test terminal, and verify the reliability of the communication link.

[0093] The main control center receives the operation data transmitted by the remote communication module and centrally monitors and analyzes the data. The main control center includes a data storage unit, a data analysis unit, a fault determination unit, and a fault handling planning unit, each of which performs data storage, analysis based on a deep learning neural network algorithm, fault determination based on a threshold determination method, and maintenance path planning using a shortest path algorithm.

[0094] Hardware setup:

[0095] High-performance servers are equipped to meet the computing resource requirements for data storage, analysis, and complex algorithm operation.

[0096] Large-capacity storage devices such as disk arrays are configured to build the data storage unit, ensuring long-term storage of a large amount of cable operation data.

[0097] Software system deployment:

[0098] Data storage unit:

[0099] Install a database management system (such as MySQL, Oracle, etc.), and design a reasonable data table structure to store cable operation data such as temperature, current, voltage, and information such as collection time and sensor location, facilitating subsequent queries and analysis.

[0100] Data analysis unit:

[0101] Build a neural network model based on a deep learning framework (such as TensorFlow, PyTorch) to analyze operation data.

[0102] Collect a large amount of historical data of normal operation and fault state of the cable as training samples, and the loss function formula of the constructed neural network model is:

[0103]

[0104] Where L(θ) is the loss function, m is the number of samples, h θ (x i ) is the predicted value of the neural network model, y i is the actual value, and θ is the parameter of the neural network model.

[0105] Use the training samples to train the model, and continuously adjust the parameters θ to minimize the loss function to improve the accuracy of cable operation state analysis.

[0106] Fault determination unit:

[0107] According to the statistical analysis of the historical data of the cable operation, determine the mean value Standard deviation σ and threshold coefficient k. These parameters are configured into the fault determination unit, and the threshold determination formula is as follows:

[0108]

[0109] Real-time judgment of cable operation data is abnormal, and when F = 1, it is determined that a fault has occurred.

[0110] k = 1.96 x (1-β)

[0111] Where β (0 < β < 1) is a risk adjustment factor, β is determined according to the trade-off between the severity of the fault consequences and the false alarm cost, and is obtained through a large amount of historical data and in-depth analysis of the actual situation of cable operation.

[0112] Fault handling planning unit:

[0113] Based on the geographical information and topological structure of the cable network, a path model is constructed. For each edge e in the path, set the weight w(e), which comprehensively considers the path length, traffic conditions, repair resource distribution, cable operation state and fault type, etc. When a fault occurs, according to the objective function of the shortest path algorithm:

[0114]

[0115] The optimal repair path from the repair personnel's location (or repair equipment storage point) to the fault point is calculated.

[0116] The intelligent monitoring process of cable operation state based on the above system is as follows:

[0117] Real-time sampling of cable operation data is performed using a high-precision sensor module;

[0118] The operation data obtained by sampling is transmitted to the main control center through the remote communication module;

[0119] The data storage unit of the main control center stores the received operation data;

[0120] The data analysis unit of the main control center analyzes the operation data;

[0121] The fault determination unit of the main control center determines the abnormal data;

[0122] When a fault is determined, the fault handling planning unit of the main control center uses the shortest path algorithm to plan the repair path.

[0123] In specific implementation, the steps of sensor calibration, data sampling, transmission, storage, analysis, fault determination and repair path planning are covered:

[0124] Sensor calibration: At the initial start-up of the system or during regular maintenance, the high-precision sensor module is calibrated. For temperature sensors, follow the calibration process described above and use the temperature measurement formula to calculate the calibration parameters. For current sensors, perform calibration operations according to the current measurement formula to ensure the accuracy of sensor measurement data.

[0125] Data sampling: The high-precision sensor module continuously collects real-time temperature, current, and voltage data of the cable at a set sampling frequency (e.g., multiple times per second). The sampling frequency can be adjusted according to the stability of the cable operation and the monitoring accuracy requirements to ensure timely capture of data changes.

[0126] Data transmission: The remote communication module sends the data collected by the sensor to the main control center through the 5G network according to the established transmission protocol. During transmission, the data is encrypted to prevent data leakage and tampering, ensuring the security and integrity of data transmission.

[0127] Data storage: After receiving the data, the data storage unit of the main control center stores the data in the database according to the pre-designed data table structure. At the same time, the stored data is backed up to prevent data loss and facilitate subsequent traceability analysis.

[0128] Data analysis: The data analysis unit reads real-time data from the database and inputs it into the trained neural network model for analysis. The model outputs an evaluation of the current operating state of the cable, including whether it is running normally, potential fault risks, and other information.

[0129] Fault determination: The fault determination unit determines the data based on the threshold determination formula according to the data analysis results. If the determination is a fault (F = 1), a fault alarm information is generated, recording the fault occurrence time, fault type (determined according to abnormal data characteristics), and other information.

[0130] Fault handling plan: When the fault determination unit issues a fault alarm, the fault handling plan unit plans a maintenance path based on the fault type and real-time operating state of the cable using the shortest path algorithm. The planned path information is sent to the terminal device (such as a mobile phone or tablet computer) of the maintenance personnel, and maintenance guidance suggestions such as required maintenance tools and precautions are provided to assist maintenance personnel in quickly reaching the fault point and repairing it.

[0131] Embodiment 3 of the present application provides a terminal comprising a processor and a storage medium; the storage medium is used to store instructions;

[0132] The processor is used to operate according to the instructions to perform the steps of the method.

[0133] Embodiment 4 of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method.

[0134] Compared with the prior art, the present application has at least the following beneficial effects:

[0135] 1、The temperature and current sensor of the present application can accurately collect cable temperature and current data by using precise fiber Bragg grating temperature sensing and Rogowski coil current sensing measurement formula, and in combination with high-precision voltage measurement means, to provide reliable data basis for subsequent analysis; and the high-precision sensor module is calibrated before data sampling, and regular maintenance calibration ensures that the sensor measurement data is accurate and reliable.

[0136] 2、When transmitting the cable operation data to the main control center, the present application selects 5G communication technology, optimizes parameters according to the data transmission rate formula, ensures that the operation data is quickly and stably transmitted to the main control center, avoids data loss or delay, guarantees the real-time performance of the monitoring system, and enables the main control center to timely obtain the cable operation information.

[0137] 3、The main control center analyzes the cable operation state based on the neural network model, and determines whether a fault occurs based on the threshold coefficient; the determination based on the threshold coefficient is used as an independent rule engine, which can cross-verify the neural network output, and avoid monitoring loopholes caused by single algorithm defects.

[0138] 4、The main control center uses a neural network based on deep learning to analyze the cable operation state; the model minimizes the loss function by continuously optimizing parameters, significantly improves the accuracy of cable operation state analysis, and timely discovers potential fault hazards.

[0139] 5、When the main control center determines whether a fault occurs based on the threshold coefficient, the mean, standard deviation and threshold coefficient of normal operation data are determined in combination with a large amount of historical data, which can quickly and accurately determine whether the cable operation data is abnormal; once a fault occurs, the related processing mechanism is triggered immediately, reducing the fault duration and impact range.

[0140] 6、The main control center uses a shortest path objective function to plan a repair path, sets the weight of the edge by comprehensively considering the path length, traffic conditions, repair resource distribution and other factors, can quickly calculate the optimal path from the repair personnel's location or repair equipment storage point to the fault point, improve the repair efficiency, reduce the repair cost, shorten the cable fault repair time, and guarantee the stability of power supply.

[0141] 7、The main control center uses high-performance servers and large-capacity storage devices, and the software system units work cooperatively, from data storage, analysis to fault handling, to comprehensively guarantee the stable operation of the system, reduce the system failure rate, and prolong the service life of the system.

[0142] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0143] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a

[0144] The computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0145] Computer readable program instructions for carrying out operations of the present disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0146] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced, and any modification or replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for intelligent monitoring of the state of a cable run, characterized by, The method comprises the following steps: Step 1: Real-time acquisition of cable operation data by using pre-calibrated sensors and transmission to the master control center; Step 2: Centralized monitoring of cable operation data by the master control center and analysis of the cable operation state based on a neural network model and a threshold criterion respectively.

2. The intelligent cable operation state monitoring method according to claim 1, characterized in that: The sensors comprise fiber grating temperature sensors, Rogowski coil current sensors and voltage sensors, wherein the fiber grating temperature sensors obtain the temperature of the cable based on the change in Bragg wavelength; and the Rogowski coil current sensors obtain the current of the cable based on induced voltage.

3. The intelligent cable operation state monitoring method according to claim 1, characterized in that: In step 1, 5G remote communication technology is used for data transmission, and transmission parameters are dynamically optimized according to a data transmission rate model to realize fast and stable data transmission.

4. The intelligent cable operation state monitoring method according to claim 1, characterized in that: In step 2, a neural network model based on deep learning is used to analyze the cable operation data, and model parameters are continuously adjusted to minimize the loss function, so as to improve the accuracy of the analysis of the cable operation state.

5. The intelligent cable operation state monitoring method according to claim 1, characterized in that: The threshold criterion in step 2 is: Wherein, when F = 1, it is determined that the cable is faulty, x is the real-time monitored operation data, is the mean of historical normal operation data, σ is the standard deviation of historical normal operation data, and k is a threshold coefficient.

6. The intelligent cable operation state monitoring method according to claim 5, characterized in that: The value formula of the threshold coefficient is: k＝1.96×(1-β) wherein 0<β<1 is a risk adjustment factor obtained through historical data and in-depth analysis of the actual situation of the cable operation.

7. The intelligent cable operation state monitoring method according to claim 1, characterized in that: Further comprising: when a cable fault is analyzed, a shortest path algorithm is used to plan a maintenance path, and the objective function of the shortest path algorithm is: wherein D(s,t) is the shortest path length from the starting point s to the fault point t, R is the path set from the starting point s to the fault point t, and w(e) is the weight of the edge e in the path set P.

8. An intelligent cable routing condition monitoring system for implementing the method of any one of claims 1 to 7, characterized by The system comprises a high-precision sensor module, a remote communication module and a master control center: The high-precision sensor module is distributed in the cable system and is used to acquire cable operation data in real time by using pre-calibrated sensors; The remote communication module is connected with the high-precision sensor module and is used to transmit the cable operation data acquired by the high-precision sensor module to the master control center; The master control center is used to centrally monitor the cable operation data and analyze the cable operation state based on a neural network model and a threshold criterion respectively.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to perform the steps of the method according to any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that The program is executed by the processor to realize the steps of the method according to any one of claims 1-7.

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