Cable temperature abnormity early warning method, device and equipment and storage medium

Abstract

The invention discloses a cable temperature abnormity early warning method, device and equipment and a storage medium, and relates to the technical field of cable detection, and the method comprises the steps: training a deep learning model constructed based on a recurrent neural network according to the historical cable data of a high-voltage cable under different load and temperature conditions, obtaining a trend prediction model, and carrying out the early warning of the temperature abnormity of the high-voltage cable; the deep learning model is used for learning the relationship among the load, the temperature and the cable insulation performance of the high-voltage cable; predicting the current cable data based on a trend prediction model to obtain temperature trend data; and when the temperature trend data is abnormal, carrying out abnormal early warning on the high-voltage cable. The relationship among the load, the temperature and the cable insulation performance of the high-voltage cable is learned by using the deep learning model, and the temperature trend data of the current cable data can be predicted through the obtained trend prediction model, so that abnormity can be found in time and early warning can be carried out. Therefore, the temperature of the cable is ensured to be within a safe range and the cable is prevented from overheating.

Description

Technical Field

[0001] The present application relates to the technical field of cable detection, and particularly to a method, device, equipment and storage medium for early warning of abnormal cable temperature. Background Art

[0002] With the vigorous development of electric power, the density of power generation projects is also increasing day by day. Power cables undertake the tasks of electric energy transmission and distribution in power generation projects. As an important part connecting various electrical equipment, the safe and stable operation of cables is crucial. For example, when a cable line fails, not only will there be a power outage, but in severe cases, there may be an explosion at the joint, causing fires and casualties.

[0003] Especially for offshore wind power projects, due to environmental factors such as humid environment and direct sunlight, high-voltage cables have problems such as overheating during power transmission, which may lead to cable accidents. Therefore, how to monitor the cable temperature in real time and prevent the cable from overheating is an urgent problem to be solved. Summary of the Invention

[0004] The main purpose of the present application is to provide a method, device, equipment and storage medium for early warning of abnormal cable temperature, aiming to solve the technical problem that in offshore wind power projects, high-voltage cables overheat during power transmission due to environmental factors, resulting in cable accidents.

[0005] To achieve the above object, the present application proposes a method for early warning of abnormal cable temperature, the method comprising:

[0006] Collecting historical cable data of a high-voltage cable under different load and temperature conditions;

[0007] Training a deep learning model constructed based on a recurrent neural network according to the historical cable data to obtain a trend prediction model, the deep learning model being used to learn the relationship between the load, temperature and cable insulation performance of the high-voltage cable;

[0008] Predicting according to the current cable data of the high-voltage cable based on the trend prediction model to obtain temperature trend data;

[0009] When the temperature trend data is abnormal, giving an abnormal warning to the high-voltage cable.

[0010] In one embodiment, the step of giving an abnormal warning to the high-voltage cable when the temperature trend data is abnormal includes:

[0011] Judging whether the temperature trend data reaches a preset temperature threshold;

[0012] When the temperature trend data reaches the preset temperature threshold, it is determined that the cable insulation condition of the high-voltage cable is abnormal;

[0013] Determine the cable sheath grounding data of the high-voltage cable according to the temperature trend data and the current cable data;

[0014] Based on the cable sheath grounding data, give an abnormal warning for the cable insulation condition.

[0015] In one embodiment, the step of determining the cable sheath grounding data of the high-voltage cable according to the temperature trend data and the current cable data includes:

[0016] Obtain the current cable load and the current ambient temperature in the current cable data;

[0017] Based on the trend prediction model, determine the load-temperature-grounding correlation relationship of the high-voltage cable;

[0018] Analyze the current cable load and the current ambient temperature through the load-temperature-grounding correlation relationship to obtain the cable sheath grounding data of the high-voltage cable.

[0019] In one embodiment, before the step of training a deep learning model based on a recurrent neural network according to the historical cable data to obtain a trend prediction model, it further includes:

[0020] Conduct a temperature analysis on the historical cable data to obtain the temperature change curve of the high-voltage cable under different operating conditions;

[0021] Conduct a correlation analysis on the historical cable data to obtain the correlation data between the cable temperature of the high-voltage cable and the cable operating condition factors, where the cable operating condition factors include cable load and ambient temperature;

[0022] Correspondingly, after the step of giving an abnormal warning to the high-voltage cable when the temperature trend data is abnormal, it further includes:

[0023] Based on the temperature change curve and the correlation data, optimize the operating condition of the high-voltage cable through the temperature trend data.

[0024] In one embodiment, the step of training a deep learning model based on a recurrent neural network according to the historical cable data to obtain a trend prediction model includes:

[0025] Conduct data preprocessing on the historical cable data to obtain preprocessed cable data;

[0026] Aiming at learning the relationship between the load, temperature and cable insulation performance of the high-voltage cable, a deep learning model is constructed through a recurrent neural network;

[0027] Input the preprocessed cable data, the temperature change curve and the correlation data into the deep learning model for training to obtain a trend prediction model.

[0028] In one embodiment, the step of inputting the preprocessed cable data, the temperature change curve and the correlation data into the deep learning model for training to obtain a trend prediction model includes:

[0029] Input the preprocessed cable data, the temperature change curve and the correlation data into the deep learning model for training to obtain an initial prediction model;

[0030] Judge whether the accuracy of the output result of the initial prediction model reaches a preset threshold;

[0031] If the accuracy does not reach the preset threshold, adjust the model parameters of the initial prediction model and return to execute the step of inputting the preprocessed cable data, the temperature change curve and the correlation data into the deep learning model for training until the accuracy reaches the preset threshold, and use the model corresponding to reaching the preset threshold as the trend prediction model.

[0032] In addition, to achieve the above object, the present application also proposes a cable temperature anomaly warning device, and the device includes:

[0033] A data acquisition module for collecting historical cable data of the high-voltage cable under different load and temperature conditions;

[0034] A model training module for training a deep learning model constructed based on a recurrent neural network according to the historical cable data to obtain a trend prediction model, and the deep learning model is used to learn the relationship between the load, temperature and cable insulation performance of the high-voltage cable;

[0035] A temperature prediction module for predicting based on the trend prediction model according to the current cable data of the high-voltage cable to obtain temperature trend data;

[0036] An anomaly warning module for performing anomaly warning on the high-voltage cable when the temperature trend data is abnormal.

[0037] In addition, to achieve the above object, the present application further provides a cable temperature anomaly warning device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the cable temperature anomaly warning method as described above.

[0038] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the cable temperature anomaly warning method as described above are implemented.

[0039] In addition, to achieve the above object, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the cable temperature anomaly warning method as described above are implemented.

[0040] One or more technical solutions proposed by the present application have at least the following technical effects: The cable temperature anomaly warning method of the present application includes: collecting historical cable data of high-voltage cables under different loads and temperature conditions; training a deep learning model constructed based on a recurrent neural network according to the historical cable data to obtain a trend prediction model, and the deep learning model is used to learn the relationship between the load, temperature and cable insulation performance of the high-voltage cable; based on the trend prediction model, predicting according to the current cable data of the high-voltage cable to obtain temperature trend data; when the temperature trend data is abnormal, performing an anomaly warning on the high-voltage cable.

[0041] Since the present application learns the relationship between the load, temperature and cable insulation performance of the high-voltage cable through a deep learning model constructed based on a recurrent neural network to obtain a trend prediction model; then uses the trend prediction model to monitor and predict the temperature trend data of the current cable data to timely discover data anomalies and perform anomaly warnings on the high-voltage cable. Thereby, the cable temperature can be ensured to be within a safe range, preventing the cable from overheating and ensuring the safe operation of the high-voltage cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0043] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for describing the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a schematic flowchart provided for the first embodiment of the cable temperature anomaly warning method of this application;

[0045] Figure 2 It is a schematic flowchart provided for the second embodiment of the cable temperature anomaly warning method of this application;

[0046] Figure 3 It is a schematic module structure diagram of the cable temperature anomaly warning device in the embodiment of this application;

[0047] Figure 4 It is a schematic device structure diagram of the hardware operating environment involved in the cable temperature anomaly warning method in the embodiment of this application.

[0048] The realization, functional characteristics, and advantages of the purpose of this application will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific Embodiments

[0049] It should be understood that the specific embodiments described herein are only used to explain the technical solution of this application and are not used to limit this application.

[0050] In order to better understand the technical solution of this application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific embodiments.

[0051] It should be noted that the execution subject of this embodiment can be a computing service device with data acquisition, model training, and temperature anomaly warning functions, such as a personal computer, a monitor, etc., or an electronic device capable of implementing the above functions, a cable temperature anomaly warning device (abbreviated as a warning device) that executes the cable temperature anomaly warning method of this application, etc. This embodiment does not limit this. The following takes the warning device as an example to illustrate this embodiment and the following embodiments.

[0052] Based on this, the first embodiment of this application is proposed. The embodiment of this application provides a cable temperature anomaly warning method, referring to Figure 1 , Figure 1 It is a schematic flowchart provided for the first embodiment of the cable temperature anomaly warning method of this application.

[0053] In this embodiment, the cable temperature anomaly warning method includes steps S10 to S40:

[0054] Step S10: Collect historical cable data of high-voltage cables under different loads and temperature conditions.

[0055] It should be noted that the historical cable data can be parameter information corresponding to the historical operating state of the high-voltage cable under various different load levels (such as light load, medium load, heavy load, etc.) and different environmental temperature conditions (such as low temperature, normal temperature, high temperature, etc.).

[0056] Exemplarily, it may include data such as the current value, voltage value, insulation resistance value, partial discharge amount, temperature value, etc. when the high-voltage cable is operating.

[0057] In a specific implementation, the early warning device first collects historical cable data of the high-voltage cable under different load levels and different ambient temperature conditions to better understand the performance law of the high-voltage cable under different combinations of load and temperature.

[0058] Step S20: Train a deep learning model constructed based on a recurrent neural network according to the historical cable data to obtain a trend prediction model. The deep learning model is used to learn the relationship between the load, temperature and cable insulation performance of the high-voltage cable.

[0059] It should be noted that a recurrent neural network (RNN) can be an artificial neural network that can process historical cable data with time series characteristics.

[0060] The deep learning model constructed by the recurrent neural network can learn historical cable data, analyze and understand the association and law existing among the load condition, temperature condition and cable insulation performance of the high-voltage cable, and form a trend prediction model. The trend prediction model can use these laws to predict new data.

[0061] In a specific implementation, during the process of model training, first construct a deep learning model based on a recurrent neural network, and then use a large amount of data on load, temperature and corresponding insulation performance as training samples. The deep learning model learns and trains through these training samples, and gradually masters the association and law existing among the load condition, temperature condition and cable insulation performance of the high-voltage cable, and generates a trend prediction model.

[0062] Step S30: Based on the trend prediction model, predict according to the current cable data of the high-voltage cable to obtain temperature trend data.

[0063] It should be noted that the current cable data can be parameter information of the high-voltage cable in the current operating state, such as the current value, voltage value, insulation resistance value, partial discharge amount, etc.

[0064] It can be understood that the temperature trend data can be data predicted by the trend prediction model for the cable temperature in a future period according to the current cable data, which reflects the trend and situation of the cable temperature changing with time.

[0065] Through the temperature trend data, it is possible to predict in advance whether the cable temperature is rising, falling or remaining relatively stable, and whether there is an overheating risk.

[0066] Step S40: When the temperature trend data is abnormal, an abnormal warning is given to the high-voltage cable.

[0067] In a specific implementation, when the temperature trend data shows characteristics that do not conform to the normal situation (for example, when the temperature suddenly rises or drops rapidly, or exceeds the normal fluctuation range), it indicates that there are conditions such as local overheating and insulation damage in the high-voltage cable. At this time, a warning can be issued for the abnormal situation of the high-voltage cable.

[0068] In a feasible implementation manner, step S40 of this embodiment may include the steps of: determining whether the temperature trend data reaches a preset temperature threshold; when the temperature trend data reaches the preset temperature threshold, determining that the cable insulation condition of the high-voltage cable is abnormal; determining the cable sheath grounding data of the high-voltage cable according to the temperature trend data and the current cable data; and based on the cable sheath grounding data, giving an abnormal warning to the cable insulation condition.

[0069] It should be noted that the preset temperature threshold is a numerically value preset for determining whether the temperature trend data is abnormal. For example, 20% exceeding the normal temperature fluctuation range of the cable can be set as an abnormal condition, or it can be set according to the rate of change of the rapid rise or fall of the temperature. This embodiment does not limit this.

[0070] It can be understood that the cable insulation condition can be the state of the insulating material used to isolate the current in the high-voltage cable. Generally, if the insulation condition of the high-voltage cable is poor (for example, the insulating material is incomplete, there are damages, or there is an aging degree), it may lead to faults such as electric leakage, arc, and breakdown, thereby causing the cable temperature to rise.

[0071] Therefore, when the temperature trend data is abnormal, it can be initially determined that the cable insulation condition of the high-voltage cable is abnormal. At this time, the cable sheath grounding data related to the grounding of the metal sheath of the high-voltage cable (such as the value of the grounding resistance, the magnitude of the grounding current, etc.) can be obtained to determine the quality of the cable insulation condition.

[0072] In this embodiment, when the temperature trend data reaches the preset temperature threshold, it indicates that there is an abnormality in the cable insulation condition of the high-voltage cable. At this time, the cable sheath grounding data of the high-voltage cable can be obtained according to the temperature trend data and the current cable data; and an abnormal warning is given to the cable insulation condition. Thus, by comparing the predicted temperature trend data with the preset temperature threshold and combining the cable sheath grounding data of the high-voltage cable, it can be determined that the cable insulation condition of the high-voltage cable is abnormal and a warning indicates the fault location, providing maintenance guidance for maintenance personnel.

[0073] In a feasible implementation manner, the step of determining the cable sheath grounding data of the high-voltage cable according to the temperature trend data and the current cable data in this embodiment includes: obtaining the current cable load and the current ambient temperature in the current cable data; based on the trend prediction model, determining the load-temperature-grounding correlation relationship of the high-voltage cable; through the load-temperature-grounding correlation relationship, analyzing the current cable load and the current ambient temperature to obtain the cable sheath grounding data of the high-voltage cable.

[0074] It should be noted that the current cable load can be the magnitude of the current carried by the high-voltage cable at the current moment, which reflects the power level being transmitted by the high-voltage cable. The current ambient temperature can refer to the instantaneous temperature condition of the surrounding environment where the high-voltage cable is located.

[0075] It can be understood that the load-temperature-grounding correlation relationship can be the law existing among the load condition, temperature condition, and cable insulation performance of the high-voltage cable learned by the above deep learning model when learning historical cable data. For example, when the cable load is large, the current passing through the cable will generate heat, resulting in an increase in the cable temperature. If the ambient temperature itself is also high and the cable has poor heat dissipation, the temperature may further rise, and too high a temperature will affect the insulation performance and service life of the cable.

[0076] In this implementation manner, when determining the cable sheath grounding data, the warning device can first obtain the current cable load and the current ambient temperature in the current cable data; then determine the load-temperature-grounding correlation relationship of the high-voltage cable according to the law learned by the trend prediction model. Finally, through the load-temperature-grounding correlation relationship, the current cable load and the current ambient temperature can be analyzed to determine the cable sheath grounding data of the high-voltage cable. When any of the above temperature trend data or grounding current exceeds the set range, an alarm is issued, and the insulation condition of the high-voltage cable can be grasped in real time according to the cable sheath grounding data.

[0077] Furthermore, before step S20 in this embodiment, it can include the steps of: performing temperature analysis on the historical cable data to obtain the temperature change curve of the high-voltage cable under different operating conditions; performing correlation analysis on the historical cable data to obtain the correlation data between the cable temperature and the cable operating condition factors, where the cable operating condition factors include cable load and ambient temperature; correspondingly, step S40 can include the steps of: based on the temperature change curve and the correlation data, optimizing the operating condition of the high-voltage cable through the temperature trend data.

[0078] It should be noted that the temperature change curve can be a curve graph depicting the temperature change of a high-voltage cable over time under various different working conditions (such as different load currents, different ambient temperatures, different heat dissipation conditions, etc.).

[0079] Specifically, the warning device can regularly analyze the temperature trend of the high-voltage cable to understand the temperature change law (i.e., the temperature change curve) of the high-voltage cable under different operating conditions. Thus, the load of the cable can be adjusted according to the temperature trend to avoid overheating of the cable and improve the operating efficiency and service life of the cable.

[0080] It can be understood that the correlation data can be data on the mutual relationship between the temperature of the high-voltage cable and working condition factors such as cable load and ambient temperature. For example, the amplitude and trend of temperature rise when the load increases, how the cable temperature rises or falls when the ambient temperature increases, etc., and this embodiment does not limit this.

[0081] Specifically, the warning device can analyze the correlation data between the cable temperature and other factors (such as load, ambient temperature, etc.). In this way, by calculating the correlation coefficient or performing regression analysis, the influence degree and direction of the cable working condition factors on the cable temperature can be understood, providing a basis for optimizing the cable operation.

[0082] In this embodiment, the warning device can first perform temperature analysis on historical cable data to obtain the temperature change curve of the high-voltage cable under different operating conditions; then perform correlation analysis on the historical cable data to obtain the correlation data between the cable temperature of the high-voltage cable and the cable working condition factors. Through the temperature change curve and the correlation data, the influence degree and direction of the cable working condition factors on the cable temperature can be clarified, providing a basis for optimizing the cable operation.

[0083] In the technical solution provided in this embodiment, the warning device first collects historical cable data of high-voltage cables under different load levels and different ambient temperature conditions to better understand the performance law of high-voltage cables under different load and temperature combinations. Then, during the model training process, a deep learning model is first constructed based on a recurrent neural network, and a large amount of data on load, temperature, and corresponding insulation performance is used as training samples. The deep learning model learns and trains through these training samples, gradually mastering the relationship and law existing among the load condition, temperature condition, and cable insulation performance of the high-voltage cable, and generating a trend prediction model. Then, based on the trend prediction model, predictions are made according to the current cable data of the high-voltage cable to obtain temperature trend data. When the temperature trend data shows characteristics that do not conform to the normal situation (for example, when the temperature suddenly rises or drops rapidly, or exceeds the normal fluctuation range), it indicates that there are conditions such as local overheating and insulation damage in the high-voltage cable. At this time, a warning can be issued for the abnormal situation of the high-voltage cable. Since in this embodiment, a deep learning model constructed based on a recurrent neural network is used to learn the relationship among the load, temperature, and cable insulation performance of the high-voltage cable to obtain a trend prediction model; then the trend prediction model is used to monitor and predict the temperature trend data of the current cable data to timely detect data anomalies and issue abnormal warnings for the high-voltage cable. Thus, the cable temperature can be ensured to be within a safe range, overheating of the cable can be prevented, cable accidents can be prevented, and the safe operation of the high-voltage cable can be guaranteed.

[0084] Based on the above-mentioned first embodiment of the present application, the second embodiment of the present application is proposed. In the second embodiment of the present application, the same or similar content as that in the above-mentioned first embodiment can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 2 , Figure 2 which is a schematic flowchart provided for the second embodiment of the cable temperature abnormal warning method of the present application.

[0085] Step S20 of this example includes steps S21 to S23:

[0086] Step S21: Perform data preprocessing on the historical cable data to obtain preprocessed cable data.

[0087] In a specific implementation, the collected historical cable data can be preprocessed such as cleaning, denoising, and normalizing to improve the data quality and usability.

[0088] Step S22: With the goal of learning the relationship among the load, temperature, and cable insulation performance of the high-voltage cable, construct a deep learning model through a recurrent neural network.

[0089] Step S23: Input the preprocessed cable data, the temperature change curve, and the correlation data into the deep learning model for training to obtain a trend prediction model.

[0090] In a specific implementation, during the process of training the model, the collected historical cable data can be preprocessed such as cleaning, denoising, and normalizing to improve the data quality and usability. Then, a deep learning model is constructed through a recurrent neural network, and the preprocessed cable data, the temperature change curve, and the correlation data are input into the deep learning model for training to obtain a trend prediction model. This is to learn the relationship between the load, temperature, and cable insulation performance of high-voltage cables.

[0091] In a feasible implementation manner, step S23 of this example includes the steps of: inputting the preprocessed cable data, the temperature change curve, and the correlation data into the deep learning model for training to obtain an initial prediction model; judging whether the accuracy of the output result of the initial prediction model reaches a preset threshold; if the accuracy does not reach the preset threshold, adjusting the model parameters of the initial prediction model, and returning to execute the step of inputting the preprocessed cable data, the temperature change curve, and the correlation data into the deep learning model for training until the accuracy reaches the preset threshold, and taking the model corresponding to reaching the preset threshold as the trend prediction model.

[0092] It should be noted that the preset threshold can be a value used to judge whether the accuracy of the output result of the model is qualified. For example, the accuracy reaches 90% or 95%. This embodiment does not limit this.

[0093] In this embodiment, the warning device inputs the preprocessed cable data, the temperature change curve, and the correlation data between the cable temperature and the working condition factors into the deep learning model for training to obtain a preliminary prediction model. Then, the accuracy of the output result of the initial prediction model is tested to see if it reaches the preset threshold set in advance. If the accuracy does not reach the preset threshold, the model parameters (such as the network structure, weights, etc.) of the initial prediction model need to be adjusted, and then a new round of training is carried out according to the above data to continuously improve and optimize the model to make its prediction result more accurate until the requirements of the preset threshold are met. Through the above cyclic training, a relatively accurate and reliable trend prediction model can be finally obtained.

[0094] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation to the cable temperature anomaly warning method of the present application. Based on this technical concept, more forms of simple transformations are within the protection scope of the present application.

[0095] The present application also provides a cable temperature anomaly warning device. Please refer to Figure 3 , Figure 3 , which is a schematic diagram of the module structure of the cable temperature anomaly warning device according to an embodiment of the present application; the cable temperature anomaly warning device includes:

[0096] A data acquisition module 301, configured to acquire historical cable data of a high-voltage cable under different loads and temperature conditions;

[0097] A model training module 302, configured to train a deep learning model constructed based on a recurrent neural network according to the historical cable data to obtain a trend prediction model, where the deep learning model is used to learn the relationship between the load, temperature, and cable insulation performance of the high-voltage cable;

[0098] A temperature prediction module 303, configured to predict based on the trend prediction model according to the current cable data of the high-voltage cable to obtain temperature trend data;

[0099] An anomaly warning module 304, configured to perform anomaly warning on the high-voltage cable when the temperature trend data is abnormal.

[0100] As an implementation manner, the anomaly warning module 304 is further configured to determine whether the temperature trend data reaches a preset temperature threshold; when the temperature trend data reaches the preset temperature threshold, determine that the cable insulation condition of the high-voltage cable is abnormal; determine the cable sheath grounding data of the high-voltage cable according to the temperature trend data and the current cable data; and perform anomaly warning on the cable insulation condition based on the cable sheath grounding data.

[0101] As an implementation manner, the anomaly warning module 304 is further configured to obtain the current cable load and the current ambient temperature in the current cable data; determine the load-temperature-ground connection relationship of the high-voltage cable based on the trend prediction model; and analyze the current cable load and the current ambient temperature through the load-temperature-ground connection relationship to obtain the cable sheath grounding data of the high-voltage cable.

[0102] As an implementation manner, the model training module 302 is further configured to perform temperature analysis on the historical cable data to obtain a temperature change curve of the high-voltage cable under different operating conditions; perform correlation analysis on the historical cable data to obtain correlation data between the cable temperature and cable operating condition factors of the high-voltage cable, where the cable operating condition factors include cable load and ambient temperature; correspondingly, the anomaly warning module 304 is further configured to optimize the operating condition of the high-voltage cable through the temperature trend data based on the temperature change curve and the correlation data.

[0103] As an implementation manner, the model training module 302 is further configured to perform data preprocessing on the historical cable data to obtain preprocessed cable data; construct a deep learning model through a recurrent neural network with the goal of learning the relationship between the load, temperature, and cable insulation performance of the high-voltage cable; and input the preprocessed cable data, the temperature change curve, and the correlation data into the deep learning model for training to obtain a trend prediction model.

[0104] As an implementation manner, the model training module 302 is further configured to input the preprocessed cable data, the temperature change curve, and the correlation data into the deep learning model for training to obtain an initial prediction model; determine whether the accuracy of the output result of the initial prediction model reaches a preset threshold; if the accuracy does not reach the preset threshold, adjust the model parameters of the initial prediction model, and return to execute the step of inputting the preprocessed cable data, the temperature change curve, and the correlation data into the deep learning model for training until the accuracy reaches the preset threshold, and use the model corresponding to when the preset threshold is reached as the trend prediction model.

[0105] Other embodiments or specific implementation manners of the cable temperature anomaly warning device of the present application may refer to the above method embodiments, and will not be elaborated here.

[0106] The cable temperature anomaly warning device provided by the present application adopts the cable temperature anomaly warning method in the above embodiment, and can solve the technical problem that in an offshore wind power project, the high-voltage cable overheats during power transmission due to environmental factors, resulting in cable accidents. Compared with the prior art, the beneficial effects of the cable temperature anomaly warning device provided by the present application are the same as those of the cable temperature anomaly warning method provided by the above embodiment, and other technical features in the cable temperature anomaly warning device are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0107] The present application provides a cable temperature anomaly warning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the cable temperature anomaly warning method in the first embodiment above.

[0108] Next, refer to Figure 4 , Figure 4The figure is a schematic diagram of the device structure of the hardware operating environment involved in the cable temperature anomaly warning method in the embodiments of the present application, which shows a schematic diagram of the structure of the cable temperature anomaly warning device suitable for implementing the embodiments of the present application. The cable temperature anomaly warning device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The shown cable temperature anomaly warning device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0109] As Figure 4 shown, the cable temperature anomaly warning device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the cable temperature anomaly warning device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the cable temperature anomaly warning device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a cable temperature anomaly warning device with various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be implemented or had alternatively.

[0110] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0111] The cable temperature anomaly warning device provided by the present application adopts the cable temperature anomaly warning method in the above-mentioned embodiment, and can solve the technical problem that in a marine wind power project, high-voltage cables overheat during power transmission due to environmental factors, resulting in cable accidents. Compared with the prior art, the beneficial effects of the cable temperature anomaly warning device provided by the present application are the same as those of the cable temperature anomaly warning method provided by the above-mentioned embodiment, and other technical features in the cable temperature anomaly warning device are the same as the features disclosed in the method of the previous embodiment, which will not be elaborated here.

[0112] It should be understood that the various parts disclosed in the present application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0113] As described above, only the specific embodiments of the present application are provided, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0114] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the cable temperature anomaly warning method in the above-mentioned embodiment.

[0115] The computer-readable storage medium provided by the present application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium can be transmitted by any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0116] The above computer-readable storage medium can be included in the cable temperature anomaly warning device; or it can exist independently and not be assembled into the cable temperature anomaly warning device.

[0117] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by the cable temperature anomaly warning device, the cable temperature anomaly warning device is enabled to: collect historical cable data of the high-voltage cable under different load and temperature conditions; train a deep learning model constructed based on a recurrent neural network according to the historical cable data to obtain a trend prediction model, where the deep learning model is used to learn the relationship between the load, temperature, and cable insulation performance of the high-voltage cable; based on the trend prediction model, predict according to the current cable data of the high-voltage cable to obtain temperature trend data; and issue an anomaly warning for the high-voltage cable when the temperature trend data is abnormal.

[0118] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by connecting through the Internet using an Internet service provider).

[0119] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order from that marked in the accompanying drawings. For example, two consecutively represented blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0120] The modules described in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

[0121] The readable storage medium provided by this application is a computer-readable storage medium, and the computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned cable temperature anomaly warning method, which can solve the technical problem that in offshore wind power projects, high-voltage cables overheat during power transmission due to environmental factors, resulting in cable accidents. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the cable temperature anomaly warning method provided by the above embodiments, and will not be elaborated here.

[0122] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the cable temperature anomaly warning method as described above.

[0123] The computer program product provided by the present application can solve the technical problem that in an offshore wind power project, high-voltage cables overheat during power transmission due to environmental factors, resulting in cable accidents. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the cable temperature anomaly warning method provided in the above embodiments, and will not be elaborated here.

[0124] The foregoing are only partial embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the specification and drawings of the present application under the technical concept of the present application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A cable temperature abnormality early warning method, characterized in that: The method comprises: Collect historical cable data of high voltage cables under different load and temperature conditions; A deep learning model constructed based on a recurrent neural network is trained according to the historical cable data to obtain a trend prediction model, wherein the deep learning model is used to learn the relationship between the load, temperature and insulation performance of the high-voltage cable; Based on the trend prediction model, prediction is performed according to current cable data of the high-voltage cable to obtain temperature trend data; When the temperature trend data is abnormal, an abnormal warning is issued to the high-voltage cable.

2. The method according to claim 1, characterized in that The step of providing an abnormal warning to the high-voltage cable when the temperature trend data is abnormal includes: Determining whether the temperature trend data reaches a preset temperature threshold; When the temperature trend data reaches the preset temperature threshold, determining that the cable insulation condition of the high-voltage cable is abnormal; Determine the cable sheath grounding data of the high-voltage cable according to the temperature trend data and the current cable data; Based on the cable sheath grounding data, an abnormal warning is issued for the cable insulation condition.

3. The method according to claim 2, characterized in that The step of determining the cable sheath grounding data of the high-voltage cable according to the temperature trend data and the current cable data comprises: Acquire the current cable load and the current ambient temperature in the current cable data; Based on the trend prediction model, determining the load-temperature-grounding association relationship of the high-voltage cable; The current cable load and the current ambient temperature are analyzed through the load-temperature-grounding association relationship to obtain the cable sheath grounding data of the high-voltage cable.

4. The method according to any one of claims 1 to 3, characterized in that Before the step of training the deep learning model constructed based on the recurrent neural network according to the historical cable data to obtain the trend prediction model, the step further includes: Performing temperature analysis on the historical cable data to obtain a temperature variation curve of the high-voltage cable under different operating conditions; Performing correlation analysis on the historical cable data to obtain correlation data between the cable temperature of the high-voltage cable and cable operating factors, wherein the cable operating factors include cable load and ambient temperature; Correspondingly, after the step of providing an abnormal warning to the high-voltage cable when the temperature trend data is abnormal, the method further includes: Based on the temperature variation curve and the correlation data, the operating condition of the high-voltage cable is optimized through the temperature trend data.

5. The method according to claim 4, characterized in that The step of training the deep learning model constructed based on the recurrent neural network according to the historical cable data to obtain the trend prediction model includes: Performing data preprocessing on the historical cable data to obtain preprocessed cable data; A deep learning model is constructed by using a recurrent neural network with the goal of learning the relationship between the load, temperature and insulation performance of the high-voltage cable; The preprocessed cable data, the temperature change curve and the correlation data are input into the deep learning model for training to obtain a trend prediction model.

6. The method according to claim 5, characterized in that The step of inputting the pre-processed cable data, the temperature change curve and the correlation data into the deep learning model for training to obtain a trend prediction model includes: Inputting the preprocessed cable data, the temperature change curve and the correlation data into the deep learning model for training to obtain an initial prediction model; Determining whether the accuracy of the output result of the initial prediction model reaches a preset threshold; If the accuracy does not reach the preset threshold, the model parameters of the initial prediction model are adjusted, and the step of inputting the preprocessed cable data, the temperature change curve and the correlation data into the deep learning model for training is returned to execute until the accuracy reaches the preset threshold, and the model corresponding to the preset threshold is used as the trend prediction model.

7. A cable temperature abnormality warning device, characterized in that: The device comprises: Data acquisition module, used to collect historical cable data of high voltage cables under different load and temperature conditions; A model training module, used to train a deep learning model based on a recurrent neural network according to the historical cable data to obtain a trend prediction model, wherein the deep learning model is used to learn the relationship between the load, temperature and insulation performance of the high-voltage cable; A temperature prediction module, used to make a prediction based on the trend prediction model and the current cable data of the high-voltage cable to obtain temperature trend data; The abnormal warning module is used to issue an abnormal warning to the high-voltage cable when the temperature trend data is abnormal.

8. A cable temperature abnormality warning device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the cable temperature abnormality early warning method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the cable temperature abnormality warning method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the cable temperature abnormality early warning method according to any one of claims 1 to 6 are implemented.

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