Monitoring method and device of electrical equipment, storage medium and equipment

Abstract

The invention relates to an electrical equipment monitoring method and device, a storage medium and equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining temperature data and current data of electrical equipment in a preset time period; generating a characteristic change curve of the electrical equipment according to the temperature data and the current data; determining whether the characteristic change curve meets a preset abnormal condition or not; and under the condition that the characteristic change curve meets a preset abnormal condition, determining that the electrical equipment has a fault. According to the method, the time sequence state change curve is obtained according to the temperature data and the current data of the electrical equipment in the preset time period, and then the characteristic change curve is compared with the preset abnormal condition in time, so that whether the state of the electrical equipment has a fault can be quickly analyzed, and abnormal monitoring on the state of the electrical equipment is realized; and the use reliability of the electrical equipment is effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular, to a monitoring method, device, storage medium and device for electrical equipment. Background Art

[0002] Electrical equipment is a general term for generators, transformers, power lines, circuit breakers and other equipment in the power system. The important role that electricity plays in life and production cannot be ignored. It has brought great convenience and has become an important energy source in production and life. The most critical factor that enables the normal operation and transmission of electricity in power plants is electrical equipment.

[0003] Abnormal operation of electrical equipment may cause equipment failure and lead to safety problems, so it is necessary to monitor the operation of electrical equipment in a timely manner. The traditional way to monitor the operation of electrical equipment is to analyze and judge the temperature changes displayed by the temperature sensor of the recording equipment at different time points, or to monitor the internal environment of the electrical equipment through the internal environment monitoring device, including temperature, humidity, leakage, etc.

[0004] However, with the rapid development of the power system, the number of electrical equipment is increasing, the functions are becoming more and more complex, and the monitoring of electrical equipment is becoming more and more difficult. Traditional monitoring methods cannot accurately distinguish the changing data under the operating status of the equipment. They have certain limitations and general decision-making, and it is difficult to truly reflect the actual situation. Summary of the invention

[0005] In order to overcome the problems existing in the related art, the present disclosure provides a monitoring method, device, storage medium and equipment for electrical equipment.

[0006] According to a first aspect of an embodiment of the present disclosure, there is provided a method for monitoring an electrical device, comprising: Obtaining temperature data and current data of electrical equipment within a preset time period; generating a characteristic change curve of the electrical device according to the temperature data and the current data; Determining whether the characteristic change curve meets a preset abnormal condition; When the characteristic change curve meets a preset abnormal condition, it is determined that the electrical equipment has a fault.

[0007] In one embodiment, determining whether the characteristic change curve meets a preset abnormal condition includes: Obtaining an abnormal data point sequence according to the characteristic change curve and a preset standard curve; According to the abnormal data point sequence, it is determined whether the characteristic change curve meets a preset abnormal condition.

[0008] In one embodiment, determining whether the characteristic change curve satisfies a preset abnormal condition according to the abnormal data point sequence includes: Determining the degree of mutation of the abnormal data point sequence; When the mutation degree is within a preset range, determining that the characteristic change curve meets a preset abnormal condition; or, When the mutation degree does not fall within the preset range, it is determined that the characteristic change curve does not meet the preset abnormal condition.

[0009] In one embodiment, the method further comprises: When the characteristic change curve meets a preset abnormal condition, determining the abnormal type of the electrical equipment; Output the fault prompt information corresponding to the abnormal type.

[0010] In one embodiment, when the characteristic change curve meets a preset abnormal condition, determining the abnormal type of the electrical equipment includes: When the characteristic change curve meets a preset abnormal condition, obtaining a slow-changing signal of the electrical device; The abnormal type of the electrical device is determined according to the slowly varying signal and a predetermined normal threshold range of each type of slowly varying signal.

[0011] According to a second aspect of an embodiment of the present disclosure, there is provided a monitoring device for an electrical device, comprising: An acquisition module, used to acquire temperature data and current data of the electrical equipment within a preset time period; A processing module, used for generating a characteristic change curve of the electrical device according to the temperature data and the current data; A determination module, used to determine whether the characteristic change curve meets a preset abnormal condition; The determination module is further configured to determine that a fault exists in the electrical equipment when the characteristic change curve meets a preset abnormal condition.

[0012] In one embodiment, the determining module is further used to: Obtaining an abnormal data point sequence according to the characteristic change curve and a preset standard curve; According to the abnormal data point sequence, it is determined whether the characteristic change curve meets a preset abnormal condition.

[0013] In one embodiment, the determining module is further used to: Determining the degree of mutation of the abnormal data point sequence; When the mutation degree is within a preset range, determining that the characteristic change curve meets a preset abnormal condition; or, When the mutation degree does not fall within the preset range, it is determined that the characteristic change curve does not meet the preset abnormal condition.

[0014] In one embodiment, the determining module is further used to: When the characteristic change curve meets a preset abnormal condition, determining the abnormal type of the electrical equipment; Output the fault prompt information corresponding to the abnormal type.

[0015] In one embodiment, the determining module is further used to: When the characteristic change curve meets a preset abnormal condition, obtaining a slow-changing signal of the electrical device; The abnormal type of the electrical device is determined according to the slowly varying signal and a predetermined normal threshold range of each type of slowly varying signal.

[0016] According to a third aspect of an embodiment of the present disclosure, there is provided a non-temporary computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0017] According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the steps of the method described in the first aspect.

[0018] Through the above technical scheme, a time series state change curve is first obtained based on the temperature data and current data of the electrical equipment within a preset time period, and then the characteristic change curve is timely compared with the preset abnormal conditions. It is possible to quickly analyze whether there is a fault in the state of the electrical equipment, realize abnormal monitoring of the state of the electrical equipment, and effectively improve the reliability of the use of the electrical equipment.

[0019] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 The present invention is a flow chart of a method for monitoring an electrical device according to an exemplary embodiment.

[0021] Figure 2The present invention is a flow chart of a method for monitoring an electrical device according to an exemplary embodiment.

[0022] Figure 3 The present invention is a flow chart of a method for monitoring an electrical device according to an exemplary embodiment.

[0023] Figure 4 The present invention is a block diagram of a monitoring device for electrical equipment according to an exemplary embodiment.

[0024] Figure 5 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0025] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0026] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0027] The terms "first", "second", etc. in the specification and claims of the present disclosure and the above drawings are used to distinguish similar objects and do not have to be understood as a specific order or sequence. In addition, in the description with reference to the drawings, the same symbols in different drawings represent the same elements.

[0028] In the description of the present disclosure, unless otherwise specified, "multiple" means two or more than two, and other quantifiers are similar thereto; "at least one item", "one item or multiple items" or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one item a can represent any number of a; for another example, one item or multiple items among a, b and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple; "and / or" is a kind of description of the association relationship of associated objects, indicating that there can be three kinds of relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " indicates that the associated objects before and after are in an "or" relationship.

[0029] Although operations or steps are described in a specific order in the drawings in the embodiments of the present disclosure, it should not be understood that it is required to perform these operations or steps in the specific order shown or in a serial order, or to perform all the operations or steps shown to obtain the desired results. In the embodiments of the present disclosure, these operations or steps can be performed in series; these operations or steps can also be performed in parallel; or some of these operations or steps can be performed.

[0030] First, the application scenarios of the present disclosure are described.

[0031] Electrical equipment is a general term for generators, transformers, power lines, circuit breakers and other equipment in the power system. The important role that electricity plays in life and production cannot be ignored. It has brought great convenience and has become an important energy source in production and life. The most critical factor that enables the normal operation and transmission of electricity in power plants is electrical equipment.

[0032] Basic elements of electrical equipment use Electrical insulation, maintaining good insulation of distribution lines and electrical equipment is the most basic element to ensure personal safety and normal operation of electrical equipment. Whether the performance of electrical insulation is good can be measured by measuring its insulation resistance, withstand voltage, leakage current and dielectric loss and other parameters. Safety distance. Electrical safety distance refers to the safe and reliable distance at which human body, objects, etc. approach the live body without danger. For example, a certain distance should be maintained between the live body and the ground, between the live body and the live body, between the live body and the human body, and between the live body and other facilities and equipment. Usually, when working near distribution lines and transformers and distribution devices, the line safety distance, transformer and distribution device safety distance, maintenance safety distance and operation safety distance should be considered. Safe current carrying capacity, the safe current carrying capacity of the conductor refers to the amount of current allowed to continuously pass through the conductor. If the current continuously passing through the conductor exceeds the safe current carrying capacity, the heat of the conductor will exceed the allowable value, resulting in insulation damage, and even leakage and fire. Therefore, it is very important to determine the conductor cross-section and select equipment according to the safe current carrying capacity of the conductor. Signs, obvious, accurate and unified signs are important factors to ensure the safety of electricity use. Signs generally include color signs, nameplate signs and model signs. Color signs indicate wires of different properties and uses. Nameplate signs are generally used as signs for hazardous locations, and model signs are used as signs for special equipment structures.

[0033] Abnormal operating status of electrical equipment may cause equipment failure and lead to safety problems. Therefore, it is necessary to monitor the operating status of electrical equipment in a timely manner. Among them, temperature monitoring of electrical equipment is the most important monitoring link. By monitoring the temperature changes of electrical equipment, abnormal conditions such as overheating and overload of the equipment can be discovered in time, so that corresponding measures can be taken to deal with them to avoid equipment failure or damage. At the same time, timely monitoring and handling of temperature abnormalities can effectively improve the safety of equipment and extend the service life of electrical equipment, avoiding the economic costs caused by frequent equipment replacement.

[0034] The traditional way to monitor the operating status of electrical equipment is to analyze and judge the temperature value changes displayed by the temperature sensor of the recording equipment at different time nodes, compare the current temperature value with the standard value, and if it does not fall within the threshold range required by the standard value, it is considered as abnormal temperature data, and it is determined that the time node corresponding to the current temperature value has an abnormal temperature of the electrical equipment. Alternatively, the internal environment of the electrical equipment is monitored through an internal environment monitoring device, and the monitoring content includes temperature, humidity, leakage, etc.

[0035] However, with the rapid development of the power system, the number of electrical equipment is increasing, the functions are becoming more and more complex, and its monitoring is becoming more and more difficult. The traditional monitoring method cannot accurately distinguish the temperature change data under the operation state of the equipment, and has certain limitations and general decision-making, resulting in the inability to monitor in the entire time domain. In addition, the overload alarm of the internal environment monitoring device only judges the current current value, which is difficult to truly reflect the actual situation.

[0036] In order to solve the above problems, the present disclosure provides a monitoring method, device, storage medium and equipment for electrical equipment, which obtains the temperature data and current data of the electrical equipment within a preset time period; generates a characteristic change curve of the electrical equipment according to the temperature data and the current data; determines whether the characteristic change curve meets the preset abnormal condition; and determines that the electrical equipment has a fault when the characteristic change curve meets the preset abnormal condition. Through the above method, a time-series state change curve is first obtained based on the temperature data and current data of the electrical equipment within a preset time period, and then the characteristic change curve is compared with the preset abnormal condition in a timely manner, which can quickly analyze whether the state of the electrical equipment has a fault, realize abnormal monitoring of the state of the electrical equipment, and effectively improve the reliability of the use of the electrical equipment.

[0037] The specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0038] Figure 1 is a flow chart of a method for monitoring an electrical device according to an exemplary embodiment. Figure 1 As shown, the following steps are included.

[0039] In step S101 , temperature data and current data of an electrical device within a preset time period are acquired.

[0040] In this embodiment, step S101 may include: periodically acquiring temperature data and current data of the electrical device within a preset time period. Optionally, the preset time period may be 1 minute, and acquiring the temperature data and current data may be 1 second.

[0041] In some embodiments, temperature data can be obtained by a temperature sensor disposed at a preset position of the electrical device. The temperature sensor can be a contact or non-contact temperature sensor, which is not limited in this embodiment; current data can be obtained by a current sensor disposed inside the electrical device.

[0042] In step S102, a characteristic variation curve of the electrical device is generated according to the temperature data and the current data.

[0043] In this embodiment, the characteristic change curve of the electrical device may be a time series curve of temperature characteristic change and current characteristic change.

[0044] Exemplarily, time series data of the temperature of electrical equipment changing with time is obtained, and a feature space is constructed. The feature space uses the numerical value of each detected time node in the time series data as the horizontal coordinate, where the interval between time nodes is 1 second, and the unit of the horizontal coordinate is seconds. The detected temperature data corresponding to the current time node is used as the vertical coordinate of the feature space, and the unit of the vertical coordinate is degrees Celsius. The acquired temperature data of the electrical equipment and its corresponding time nodes are marked in the spatial coordinate system to obtain data points, and the data points are connected to construct a time series temperature change characteristic curve. Constructing the time series temperature change characteristic curve can more objectively show the changing and fluctuating characteristics of the equipment temperature in the current time period.

[0045] In step S103, it is determined whether the characteristic change curve meets a preset abnormal condition.

[0046] In this embodiment, the preset abnormal condition may be that the difference between the characteristic change curve and the preset standard curve is greater than or equal to the abnormal threshold.

[0047] In some embodiments, step S103 includes: comparing the target parameter curve with a preset standard parameter curve to obtain a parameter comparison result; wherein the parameter comparison result may include a deviation value of the parameter curve, a deviation rate, a time point (or time period) when the deviation occurs, etc. The generated parameter comparison result can characterize the deviation between the target parameter curve and the standard parameter curve.

[0048] In a possible implementation, the preset abnormal condition may be obtained based on abnormality detection. For example, when there is a fault in the electrical equipment, the abnormal condition is obtained based on clustering, classification, statistics, or the like.

[0049] In step S104, when the characteristic variation curve meets the preset abnormal condition, it is determined that the electrical equipment has a fault.

[0050] In a possible implementation, after step S104, the above-mentioned electrical equipment monitoring method further includes: step S202 may include: sending the abnormal type of the electrical equipment to the maintenance personnel through communication means, so that the maintenance personnel can understand the cause of the fault without complicated on-site detection.

[0051] Through the above technical scheme, a time series state change curve is first obtained based on the temperature data and current data of the electrical equipment within a preset time period, and then the characteristic change curve is timely compared with the preset abnormal conditions. It is possible to quickly analyze whether there is a fault in the state of the electrical equipment, realize abnormal monitoring of the state of the electrical equipment, and effectively improve the reliability of the use of the electrical equipment.

[0052] In some embodiments, see Figure 2 , step S103 includes: In step S1031, a sequence of abnormal data points is obtained according to the characteristic change curve and a preset standard curve.

[0053] In a possible implementation, obtaining an abnormal data point sequence according to the characteristic change curve and a preset standard curve includes: obtaining temperature data difference values ​​of all time nodes in the time series temperature change characteristic curve; when the temperature data difference value is greater than or equal to a preset difference value threshold, determining the time node as an abnormal data point sequence, and marking the data point corresponding to the abnormal temperature data in the time series temperature change characteristic curve. Exemplarily, the preset difference value threshold is 5 degrees Celsius.

[0054] In step S1032, it is determined whether the characteristic change curve meets a preset abnormal condition according to the abnormal data point sequence.

[0055] In this embodiment, the abnormal data point sequence may be abnormal temperature data or current data in the characteristic change curve, and a corresponding data point sequence in the change characteristic curve.

[0056] In a possible implementation, the abnormal data point sequence is a data point whose deviation value from the normal state at a certain time sequence of the characteristic change curve is greater than a preset deviation threshold. For example, if there is a data point in the characteristic change curve whose deviation value from the normal state is greater than 80%, the data point and its corresponding time sequence are regarded as the abnormal data point sequence.

[0057] In some embodiments, step S1032 includes: Determine the degree of mutation of the sequence of abnormal data points; When the mutation degree is within the preset range, it is determined that the characteristic change curve meets the preset abnormal condition; or, When the mutation degree does not fall within the preset range, it is determined that the characteristic change curve does not meet the preset abnormal condition.

[0058] In a possible implementation, the temperature data of each time node in the time series temperature change characteristic curve is judged by setting the upper limit of the standard temperature reference value of the electrical equipment, and discrete abnormal data points exceeding the upper limit of the standard temperature reference value are obtained. Due to the diversity of the causes of the discrete abnormalities, it is necessary to analyze and distinguish them according to the fluctuation characteristics of each abnormal data point. If the abnormal temperature data at the current time node is because the temperature data of the electrical equipment is normal but the test or contact of the temperature sensor causes an instantaneous abnormal temperature mutation, that is, the temperature data is abnormal due to the sensor's own failure, the degree of abnormal mutation of the data point at this time is relatively large; because the temperature rise under normal circumstances is not instantaneous, but gradually increases in adjacent time nodes, it is possible to judge whether it is a normal temperature rise based on the fluctuation of the temperature data of the adjacent time nodes in the current abnormal data point; for short-term temperature fluctuations caused by factors such as the environment and load, the temperature data of the adjacent time nodes in the abnormal data point are similar to the abnormal changes in temperature data with greater hazards such as overload and short circuit, and a more in-depth analysis is required to finally obtain an impact value assessment model for the abnormal data point.

[0059] Exemplarily, the data points corresponding to the abnormal temperature data in the time-series temperature change characteristic curve are taken as abnormal data points, all abnormal data points in the time-series temperature change characteristic curve are obtained, all abnormal data points are arranged in the order of time nodes in the time-series temperature change characteristic curve to obtain an abnormal data point sequence, and the degree of mutation of the abnormal temperature data in the abnormal data point sequence is obtained based on the abnormal temperature data of the time nodes in the abnormal data point sequence and the temperature data of adjacent time nodes.

[0060] In this embodiment, the preset range can be set by the technician.

[0061] In a possible implementation, the above-mentioned monitoring method for electrical equipment further includes: evaluating the impact value of abnormal data points with a high degree of mutation.

[0062] It should be noted that the data obtained by the traditional monitoring method is discrete data compared with the given standard reference value, but the reasons for its discreteness are diverse. If the current device is running under high load for a long time, the current density of the internal components of the device increases, resulting in a rapid increase in the internal temperature, or the electrical equipment is short-circuited, causing the current to increase rapidly and concentrate at the short-circuit to form a high-temperature area, thereby causing abnormal temperature data, etc., it will cause great harm to the electrical equipment and bring great safety hazards. Therefore, the abnormal data obtained in this case has a higher impact value; the reason why the data is discrete from the standard temperature reference value may also be the fault of the temperature sensor itself, that is, the current device temperature data is normal but the test or contact of the sensor causes an instantaneous abnormal temperature mutation; or due to short-term temperature fluctuations caused by environmental, load and other factors, such abnormal data is not enough to cause equipment overheating or other safety problems; then the impact value of the abnormal data caused by this type of situation is relatively low; therefore, the different data point fluctuation characteristics in the timing curve caused by different abnormal reasons are combined to evaluate and calculate them; the impact value factor of each abnormal data is obtained, and then refinement and differentiation are achieved.

[0063] Exemplarily, a time-series temperature change characteristic curve is obtained after all abnormal data points less than or equal to the mutation degree threshold are removed. For ease of description, the time-series temperature change characteristic curve after all abnormal data points less than or equal to the mutation degree threshold are removed is recorded as the first characteristic curve, and any abnormal data point greater than the mutation degree threshold in the first characteristic curve is recorded as the th abnormal data point. In the first characteristic curve, the interval consisting of the th data points adjacent to the left and right is taken as the fluctuation interval of the th abnormal data point with the th abnormal data point as the center; the abnormal data is classified and marked, wherein all abnormal data less than or equal to the mutation degree threshold are removed. These abnormal data are very likely to be sensor Abnormal data with low impact value are very likely to be short-term temperature fluctuations caused by environmental and load factors, and can be temporarily not processed, but it is necessary to determine whether the frequency of such abnormal data increases in subsequent monitoring before conducting subsequent inspections and maintenance; and for abnormal data with high impact value, it is necessary to conduct statistics on time nodes in a timely manner. Such abnormal data are important and accurate abnormal temperature data of electrical equipment. When temperature abnormalities occur, possible risks can be reduced through detection and maintenance of electrical equipment. According to the above classification and processing, intelligent monitoring of temperature abnormalities in the operating state of electrical equipment can be achieved.

[0064] Through the above technical solution, the fluctuation range of any abnormal data point in the first characteristic curve is obtained, the impact value of the abnormal data point is obtained according to the proportion of the abnormal data point in the fluctuation range, and the abnormal temperature data of the electrical equipment is processed according to the impact value to achieve the effect of intelligent monitoring of abnormal data.

[0065] In some embodiments, see Figure 3 , the above-mentioned electrical equipment monitoring method further includes: In step S201, when the characteristic variation curve meets the preset abnormal condition, the abnormal type of the electrical equipment is determined.

[0066] In a possible implementation, the abnormality type of the electrical equipment may be set according to actual experimental results or experience, that is, the fault cause corresponding to the combination of different abnormal data is determined according to the combination.

[0067] In step S202, fault prompt information corresponding to the abnormal type is output.

[0068] Through the above technical solution, the abnormal type of electrical equipment is judged and the abnormal type of electrical equipment is output, so that maintenance personnel can learn the cause of the failure of the electrical equipment without complicated on-site detection, so that maintenance personnel can quickly complete the maintenance of the electrical equipment according to the cause of the failure, thereby improving the efficiency of maintenance work.

[0069] In some embodiments, step S201 includes: When the characteristic change curve meets the preset abnormal condition, a slow-changing signal of the electrical equipment is obtained; The abnormal type of the electrical equipment is determined according to the slowly varying signal and a predetermined normal threshold range of each type of slowly varying signal.

[0070] In one possible implementation, determining the abnormal type of the electrical equipment based on the slowly varying signal and the predetermined normal threshold range of each type of slowly varying signal includes: performing data quantization conversion according to the state of the slowly varying signal to obtain a fault data item set; and determining the abnormal type of the electrical equipment using a preset analysis algorithm based on the fault data item set and preset analysis parameters.

[0071] Exemplarily, data quantization conversion is performed according to the state of the slowly varying signal to obtain a set of fault data items, including: determining a slowly varying signal state matrix according to the state of the slowly varying signal; wherein each row of the slowly varying signal state matrix corresponds to a group of slowly varying signals, and each column corresponds to a type of slowly varying signal; when a slowly varying signal state is normal, the corresponding position in the slowly varying signal state matrix is ​​marked as 0, and when a slowly varying signal state is abnormal, the corresponding position in the slowly varying signal state matrix is ​​marked as 1; according to the column number index number corresponding to the value of 1 in each row of the slowly varying signal state matrix, the fault data items of each row are organized; and the fault data item set is obtained according to the fault data items of each row.

[0072] Exemplarily, the preset analysis algorithm may be: an FPgrowth algorithm or an Aprior algorithm with preset confidence and support.

[0073] Through the above technical scheme, the abnormal type of electrical equipment is judged by the slowly varying signal and the normal threshold values ​​of various types of slowly varying signals determined in advance, and the abnormal type of the electrical equipment is output, so that maintenance personnel can know the cause of the failure of the electrical equipment without complicated on-site detection, effectively understand the changes in the operating sequence of the electrical equipment, and the analysis is more accurate.

[0074] Figure 4 is a block diagram of a monitoring device for electrical equipment according to an exemplary embodiment. Figure 4 The monitoring device 400 for electrical equipment includes an acquisition module 401 , a processing module 402 and a determination module 403 .

[0075] An acquisition module 401 is used to acquire temperature data and current data of the electrical equipment within a preset time period; The processing module 402 is used to generate a characteristic change curve of the electrical device according to the temperature data and the current data; A determination module 403 is used to determine whether the characteristic change curve meets a preset abnormal condition; The determination module 403 is further configured to determine that a fault exists in the electrical equipment when the characteristic change curve meets a preset abnormal condition.

[0076] In one embodiment, the determination module 403 is further configured to: According to the characteristic change curve and the preset standard curve, a sequence of abnormal data points is obtained; According to the abnormal data point sequence, determine whether the characteristic change curve meets the preset abnormal condition.

[0077] In one embodiment, the determination module 403 is further configured to: Determine the degree of mutation of the sequence of abnormal data points; When the mutation degree is within the preset range, it is determined that the characteristic change curve meets the preset abnormal condition; or, When the mutation degree does not fall within the preset range, it is determined that the characteristic change curve does not meet the preset abnormal condition.

[0078] In one embodiment, the determination module 403 is further configured to: When the characteristic change curve meets the preset abnormal condition, determine the abnormal type of the electrical equipment; Output fault prompt information corresponding to the abnormal type.

[0079] In one embodiment, the determination module 403 is further configured to: When the characteristic change curve meets the preset abnormal condition, a slow-changing signal of the electrical equipment is obtained; The abnormal type of the electrical equipment is determined according to the slowly varying signal and a predetermined normal threshold range of each type of slowly varying signal.

[0080] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0081] Figure 5 FIG. 5 is a block diagram of an electronic device 500 according to an exemplary embodiment. Figure 5 As shown, the electronic device 500 may include: a processor 501 , a memory 502 . The electronic device 500 may also include one or more of a multimedia component 503 , an input / output (I / O) interface 504 , and a communication component 505 .

[0082] The processor 501 is used to control the overall operation of the electronic device 500 to complete all or part of the steps in the above-mentioned monitoring method of the electrical device. The memory 502 is used to store various types of data to support the operation of the electronic device 500. For example, these data may include instructions for any application or method used to operate on the electronic device 500, and application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory 502 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 503 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 502 or sent through the communication component 505. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 504 provides an interface between the processor 501 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 505 is used for wired or wireless communication between the electronic device 500 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 505 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0083] In an exemplary embodiment, the electronic device 500 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned electrical equipment monitoring method.

[0084] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned monitoring method of the electrical device are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 502 including program instructions, and the above-mentioned program instructions can be executed by the processor 501 of the electronic device 500 to complete the above-mentioned monitoring method of the electrical device.

[0085] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program executable by a programmable device. The computer program has a code portion for executing the above-mentioned monitoring method of the electrical equipment when executed by the programmable device.

[0086] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0087] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0088] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A method for monitoring electrical equipment, characterized in that: include: Obtaining temperature data and current data of electrical equipment within a preset time period; generating a characteristic change curve of the electrical device according to the temperature data and the current data; Determining whether the characteristic change curve meets a preset abnormal condition; When the characteristic change curve meets a preset abnormal condition, it is determined that the electrical equipment has a fault.

2. The method for monitoring electrical equipment according to claim 1, characterized in that: Determining whether the characteristic change curve meets a preset abnormal condition includes: Obtaining an abnormal data point sequence according to the characteristic change curve and a preset standard curve; According to the abnormal data point sequence, it is determined whether the characteristic change curve meets a preset abnormal condition.

3. The method for monitoring electrical equipment according to claim 2, characterized in that: The determining, according to the abnormal data point sequence, whether the characteristic change curve meets a preset abnormal condition comprises: Determining the degree of mutation of the abnormal data point sequence; When the mutation degree is within a preset range, determining that the characteristic change curve meets a preset abnormal condition; or, When the mutation degree does not fall within the preset range, it is determined that the characteristic change curve does not meet the preset abnormal condition.

4. The method for monitoring electrical equipment according to claim 1, characterized in that: The method further comprises: When the characteristic change curve meets a preset abnormal condition, determining the abnormal type of the electrical equipment; Output the fault prompt information corresponding to the abnormal type.

5. The method for monitoring electrical equipment according to claim 4, characterized in that: When the characteristic change curve meets the preset abnormal condition, determining the abnormal type of the electrical equipment includes: When the characteristic change curve meets a preset abnormal condition, obtaining a slow-changing signal of the electrical device; The abnormal type of the electrical device is determined according to the slowly varying signal and a predetermined normal threshold range of each type of slowly varying signal.

6. A monitoring device for electrical equipment, characterized in that: include: An acquisition module, used to acquire temperature data and current data of the electrical equipment within a preset time period; A processing module, used for generating a characteristic change curve of the electrical device according to the temperature data and the current data; A determination module, used to determine whether the characteristic change curve meets a preset abnormal condition; The determination module is further configured to determine that a fault exists in the electrical equipment when the characteristic change curve satisfies a preset abnormal condition.

7. The monitoring device for electrical equipment according to claim 6, characterized in that: The determining module is further used for: Obtaining an abnormal data point sequence according to the characteristic change curve and a preset standard curve; According to the abnormal data point sequence, it is determined whether the characteristic change curve meets a preset abnormal condition.

8. The monitoring device for electrical equipment according to claim 7, characterized in that: The determining module is further used for: Determining the degree of mutation of the abnormal data point sequence; When the mutation degree is within a preset range, determining that the characteristic change curve meets a preset abnormal condition; or, When the mutation degree does not fall within the preset range, it is determined that the characteristic change curve does not meet the preset abnormal condition.

9. The monitoring device for electrical equipment according to claim 6, characterized in that: The determining module is further used for: When the characteristic change curve meets a preset abnormal condition, determining the abnormal type of the electrical equipment; Output the fault prompt information corresponding to the abnormal type.

10. The monitoring device for electrical equipment according to claim 9, characterized in that: The determining module is further used for: When the characteristic change curve meets a preset abnormal condition, obtaining a slow-changing signal of the electrical device; The abnormal type of the electrical device is determined according to the slowly varying signal and a predetermined normal threshold range of each type of slowly varying signal.

11. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

12. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 5.

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