Data processing method, device, electronic device and storage medium

Abstract

The embodiment of the present invention discloses a data processing method, device, electronic device and storage medium. The method includes: receiving detection data and positioning information corresponding to at least one monitoring device; processing the detection data based on a target voltage correction model to obtain a voltage correction attribute value; correcting the power information to be detected based on the voltage correction attribute value to obtain a target correction voltage corresponding to the power data to be used in each phase; if there is a target correction voltage in each phase target correction voltage that is not within a preset voltage range, then based on the target correction voltage and the positioning information corresponding to the corresponding monitoring device, generate an early warning prompt information and send it to the target device. The problem of poor data availability and low detection accuracy caused by abnormal detection of monitoring equipment based on measured power data in the prior art is solved, and the accuracy of voltage determination is improved while improving the accuracy and timeliness of abnormal detection, so as to achieve the effect of maintaining the operation of the power grid.

Description

Technical Field

[0001] The embodiments of the present invention relate to computer processing technology, and more particularly to a data processing method, device, electronic device and storage medium. Background Art

[0002] With the development of distribution networks, the use of drop-out fuses is huge. It is common for drop-out fuses to drop (burn out) due to abnormal events. If the drop-out fuse drops, the transformer will be in phase-loss operation, damaging the transformer and posing a safety hazard. At the same time, phase-loss operation will cause residential electrical appliances and other equipment to fail to start normally, affecting the power supply quality. Therefore, real-time monitoring of the working status of the drop-out fuse is particularly important.

[0003] The existing monitoring method usually uses power parameter data such as voltage and current to detect the drop-out fuse. If there is an abnormal fluctuation in voltage or current, it is considered that the drop-out fuse is in an abnormal state.

[0004] However, in actual scenarios, the power parameter data of the fuse will be affected by a large number of external factors such as the environment or magnetic field, causing the collected power parameter information to be inaccurate and unstable, resulting in low detection accuracy and poor timeliness of abnormality detection. Summary of the invention

[0005] The embodiments of the present invention provide a data processing method, device, electronic device and storage medium to improve the accuracy of voltage determination while improving the accuracy and timeliness of abnormality detection, thereby achieving the technical effect of maintaining power grid operation.

[0006] In a first aspect, an embodiment of the present invention provides a data processing method, the method comprising:

[0007] Receive detection data and positioning information corresponding to at least one monitoring device; wherein the detection data includes temperature information, humidity information and power information to be detected, and the power information to be detected includes at least one phase of power data to be used;

[0008] Processing the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value;

[0009] Correcting the power information to be detected based on the voltage correction attribute value to obtain a target correction voltage corresponding to the power data to be used of each phase;

[0010] If a target corrected voltage among the target corrected voltages of each phase is not within a preset voltage range, an early warning prompt information is generated based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring device, and the early warning prompt information is sent to the target device.

[0011] In a second aspect, an embodiment of the present invention further provides a data processing device, the device comprising:

[0012] A data receiving module, used to receive detection data and positioning information corresponding to at least one monitoring device; wherein the detection data includes temperature information, humidity information and power information to be detected, and the power information to be detected includes at least one phase of power data to be used;

[0013] A voltage correction attribute value determination module, used for processing the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value;

[0014] A target correction voltage determination module, used to correct the power information to be detected based on the voltage correction attribute value to obtain a target correction voltage corresponding to the power data to be used of each phase;

[0015] The early warning prompt information generation module is used to generate early warning prompt information based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring device if the target corrected voltage of each phase is not within the preset voltage range, and send the early warning prompt information to the target device.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, the device comprising:

[0017] one or more processors;

[0018] a storage device for storing one or more programs,

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method as described in any one of the embodiments of the present invention.

[0020] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a data processing method as described in any one of the embodiments of the present invention.

[0021] The technical solution of the embodiment of the present invention receives detection data such as temperature information, humidity information, and power information to be detected, as well as positioning information corresponding to at least one monitoring device, and then processes the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value, and then corrects the power information to be detected based on the voltage correction attribute value to obtain a target corrected voltage corresponding to the power data to be used of each phase; if there is a target corrected voltage in the target corrected voltage of each phase that is not within a preset voltage range, then based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring device, an early warning prompt information is generated, and the early warning prompt information is sent to the target device, which solves the problem of poor availability of power data in the prior art of performing abnormal detection on the drop-out type fuse based on the power data actually measured by the device. , low practicality, which leads to the problem of low detection accuracy. It is realized that the voltage value actually measured by the equipment is corrected by obtaining the parameter information corresponding to the influencing factors affecting the actually measured voltage value of the drop-out type fuse, so as to obtain the corrected voltage information with high accuracy, so as to judge the operating state of the drop-out type fuse based on the corrected voltage. When the target corrected voltage of each phase exists in the target corrected voltage that is not within the preset voltage range, it is considered that the drop-out type fuse is in an abnormal operating state, and based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring equipment, an early warning prompt information is generated and sent to the target device, so as to achieve the goal of early monitoring of the abnormality, so as to enable the target user to accurately locate the problem based on the data in the early warning prompt information in time, thereby improving the accuracy and timeliness of abnormality detection and achieving the technical effect of maintaining the operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required to describe the embodiments. Obviously, the drawings introduced are only drawings of a part of the embodiments to be described in the present invention, rather than all the drawings. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flowchart of a data processing method provided in Embodiment 1 of the present invention;

[0024] Figure 2 A flowchart of a data processing method provided in Embodiment 2 of the present invention;

[0025] Figure 3 A schematic diagram of a data processing method provided in Embodiment 3 of the present invention;

[0026] Figure 4 A structural block diagram of a data processing device provided in Embodiment 4 of the present invention;

[0027] Figure 5This is a schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0029] Embodiment 1

[0030] Figure 1 This is a flow chart of a data processing method provided in the first embodiment of the present invention. This embodiment is applicable to the case of abnormal monitoring of equipment. The method can be executed by the data processing device in the embodiment of the present invention. The device can be implemented in software and / or hardware. Optionally, it can be implemented by an electronic device, which can be a mobile terminal, a PC or a server. The device can be configured in a computing device. The data processing method provided in this embodiment specifically includes the following steps:

[0031] S110: Receive detection data and positioning information corresponding to at least one monitoring device.

[0032] The monitoring device may be a drop-out fuse, for example, an outdoor AC high-voltage drop-out fuse. The detection data includes temperature information, humidity information, and power information to be detected, and the power information to be detected includes at least one phase of power data to be used. The power data to be used may include voltage and current.

[0033] In practical applications, for monitoring equipment, a temperature sensor can be used to detect the temperature information corresponding to the current monitoring equipment, and a humidity sensor can be used to detect the temperature information corresponding to the current monitoring equipment, and a current sensor can be used to detect the current information corresponding to the A phase, B phase, and C phase of the current monitoring equipment, and a voltage sensor can be used to detect the voltage information corresponding to the A phase, B phase, and C phase of the current monitoring equipment. When the temperature information, temperature information, current information, and voltage information uploaded by these four sensors, and the positioning information uploaded by the positioning device are received, it can be considered that the detection data and positioning information corresponding to the current monitoring equipment have been received. Correspondingly, the detection data and positioning information corresponding to each monitoring equipment are received. This allows the subsequent determination of whether the monitoring equipment is abnormal based on the detection of the detection data, and the accurate positioning of the abnormality based on the positioning information.

[0034] S120 : Processing the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value.

[0035] The target voltage correction model may be understood as a pre-trained voltage correction value used to determine the voltage correction value corresponding to the actual measured voltage.

[0036] Specifically, the temperature information, humidity information, current information and voltage information in the detection data can be used as inputs of the target voltage correction model to output a correction value corresponding to the voltage information, i.e., a voltage correction attribute value, so that the voltage information can be subsequently corrected based on the voltage correction attribute value to improve the voltage accuracy.

[0037] It should be noted that in actual scenarios, the voltage parameter data in the drop-out fuse is often affected by the environment and magnetic field, such as temperature, humidity, and current fluctuations, resulting in poor accuracy of the collected voltage information and low accuracy of abnormal detection of the drop-out fuse. At the same time, if a single-phase, two-phase, or three-phase drop occurs in the drop-out fuse, the current or voltage amplitudes of the three phases A, B, and C will be inconsistent, and the amplitude difference will exceed the specified range. Therefore, the three-phase imbalance can be used as a judgment indicator in the abnormality assessment to improve the accuracy of abnormality detection. A blind box relationship model between the voltage monitoring error, that is, the target voltage correction model, can be established in advance based on multi-dimensional influencing factors such as temperature, humidity, and three-phase imbalance. The target voltage correction model is used to process each influencing factor and output the voltage monitoring error, that is, the voltage correction attribute value.

[0038] Optionally, the detection data is processed based on a pre-trained target voltage correction model to obtain a voltage correction attribute value, including: balancing the power data to be used in each phase to obtain a three-phase unbalanced attribute value; inputting the temperature information, the humidity information and the three-phase unbalanced attribute value into a pre-trained target voltage correction model to obtain a voltage correction attribute value.

[0039] Among them, the three-phase imbalance attribute value includes the three-phase voltage imbalance attribute value and the three-phase current imbalance attribute value. The three-phase voltage imbalance attribute value can be used to characterize the degree of imbalance of the three-phase voltages of A, B, and C. Optionally, it can be expressed as the percentage of the root mean square value of the negative-sequence component of the voltage to the positive-sequence component. The three-phase current imbalance attribute value can be used to characterize the degree of imbalance of the three-phase currents of A, B, and C. Optionally, it can be expressed as the percentage of the root mean square value of the negative-sequence component of the current to the positive-sequence component.

[0040] In practical applications, after obtaining the current and voltage corresponding to the three phases A, B, and C, the current imbalance corresponding to A, B, and C can be calculated based on the three-phase current, that is, as the three-phase current imbalance attribute value, and the voltage imbalance corresponding to A, B, and C can also be calculated based on the three-phase voltage, that is, as the three-phase current imbalance attribute value. The three-phase current imbalance attribute value, the three-phase current imbalance attribute value, the temperature information, and the humidity information can be used as input parameters of the target voltage correction model to obtain the voltage correction attribute value.

[0041] S130: Correct the power information to be detected based on the voltage correction attribute value to obtain a target correction voltage corresponding to the power data to be used of each phase.

[0042] Specifically, after obtaining the voltage correction attribute value, the power data of each phase to be used in the power information to be detected can be corrected based on the voltage correction attribute value to obtain corrected voltage information. These three corrected voltage information can be used as target correction voltages.

[0043] It should be noted that when the power information to be detected is corrected based on the voltage correction attribute value to obtain the target corrected voltage corresponding to the power data to be used in each phase, the voltage correction attribute value and the voltage information in the power data to be used in each phase can be processed, such as merging. For example, the voltage correction attribute value can be added to the A phase voltage information to obtain the corrected voltage of the A phase, that is, the target corrected voltage.

[0044] Optionally, the power information to be detected is corrected based on the voltage correction attribute value to obtain the target corrected voltage corresponding to the power data to be used for each phase, including: determining the voltage data to be used corresponding to the power data to be used for each phase in the power information to be detected; and fusing the voltage correction attribute value with the voltage data to be used for each phase to obtain the target corrected voltage corresponding to the voltage data to be used for each phase.

[0045] In practical applications, after obtaining the voltage correction attribute value, the voltage data to be used for each phase in the power information to be detected can be processed based on the voltage correction attribute value, such as merging. For example, if the voltage correction attribute value (error value) is 0.1v and the measured voltage value of phase A is 10v, the voltage correction attribute value plus the measured voltage value can be used as the corrected voltage, that is, 10.1v. Accordingly, the corrected voltage information of phases A, B, and C can be obtained respectively, all as the target corrected voltage.

[0046] S140. If a target corrected voltage among the target corrected voltages of each phase is not within a preset voltage range, generate warning prompt information based on the target corrected voltage and positioning information corresponding to the corresponding monitoring device, and send the warning prompt information to the target device.

[0047] In this embodiment, each corrected target correction voltage can be compared with a voltage threshold within a preset voltage range. If at least one phase of the target correction voltages of each phase is not within the preset voltage range, it means that the monitoring equipment may be operating in a phase-missing state, that is, a drop-out fuse failure may exist. At this time, the target correction voltage, the positioning information of the corresponding monitoring equipment, and the phase sequence information corresponding to the target correction voltage can be used as early warning prompt information, and the early warning prompt information can be sent to the target terminal device to push the early warning prompt information to the staff so that the monitoring equipment can be inspected and repaired in time.

[0048] It should be noted that in order to accurately report and prompt equipment abnormalities, improve monitoring efficiency and subsequent maintenance efficiency, when generating early warning prompt information based on the target correction voltage and the positioning information corresponding to the corresponding monitoring equipment, the target correction voltage and positioning information can be transformed based on pre-set prompt requirements, such as enlarging the prompt information font, highlighting the color, etc., and then the processed early warning prompt information can be sent to the target device, so that the target user can promptly discover the abnormality based on the abnormal information in the early warning prompt information, and determine the source of the abnormality.

[0049] Optionally, based on the target correction voltage and the positioning information corresponding to the corresponding monitoring device, early warning prompt information is generated and sent to the target device, including: filling the target correction voltage and the positioning information corresponding to the corresponding monitoring device into a preset early warning prompt template, generating early warning prompt information, and sending the early warning prompt information to the target device, so that the target user corresponding to the target device can maintain the monitoring device.

[0050] The positioning information includes position information and phase sequence. For example, the phase sequence may include at least one of phase A, phase B, and phase C.

[0051] In this embodiment, the abnormal target correction voltage, the corresponding phase sequence, and the positioning information corresponding to the abnormal monitoring device can be filled into the preset warning prompt template to generate warning prompt information. For example, assuming that the corresponding target correction voltage of phase A in the monitoring device 1 is not within the preset voltage range, the warning prompt information can be the drop of phase A in the monitoring device 1. The warning prompt information can be sent to the target device so that the user can locate the problem of the abnormality based on the abnormal data in the warning prompt information, and then determine the equipment maintenance method in time.

[0052] The technical solution of this embodiment receives detection data such as temperature information, humidity information, and power information to be detected, as well as positioning information corresponding to at least one monitoring device, and then processes the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value, and then corrects the power information to be detected based on the voltage correction attribute value to obtain a target corrected voltage corresponding to the power data to be used of each phase; if there is a target corrected voltage in the target corrected voltage of each phase that is not within a preset voltage range, then based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring device, an early warning prompt information is generated, and the early warning prompt information is sent to the target device, which solves the problem in the prior art of performing abnormal detection of drop-out fuses based on the power data actually measured by the equipment, and there is a problem in which the power data is not within the preset voltage range. In order to solve the problem of poor availability and low practicality, which leads to the problem of low detection accuracy, it is realized that the voltage value actually measured by the equipment is corrected by obtaining parameter information corresponding to the influencing factors affecting the actually measured voltage value of the drop-out type fuse, so as to obtain the corrected voltage information with high accuracy, so as to judge the operating state of the drop-out type fuse based on the corrected voltage. When there is a target corrected voltage in the target corrected voltage of each phase that is not within the preset voltage range, it is considered that the drop-out type fuse is in an abnormal operating state, and based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring equipment, an early warning prompt information is generated and sent to the target device, so that the target user can accurately locate the problem based on the data in the early warning prompt information in time, thereby improving the accuracy and timeliness of abnormal detection and achieving the technical effect of maintaining the operation of the power grid.

[0053] Embodiment 2

[0054] Figure 2 This is a flow chart of a data processing method provided by Embodiment 2 of the present invention. Based on the above embodiment, the method further includes training to obtain the target voltage correction model. The specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here.

[0055] like Figure 2 As shown, the method specifically comprises the following steps:

[0056] S210: Obtain a training sample set.

[0057] In order to improve the accuracy of the model, as many and rich training samples as possible can be obtained. The training sample set includes at least one training sample, and the training sample includes the training data to be used and the theoretical error voltage corresponding to the training data to be used. The training data to be used is the data of the monitoring device collected based on the sensor device. For example, in practical applications, when the monitoring device fails, the power parameter data corresponding to the monitoring device can be collected as the training data to be used. The training data to be used may include temperature information, humidity information, voltage information, current information and at least one indicator data in the three-phase unbalanced attribute value. The three-phase unbalanced attribute value includes the three-phase voltage unbalanced attribute value and the three-phase current unbalanced attribute value. At this time, the voltage information in the training data to be used is the actual measured voltage that has not been corrected. The theoretical error voltage is determined in advance based on the voltage information when the faulty device fails and the voltage information at the moment before the failure occurs. At this time, the theoretical error voltage is the voltage correction value expected to be output by the voltage correction model to be trained, so as to correct the voltage information based on the voltage correction value.

[0058] In this embodiment, determining the training data to be used in each training sample can be: obtaining fault information corresponding to at least one monitoring device, and balancing the fault information to obtain a three-phase unbalanced attribute value; normalizing the temperature information, humidity information and three-phase unbalanced attribute value to obtain the training data to be used.

[0059] The fault information includes temperature information, humidity information, voltage information and current information. The voltage information includes the voltages corresponding to the three phases A, B and C. The current information includes the voltage flows corresponding to the three phases A, B and C.

[0060] In practical applications, four fault data items, including temperature information, humidity information, voltage information, and current information, can be obtained when at least one monitoring device fails. The voltages corresponding to the three phases A, B, and C can be processed to obtain the three-phase voltage imbalance attribute value. The currents corresponding to the three phases A, B, and C can be processed to obtain the three-phase current imbalance attribute value. The three-phase voltage imbalance attribute value, the three-phase current imbalance attribute value, the temperature information, and the humidity information can be normalized using deviation standardization to obtain the normalized data as training data to be used. For example, assuming that a drop-out type fuse has a fuse drop failure at time T, the monitoring device can be used to obtain four data items, including temperature, humidity, three-phase voltage imbalance, and three-phase current imbalance, corresponding to time T. Each data item can be used as a feature, and all data under one feature constitute a feature vector. For example, the temperature feature vector is ( ), the humidity feature vector is ( , ). Accordingly, the feature vectors can be concatenated to obtain the total feature vector X, the dimension of X is n 4, n represents the number of data samples, and 4 represents the four characteristic parameters that cause the error. The total characteristic vector X can be normalized using formula (1).

[0061] (1)

[0062] in, It can be represented as the kth eigenvector in X, such as the temperature eigenvector. represents the kth eigenvector The i-th characteristic element of . can be expressed as the minimum value in the eigenvector, can be expressed as the maximum value in the eigenvector, It can be expressed as the kth eigenvector The data of the ith characteristic element of is standardized by deviation. Correspondingly, the characteristic vector X after the deviation standardization of the total characteristic vector X can be obtained. ， , as the training data to be used.

[0063] It should be noted that when a monitoring device fails, its voltage fluctuates greatly. At the same time, the monitoring device was in a normal voltage state at the moment before the failure. The theoretical voltage value at the moment of failure can be calculated based on the voltage in the normal state at the moment before the failure. At this time, the theoretical voltage value is different from the voltage detected by the monitoring device affected by external factors. Accordingly, the existing voltage error can be obtained based on the theoretical voltage value and the equipment detection voltage.

[0064] In this embodiment, determining the theoretical error voltage in each training sample may be: determining a normal voltage value corresponding to a moment before the acquisition moment corresponding to the fault information; and determining the theoretical error voltage based on the voltage information in the fault information and the normal voltage value.

[0065] In practical applications, when a monitoring device fails and voltage information of the device is collected, the voltage information of the previous moment corresponding to the collection moment corresponding to the voltage information can be obtained. The voltage information of the previous moment is the voltage when the device is operating normally, which is used as the normal voltage value. The theoretical voltage value can be calculated based on the normal voltage value, and the difference between the theoretical voltage value and the device detection voltage can be used as the theoretical error voltage. For example, assuming that a fuse drops at time T, the voltage information corresponding to time T is obtained from the monitoring device, denoted as A. The voltage data at the moment before time T, that is, at time T-1, is the voltage data under normal conditions, denoted as B. The theoretical voltage value at time T can be calculated based on B, as shown in FIG. , recorded as C. The corresponding error value can be -A, recorded as D. D can be regarded as the theoretical error voltage.

[0066] S220 . For each training sample, use the to-be-used training data of the current training sample as the input of the voltage correction model to be trained, use the theoretical error voltage as the output of the voltage correction model to be trained, and train the voltage correction model to be trained.

[0067] The voltage correction model to be trained refers to a voltage correction value output model that needs to be trained. After the training is completed, a target voltage correction model can be obtained.

[0068] It should be noted that the processing method for each training sample is the same, and the processing of one training sample is taken as an example for explanation.

[0069] Specifically, the voltage correction model to be trained can be trained based on each training sample in the training sample set to obtain a target voltage correction model. Each training data to be used in the training sample can be input into the voltage correction model to be trained, the model can process the training data to be used, and can output the voltage correction attribute value corresponding to the training data to be used, and then the algorithm can be used to perform loss processing on the error between the output voltage correction attribute value and the expected output theoretical voltage to obtain a loss value, so that the model parameters in the voltage correction model to be trained can be corrected based on the loss value, and the voltage correction model to be trained can be trained.

[0070] S230, taking the convergence of the loss function of the voltage correction model to be trained as a training target to obtain the target voltage correction model.

[0071] Among them, the convergence of the preset loss function can be used as a training target. When it is determined that the preset loss function of the voltage correction model to be trained converges, it means that this model can be used as a target voltage correction model.

[0072] It should be noted that since the model parameters in the voltage correction model to be trained are uncorrected, the voltage correction value output by the model at this time may be different from the training data to be used corresponding to the current training sample. The model parameters in the voltage correction model to be trained can be continuously corrected so that the voltage correction model to be trained can output the expected theoretical error voltage.

[0073] Specifically, the loss function in the voltage correction model to be trained can be used to compare the voltage correction value corresponding to the current training sample with the theoretical voltage error of the expected output, and the loss value can be calculated to correct the model parameters of the voltage correction model to be trained according to the obtained loss result. Furthermore, the training error of the loss function, that is, the loss parameter, can be used as a condition for detecting whether the loss function has reached convergence at present, such as whether the training error is less than the preset error or whether the error change trend tends to be stable, or whether the current number of iterations is equal to the preset number. If the convergence condition is detected, such as the training error of the loss function is less than the preset error or the error change tends to be stable, it indicates that the training of the voltage correction model to be trained is completed, and the iterative training can be stopped at this time. If it is detected that the convergence condition is not reached at present, further training samples can be obtained to continue training the voltage correction model to be trained until the training error of the loss function is within the preset range. When the training error of the loss function reaches convergence, it can be considered that the voltage correction model to be trained is trained, that is, the target voltage correction model is obtained, so that when the temperature information, humidity information, and three-phase imbalance attribute value corresponding to a certain monitoring device are input, the target voltage correction model can output accurate voltage correction attribute values, and then correct the actual measured voltage of the device based on the voltage correction attribute value.

[0074] S240: Receive detection data and positioning information corresponding to at least one monitoring device.

[0075] S250: Process the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value.

[0076] S260: Correct the power information to be detected based on the voltage correction attribute value to obtain a target correction voltage corresponding to the power data to be used of each phase.

[0077] S270: If a target corrected voltage among the target corrected voltages of each phase is not within a preset voltage range, generate warning information based on the target corrected voltage and positioning information corresponding to the corresponding monitoring device, and send the warning information to the target device.

[0078] The technical solution of this embodiment obtains a target voltage correction model by pre-training based on the training data to be used and the theoretical voltage error value, and realizes outputting accurate voltage correction attribute values ​​by utilizing the trained target voltage correction model, so as to judge the operating state of the drop-out type fuse based on the corrected voltage. When there is a target correction voltage in each phase target correction voltage that is not within the preset voltage range, it is considered that the drop-out type fuse is in an abnormal operating state, and based on the target correction voltage and the positioning information corresponding to the corresponding monitoring device, an early warning prompt information is generated and sent to the target device, so that the target user can accurately locate the problem based on the data in the early warning prompt information in time, thereby improving the accuracy and timeliness of abnormality detection and achieving the technical effect of maintaining the operation of the power grid.

[0079] Embodiment 3

[0080] As an alternative embodiment of the above embodiment, Figure 3 This is a schematic diagram of a data processing method provided by Embodiment 3 of the present invention. For details, please refer to the following specific content.

[0081] See also Figure 3The technical solution of this embodiment can be implemented by monitoring equipment, positioning devices, temperature sensors, humidity sensors, voltage sensors, current sensors, three-phase unbalance calculation modules, voltage error attribute value output modules, main control units, voltage devices, wireless communication devices, and intelligent terminals. Among them, the power supply device is used to provide electric energy to the main control unit and other electrical equipment, and the power supply method includes the solar panel power supply method and the live line induction power supply method. The positioning device is used to provide the control device (drop-out fuse) position information and indicate the corresponding phase sequence. The positioning device can upload information such as phase sequence and position to the main control unit. The monitoring terminal is equipped with a voltage sensor, a current sensor, a temperature sensor, and a humidity sensor. The voltage and current sensors are used to measure the voltage and current of each phase of the drop-out fuse, so that the voltage and current of each phase can be input into the three-phase unbalance calculation module to calculate the three-phase unbalance. The three-phase unbalance includes the three-phase voltage unbalance and the three-phase current unbalance, and the three-phase voltage unbalance and the three-phase current unbalance are input into the voltage error attribute output module. The temperature sensor is used to measure the temperature of the on-site environment and upload the temperature to the voltage error attribute output module. The humidity sensor is used to measure the humidity of the on-site environment and upload the humidity to the voltage error attribute output module. The voltage error attribute output module considers the factors that cause voltage errors on site, processes the temperature, humidity, three-phase voltage imbalance and three-phase current imbalance through the target voltage correction model, calculates the voltage error attribute value, and inputs the voltage error attribute value into the voltage correction module in the main control unit, which is used to correct the voltage information input by the voltage sensor, obtain the corrected target correction voltage, and determine whether the corrected target correction voltage is within the preset voltage range. If not, it is determined that the drop-out fuse has a fault, and then the corresponding warning prompt information can be generated based on the temperature, humidity, three-phase voltage imbalance and three-phase current imbalance of the drop-out fuse, current information, target correction voltage and the corresponding transformer model, wiring method and other basic information, and the warning prompt information is transmitted to the intelligent terminal, that is, the target device, through a wireless communication device, so that the user corresponding to the target device can maintain the drop-out fuse in time.

[0082] On the basis of the above scheme, it is necessary to pre-train to obtain the target voltage correction model, and first of all, it is necessary to obtain training samples, wherein the training samples include the training data to be used and the theoretical error voltage. The method for obtaining the training data to be used can be: obtaining four fault data such as temperature information, humidity information, voltage information and current information of at least one monitoring device when a fault occurs, and processing the voltage corresponding to the three phases A, B and C to obtain the three-phase voltage imbalance attribute value. The current corresponding to the three phases A, B and C can be processed to obtain the three-phase current imbalance attribute value. The three-phase voltage imbalance attribute value, the three-phase current imbalance attribute value, the temperature information and the humidity information can be normalized by using deviation standardization to obtain the normalized data as the training data to be used. For example, assuming that a drop-out type fuse has a fuse drop fault at time T, the monitoring device can be used to obtain the four data corresponding to time T, such as temperature, humidity, three-phase voltage imbalance, and three-phase current imbalance. Each data can be used as a feature, and all data under one feature constitute a feature vector. For example, the temperature feature vector is ( ), the humidity feature vector is ( , ). Accordingly, the feature vectors can be concatenated to obtain the total feature vector X, the dimension of X is n 4, n represents the number of data samples, and 4 represents the four characteristic parameters that cause the error. The total characteristic vector X can be normalized using formula (1).

[0083] (1)

[0084] in, It can be represented as the kth eigenvector in X, such as the temperature eigenvector. represents the kth eigenvector The i-th characteristic element of . can be expressed as the minimum value in the eigenvector, can be expressed as the maximum value in the eigenvector, It can be expressed as the kth eigenvector The data of the ith characteristic element of is standardized by deviation. Correspondingly, the characteristic vector X after the deviation standardization of the total characteristic vector X can be obtained. ， , as the training data to be used.

[0085] On the basis of the above scheme, the theoretical error voltage can be obtained in the following way: when the voltage information of the device is collected when the monitoring device fails, the voltage information of the previous moment corresponding to the collection moment corresponding to the voltage information can be obtained. The voltage information of the previous moment is the voltage when the device is operating normally, which is used as the normal voltage value. The theoretical voltage value can be calculated based on the normal voltage value, and the difference between the theoretical voltage value and the device detection voltage can be used as the theoretical error voltage. For example, assuming that a fuse drops at time T, the voltage information corresponding to time T is obtained from the monitoring device, which is recorded as A. The voltage data at the moment before time T, that is, time T-1, is the voltage data under normal conditions, which is recorded as B. The theoretical voltage value at time T can be calculated based on B, as shown in FIG. , recorded as C. The corresponding error value can be -A, recorded as D. D can be used as the theoretical error voltage. Accordingly, the training data to be used and the corresponding theoretical error voltage can be obtained. The training data to be used can be used as the input of the voltage correction model to be trained, and the theoretical error voltage can be used as the output of the voltage correction model to be trained to obtain the target voltage correction model. When the temperature information, humidity information, and three-phase unbalanced attribute value corresponding to a certain monitoring device are input, the voltage correction model to be trained can output an accurate voltage correction attribute value, and then correct the voltage actually measured by the device based on the voltage correction attribute value.

[0086] The technical solution of the present embodiment receives detection data such as temperature information, humidity information, and power information to be detected, as well as positioning information corresponding to at least one monitoring device, and then processes the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value, and then corrects the power information to be detected based on the voltage correction attribute value to obtain a target corrected voltage corresponding to the power data to be used of each phase; if there is a target corrected voltage in the target corrected voltage of each phase that is not within a preset voltage range, then based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring device, an early warning prompt information is generated, and the early warning prompt information is sent to the target device, which solves the problem in the prior art of performing abnormal detection of drop-out fuses based on the power data actually measured by the equipment, and there is a problem in which the power data is not within the preset voltage range. In order to solve the problem of poor availability and low practicality, which leads to the problem of low detection accuracy, it is realized that the voltage value actually measured by the equipment is corrected by obtaining parameter information corresponding to the influencing factors affecting the actually measured voltage value of the drop-out type fuse, so as to obtain the corrected voltage information with high accuracy, so as to judge the operating state of the drop-out type fuse based on the corrected voltage. When there is a target corrected voltage in the target corrected voltage of each phase that is not within the preset voltage range, it is considered that the drop-out type fuse is in an abnormal operating state, and based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring equipment, an early warning prompt information is generated and sent to the target device, so that the target user can accurately locate the problem based on the data in the early warning prompt information in time, thereby improving the accuracy and timeliness of abnormal detection and achieving the technical effect of maintaining the operation of the power grid.

[0087] Embodiment 4

[0088] Figure 4 This is a structural block diagram of a data processing device provided by the fourth embodiment of the present invention. The device comprises: a data receiving module 410 , a voltage correction attribute value determination module 420 , a target correction voltage determination module 430 and a warning prompt information generation module 440 .

[0089] Among them, the data receiving module 410 is used to receive the detection data and positioning information corresponding to at least one monitoring device; wherein the detection data includes temperature information, humidity information and power information to be detected, and the power information to be detected includes at least one phase of power data to be used; the voltage correction attribute value determination module 420 is used to process the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value; the target correction voltage determination module 430 is used to correct the power information to be detected based on the voltage correction attribute value to obtain the target corrected voltage corresponding to the power data to be used of each phase; the early warning prompt information generation module 440 is used to generate early warning prompt information based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring device if there is a target corrected voltage in each phase target corrected voltage that is not within a preset voltage range, and send the early warning prompt information to the target device.

[0090] The technical solution of the present embodiment receives detection data such as temperature information, humidity information, and power information to be detected, as well as positioning information corresponding to at least one monitoring device, and then processes the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value, and then corrects the power information to be detected based on the voltage correction attribute value to obtain a target corrected voltage corresponding to the power data to be used of each phase; if there is a target corrected voltage in the target corrected voltage of each phase that is not within a preset voltage range, then based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring device, an early warning prompt information is generated, and the early warning prompt information is sent to the target device, which solves the problem in the prior art of performing abnormal detection of drop-out fuses based on the power data actually measured by the equipment, and there is a problem in which the power data is not within the preset voltage range. In order to solve the problem of poor availability and low practicality, which leads to the problem of low detection accuracy, it is realized that the voltage value actually measured by the equipment is corrected by obtaining parameter information corresponding to the influencing factors affecting the actually measured voltage value of the drop-out type fuse, so as to obtain the corrected voltage information with high accuracy, so as to judge the operating state of the drop-out type fuse based on the corrected voltage. When there is a target corrected voltage in the target corrected voltage of each phase that is not within the preset voltage range, it is considered that the drop-out type fuse is in an abnormal operating state, and based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring equipment, an early warning prompt information is generated and sent to the target device, so that the target user can accurately locate the problem based on the data in the early warning prompt information in time, thereby improving the accuracy and timeliness of abnormal detection and achieving the technical effect of maintaining the operation of the power grid.

[0091] On the basis of the above device, optionally, the voltage correction property value determination module 420 includes a three-phase unbalance property value determination unit and a voltage correction property value determination unit.

[0092] A three-phase unbalanced attribute value determination unit, configured to perform a balancing process on the power data to be used of each phase to obtain a three-phase unbalanced attribute value;

[0093] The voltage correction attribute value determining unit is used to input the temperature information, the humidity information and the three-phase unbalance attribute value into a pre-trained target voltage correction model to obtain a voltage correction attribute value.

[0094] On the basis of the above device, optionally, the target correction voltage determination module 430 includes a to-be-used voltage data determination unit and a target correction voltage determination unit.

[0095] a voltage data to be used determining unit, used to determine the voltage data to be used corresponding to the power data to be used of each phase in the power information to be detected;

[0096] The target correction voltage determination unit is used to fuse the voltage correction attribute value with the voltage data to be used of each phase respectively to obtain the target correction voltage corresponding to the voltage data to be used of each phase.

[0097] On the basis of the above-mentioned device, optionally, the warning prompt information generation module 440 includes a warning prompt information generation unit.

[0098] The warning prompt information generating unit is used to fill the target correction voltage and the positioning information corresponding to the corresponding monitoring device into the preset warning prompt template, generate the warning prompt information, and send the warning prompt information to the target device so that the target user corresponding to the target device can maintain the monitoring device.

[0099] On the basis of the above device, optionally, the device further includes a target voltage correction model determination module, wherein the target voltage correction model determination module includes a training sample set determination unit, a voltage correction model to be trained training unit and a target voltage correction model determination unit.

[0100] A training sample set determination unit, configured to obtain a training sample set; wherein the training sample set includes at least one training sample, the training sample includes training data to be used and a theoretical error voltage corresponding to the training data to be used, and the training data to be used includes at least one indicator data among temperature information, humidity information and a three-phase unbalanced attribute value;

[0101] A training unit for the voltage correction model to be trained is used to train the voltage correction model to be trained by taking the training data to be used of the current training sample as the input of the voltage correction model to be trained and taking the theoretical error voltage as the output of the voltage correction model to be trained for each training sample;

[0102] The target voltage correction model determining unit is used to take the loss function convergence of the voltage correction model to be trained as a training target to obtain the target voltage correction model.

[0103] On the basis of the above device, optionally, the training sample set determination unit includes a three-phase unbalanced attribute value determination subunit and a to-be-used training data determination subunit.

[0104] A three-phase unbalanced attribute value determination subunit, used to obtain fault information corresponding to at least one monitoring device, and balance the fault information to obtain a three-phase unbalanced attribute value; wherein the fault information includes temperature information, humidity information, voltage information and current information;

[0105] The training data to be used determining subunit is used to normalize the temperature information, humidity information and three-phase unbalanced attribute value to obtain the training data to be used.

[0106] On the basis of the above device, optionally, the training sample set determination unit includes a normal voltage value determination subunit and a theoretical error voltage determination subunit.

[0107] A normal voltage value determination subunit, used to determine a normal voltage value corresponding to a moment before the acquisition moment corresponding to the fault information;

[0108] The theoretical error voltage determination subunit is used to determine the theoretical error voltage based on the voltage information in the fault information and the normal voltage value.

[0109] The data processing device provided in the embodiment of the present invention can execute the data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0110] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present invention.

[0111] Embodiment 5

[0112] Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present invention. Figure 5 A block diagram of an exemplary electronic device 50 suitable for implementing exemplary embodiments of the present invention is shown. Figure 5 The electronic device 50 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0113] like Figure 5As shown, the electronic device 50 is in the form of a general-purpose computing device. The components of the electronic device 50 may include, but are not limited to: one or more processors or processing units 501, a system memory 502, and a bus 503 connecting different system components (including the system memory 502 and the processing unit 501).

[0114] Bus 503 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0115] The electronic device 50 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 50, including volatile and non-volatile media, removable and non-removable media.

[0116] The system memory 502 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 504 and / or cache memory 505. The electronic device 50 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 506 may be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, usually called a "hard drive"). Although Figure 5 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 503 via one or more data medium interfaces. The memory 502 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0117] A program / utility 508 having a set (at least one) of program modules 507 may be stored, for example, in the memory 502, such program modules 507 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment. The program modules 507 generally perform the functions and / or methods of the embodiments described herein.

[0118] The electronic device 50 may also communicate with one or more external devices 609 (e.g., keyboards, pointing devices, displays 510, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 50, and / or may communicate with any device that enables the electronic device 50 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 511. Furthermore, the electronic device 50 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 512. As shown, the network adapter 512 communicates with other modules of the electronic device 50 via the bus 503. It should be understood that although Figure 5 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 50, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0119] The processing unit 501 executes various functional applications and data processing by running the programs stored in the system memory 502, such as implementing the data processing method provided in the embodiment of the present invention.

[0120] Embodiment 6

[0121] Embodiment 6 of the present invention further provides a storage medium containing computer executable instructions, wherein the computer executable instructions are used to execute a data processing method when executed by a computer processor. The method includes:

[0122] Receive detection data and positioning information corresponding to at least one monitoring device; wherein the detection data includes temperature information, humidity information and power information to be detected, and the power information to be detected includes at least one phase of power data to be used;

[0123] Processing the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value;

[0124] Correcting the power information to be detected based on the voltage correction attribute value to obtain a target correction voltage corresponding to the power data to be used of each phase;

[0125] If a target corrected voltage among the target corrected voltages of each phase is not within a preset voltage range, an early warning prompt information is generated based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring device, and the early warning prompt information is sent to the target device.

[0126] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device.

[0127] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0128] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0129] Computer program code for performing the operation of the embodiments of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate 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 may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0130] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A data processing method, characterized in that: include: Receive detection data and positioning information corresponding to at least one monitoring device; wherein the detection data includes temperature information, humidity information and power information to be detected, and the power information to be detected includes at least one phase of power data to be used; Processing the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value; Correcting the power information to be detected based on the voltage correction attribute value to obtain a target correction voltage corresponding to the power data to be used of each phase; If a target corrected voltage among the target corrected voltages of each phase is not within a preset voltage range, generating early warning information based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring device, and sending the early warning information to the target device; The process of processing the detection data based on the pre-trained target voltage correction model to obtain the voltage correction attribute value includes: Performing balancing processing on the power data to be used of each phase to obtain a three-phase unbalanced attribute value; The temperature information, the humidity information and the three-phase unbalanced attribute value are input into a pre-trained target voltage correction model to obtain a voltage correction attribute value.

2. The method according to claim 1, characterized in that The step of correcting the power information to be detected based on the voltage correction attribute value to obtain a target corrected voltage corresponding to the power data to be used of each phase includes: Determine the voltage data to be used corresponding to the power data to be used of each phase in the power information to be detected; The voltage correction attribute value is respectively merged with the voltage data to be used of each phase to obtain the target correction voltage corresponding to the voltage data to be used of each phase.

3. The method according to claim 1, characterized in that The generating of early warning information based on the target correction voltage and the positioning information corresponding to the corresponding monitoring device, and sending the early warning information to the target device, comprises: The target correction voltage and the positioning information corresponding to the corresponding monitoring device are filled into a preset early warning prompt template to generate early warning prompt information, and the early warning prompt information is sent to the target device, so that the target user corresponding to the target device can maintain the monitoring device.

4. The method according to claim 1, characterized in that: Also includes: Training to obtain the target voltage correction model; The training to obtain the target voltage correction model includes: Acquire a training sample set; wherein the training sample set includes at least one training sample, the training sample includes training data to be used and a theoretical error voltage corresponding to the training data to be used, and the training data to be used includes at least one indicator data among temperature information, humidity information and a three-phase unbalanced attribute value; For each training sample, the training data to be used of the current training sample is used as the input of the voltage correction model to be trained, and the theoretical error voltage is used as the output of the voltage correction model to be trained, so as to train the voltage correction model to be trained; The loss function convergence of the voltage correction model to be trained is taken as a training target to obtain the target voltage correction model.

5. The method according to claim 4, characterized in that Determining the training data to be used in each first training sample in the first training sample set includes: Acquire fault information corresponding to at least one monitoring device, and perform balancing processing on the fault information to obtain a three-phase unbalanced attribute value; wherein the fault information includes temperature information, humidity information, voltage information, and current information; The temperature information, humidity information and three-phase unbalanced attribute value are normalized to obtain training data to be used.

6. The method according to claim 5, characterized in that Determining a theoretical error voltage in each first training sample in the first training sample set includes: Determine a normal voltage value corresponding to a moment before the acquisition moment corresponding to the fault information; A theoretical error voltage is determined based on the voltage information in the fault information and the normal voltage value.

7. A data processing device, characterized in that: include: A data receiving module, used to receive detection data and positioning information corresponding to at least one monitoring device; wherein the detection data includes temperature information, humidity information and power information to be detected, and the power information to be detected includes at least one phase of power data to be used; A voltage correction attribute value determination module, used for processing the detection data based on a pre-trained target voltage correction model to obtain a voltage correction attribute value; A target correction voltage determination module, used to correct the power information to be detected based on the voltage correction attribute value to obtain a target correction voltage corresponding to the power data to be used of each phase; A warning prompt information generating module is used to generate warning prompt information based on the target corrected voltage and the positioning information corresponding to the corresponding monitoring device if there is a target corrected voltage among the target corrected voltages of each phase that is not within the preset voltage range, and send the warning prompt information to the target device; Wherein, the voltage correction attribute value determination module includes: A three-phase unbalanced attribute value determination unit, configured to perform a balancing process on the power data to be used of each phase to obtain a three-phase unbalanced attribute value; The voltage correction attribute value determining unit is used to input the temperature information, the humidity information and the three-phase unbalance attribute value into a pre-trained target voltage correction model to obtain a voltage correction attribute value.

8. An electronic device, characterized in that: The device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the data processing method as described in any one of claims 1 to 6 is implemented.

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