Distribution transformer safety data verification and correction method and system

Abstract

The invention relates to the technical field of intelligent power grids, and discloses a distribution transformer safety data verification and correction method and system, and the method comprises the steps: extracting a voltage parameter, a current parameter and a temperature parameter in the operation of a distribution transformer, normalizing the format of the parameters, and unifying the unit, thereby obtaining an operation parameter; marking abnormal parameters of the operation parameters to obtain an abnormal marking result; identifying a continuous abnormal interval existing in the abnormal marking result, retrieving a transient process feature library of the distribution transformer in a historical normal operation state, and extracting transient waveform feature parameters; establishing a waveform mapping relation between an abnormal parameter segment and a normal transient process according to a waveform rule of transient waveform characteristic parameters, and deducing a characteristic retention constraint condition; guiding data recovery processing of the abnormal parameter segment, and reconstructing to obtain a parameter sequence; verifying the continuity and rationality of the parameter sequence in the time dimension to obtain security data; the safety data verification and correction efficiency of the distribution transformer can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid, and particularly relates to a power distribution transformer safety data verification correction method and system. BACKGROUND

[0002] The prior art fails to reorganize the collected voltage, current and temperature related data in a standard format and unify the units, and fails to align the time identifiers of the data to a unified benchmark, resulting in disordered data. At the same time, the abnormality determination of the operation data only stays in simple numerical comparison, and fails to accurately locate the time position of the abnormal point and bind parameters, so that the abnormality marking result lacks completeness and relevance, and cannot provide reliable basis for subsequent data processing.

[0003] The prior art fails to effectively identify the continuous abnormal interval in the operation data of the power distribution transformer, and also fails to combine the transient process characteristics under the historical normal operation state of the equipment, and lacks effective means to establish the mapping relationship between the abnormal parameter segment and the normal transient process. In the data recovery processing, there is no clear constraint condition to guide, and the waveform amplitude continuity and phase smoothness of the corrected data cannot be guaranteed, and the reconstructed parameter sequence is not comprehensively verified for time continuity and rationality, so that the finally output data is difficult to meet the monitoring needs of the safe operation of the power distribution transformer. SUMMARY

[0004] The present application provides a power distribution transformer safety data verification correction method and system to solve the problems raised in the background.

[0005] To achieve the above-mentioned purpose, the present application provides a power distribution transformer safety data verification correction method, which comprises:

[0006] S1, extracting voltage parameters, current parameters and temperature parameters in the operation of the power distribution transformer, normalizing the format and unifying the units of the parameters, and obtaining the operation parameters of the power distribution transformer;

[0007] S2, based on the preset safe operation threshold, marking the abnormal parameters in the operation parameters that exceed the safe operation threshold, and obtaining the abnormal marking result of the power distribution transformer;

[0008] S3, traversing the abnormal marking result, identifying the continuous abnormal interval existing in the abnormal marking result, and retrieving the transient process characteristic library of the power distribution transformer under the historical normal operation state, and extracting the transient waveform characteristic parameters of the power distribution transformer;

[0009] S4, according to the waveform law of the transient waveform characteristic parameters, establishing the waveform mapping relationship between the abnormal parameter segment and the normal transient process, and deducing the characteristic maintaining constraint condition of the power distribution transformer;

[0010] S5, based on the feature retention constraint condition, guiding the data recovery processing of the abnormal parameter segment, and reconstructing to obtain the parameter sequence of the power distribution transformer;

[0011] S6, verifying the continuity and rationality of the parameter sequence in the time dimension, and obtaining the safe data of the power distribution transformer.

[0012] In a preferred embodiment, the voltage parameter, current parameter and temperature parameter in the operation of the power distribution transformer are extracted, the format of the parameter is regularized and the unit is unified, and the operation parameter of the power distribution transformer is obtained, including:

[0013] The voltage signal, current signal and temperature signal output by the power distribution transformer monitoring device are collected, and the original parameter set of the power distribution transformer is obtained;

[0014] The data organization form in the original parameter set is recombined, and the time identifier of the original parameter set is aligned to the preset time reference, and the standardized parameter of the power distribution transformer is obtained;

[0015] The measurement unit in the standardized parameter is standardized, and the converted parameter is integrated to form the operation parameter of the power distribution transformer.

[0016] In a preferred embodiment, based on the preset safe operation threshold, the abnormal parameters in the operation parameter exceeding the threshold are marked to obtain the abnormal marking result of the power distribution transformer, including:

[0017] The parameter value in the operation parameter is read, the parameter value is compared with the preset safe operation threshold, and the abnormal parameter point of the power distribution transformer is determined according to the comparison result;

[0018] The specific position of the abnormal parameter point in the time sequence is located, and a time stamp identifier is attached, and the abnormal position identifier of the power distribution transformer is obtained;

[0019] Based on the voltage parameter, current parameter and temperature parameter of the abnormal parameter point, the parameter binding of the abnormal position is carried out, and the abnormal marking result of the power distribution transformer is obtained.

[0020] In a preferred embodiment, the abnormal marking result is traversed, the continuous abnormal interval existing in the abnormal marking result is identified, and the transient process feature library of the power distribution transformer in the historical normal operation state is searched, and the transient waveform feature parameter of the power distribution transformer is extracted, including:

[0021] The abnormal distribution rule in the abnormal marking result is analyzed, the start time boundary and the end time boundary of the abnormal parameter segment are defined, and the continuous abnormal interval of the power distribution transformer is obtained;

[0022] accessing a transient process feature library of the power distribution transformer, screening a normal transient process record corresponding to the continuous abnormal interval time window;

[0023] intercepting waveform data in the normal transient process record to obtain a normal waveform record of the power distribution transformer;

[0024] selecting a key waveform attribute in the normal waveform record and calibrating a characteristic parameter of the waveform attribute to obtain a transient waveform characteristic parameter of the power distribution transformer.

[0025] In a preferred embodiment, the waveform mapping relationship between the abnormal parameter segment and the normal transient process is established according to the waveform rule of the transient waveform characteristic parameter, and a feature preservation constraint condition of the power distribution transformer is derived, including:

[0026] analyzing periodic variation characteristics and amplitude envelope patterns in the transient waveform characteristic parameter, and distinguishing waveform characteristic parameters of steady-state stages and transient stages of the normal transient process;

[0027] taking the time axis of the abnormal parameter segment as a reference and taking the spatial distribution of the normal transient process as a reference, a space-time correspondence relationship of the power distribution transformer is constructed;

[0028] based on the space-time correspondence relationship, a mapping dimension of a data point in the abnormal parameter segment on a time sequence is defined, an association rule between the data point and the normal waveform is established, and a dynamic mapping rule of the power distribution transformer is obtained;

[0029] according to the dynamic mapping rule, a waveform amplitude continuity constraint and a phase smoothness constraint are defined, and the feature preservation constraint condition of the power distribution transformer is combined.

[0030] In a preferred embodiment, the waveform amplitude continuity constraint and the phase smoothness constraint are defined according to the dynamic mapping rule, and the feature preservation constraint condition of the power distribution transformer is combined, including:

[0031] based on the dynamic mapping rule, a variation boundary range of a waveform amplitude of the power distribution transformer on a time dimension is defined, and an amplitude continuity constraint of the power distribution transformer is constructed;

[0032] according to the dynamic mapping rule, a variation characteristic of a waveform phase of the power distribution transformer in a transient process is evaluated, and a smoothness standard of phase variation is formulated, and a phase smoothness constraint of the power distribution transformer is obtained;

[0033] the technical requirements of the amplitude continuity constraint and the phase smoothness constraint are coordinated, and a complete constraint system is combined to construct the feature preservation constraint condition of the power distribution transformer.

[0034] In a preferred embodiment, the feature preservation degree of the feature preservation constraint is calculated using the following formula:

[0035] ;

[0036] In the formula, The feature preservation degree, The number of data points in the abnormal parameter segment. The first of the abnormal parameter segments The magnitude value of each data point The first of the abnormal parameter segments The magnitude value of each data point The first of the abnormal parameter segments Phase values ​​of each data point The first of the abnormal parameter segments Phase values ​​of each data point The maximum amplitude value within the amplitude reference range of the normal waveform. The minimum amplitude value within the amplitude reference range. This refers to the maximum phase value within the phase reference range of the normal waveform. The minimum phase value within the stated phase reference range. The preset balance amplitude weighting coefficient, This is the preset phase change weighting coefficient.

[0037] In a preferred embodiment, the step of guiding the data recovery process of the abnormal parameter segment based on the feature preservation constraint to reconstruct the parameter sequence of the distribution transformer includes:

[0038] The aforementioned features are applied to maintain the constraints to the abnormal parameter segment;

[0039] Based on the amplitude continuity requirement in the feature-preserving constraint, the amplitude values ​​of data points that do not meet the amplitude continuity requirement in the parameter segment after constraint are corrected to obtain the amplitude continuity parameter segment of the distribution transformer.

[0040] Based on the phase smoothness requirement in the characteristic retention constraint, the phase jump in the amplitude continuous parameter segment is eliminated to obtain the phase smoothing parameter segment of the distribution transformer;

[0041] The phase smoothing parameter segment is connected and recombined with the normal parameter segment of the distribution transformer to construct the parameter sequence of the distribution transformer.

[0042] In a preferred embodiment, the time stamp interval between adjacent data points in the parameter sequence is checked to see if it meets a preset continuity criterion, and a continuity verification result of the distribution transformer is obtained;

[0043] The parameter sequence is checked to see if there is an abnormal data jump in the gradual change characteristic of the data value, and a rationality verification result of the distribution transformer is obtained;

[0044] The continuity verification result and the rationality verification result are combined to determine whether the parameter sequence meets a safe operation threshold, and safe data of the distribution transformer is obtained.

[0045] To solve the above problems, the application also provides a distribution transformer safe data verification and correction system, which comprises:

[0046] An operating parameter extraction and regularization module is configured to extract voltage parameters, current parameters and temperature parameters in the operation of the distribution transformer, regularize the format of the parameters and unify the units, and obtain operating parameters of the distribution transformer;

[0047] An abnormal parameter marking module is configured to mark abnormal parameters in the operating parameters that exceed a preset safe operation threshold, and obtain an abnormal marking result of the distribution transformer;

[0048] A continuous abnormality identification and transient feature extraction module is configured to traverse the abnormal marking result, identify a continuous abnormality interval in the abnormal marking result, search a transient process feature library of the distribution transformer in a historical normal operation state, and extract transient waveform feature parameters of the distribution transformer;

[0049] A waveform mapping relationship establishment and constraint condition derivation module is configured to establish a waveform mapping relationship between an abnormal parameter segment and a normal transient process according to the waveform law of the transient waveform feature parameters, and derive feature maintenance constraint conditions of the distribution transformer;

[0050] An abnormal data recovery and parameter sequence reconstruction module is configured to guide data recovery processing of the abnormal parameter segment based on the feature maintenance constraint conditions, and reconstruct a parameter sequence of the distribution transformer;

[0051] A parameter sequence verification and safe data generation module is configured to verify the continuity and rationality of the parameter sequence in the time dimension, and obtain safe data of the distribution transformer.

[0052] Compared with the prior art, the application has the following beneficial effects:

[0053] 1.The technology extracts voltage parameters, current parameters and temperature parameters in the operation of the power distribution transformer, reorganizes the organization form of the original data, aligns the time identifier to the preset time reference and standardizes the measurement unit, forms the standard operation parameters, ensures the uniformity of the data format, the consistency of the unit and the regularity of the time dimension, judges the abnormal parameter points based on the preset safe operation threshold, locates the specific position of the abnormal parameter points in the time sequence and adds the timestamp identifier, completes the binding of the abnormal position and the corresponding parameters to obtain the abnormal marking result, and extracts the transient waveform characteristic parameters in combination with the searched historical normal transient process characteristic library, so as to provide accurate and standard basic data for subsequent abnormal data processing, and significantly improve the pre-accuracy and reliability of data processing.

[0054] 2.The technology establishes the waveform mapping relationship between the abnormal parameter segment and the normal transient process according to the waveform law of the transient waveform characteristic parameters, deduces the feature preservation constraint condition containing the amplitude continuity constraint and the phase smoothness constraint, guides the data recovery of the abnormal parameter segment, corrects the data points that do not meet the amplitude requirement and eliminates the phase jump, reorganizes the parameter sequence by connecting the processed parameter segment and the normal parameter segment, verifies the time stamp interval continuity and the data value gradual change characteristic rationality of the parameter sequence, and finally obtains the power distribution transformer safety data that can accurately reflect the actual operation state of the equipment, effectively improves the effectiveness of data verification and correction and the reliability of safety data, and provides reliable data support for the safe operation monitoring of the power distribution transformer. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 A flowchart of a power distribution transformer safety data verification and correction method provided by an embodiment of the present application is shown.

[0056] Figure 2 A functional module diagram of a power distribution transformer safety data verification and correction system provided by an embodiment of the present application is shown.

[0057] The implementation, functional characteristics and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0058] It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0059] The embodiment of the present application provides a power distribution transformer safety data verification correction method. The execution subject of the power distribution transformer safety data verification correction method includes but is not limited to at least one of the electronic devices such as a server and a terminal which can be configured to execute the method provided by the embodiment of the present application. In other words, the power distribution transformer safety data verification correction method can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be a stand-alone server, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0060] Referring to Figure 1 Fig. 1 is a flowchart of a power distribution transformer safety data verification correction method provided by an embodiment of the present application. In the embodiment, the power distribution transformer safety data verification correction method includes the following steps.

[0061] S1, extracting voltage parameters, current parameters and temperature parameters in the operation of a power distribution transformer, normalizing the format of the parameters and unifying the units to obtain operation parameters of the power distribution transformer;

[0062] In the embodiment of the present application, the extracting voltage parameters, current parameters and temperature parameters in the operation of a power distribution transformer, normalizing the format of the parameters and unifying the units to obtain operation parameters of the power distribution transformer includes:

[0063] Collecting voltage signals, current signals and temperature signals output by a power distribution transformer monitoring device to obtain a raw parameter set of the power distribution transformer;

[0064] Reorganizing the data organization form in the raw parameter set, and aligning the time identifier of the raw parameter set to a preset time reference to obtain standardized parameters of the power distribution transformer;

[0065] Standardizing the measurement units in the standardized parameters, and integrating the converted parameters to form the operation parameters of the power distribution transformer.

[0066] Specifically, through the voltage sensor, current sensor and temperature sensor deployed on the power distribution transformer, the voltage fluctuation signal, current flow signal and temperature change signal of the winding and iron core during the operation of the device are captured in real time. After the physical signals captured by these sensors are converted into electrical signals, they are transmitted to the dedicated data acquisition terminal. The data acquisition terminal decodes the received electrical signals, converts them into digital data that can be stored, and then preliminarily collects these digital data according to the categories of voltage, current and temperature. Finally, the original parameter set of the power distribution transformer is obtained.

[0067] Further, according to the fixed structure of "parameter type-acquisition time", the voltage, current and temperature data mixed in the original parameter set are rearranged, and parameters of the same type are classified into the corresponding parameter directory. Each parameter entry is bound to a unique acquisition time record. At the same time, the standard time specified by the State Grid is used as the preset time reference, and the time calibration function built-in the data acquisition terminal is used to compare the acquisition time of each data entry in the original parameter set with the standard time one by one. If there is a time deviation, the acquisition time record is adjusted according to the deviation value, so that the time identifiers of all data entries are completely consistent with the preset time reference. Through the above data organization form reorganization and time identifier alignment operation, the standardized parameters of the power distribution transformer are obtained.

[0068] Further, determine voltage parameters in volts, current parameters in amperes, and temperature parameters in degrees Celsius as the unified standard unit of measurement. Check the units of all data in the standardized parameters. If there are data in kilovolts in the voltage parameters, convert them to volts according to the fixed conversion ratio between kilovolts and volts. If there are data in milliamperes in the current parameters, convert them to amperes according to the fixed conversion ratio between amperes and milliamperes. If there are data in Fahrenheit in the temperature parameters, convert them to Celsius according to the fixed conversion rule between Fahrenheit and Celsius. After the units of all standardized parameters are unified, arrange and integrate the converted voltage parameters, current parameters and temperature parameters in the order of the time sequence of the acquisition to form the operating parameters of the power distribution transformer.

[0069] In summary, through the special sensors on the power distribution transformer monitoring device, the voltage signal, current signal and temperature signal during the operation of the device are captured in real time. After the physical signals are converted into electrical signals by the sensors, they are transmitted to the data acquisition terminal, decoded and processed into digital data that can be stored, and then preliminarily classified according to the signal type. Finally, the original parameter set of the power distribution transformer is obtained.

[0070] In general, the various types of data in the original parameter set are rearranged according to a fixed data structure, parameters of the same type are classified into corresponding directories and bound to a unique acquisition time, and the time identifiers of each data entry are compared and adjusted for deviation based on a preset standard time to ensure consistency of all data time identifiers, thereby obtaining standardized parameters of the distribution transformer.

[0071] In general, the unified standard measurement units corresponding to the voltage, current and temperature parameters are determined, the various types of data in the standardized parameters are unit checked, and the data with different measurement units are converted according to a fixed conversion rule. After the measurement units of all data are unified, the various types of parameters are integrated according to the acquisition time sequence to form the operation parameters of the distribution transformer.

[0072] S2, based on a preset safe operation threshold, marking the abnormal parameters in the operation parameters that exceed the safe operation threshold, obtaining an abnormal marking result of the distribution transformer;

[0073] In the embodiment of the application, based on the preset safe operation threshold, the abnormal parameters in the operation parameters that exceed the safe operation threshold are marked to obtain the abnormal marking result of the distribution transformer, which comprises:

[0074] The parameter values in the operation parameters are read, the parameter values are compared with the preset safe operation threshold for compliance determination, and the abnormal parameter points of the distribution transformer are determined according to the comparison result;

[0075] The specific position of the abnormal parameter points in the time sequence is located, and a timestamp identifier is attached to obtain the abnormal position identifier of the distribution transformer;

[0076] Based on the voltage parameter, current parameter and temperature parameter of the abnormal parameter points, the abnormal position identifier is parameter bound to obtain the abnormal marking result of the distribution transformer.

[0077] Specifically, the operation parameters of the distribution transformer archived by category in the data storage unit are taken as objects, the related values of voltage, current, temperature, etc. are retrieved one by one through a special data reading interface, the preset safe operation threshold is a fixed range value pre-set according to the design rating standard of the distribution transformer and the industry safe operation specification and stored in a special threshold database, each retrieved value is compared with the corresponding safe operation threshold one by one, and if the value exceeds the range defined by the threshold, the parameter corresponding to the value is directly determined as an abnormal parameter point of the distribution transformer.

[0078] Further, all the operation parameter data entries of the power distribution transformer form a continuous time sequence in the order of collection time, each data entry is bound with original collection time information, according to the value characteristics and category attributes of the abnormal parameter point, the entry corresponding to the completely matched value in the time sequence is found, the specific arrangement position of the entry in the time sequence is determined, the original collection time information corresponding to the abnormal parameter point is extracted from the time recording module of the data collection terminal, the time information is taken as a time stamp and added to the position record of the abnormal parameter point, and the abnormal position identification of the power distribution transformer is obtained.

[0079] Further, the complete voltage, current and temperature related data corresponding to the abnormal parameter point are extracted from the operation parameter database, it is ensured that the extracted data completely correspond to the time stamp information of the abnormal parameter point and are not missed, the extracted voltage, current and temperature parameter data are bound with the previously obtained abnormal position identification through data association technology, the abnormal position identification and the corresponding three types of parameters form a unique corresponding association and are integrated, and the abnormal marking result of the power distribution transformer is obtained.

[0080] In summary, the related values such as voltage, current and temperature in the operation parameters of the power distribution transformer are called through the special data reading interface, each value is compared with the safety operation threshold preset and stored in the special threshold database according to the equipment design rated standard and industry safety operation specification, and whether the value exceeds the threshold range is determined to determine the abnormal parameter point of the power distribution transformer.

[0081] In summary, relying on the continuous time sequence formed in the order of collection time, the matching entry is found in the sequence to determine the specific arrangement position according to the value characteristics and category attributes of the abnormal parameter point, the original collection time information corresponding to the abnormal parameter point is extracted as a time stamp and added to the position record, and the abnormal position identification of the power distribution transformer is obtained.

[0082] In summary, the complete voltage, current and temperature data completely corresponding to the time stamp of the abnormal parameter point are extracted from the operation parameter database, and the data and the abnormal position identification are uniquely related and integrated through data association technology, and the abnormal marking result of the power distribution transformer is obtained.

[0083] S3, traverse the abnormal marking result, identify the continuous abnormal interval existing in the abnormal marking result, retrieve the transient process characteristic library of the power distribution transformer in the historical normal operation state, and extract the transient waveform characteristic parameters of the power distribution transformer.

[0084] In the embodiment of the present application, the abnormal marking result is traversed, a continuous abnormal interval existing in the abnormal marking result is identified, and a transient process characteristic library of the distribution transformer in a historical normal operation state is retrieved, and transient waveform characteristic parameters of the distribution transformer are extracted, including:

[0085] The abnormal distribution law in the abnormal marking result is analyzed, the start time boundary and the end time boundary of the abnormal parameter segment are defined, and a continuous abnormal interval of the distribution transformer is obtained;

[0086] The transient process characteristic library of the distribution transformer is accessed, and normal transient process records corresponding to the continuous abnormal interval time window are screened;

[0087] The waveform data in the normal transient process records is intercepted, and normal waveform records of the distribution transformer are obtained;

[0088] Key waveform attributes in the normal waveform records are selected, and characteristic parameters of the waveform attributes are calibrated, and transient waveform characteristic parameters of the distribution transformer are obtained.

[0089] Specifically, all abnormal position identifiers in the abnormal marking result are combed one by one in chronological order, the distribution of these abnormal identifiers in the time dimension is analyzed, whether the time stamps corresponding to adjacent abnormal position identifiers are continuous and without interval is judged, and according to this continuous distribution law, the time node at which the abnormal parameter segment starts and the time node at which the abnormal parameter segment ends are clearly divided, the two time nodes together constitute the time range of the abnormal parameter segment, and finally a continuous abnormal interval of the distribution transformer is obtained.

[0090] Further, the transient process characteristic library of the distribution transformer is connected through a preset database access channel, the characteristic library stores complete records of all transient processes in the historical normal operation state of the equipment, and each record contains clear time window information. The time range constituted by the start time and the end time of the continuous abnormal interval is used as a retrieval condition, the time window of each normal transient process record in the transient process characteristic library is compared one by one, and the normal transient process record whose time window completely coincides with the retrieval condition is screened out.

[0091] Further, for the screened normal transient process record, waveform information describing voltage and current change trends is extracted, and the information is stored in the record in the form of continuous change curves. According to the time window range corresponding to the normal transient process record, all waveform change curve data in this time range is completely intercepted, ensuring that no waveform details corresponding to any time node are missed, and then normal waveform records of the distribution transformer are obtained.

[0092] Further, the waveform change curve in the normal waveform record is analyzed, core attributes affecting the transient process characteristics are identified, including the change trend of the waveform, the amplitude fluctuation characteristics, the shape of the stable stage and other key dimensions.

[0093] In summary, the abnormal marking results are sorted in chronological order, the time distribution law of the abnormal position identification is analyzed, whether the time stamps corresponding to adjacent abnormalities are continuous and uninterrupted is judged, the start and end time nodes of the abnormal parameter section are determined, and then the continuous abnormal interval of the distribution transformer is obtained.

[0094] In summary, the transient process characteristic library of the distribution transformer is connected through the preset database access channel, the time window of the normal transient process record is compared one by one in the characteristic library taking the time range of the continuous abnormal interval as the retrieval condition, and the normal transient process record with the completely coincident time window is screened out.

[0095] In summary, from the screened normal transient process record, the waveform information describing the voltage and current change trend is extracted, all the waveform change curve data are completely intercepted according to the corresponding time window range, and no waveform details of any time node are missed, and the normal waveform record of the distribution transformer is obtained.

[0096] In summary, the change curve in the normal waveform record is analyzed, core waveform attributes affecting the transient process characteristics are identified, each key attribute is defined and specifically described, these attributes are taken as characteristic parameters for calibration, all calibrated characteristic parameters are integrated, and the transient waveform characteristic parameters of the distribution transformer are obtained.

[0097] S4, according to the waveform rule of the transient waveform characteristic parameter, the waveform mapping relationship between the abnormal parameter section and the normal transient process is established, and the feature retention constraint condition of the distribution transformer is derived;

[0098] In the embodiment of the application, the waveform mapping relationship between the abnormal parameter section and the normal transient process is established according to the waveform rule of the transient waveform characteristic parameter, and the feature retention constraint condition of the distribution transformer is derived, including:

[0099] The periodic change characteristics and amplitude envelope shape in the transient waveform characteristic parameters are analyzed, and the waveform characteristic parameters of the steady state stage and the transition stage of the normal transient process are distinguished;

[0100] Taking the time axis of the abnormal parameter section as the reference and the spatial distribution of the normal transient process as the reference, the space-time correspondence relationship of the distribution transformer is constructed.

[0101] Based on the spatiotemporal correspondence, the mapping dimension of the data points in the abnormal parameter segment on the time series is defined, the association rule between the data points and the normal waveform is established, and the dynamic mapping rule of the distribution transformer is obtained.

[0102] Based on the dynamic mapping rules, waveform amplitude continuity constraints and phase smoothness constraints are defined and combined to form the characteristic retention constraints of the distribution transformer.

[0103] Based on the dynamic mapping rules, waveform amplitude continuity constraints and phase smoothness constraints are defined and combined to form the characteristic retention constraints of the distribution transformer, including:

[0104] Based on the dynamic mapping rule, the time dimension variation range of the waveform amplitude of the distribution transformer is defined, and the amplitude continuity constraint of the distribution transformer is constructed.

[0105] Based on the dynamic mapping rule, the waveform phase change characteristics of the distribution transformer during the transition process are evaluated, and a smoothness standard for phase change is established to obtain the phase smoothness constraint of the distribution transformer.

[0106] The technical requirements for coordinating the amplitude continuity constraint and the phase smoothness constraint are combined to form a complete constraint system, thereby constructing the characteristic retention constraint conditions of the distribution transformer.

[0107] The formula for calculating the feature preservation degree of the feature preservation constraint is as follows:

[0108] ;

[0109] In the formula, The feature preservation degree, The number of data points in the abnormal parameter segment. The first of the abnormal parameter segments The magnitude value of each data point The first of the abnormal parameter segments The magnitude value of each data point The first of the abnormal parameter segments Phase values ​​of each data point The first of the abnormal parameter segments Phase values ​​of each data point The maximum amplitude value within the amplitude reference range of the normal waveform. The minimum amplitude value within the amplitude reference range. This refers to the maximum phase value within the phase reference range of the normal waveform. a phase minimum value in the phase reference range, a preset balance amplitude weight coefficient, a preset phase change weight coefficient.

[0110] Specifically, the change law presented by the transient waveform feature parameter is analyzed in depth, the change mode repeatedly appearing in the time dimension of the waveform is observed, the manifestation of the periodic change is determined, and the overall change range and contour form of the waveform amplitude are carefully depicted to grasp the fluctuation characteristics of the amplitude envelope. By comparing the stability of the waveform in different time periods, the steady state stage where the waveform remains stable without obvious fluctuation in the normal transient process and the transition stage where the waveform changes from one state to another with obvious change are distinguished, and the waveform feature parameters corresponding to the two stages are extracted respectively.

[0111] Further, the time axis formed by arranging the abnormal parameter segments in the order of collection time is taken as the reference framework, the specific position of each abnormal data point on the time axis is determined, and the spatial distribution corresponding to the change trend of the voltage, current and other physical quantities in the normal transient process is taken as the reference basis. Each time node on the abnormal parameter segment time axis is matched with the spatial state of the corresponding time node in the normal transient process one by one, a one-to-one correspondence between each abnormal time point and the normal transient spatial state is established, and the space-time correspondence of the distribution transformer is constructed.

[0112] Further, based on the constructed space-time correspondence, the correlation direction and corresponding dimension of each data point in the abnormal parameter segment in the time sequence are determined, which directly corresponds to the time change dimension of the normal waveform, ensuring that the time attribute of the abnormal data point is consistent with the normal waveform. According to the matching result of the abnormal data point and the normal transient spatial state in the space-time correspondence, the corresponding rules between the data points in the abnormal parameter segment and the normal waveform are formulated, the normal waveform rules that the data points need to follow in the change trend and morphological characteristics are determined, and the dynamic mapping rule of the distribution transformer is obtained.

[0113] Further, according to the corresponding requirements of the abnormal data points and the normal waveform in the dynamic mapping rule, the waveform amplitude continuity constraint is defined, the amplitude change of adjacent data points is required to be smooth transition, and irregular mutation is not allowed, so as to ensure that the amplitude change conforms to the amplitude change logic of the normal waveform. At the same time, the phase smoothness constraint is defined, which stipulates that the change of the waveform phase should be gradual, avoiding sudden jump or interruption, and ensuring that the phase change is consistent with the phase change characteristics of the normal transient process. The requirements of the two constraints are integrated and coordinated to form a complete constraint system, and the feature preservation constraint condition of the distribution transformer is constructed.

[0114] Specifically, based on the correspondence between the abnormal parameter segment data points and the normal waveform in the dynamic mapping rule, the maximum fluctuation range and the stable change interval of the amplitude change in the time dimension of the normal waveform are extracted. With the range and interval as a reference, the upper and lower limits of the waveform amplitude in the abnormal parameter segment are determined, and the allowed amplitude of the amplitude change between the adjacent data points is defined to ensure that the amplitude change of the subsequent data point relative to the previous data point does not exceed the allowed amplitude, thereby constructing the amplitude continuity constraint of the distribution transformer.

[0115] Further, according to the phase change law of the normal transient process specified in the dynamic mapping rule, the phase evolution path of the normal waveform in the transition stage is analyzed in detail, the change trend and rhythm of the phase from the initial state to the stable state are observed, and the smoothness and gradual change characteristics of the phase change are evaluated. Based on these analysis results, a clear phase change standard is formulated, which requires that the phase change in the abnormal parameter segment must follow the standard and cannot have sudden jumps or reverse mutations without basis, ensuring that the phase change is consistent with the phase change logic of the normal transient process, and obtaining the phase smoothness constraint of the distribution transformer.

[0116] Further, the technical details of the amplitude continuity constraint and the phase smoothness constraint are comprehensively sorted out, and whether there is a conflict or contradiction between the two constraints in the time dimension and the change logic is checked. In view of the possible overlapping requirements, the core principle of conforming to the waveform law of the normal transient process is used for coordination, and the priority following rule when the requirements of the two constraints for the same data point overlap is specified. The integrated amplitude continuity constraint and phase smoothness constraint are systematically integrated, forming a logically consistent and comprehensive constraint system, and constructing the feature preservation constraint condition of the distribution transformer.

[0117] Specifically, each item of data related to feature preservation degree is obtained from the corresponding product generated in the safety data verification and correction process of the distribution transformer. The number of data points in the abnormal parameter segment is directly taken from the continuous abnormal interval identified in the abnormal parameter segment. The amplitude and phase values of adjacent data points in the abnormal parameter segment are directly extracted from the original record of the abnormal parameter segment.

[0118] Further, the maximum and minimum amplitudes of the amplitude reference range in the normal waveform come from the normal waveform record corresponding to the selected normal transient process record, which is determined by analyzing the amplitude change range of the normal waveform record. The maximum and minimum phases of the phase reference range in the normal waveform also come from the normal waveform record, which is obtained by analyzing the phase change interval of the normal waveform.

[0119] Further, balancing the amplitude weight coefficient and the phase change weight coefficient is to set a fixed value in advance according to the technical requirements of the feature preservation constraint condition, which is used to coordinate the influence degree of the amplitude continuity constraint and the phase smoothness constraint in the calculation.

[0120] Further, the core of the calculation is to measure the degree to which the waveform of the abnormal parameter segment after data recovery processing conforms to the characteristics of the normal transient process in amplitude continuity and phase smoothness.

[0121] Further, when calculating, first obtain the amplitude difference and phase difference of each two adjacent data points in the abnormal parameter segment, divide the amplitude difference by the difference between the maximum and minimum amplitudes of the normal waveform amplitude reference range, divide the phase difference by the difference between the maximum and minimum phases of the normal waveform phase reference range, and then multiply the two results by the corresponding preset weight coefficients, respectively.

[0122] Further, all the above calculation results corresponding to adjacent data points are accumulated, and then divided by the number of data points in the abnormal parameter segment to obtain the average value. Subtract 1 from the average value, and the final result is the feature retention degree.

[0123] Further, the result of the feature retention degree can directly reflect the degree of fit between the corrected abnormal parameter segment and the waveform characteristics of the normal transient process. The higher the value, the more the corrected parameter segment conforms to the characteristics of the normal transient process in amplitude continuity and phase smoothness, and the better the feature retention effect.

[0124] In summary, the periodic variation characteristics and overall shape of the amplitude envelope presented in the transient waveform characteristic parameters are analyzed and extracted, and by comparing the stability of the waveform at different time periods, the waveform characteristic parameters corresponding to the stable state phase where the waveform remains stable and the transition phase where the waveform changes in the normal transient process are distinguished.

[0125] In summary, taking the time axis formed by the abnormal parameter segment in chronological order as the basic framework, taking the spatial distribution of the changes of physical quantities such as voltage and current in the normal transient process as the reference basis, matching the nodes on the time axis of the abnormal parameter segment with the spatial state of the normal transient process, and constructing the time-space correspondence relationship of the distribution transformer.

[0126] In summary, based on the constructed time-space correspondence relationship, the mapping direction and dimension of each data point in the abnormal parameter segment in the time sequence are determined, the normal waveform rules that these data points need to follow in terms of change trend and morphological characteristics are formulated, the corresponding association rules between the data points and the normal waveform are established, and the dynamic mapping rules of the distribution transformer are obtained.

[0127] In summary, according to the association requirements of the data points and the normal waveform in the dynamic mapping rules, the waveform amplitude continuity constraint that ensures the smooth change of the waveform amplitude and the phase smoothness constraint that ensures the gradual change of the waveform phase are defined, respectively, and the technical requirements of these two constraints are integrated to form the feature retention constraint condition of the distribution transformer.

[0128] In summary, based on the correlation requirements between abnormal parameter segment data points and normal waveforms in the dynamic mapping rules, the upper and lower limits of the allowable variation of the waveform amplitude of the distribution transformer in the time dimension are clarified. At the same time, the allowable amplitude variation between adjacent data points is determined, thereby defining the boundary range of amplitude variation and constructing the amplitude continuity constraint of the distribution transformer.

[0129] In summary, based on the phase change logic of the normal transient process as defined by the dynamic mapping rules, the evolution trend and stability of the waveform phase of the distribution transformer during the transition process are analyzed, its change characteristics are evaluated, and the stability standard that the phase change must follow is formulated based on the evaluation results, thus obtaining the phase smoothness constraint of the distribution transformer.

[0130] In summary, the technical requirements of amplitude continuity constraints and phase smoothness constraints are comprehensively reviewed, their consistency in time dimension and change logic is checked, any conflicting or overlapping requirements are adjusted and coordinated, and the two constraints are integrated into a logically self-consistent and comprehensive constraint system to construct the characteristic retention constraints of distribution transformers.

[0131] S5. Based on the feature preservation constraints, guide the data recovery processing of the abnormal parameter segment to reconstruct the parameter sequence of the distribution transformer;

[0132] In this embodiment of the invention, the step of guiding the data recovery process of the abnormal parameter segment based on the feature preservation constraint to reconstruct the parameter sequence of the distribution transformer includes:

[0133] The aforementioned features are applied to maintain the constraints to the abnormal parameter segment;

[0134] Based on the amplitude continuity requirement in the feature-preserving constraint, the amplitude values ​​of data points that do not meet the amplitude continuity requirement in the parameter segment after constraint are corrected to obtain the amplitude continuity parameter segment of the distribution transformer.

[0135] Based on the phase smoothness requirement in the characteristic retention constraint, the phase jump in the amplitude continuous parameter segment is eliminated to obtain the phase smoothing parameter segment of the distribution transformer;

[0136] The phase smoothing parameter segment is connected and recombined with the normal parameter segment of the distribution transformer to construct the parameter sequence of the distribution transformer.

[0137] Specifically, from the database storing the feature-preserving constraints, the complete technical requirements including the waveform amplitude continuity constraint and the phase smoothness constraint are retrieved, each data point in the abnormal parameter segment is sequentially compared with the variation boundary range of the amplitude continuity constraint and the smoothness standard of the phase smoothness constraint in time sequence, and the data points in the abnormal parameter segment that do not meet the amplitude continuity requirement or the phase smoothness requirement are marked, to complete the application of the feature-preserving constraints on the abnormal parameter segment.

[0138] Further, the variation boundary range of the waveform amplitude in the time dimension defined by the amplitude continuity requirement in the feature-preserving constraints is determined, for the data points marked after the application of the constraints and not meeting the amplitude continuity requirement, the amplitude variation trend of the adjacent data points before and after the data point and meeting the amplitude continuity requirement is referred to, to calculate the reasonable amplitude value to which the data point should be adjusted, the original amplitude value of the data point is replaced by the calculated reasonable amplitude value, and after the correction of all the data points not meeting the amplitude continuity requirement is completed, the amplitude continuous parameter segment of the distribution transformer is obtained.

[0139] Further, the phase variation smoothness standard formulated by the phase smoothness requirement in the feature-preserving constraints is used to check the phase data in the amplitude continuous parameter segment point by point, to identify the phase jump points where the phase value suddenly jumps and does not meet the smoothness standard, the variation rhythm and evolution path of the normal phase data before and after the jump point are referred to, to determine the phase value to which the jump point should be adjusted, the original phase value of the jump point is replaced by the adjusted phase value, to make the phase variation meet the smoothness standard, and after all the phase jumps are eliminated, the phase smooth parameter segment of the distribution transformer is obtained.

[0140] Further, the normal parameter segment without abnormality is extracted from the operation parameter database of the distribution transformer, the time range of the normal parameter segment and the time range of the phase smooth parameter segment are determined, the connection nodes in the time sequence are found, to ensure that the last data point of the normal parameter segment and the first data point of the phase smooth parameter segment are continuous and without interval in the time stamp at the connection nodes, the phase smooth parameter segment is inserted into the corresponding time position of the normal parameter segment in time sequence, to integrate and form a complete data set arranged in time sequence, and the parameter sequence of the distribution transformer is constructed.

[0141] In summary, the complete technical requirements including the waveform amplitude continuity constraint and the phase smoothness constraint are retrieved from the database storing the feature-preserving constraints, each data point of the abnormal parameter segment is compared with the constraint requirement in time sequence, the points not meeting the constraint are marked, and the application of the feature-preserving constraints on the abnormal parameter segment is completed.

[0142] In summary, according to the amplitude variation boundary range defined by the amplitude continuity requirement in the feature preservation constraint condition, for the data points in the constrained parameter segment that do not meet the requirement, the amplitude values of these data points are calculated and adjusted by referring to the amplitude variation trend of the data points before and after the data points, and after the correction of all non-compliant points, the amplitude continuous parameter segment of the distribution transformer is obtained.

[0143] In summary, according to the phase variation standard formulated according to the phase smoothness requirement in the feature preservation constraint condition, the phase data in the amplitude continuous parameter segment is checked, the phase jump point is identified, and the phase value of the jump point is adjusted by referring to the variation rhythm of the normal phase before and after the jump point, and after eliminating all jumps, the phase smooth parameter segment of the distribution transformer is obtained.

[0144] In summary, the normal parameter segment of the distribution transformer is extracted from the operation parameter database, the time connection node of the normal parameter segment and the phase smooth parameter segment is determined, and the two are integrated in chronological order, so that the data is continuous without interval in time sequence, and the parameter sequence of the distribution transformer is constructed.

[0145] S6, verifying the continuity and rationality of the parameter sequence in the time dimension, obtaining the safety data of the distribution transformer.

[0146] In the embodiment of the application, the verification of the continuity and rationality of the parameter sequence in the time dimension, obtaining the safety data of the distribution transformer, comprises:

[0147] Verifying whether the time stamp interval between adjacent data points in the parameter sequence meets the preset continuity standard, obtaining the continuity verification result of the distribution transformer;

[0148] Detecting whether there is an abnormal data jump in the gradual change characteristics of the data value in the parameter sequence, obtaining the rationality verification result of the distribution transformer;

[0149] Comprehensive judgment conclusion of the continuity verification result and the rationality verification result, judging whether the parameter sequence meets the safety operation threshold, obtaining the safety data of the distribution transformer.

[0150] Specifically, a continuity criterion set for the parameter sequence timestamp interval is called from the preset criterion storage module, which specifies the fixed interval range that the timestamp interval of adjacent data points needs to maintain. In the time sequence of the data points in the parameter sequence, the timestamp information of adjacent two data points is extracted one by one, the interval length of the timestamp of the latter data point and the timestamp of the former data point is calculated, and the calculated interval length is compared with the preset continuity criterion one by one. If the interval length is within the range defined by the criterion, it is determined that the timestamp interval of the group of adjacent data points meets the requirements, and if it exceeds the range, it is determined that it does not meet the requirements. The comparison results of all adjacent data points are summarized and recorded to obtain the continuity verification result of the power transformer.

[0151] Further, in the time sequence of the data points in the parameter sequence, the data values of each data point are extracted in turn, the change trend of the data values of adjacent data points is observed, and it is determined whether the change of the data value of the latter data point relative to the data value of the former data point presents a steady and gradual change state. If there is an irregular large increase and decrease between the data values of adjacent data points, and the change does not conform to the gradual change logic of the data value under normal operating state, it is determined that there is an abnormal data jump; if all adjacent data point data values remain steady and gradually change without such large abnormal changes, it is determined that there is no abnormal data jump. The detection situation of the entire parameter sequence is recorded to obtain the rationality verification result of the power transformer.

[0152] Further, it is first checked whether the continuity verification result is that all adjacent data point timestamp intervals meet the preset continuity criterion, and it is also checked whether the rationality verification result is that there is no abnormal data jump in the parameter sequence. Only when both verification results meet the qualified requirements, the parameter sequence is included in the subsequent safety threshold judgment link. The preset safety operating threshold corresponding to the operating parameter of the power transformer is called from the safety threshold database, and the data value of each data point in the parameter sequence is compared with the corresponding safety operating threshold one by one. If the data values of all data points are within the range defined by the safety operating threshold, it is determined that the parameter sequence meets the safety operating threshold, and the parameter sequence is output as the final result to obtain the safety data of the power transformer.

[0153] In summary, the preset continuity criterion corresponding to the parameter sequence timestamp interval is called from the preset criterion storage module, the timestamps of adjacent data points are extracted in the time sequence of the data points in the parameter sequence, the interval length is calculated and compared with the preset continuity criterion one by one, and all comparison results are summarized to obtain the continuity verification result of the power transformer.

[0154] In general, the data value of each data point is extracted according to the time sequence of the parameter sequence data points, whether the change of the data values of adjacent data points is in a stable gradual change state is observed, whether there is irregular large abnormal increase or decrease is judged, the detection situation of the entire parameter sequence is recorded, and a rationality verification result of the power distribution transformer is obtained.

[0155] In general, it is first confirmed whether the continuity verification result is that the time stamp interval of all adjacent data points meets the standard and whether the rationality verification result is that there is no abnormal data jump, and under the premise that both results are qualified, the preset safe operation threshold value is retrieved and compared with the data values of the data points of the parameter sequence one by one, if all the data values are within the threshold value range, the parameter sequence is taken as the result output, and safe data of the power distribution transformer is obtained.

[0156] As shown in Figure 2 Fig. 1 is a functional module diagram of a power distribution transformer safe data verification and correction system according to an embodiment of the present application.

[0157] The power distribution transformer safe data verification and correction system 100 can be installed in an electronic device. According to the functions implemented, the power distribution transformer safe data verification and correction system 100 can include a running parameter extraction and regularization module 101, an abnormal parameter marking module 102, a continuous abnormality identification and transient feature extraction module 103, a waveform mapping relationship establishment and constraint condition derivation module 104, an abnormal data recovery and parameter sequence reconstruction module 105, and a parameter sequence verification and safe data generation module 106. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0158] In this embodiment, the functions of each module / unit are as follows:

[0159] The running parameter extraction and regularization module 101 is used to extract voltage parameters, current parameters and temperature parameters in the running of the power distribution transformer, regularize the format and unify the units of the parameters, and obtain the running parameters of the power distribution transformer;

[0160] The abnormal parameter marking module 102 is used to mark abnormal parameters in the running parameters that exceed the preset safe operation threshold value based on the safe operation threshold value, and obtain an abnormal marking result of the power distribution transformer;

[0161] The continuous abnormality identification and transient feature extraction module 103 is used to traverse the abnormal marking result, identify the continuous abnormal interval existing in the abnormal marking result, search the transient process feature library of the power distribution transformer in the historical normal running state, and extract the transient waveform feature parameters of the power distribution transformer;

[0162] The waveform mapping relationship establishing and constraint condition deriving module 104 is configured to establish a waveform mapping relationship between an abnormal parameter segment and a normal transient process according to a waveform rule of the transient waveform characteristic parameter, and derive a characteristic maintaining constraint condition of the power distribution transformer.

[0163] The abnormal data recovery and parameter sequence reconstruction module 105 is configured to guide data recovery processing of the abnormal parameter segment based on the characteristic maintaining constraint condition, and reconstruct a parameter sequence of the power distribution transformer.

[0164] The parameter sequence verification and secure data generation module 106 is configured to verify continuity and rationality of the parameter sequence in a time dimension, and obtain secure data of the power distribution transformer.

[0165] In several embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and another division mode can be used in actual implementation.

[0166] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs.

[0167] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0168] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0169] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Artificial intelligence is a theory, method, technology and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by a digital computer, perceiving an environment, acquiring knowledge and using knowledge to obtain optimal results.

[0170] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for verifying and correcting safety data of a distribution transformer, characterized in that, The method includes: S1. Extract the voltage, current and temperature parameters of the distribution transformer during operation, standardize the parameter format and unify the units to obtain the operating parameters of the distribution transformer. S2. Based on a preset safe operating threshold, mark the abnormal parameters in the operating parameters that exceed the safe operating threshold to obtain the abnormal marking result of the distribution transformer; S3. Traverse the anomaly marking results, identify continuous anomaly intervals in the anomaly marking results, and retrieve the transient process feature library of the distribution transformer under historical normal operation conditions to extract the transient waveform feature parameters of the distribution transformer. S4. Based on the waveform characteristics of the transient waveform parameters, establish the waveform mapping relationship between the abnormal parameter segment and the normal transient process, and derive the characteristic holding constraint conditions of the distribution transformer. S5. Based on the feature preservation constraints, guide the data recovery processing of the abnormal parameter segment to reconstruct the parameter sequence of the distribution transformer; S6. Verify the continuity and rationality of the parameter sequence in the time dimension to obtain the safety data of the distribution transformer.

2. The method for verifying and correcting safety data of a distribution transformer as described in claim 1, characterized in that, The process involves extracting voltage, current, and temperature parameters from the distribution transformer during operation, standardizing the parameter format, and unifying the units to obtain the operating parameters of the distribution transformer, including: The voltage, current, and temperature signals output by the distribution transformer monitoring device are collected to obtain the original parameter set of the distribution transformer. The data organization of the original parameter set is reorganized, and the time identifier of the original parameter set is aligned to a preset time base to obtain the standardized parameters of the distribution transformer; The units of measurement in the standardized parameters are standardized, and the converted parameters are integrated to form the operating parameters of the distribution transformer.

3. The method for verifying and correcting safety data of a distribution transformer as described in claim 1, characterized in that, The step of marking abnormal parameters in the operating parameters that exceed the preset safe operating threshold based on a preset safe operating threshold, and obtaining the abnormal marking result of the distribution transformer, includes: Read the parameter values ​​from the operating parameters, compare the parameter values ​​with the preset safe operating thresholds to determine compliance, and determine the abnormal parameter points of the distribution transformer based on the comparison results; Locate the specific position of the abnormal parameter point in the time series and attach a timestamp identifier to obtain the abnormal position identifier of the distribution transformer; Based on the voltage, current, and temperature parameters of the abnormal parameter points, the abnormal location markers are parameter-bound to obtain the abnormal marking results of the distribution transformer.

4. The method for verifying and correcting safety data of a distribution transformer as described in claim 1, characterized in that, The process involves traversing the anomaly marking results, identifying continuous anomaly intervals within the anomaly marking results, and retrieving the transient process feature library of the distribution transformer under historical normal operating conditions to extract transient waveform feature parameters of the distribution transformer, including: The abnormal distribution pattern in the abnormal marking results is analyzed, and the start and end time boundaries of the abnormal parameter segments are defined to obtain the continuous abnormal interval of the distribution transformer. Access the transient process feature library of the distribution transformer and filter the normal transient process records corresponding to the continuous abnormal interval time window; By extracting waveform data from the normal transient process record, the normal waveform record of the distribution transformer is obtained; Key waveform attributes are selected from the normal waveform records, and the characteristic parameters of the waveform attributes are calibrated to obtain the transient waveform characteristic parameters of the distribution transformer.

5. The method for verifying and correcting safety data of a distribution transformer as described in claim 1, characterized in that, The step of establishing a waveform mapping relationship between abnormal parameter segments and normal transient processes based on the waveform patterns of the transient waveform characteristic parameters, and deriving the characteristic holding constraints of the distribution transformer, includes: The periodic variation characteristics and amplitude envelope shape of the transient waveform feature parameters are analyzed, and the waveform feature parameters of the steady-state stage and the transition stage of the normal transient process are distinguished. Using the time axis of the abnormal parameter segment as a reference and the spatial distribution of the normal transient process as a reference, the spatiotemporal correspondence of the distribution transformer is constructed. Based on the spatiotemporal correspondence, the mapping dimension of the data points in the abnormal parameter segment on the time series is defined, the association rule between the data points and the normal waveform is established, and the dynamic mapping rule of the distribution transformer is obtained. Based on the dynamic mapping rules, waveform amplitude continuity constraints and phase smoothness constraints are defined and combined to form the characteristic retention constraints of the distribution transformer.

6. The method for verifying and correcting safety data of a distribution transformer as described in claim 5, characterized in that, Based on the dynamic mapping rules, waveform amplitude continuity constraints and phase smoothness constraints are defined and combined to form the characteristic retention constraints of the distribution transformer, including: Based on the dynamic mapping rule, the time dimension variation range of the waveform amplitude of the distribution transformer is defined, and the amplitude continuity constraint of the distribution transformer is constructed. Based on the dynamic mapping rule, the waveform phase change characteristics of the distribution transformer during the transition process are evaluated, and a smoothness standard for phase change is established to obtain the phase smoothness constraint of the distribution transformer. The technical requirements for coordinating the amplitude continuity constraint and the phase smoothness constraint are combined to form a complete constraint system, thereby constructing the characteristic retention constraint conditions of the distribution transformer.

7. The method for verifying and correcting safety data of a distribution transformer as described in claim 6, characterized in that, The formula for calculating the feature preservation degree of the feature preservation constraint is as follows: ； In the formula, The feature preservation degree, The number of data points in the abnormal parameter segment. The first of the abnormal parameter segments The magnitude value of each data point The first of the abnormal parameter segments The magnitude value of each data point The first of the abnormal parameter segments Phase values ​​of each data point The first of the abnormal parameter segments Phase values ​​of each data point The maximum amplitude value within the amplitude reference range of the normal waveform. The minimum amplitude value within the amplitude reference range. This refers to the maximum phase value within the phase reference range of the normal waveform. The minimum phase value within the stated phase reference range. The preset balance amplitude weighting coefficient, This is the preset phase change weighting coefficient.

8. The method for verifying and correcting safety data of a distribution transformer as described in claim 1, characterized in that, The data recovery process based on the feature preservation constraints, guiding the abnormal parameter segments, and reconstructing the parameter sequence of the distribution transformer, includes: The aforementioned features are applied to maintain the constraints to the abnormal parameter segment; Based on the amplitude continuity requirement in the feature-preserving constraint, the amplitude values ​​of data points that do not meet the amplitude continuity requirement in the parameter segment after constraint are corrected to obtain the amplitude continuity parameter segment of the distribution transformer. Based on the phase smoothness requirement in the characteristic retention constraint, the phase jump in the amplitude continuous parameter segment is eliminated to obtain the phase smoothing parameter segment of the distribution transformer; The phase smoothing parameter segment is connected and recombined with the normal parameter segment of the distribution transformer to construct the parameter sequence of the distribution transformer.

9. The method for verifying and correcting safety data of a distribution transformer as described in claim 1, characterized in that, The verification of the continuity and rationality of the parameter sequence in the time dimension to obtain the safety data of the distribution transformer includes: The continuity verification result of the distribution transformer is obtained by verifying whether the timestamp interval between adjacent data points in the parameter sequence meets the preset continuity standard. The rationality verification result of the distribution transformer is obtained by detecting whether there are abnormal data jumps in the gradual change characteristics of the data values ​​in the parameter sequence. Based on the combined results of the continuity verification and the rationality verification, it is determined whether the parameter sequence meets the safe operation threshold, thus obtaining the safety data of the distribution transformer.

10. A safety data verification and correction system for distribution transformers, characterized in that, The system includes: The operating parameter extraction and normalization module is used to extract voltage, current and temperature parameters during the operation of the distribution transformer, normalize the parameter format and unify the units to obtain the operating parameters of the distribution transformer. An abnormal parameter marking module is used to mark abnormal parameters in the operating parameters that exceed the preset safe operating threshold based on a preset safe operating threshold, so as to obtain the abnormal marking result of the distribution transformer; The continuous anomaly identification and transient feature extraction module is used to traverse the anomaly marking results, identify the continuous anomaly intervals in the anomaly marking results, and retrieve the transient process feature library of the distribution transformer under historical normal operation to extract the transient waveform feature parameters of the distribution transformer. The waveform mapping relationship establishment and constraint condition derivation module is used to establish the waveform mapping relationship between the abnormal parameter segment and the normal transient process based on the waveform law of the transient waveform characteristic parameters, and derive the characteristic holding constraint conditions of the distribution transformer. An abnormal data recovery and parameter sequence reconstruction module is used to guide the data recovery processing of the abnormal parameter segment based on the feature preservation constraint condition, and reconstruct the parameter sequence of the distribution transformer. The parameter sequence verification and safety data generation module is used to verify the continuity and rationality of the parameter sequence in the time dimension and obtain the safety data of the distribution transformer.

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