Electric power remote safety protection system

Abstract

The invention discloses an electric power remote safety protection system, and relates to the technical field of electric power protection, and the system comprises a monitoring module which is used for monitoring the data information of electric power terminal equipment in a power grid, and obtaining a terminal data set; the extraction module is used for performing feature extraction on the terminal data set; the analysis module is used for acquiring the detection instruction book from the test management platform and analyzing the detection instruction book to generate a detection object and a compliance index associated with the detection object; the establishing module is used for establishing a feature evaluation model based on the compliance indexes; and the analysis module is used for carrying out situation analysis on the extracted features according to the feature evaluation model, and judging whether an abnormal behavior warning is sent out or not according to a situation analysis result. According to the invention, the problem that a lot of time and manpower are consumed due to the fact that an existing protection system needs people to judge accident reasons on site is solved.

Description

Technical Field

[0001] The invention relates to the technical field of electric power protection, in particular to an electric power remote safety protection system. Background Art

[0002] At present, accidents always occur during the operation of power grids, such as short circuit of transmission lines, open circuit of transmission lines, lightning strikes on transmission lines, tripping of equipment due to overload, damage to power equipment due to external force, power outage of equipment due to illegal operation, etc. When a fault occurs in the power grid, whether in the substation, on the line, or at the equipment end, it will first cause a large-scale power outage, affecting the production of other industries and people's lives. Therefore, it has become a very necessary emergency task to determine the fault location as soon as possible and command the corresponding personnel to arrive at the scene with appropriate repair tools to repair the fault and restore power supply as soon as possible. However, the existing protection system requires people to go to the scene to determine the cause of the accident, which consumes a lot of time and manpower. Summary of the invention

[0003] In view of the problems existing in the existing electric power remote safety protection, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the existing protection system requires people to go to the scene to determine the cause of the accident, which consumes a lot of time and manpower.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a power remote safety protection system, which includes:

[0007] Monitoring module: used to monitor the data information of power terminal equipment in the power grid and obtain terminal data sets;

[0008] Extraction module: used for extracting features from the terminal data set;

[0009] Parsing module: obtains the test instructions from the test management platform, parses the test instructions to generate test objects and compliance indicators associated with the test objects;

[0010] Building module: used to build feature evaluation models based on compliance indicators;

[0011] Analysis module: Perform situation analysis on the extracted features according to the feature evaluation model, and determine whether to issue an abnormal behavior warning based on the situation analysis results.

[0012] As a preferred solution of the electric power remote safety protection system of the present invention, the monitoring module includes:

[0013] A first acquisition unit: acquiring device attributes and device types of power terminal devices in the power grid;

[0014] Setting unit: setting the data collection period, data collection rate and data items based on the device attributes and device type;

[0015] Collection unit: collects data information of each data item in the power terminal equipment according to the data collection cycle and data collection rate;

[0016] Integration unit: Integrate and process the data information of each data item of the power terminal equipment to obtain the terminal data set.

[0017] As a preferred solution of the electric power remote safety protection system of the present invention, wherein: the extraction module includes:

[0018] Screening unit: Screening the data in the acquired terminal data set by feature values ​​to obtain a basic feature value set;

[0019] A first classification unit: classifies the feature values ​​under the same data item in the basic feature value set to obtain the classification result under the same data item;

[0020] A first determining unit: determining a feature distribution of the same data item in the terminal data set based on the classification result;

[0021] Extraction unit: selects a corresponding feature extraction method from a distribution-method mapping table according to the feature distribution of different data items, and performs feature extraction on all data under the corresponding data item in the terminal data set;

[0022] Among them, the first classification unit includes:

[0023] Acquisition subunit: acquiring multiple random eigenvalues ​​in each data item from the basic eigenvalue set by a random acquisition method;

[0024] Determine the subunit: determine the edit distance between multiple random eigenvalues ​​under the same data item, and obtain a distance similarity array for each random eigenvalue;

[0025] Classification subunit: Based on the distance similarity array, the feature values ​​under the same data item are classified into similar distances to obtain the classification result.

[0026] As a preferred solution of the electric power remote safety protection system of the present invention, the analysis module includes:

[0027] Decryption unit: obtains the test instruction from the test management platform through data acquisition authority, and decrypts the test instruction;

[0028] A second acquisition unit is used to acquire multiple detection items of power terminals in the power grid;

[0029] A second determination unit: determines the detection object according to the decrypted detection guide and multiple detection items of the power terminal;

[0030] The third determination unit: parses the detection instruction to obtain the standard working parameters of each detection object, and determines the compliance indicators associated with each detection object according to the standard working parameters.

[0031] As a preferred solution of the electric power remote safety protection system of the present invention, wherein: the establishment module includes:

[0032] Construction unit: obtain the characteristic data of each compliance indicator, obtain the feedback evaluation of each characteristic data in the same compliance indicator, and construct the corresponding characteristic vector;

[0033] Establishing unit: inputting different feature vectors into the neural network model in sequence to establish a feature evaluation model.

[0034] As a preferred solution of the electric power remote safety protection system of the present invention, wherein: the analysis module includes:

[0035] Conversion unit: converts the extracted features into standardized data according to the feature-conversion mapping table;

[0036] Input unit: inputs the extracted features into the feature evaluation model to obtain the initial evaluation results;

[0037] Analysis unit: performing feature analysis on the standardized data to obtain optimization factors;

[0038] A judgment unit is configured to optimize the initial evaluation result based on the optimization factor and judge whether to issue an abnormal behavior warning.

[0039] As a preferred solution of the electric power remote safety protection system of the present invention, it also includes:

[0040] Data determination module: reads the original power data collected by the power terminal in the power grid, determines the data type of the original power data, and obtains the regenerated data;

[0041] Source matching module: tracing the data source of the regenerated data, matching the regenerated data with the data source one by one according to the source database, and obtaining classified data;

[0042] Data set acquisition module: sorts the classified data from the same data source in chronological order, and matches the data set at each time point with the load in the system one by one, so as to obtain the target load data set from the same data source;

[0043] Data clustering module: obtaining the operation characteristics of the same data source, and clustering the corresponding target load data set based on the operation characteristics to obtain a number of operation data groups;

[0044] Value determination module: sort the data values ​​of the same parameter in the same running data group in ascending order to form a value sequence.

[0045] As a preferred solution of the electric power remote safety protection system of the present invention, wherein: the mean of the value sequence is determined, and the first difference between the mean and the left boundary value of the standard range of the same parameter under the corresponding operating characteristics and the second difference between the mean and the right boundary value are determined;

[0046] If the first difference is less than 0, the data value arranged at the first position in the value sequence is removed, and the first value D1 is calculated:

[0047]

[0048] Among them, y1 1 Represents the data value arranged in the first position in the value sequence; y1 i1 represents the data value at the i1th position in the value sequence; F Z Indicates the left boundary value in the standard range of the corresponding parameter; F Y Indicates the right boundary value corresponding to the standard range of the same parameter;

[0049] If the second difference is greater than 0, the data value arranged at the last position in the value sequence is removed, and the second value D2 is calculated;

[0050]

[0051] Among them, y1 n1 represents the data value arranged at the last position in the value sequence;

[0052] Taking the first value D1 or the second value D2 as an initial value;

[0053] Otherwise, the mean of the value sequence is taken as the initial value;

[0054] Based on matching the initial value of each parameter in the same operation data group with the value mapping table, a target value of the corresponding operation data group is obtained;

[0055] Locking the operation time points involved in the same operation data group, matching the load value to the data set at each operation time point, and drawing the array load curve of the corresponding operation data group;

[0056] Curve comparison module: based on the typical daily load curve, array load curve and standard load curve under the same operating data group;

[0057] Calculate the first similarity S1 between the typical daily load curve y01 and the array load curve y0s under the same operating array and the second similarity S2 between the standard load curve y0b and the array load curve y0s under the same operating array;

[0058] S1 = sim(y01,y0s);

[0059] S2 = sim(y0b,y0s);

[0060] Among them, sim() represents the similarity function;

[0061] Performing a first comparison between the target value y01 of the typical load curve and the standard value of the standard load curve y0b, and performing a second comparison between the first similarity and the second similarity;

[0062] Determine an upper boundary and a lower boundary according to the first comparison result and the second comparison result;

[0063]

[0064]

[0065] in, represents the adjustment coefficient; F1 represents the value of the upper boundary; F2 represents the value of the lower boundary;

[0066] Based on the upper boundary and the lower boundary of the division, the data outside the boundary of the corresponding array load curve is locked and regarded as bad data;

[0067] Determine the current position of the bad data, and obtain the value of the first data and the value of the second data that are closest to the bad data, determine to obtain replacement data corresponding to the bad data, and replace it;

[0068]

[0069] Among them, J represents the value of the boundary for determining that the data at the current position is bad data; T1 represents the value of the first data; T2 represents the value of the second data; and T0 represents the value of the replacement data.

[0070] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned electric power remote safety protection system is implemented.

[0071] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program implements any step of the above-mentioned electric power remote safety protection system when executed by a processor.

[0072] The beneficial effects of the present invention are: BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0074] Figure 1 It is a structural diagram of a power remote safety protection system in an embodiment of the present invention.

[0075] Figure 2 Another structural diagram of an electric power remote safety protection system in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0077] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0078] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0079] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0080] Example 1

[0081] Reference Figure 1 , which is the first embodiment of the present invention, and provides a power remote safety protection system, comprising:

[0082] Monitoring module: used to monitor the data information of power terminal equipment in the power grid and obtain terminal data sets;

[0083] Extraction module: used for extracting features from the terminal data set;

[0084] Parsing module: obtains the test instructions from the test management platform, parses the test instructions to generate test objects and compliance indicators associated with the test objects;

[0085] Building module: used to build feature evaluation models based on compliance indicators;

[0086] Analysis module: Perform situation analysis on the extracted features according to the feature evaluation model, and determine whether to issue an abnormal behavior warning based on the situation analysis results.

[0087] In this embodiment, the power terminal equipment includes: a substation, a distribution room, a distribution box, an electric meter, a power load controller and an online monitoring device.

[0088] In this embodiment, the data information includes working parameters such as power generation, power generation capacity, and power generation efficiency of the power terminal equipment.

[0089] In this embodiment, the terminal data set refers to a data set formed by summarizing all data of the terminal device.

[0090] In this embodiment, feature extraction refers to extracting features from data in a data set. For example, the power generation of device 1 is: 20 kWh, 19 kWh, 21 kWh, etc., thereby determining the power generation characteristics of device 1.

[0091] In this embodiment, the test guide is generated by the test management platform according to the test project, and the test project is constructed by the test management platform according to the test object and the compliance indicators associated with the test object.

[0092] Among them, the detection objects refer to host devices, application systems, and network security devices. The compliance indicators associated with the detection objects are determined based on the compliance indicators stored in the compliance library. Among them, the compliance indicators refer to, for example, how many kilowatts of power generation must be achieved per hour.

[0093] In this embodiment, the feature evaluation model refers to a model for evaluating the features of a data set, and is a model trained based on a large number of data sets containing features and evaluation results of different features by experts as training samples.

[0094] In this embodiment, situation analysis refers to analyzing the advantages, disadvantages, strengths and weaknesses formed by the extracted features. For example, a current of 220 mA per hour can maximize the efficiency of the entire power generation process. At this time, it is determined that the power generation process is efficient and there is no abnormality, so there is no need to issue an abnormal behavior warning.

[0095] In this embodiment, the abnormal behavior may be: the load on the power load controller is too large, the electric meter cannot operate normally, and the current is too large instantly.

[0096] The beneficial effects of the above technical solution are: by remotely monitoring the data of the power terminal equipment of the power grid, forming a data set, and performing feature extraction, building a feature evaluation model, and performing situation analysis on the extracted signs, the cause of the abnormality can be quickly determined remotely, thereby improving judgment efficiency and saving a lot of time.

[0097] Example 2

[0098] Reference Figure 2 As shown, based on the first embodiment, this embodiment further provides a power remote safety protection system, a monitoring module, including:

[0099] A first acquisition unit: acquiring device attributes and device types of power terminal devices in the power grid;

[0100] Setting unit: setting the data collection period, data collection rate and data items based on the device attributes and device type;

[0101] Collection unit: collects data information of each data item in the power terminal equipment according to the data collection cycle and data collection rate;

[0102] Integration unit: Integrate and process the data information of each data item of the power terminal equipment to obtain the terminal data set.

[0103] In this embodiment, the device attributes include: basic information, parameters, such as brand model, rated voltage, rated current, rated frequency, etc.

[0104] In this embodiment, the equipment types include: monitoring equipment, control equipment, communication equipment, and power generation equipment.

[0105] In this embodiment, the collection cycle refers to the time interval for collecting data, for example, it is set that the data of the monitoring device is collected once every two days.

[0106] In this embodiment, the acquisition rate refers to the speed of acquiring data, for example, each acquisition speed is to complete the acquisition within 1 hour.

[0107] In this embodiment, the data items are, for example, current, voltage, and capacitance.

[0108] In this embodiment, the data information means, for example, that the current is 200 amps.

[0109] In this embodiment, the terminal data set is data information obtained based on various data items.

[0110] The beneficial effect of the above technical solution is: according to the device attributes and device types of the power terminal in the power grid, the data information of each data item of the power terminal device is collected. The data information of each data item is integrated and processed to obtain the terminal data set, which can integrate different data and make the data more centralized and unified.

[0111] Example 3

[0112] On the basis of the previous two embodiments, this embodiment further provides a power remote safety protection system, an extraction module, including:

[0113] Screening unit: Screening the data in the acquired terminal data set by feature values ​​to obtain a basic feature value set;

[0114] A first classification unit: classifies the feature values ​​under the same data item in the basic feature value set to obtain the classification result under the same data item;

[0115] A first determining unit: determining a feature distribution of the same data item in the terminal data set based on the classification result;

[0116] Extraction unit: selects the corresponding feature extraction method from the distribution-method mapping table according to the feature distribution of different data items, and performs feature extraction on all data under the corresponding data item in the terminal data set.

[0117] In this embodiment, the data in the terminal data set refers to a data set that is aggregating various types of data from all terminal devices. For example, if the variance of a feature itself is very small, it means that there is basically no difference in the samples on this feature. Then this type of feature value can be filtered out, and the remaining data together constitutes the basic feature set.

[0118] In this embodiment, the terminal data set includes data item 1, data item 2 and data item 3, wherein data item 1 includes data 1, data 2 and data 3. At this time, data 1 and data 2 are relatively close. At this time, data 1 and data 2 are regarded as a classification result, and data 3 is regarded as a classification result. At this time, the classification result under data item 1 is obtained, and data item 2 and data item 3 are similar.

[0119] In this embodiment, feature distribution refers to a situation after data contained in the same data item is classified, that is: data 1 and data 2 are in the first category, and there are 2 data in the first category, and data 3 is in the second category, and there is 1 data in the second category.

[0120] In this embodiment, the distribution-mode mapping table includes different distribution situations and extraction methods. Therefore, a feature extraction method consistent with the feature distribution can be directly obtained to perform feature extraction. The feature extraction method may be to extract data with excessive standard errors as extracted features.

[0121] The beneficial effect of the above technical solution is: by screening the feature values ​​of the data to obtain a basic feature value set, classifying the data in the set, and determining the feature distribution according to the classification results, the features of the data can be accurately extracted, making the extracted features more representative.

[0122] Example 4

[0123] On the basis of the first three embodiments, this embodiment further provides a power remote safety protection system, wherein the first classification unit includes:

[0124] Acquisition subunit: acquiring multiple random eigenvalues ​​in each data item from the basic eigenvalue set by a random acquisition method;

[0125] Determine the subunit: determine the edit distance between multiple random eigenvalues ​​under the same data item, and obtain a distance similarity array for each random eigenvalue;

[0126] Classification subunit: Based on the distance similarity array, the feature values ​​under the same data item are classified into similar distances to obtain the classification result.

[0127] In this embodiment, the random feature values ​​in data item 1 include: a1, a2 and a3, wherein the distance similarity array for a1 is: [,]; the distance similarity array for a2 is: [,], and the distance similarity array for a3 is: [,].

[0128] The beneficial effect of the above technical solution is: by randomly collecting multiple random eigenvalues ​​from the basic eigenvalue set, determining the edit distance between the multiple random eigenvalues, obtaining a distance similarity array, and classifying the eigenvalues, the eigenvalues ​​can be made more unified and centralized.

[0129] Example 5

[0130] On the basis of the previous four embodiments, this embodiment further provides a power remote safety protection system, a parsing module, including:

[0131] Decryption unit: obtains the test instruction from the test management platform through data acquisition authority, and decrypts the test instruction;

[0132] A second acquisition unit is used to acquire multiple detection items of power terminals in the power grid;

[0133] A second determination unit: determines the detection object according to the decrypted detection guide and multiple detection items of the power terminal;

[0134] The third determination unit: parses the detection instruction to obtain the standard working parameters of each detection object, and determines the compliance indicators associated with each detection object according to the standard working parameters.

[0135] In this embodiment, decryption processing refers to canceling the confidentiality level of the detection instructions, performing reverse operations on the ciphertext data according to the encryption algorithm and key, removing the noise, transformation or randomness added during encryption, and restoring the ciphertext data to the original plaintext data state. After decryption processing, the guidance process based on the detection instructions can be obtained, and the guidance process includes different objects and implementation projects of different objects.

[0136] In this embodiment, the plurality of detection items include: a capacitance detection item, a voltage detection item, a power generation detection item, and the like.

[0137] In this embodiment, objects that meet multiple detection items are determined, and then the objects that meet the requirements are regarded as detection objects, such as host devices, application systems, network security devices, etc.

[0138] In this embodiment, for example, the standard working parameters of the detection object 1 are: parameter 1, parameter 2 and parameter 3, among which parameter 2 and parameter 3 are the parameters that need to be monitored intensively, so the compliance index is an indicator that must be related to parameter 2 and parameter 3.

[0139] The beneficial effect of the above technical solution is: by obtaining the inspection instructions from the test management platform, and then decrypting the obtained inspection instructions, obtaining multiple inspection items of the power terminals in the power grid, determining the inspection objects and the compliance indicators associated with the inspection objects, it not only solves the problems of low efficiency and low accuracy of traditional compliance inspections, but also can continuously inspect the inspected objects to avoid omissions.

[0140] Example 6

[0141] On the basis of the previous five embodiments, this embodiment further provides a power remote safety protection system, establishing a module, including:

[0142] Construction unit: obtain the characteristic data of each compliance indicator, obtain the feedback evaluation of each characteristic data in the same compliance indicator, and construct the corresponding characteristic vector;

[0143] Establishing unit: inputting different feature vectors into the neural network model in sequence to establish a feature evaluation model.

[0144] In this embodiment, for example, characteristic data with a parameter description of 01 that meets the compliance index is obtained, and the characteristic data obtained are: 21 mA, 20 mA, 0 mA, 30 mA, 40 mA, etc., and the corresponding feedback evaluations are: qualified, qualified, unqualified, qualified, and exceeded. The constructed characteristic vector is: [21 mA-qualified, 20 mA-qualified, 0 mA-unqualified, 30 mA-qualified, 40 mA-exceeded].

[0145] The beneficial effect of the above technical solution is: by obtaining the characteristic data of each indicator and the feedback evaluation of the data, it is easy to construct a reasonable vector and input it into the model for training, so as to construct a characteristic evaluation model.

[0146] Example 7

[0147] On the basis of the previous six embodiments, this embodiment further provides a power remote safety protection system, an analysis module, including:

[0148] Conversion unit: converts the extracted features into standardized data according to the feature-conversion mapping table;

[0149] Input unit: inputs the extracted features into the feature evaluation model to obtain the initial evaluation results;

[0150] Analysis unit: performing feature analysis on the standardized data to obtain optimization factors;

[0151] A judgment unit is configured to optimize the initial evaluation result based on the optimization factor and judge whether to issue an abnormal behavior warning.

[0152] In this embodiment, the feature-conversion mapping table is a table based on different feature data and standardized representations of the data, and the corresponding standard results can be matched through the feature data, which facilitates subsequent optimization of the initial evaluation results.

[0153] In this embodiment, the standardized data may be normalization processing of feature data of the same type.

[0154] In this embodiment, the initial evaluation result is directly obtained based on the model and is an evaluation result for each feature.

[0155] In this embodiment, the purpose of feature analysis is to determine whether the standardized data is within a reasonable standard range, and then determine the fluctuation of each standardized data. If the fluctuations tend to be consistent and are within the standard range, the optimization factor is 0. At this time, there is no need to optimize the initial evaluation results.

[0156] In this embodiment, if there is standardized data corresponding to fluctuations that are not within the standard range, and the evaluation result of the feature data consistent with the standardized data is qualified, at this time, the two are inconsistent, and the evaluation result needs to be optimized. For example, adding an unqualified to the corresponding same feature data is the optimized result, that is, there are two labels for the same feature data, one is qualified and the other is unqualified. At this time, a behavioral warning is required.

[0157] The beneficial effect of the above technical solution is: by standardizing the features and performing feature analysis, the optimization factors are determined. At the same time, the features are analyzed based on the model to obtain evaluation results. Through the results of both, it is determined whether the results are optimized, thereby realizing abnormal behavior warnings and facilitating safety protection.

[0158] Example 8

[0159] On the basis of the previous seven embodiments, this embodiment further provides a power remote safety protection system, further comprising:

[0160] Data determination module: reads the original power data collected by the power terminal in the power grid, determines the data type of the original power data, and obtains the regenerated data;

[0161] Source matching module: tracing the data source of the regenerated data, matching the regenerated data with the data source one by one according to the source database, and obtaining classified data;

[0162] Data set acquisition module: sorts the classified data from the same data source in chronological order, and matches the data set at each time point with the load in the system one by one, so as to obtain the target load data set from the same data source;

[0163] Data clustering module: obtaining the operation characteristics of the same data source, and clustering the corresponding target load data set based on the operation characteristics to obtain a number of operation data groups;

[0164] Value determination module: sorts the data values ​​of the same parameter in the same operation data group in ascending order to form a value sequence;

[0165] Determine a mean value of the value sequence, and determine a first difference between the mean value and a left boundary value of a standard range of the same parameter under the corresponding operating characteristic, and a second difference between the mean value and a right boundary value;

[0166] If the first difference is less than 0, the data value arranged at the first position in the value sequence is removed, and the first value D1 is calculated;

[0167]

[0168] Among them, y1 1 Represents the data value arranged in the first position in the value sequence; y1 i1 represents the data value at the i1th position in the value sequence; F Z Indicates the left boundary value in the standard range of the corresponding parameter; F Y Indicates the right boundary value corresponding to the standard range of the same parameter;

[0169] If the second difference is greater than 0, the data value arranged at the last position in the value sequence is removed, and the second value D2 is calculated;

[0170]

[0171] Among them, y1 n1 represents the data value arranged at the last position in the value sequence;

[0172] Taking the first value D1 or the second value D2 as an initial value;

[0173] Otherwise, the mean of the value sequence is taken as the initial value;

[0174] Based on matching the initial value of each parameter in the same operation data group with the value mapping table, a target value of the corresponding operation data group is obtained;

[0175] Locking the operation time points involved in the same operation data group, matching the load value to the data set at each operation time point, and drawing the array load curve of the corresponding operation data group;

[0176] Curve comparison module: based on the typical daily load curve, array load curve and standard load curve under the same operating data group;

[0177] Calculate the first similarity S1 between the typical daily load curve y01 and the array load curve y0s under the same operating array and the second similarity S2 between the standard load curve y0b and the array load curve y0s under the same operating array;

[0178] S1 = sim(y01,y0s);

[0179] S2 = sim(y0b,y0s);

[0180] Among them, sim() represents the similarity function;

[0181] Performing a first comparison between the target value y01 of the typical load curve and the standard value of the standard load curve y0b, and performing a second comparison between the first similarity and the second similarity;

[0182] Determine an upper boundary and a lower boundary according to the first comparison result and the second comparison result;

[0183]

[0184]

[0185]

[0186] in, represents the adjustment coefficient; F1 represents the value of the upper boundary; F2 represents the value of the lower boundary;

[0187] Based on the upper boundary and the lower boundary of the division, the data outside the boundary of the corresponding array load curve is locked and regarded as bad data;

[0188] Determine the current position of the bad data, and obtain the value of the first data and the value of the second data that are closest to the bad data, determine to obtain replacement data corresponding to the bad data, and replace it;

[0189]

[0190] Among them, J represents the value of the boundary for determining that the data at the current position is bad data; T1 represents the value of the first data; T2 represents the value of the second data; and T0 represents the value of the replacement data.

[0191] In this embodiment, the collection parameters for the same data source at the same time point are the same, but the values ​​of the parameters may be different. Therefore, the obtained target load data set includes the collection data of the same data source at different time points.

[0192] In this embodiment, the operating characteristics are determined based on the working properties of the device itself. Since the device may have different working modes and the operating characteristics under different working modes are different, the classified data can be clustered according to the working mode to obtain several operating data groups, that is, each clustering result corresponds to an operating data group.

[0193] In this embodiment, bad data refers to data that has been collected or read incorrectly during the collection or reading process and needs to be replaced.

[0194] In this embodiment, the original power data refers to unprocessed data, that is, data directly collected by the power terminal.

[0195] In this embodiment, data types include: current type, voltage type, power type, etc., and the data is classified according to the source. For example, the determined current data includes: data 01 and data 02, the voltage data includes: data a01 and data a02, and the power data includes: g01 and g02. At this time, the source of data 01 is device @1, the source of data 02 is device @2, the source of data a01 is device @1, the source of data a02 is device #1, the source of data g01 is device #2, and the source of data g02 is device #1. Therefore, the data for device @1 includes: data 01 and data a01, the data for device @2 includes: data 02, the data for device #1 includes: data a02 and g02, and the data for device #2 includes: g01. At this time, the data classification is completed.

[0196] In this embodiment, because the original power data is collected based on the power terminal, and different data collection processes are related to the corresponding collection terminals, the source of the regenerated data can be directly traced back to the relevant terminal device.

[0197] In this embodiment, each classification data corresponds to a device terminal, so the data feature is the analysis result including multiple data obtained for the device at the same moment, that is, there will be data with multiple different parameters collected at the same moment.

[0198] In this embodiment, the value mapping table is a combination of the final values ​​of different parameters in the same array, and the values ​​including the matching target value with the combination, for example, parameter 1=r1, parameter 2=r2, the target value matching the combination parameter 1=r1, parameter 2=r2 is p1.

[0199] In this embodiment, the closest distance refers to two data with the shortest time distance, which may be on the same side of the current position or on both sides of the current position.

[0200] The beneficial effects of the above technical solution are: by classifying the original power data, regenerated data is obtained, the data source of the regenerated data is obtained, and the load data set is obtained. Further, through clustering processing and curve comparison, bad data can be obtained, data can be replaced, and data reliability can be improved, providing a basis for safety protection.

[0201] This embodiment also provides a computer device suitable for the case of a remote power safety protection system, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the remote power safety protection system proposed in the above embodiment.

[0202] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0203] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the remote power safety protection system proposed in the above embodiment is implemented.

[0204] The storage medium proposed in this embodiment and the data storage system proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0205] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention 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 invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A power remote safety protection system, characterized in that: include, Monitoring module: used to monitor the data information of power terminal equipment in the power grid and obtain terminal data sets; Extraction module: used for extracting features from the terminal data set; Parsing module: obtains the test instructions from the test management platform, parses the test instructions to generate test objects and compliance indicators associated with the test objects; Building module: used to build feature evaluation models based on compliance indicators; Analysis module: Perform situation analysis on the extracted features according to the feature evaluation model, and determine whether to issue an abnormal behavior warning based on the situation analysis results.

2. The electric power remote safety protection system according to claim 1, characterized in that: The monitoring module comprises: A first acquisition unit: acquiring device attributes and device types of power terminal devices in the power grid; Setting unit: setting the data collection period, data collection rate and data items based on the device attributes and device type; Collection unit: collects data information of each data item in the power terminal equipment according to the data collection cycle and data collection rate; Integration unit: Integrate and process the data information of each data item of the power terminal equipment to obtain the terminal data set.

3. The electric power remote safety protection system according to claim 2, characterized in that: The extraction module comprises: Screening unit: Screening the data in the acquired terminal data set by feature values ​​to obtain a basic feature value set; A first classification unit: classifies the feature values ​​under the same data item in the basic feature value set to obtain the classification result under the same data item; A first determining unit: determining a feature distribution of the same data item in the terminal data set based on the classification result; Extraction unit: selects a corresponding feature extraction method from a distribution-method mapping table according to the feature distribution of different data items, and performs feature extraction on all data under the corresponding data item in the terminal data set; Among them, the first classification unit includes: Acquisition subunit: acquiring multiple random eigenvalues ​​in each data item from the basic eigenvalue set by a random acquisition method; Determine the subunit: determine the edit distance between multiple random eigenvalues ​​under the same data item, and obtain a distance similarity array for each random eigenvalue; Classification subunit: Based on the distance similarity array, the feature values ​​under the same data item are classified into similar distances to obtain the classification result.

4. The electric power remote safety protection system according to claim 3, characterized in that: The parsing module includes: Decryption unit: obtains the test instruction from the test management platform through data acquisition authority, and decrypts the test instruction; A second acquisition unit is used to acquire multiple detection items of power terminals in the power grid; A second determination unit: determines the detection object according to the decrypted detection guide and multiple detection items of the power terminal; The third determination unit: parses the detection instruction to obtain the standard working parameters of each detection object, and determines the compliance indicators associated with each detection object according to the standard working parameters.

5. The electric power remote safety protection system according to claim 4, characterized in that: The establishment module includes: Construction unit: obtain the characteristic data of each compliance indicator, obtain the feedback evaluation of each characteristic data in the same compliance indicator, and construct the corresponding characteristic vector; Establishing unit: inputting different feature vectors into the neural network model in sequence to establish a feature evaluation model.

6. The electric power remote safety protection system according to claim 5, characterized in that: The analysis module comprises: Conversion unit: converts the extracted features into standardized data according to the feature-conversion mapping table; Input unit: inputs the extracted features into the feature evaluation model to obtain the initial evaluation results; Analysis unit: performing feature analysis on the standardized data to obtain optimization factors; A judgment unit is configured to optimize the initial evaluation result based on the optimization factor and judge whether to issue an abnormal behavior warning.

7. The electric power remote safety protection system according to claim 6, characterized in that: Also includes, Data determination module: reads the original power data collected by the power terminal in the power grid, determines the data type of the original power data, and obtains the regenerated data; Source matching module: tracing the data source of the regenerated data, matching the regenerated data with the data source one by one according to the source database, and obtaining classified data; Data set acquisition module: sorts the classified data from the same data source in chronological order, and matches the data set at each time point with the load in the system one by one, so as to obtain the target load data set from the same data source; Data clustering module: obtaining the operation characteristics of the same data source, and clustering the corresponding target load data set based on the operation characteristics to obtain a number of operation data groups; Value determination module: sort the data values ​​of the same parameter in the same running data group in ascending order to form a value sequence.

8. The electric power remote safety protection system according to claim 7, characterized in that: Determine a mean value of the value sequence, and determine a first difference between the mean value and a left boundary value of a standard range of the same parameter under the corresponding operating characteristic, and a second difference between the mean value and a right boundary value; If the first difference is less than 0, the data value arranged at the first position in the value sequence is removed, and the first value D1 is calculated: Wherein, y11 represents the data value arranged in the first position in the value sequence; y1 i1 represents the data value at the i1th position in the value sequence; F Z Indicates the left boundary value in the standard range of the corresponding parameter; F Y Indicates the right boundary value corresponding to the standard range of the same parameter; If the second difference is greater than 0, the data value arranged at the last position in the value sequence is removed, and the second value D2 is calculated; Among them, y1 n1 represents the data value arranged at the last position in the value sequence; Taking the first value D1 or the second value D2 as an initial value; Otherwise, the mean of the value sequence is taken as the initial value; Based on matching the initial value of each parameter in the same operation data group with the value mapping table, a target value of the corresponding operation data group is obtained; Locking the operation time points involved in the same operation data group, matching the load value to the data set at each operation time point, and drawing the array load curve of the corresponding operation data group; Curve comparison module: based on the typical daily load curve, array load curve and standard load curve under the same operating data group; Calculate the first similarity S1 between the typical daily load curve y01 and the array load curve y0s under the same operating array and the second similarity S2 between the standard load curve y0b and the array load curve y0s under the same operating array; S1 = sim(y01, y0s); S2 = sim(y0b, y0s); Among them, sim() represents the similarity function; Performing a first comparison between the target value y01 of the typical load curve and the standard value of the standard load curve y0b, and performing a second comparison between the first similarity and the second similarity; Determine an upper boundary and a lower boundary according to the first comparison result and the second comparison result; in, represents the adjustment coefficient; F1 represents the value of the upper boundary; F2 represents the value of the lower boundary; Based on the upper boundary and the lower boundary of the division, the data outside the boundary of the corresponding array load curve is locked and regarded as bad data; Determine the current position of the bad data, and obtain the value of the first data and the value of the second data that are closest to the bad data, determine to obtain replacement data corresponding to the bad data, and replace it; Among them, J represents the value of the boundary for determining that the data at the current position is bad data; T1 represents the value of the first data; T2 represents the value of the second data; and T0 represents the value of the replacement data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the electric power remote safety protection system according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electric power remote safety protection system according to any one of claims 1 to 8 are implemented.

Images