Power equipment data acquisition equipment

Abstract

The invention relates to the technical field of electrical equipment intellectualization, in particular to electrical equipment data acquisition equipment which comprises an information capture subsystem, an edge computing subsystem, a database, an operation processing module and an alarm display module. According to the invention, through unified data acquisition and normalization operation, the standardization demand degree of the whole power equipment data acquisition equipment in the aspects of data format, data fluctuation range and the like is reduced; the overall efficiency of the system is improved; the method comprises the following steps: performing normalization processing on collected power equipment data through an edge calculation subsystem, preprocessing the collected data through edge calculation before centralized operation, performing feature data extraction from the collected data, and then concentrating the feature data into a database and an operation processing module for logic judgment; the data potential safety hazard caused by data leakage in the data is prevented, and the leakage of sensitive information of the power system in the operation process is prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power equipment, and in particular to a data acquisition device for power equipment. Background Art

[0002] With the development of the electrical Internet of Things, electrical systems have also begun to develop towards intelligence, informatization and automation, and the importance of power equipment management in the safety protection of electrical systems has become increasingly prominent.

[0003] Power equipment is the infrastructure to ensure the operation of the entire electrical system, and its safety is a major issue involving the national economy and people's livelihood. The application of large-scale power equipment in the power system has promoted the development of intelligent and networked power systems, but it has also brought about power system security risks caused by network security. With the development of intelligent power systems, embedded networkable devices are prevalent and widely deployed in power systems, so now higher requirements are put forward for the safety protection of power equipment. The safety of power equipment has a great impact on the overall safety of the power system. Once the power equipment is attacked, it will cause the power equipment to fail, and will cause a chain reaction, threatening the normal operation and stability of a large range of power grids.

[0004] The existing data collection methods for power equipment still have many limitations and shortcomings. First, the current data collection methods for power equipment have insufficient standardization, which increases the complexity of power system construction and operation and maintenance and reduces the overall efficiency of the system.

[0005] Secondly, the existing data collection methods for power equipment have great data security risks. At present, some intelligent devices have loopholes in data security and are vulnerable to security threats such as network attacks and data leakage. This may not only cause equipment damage or paralysis, but also leak sensitive information of the power system, posing a threat to the security of the power system.

[0006] Therefore, it is necessary to propose an electric power equipment data acquisition device to solve the above problems. Summary of the invention

[0007] The purpose of the present invention is to solve the problems existing in the background technology and to propose a power equipment data acquisition device;

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A power equipment data acquisition device includes an information capture subsystem, an edge computing subsystem, a database, a calculation processing module, and an alarm display module. The information capture subsystem includes a power generation link perception module, a power transmission and transformation link perception module, and a power distribution link perception module.

[0010] Each sensing module in the information capture subsystem collects the operating data of various power equipment in the power generation link, power transmission and distribution link at a preset time interval t, specifically:

[0011] The power generation link sensing module uniquely numbers each generator, and the unique number is i1, i1 = 1, 2, 3, ..., n1; where n1 is the total number of generators. At every preset time interval t, multiple oscilloscopes and sensors are used to collect multi-dimensional operating data of each generator i1, including: stator current effective value Rotor current effective value Actual output voltage imbalance Rotor current three-phase asymmetry Generator frequency Active Power Reactive power Engine temperature Cooling medium temperature and unit noise

[0012] The sensing module of the power transmission and transformation link uniquely numbers each transformer, and the unique number is i2, i2 = 1, 2, 3, ..., n2; where n2 is the total number of transformers. At every preset time interval t, multiple oscilloscopes and sensors are used to collect multi-dimensional operating data of each transformer i2, including: the average current of the coils on both sides Average voltage of the coils on both sides Insulation resistance of transformer windings and bushings Actual load Power Factor Rated coil voltage Vibration frequency and noise intensity Get the rated current of each transformer i2 coil and coil rated voltage By formula Calculate the current deviation of each transformer i2 By formula Calculate the voltage deviation of each transformer i2

[0013] The distribution link sensing module uniquely numbers each distribution box, and the unique number is i3, i3 = 1, 2, 3, ..., n3. At every preset time interval t, multiple oscilloscopes and sensors are used to collect multi-dimensional operating data of each transformer i2, including: actual output current Actual output voltage Power Factor Current imbalance Busbar temperature Distribution box shell temperature Vibration frequency and harmonic content Get the rated output current of each distribution box i3 and rated output voltage By formula Calculate the current deviation of each distribution box i3 By formula Calculate the voltage deviation of each distribution box i3

[0014] The information capture subsystem sends the acquired multi-dimensional operation data to the corresponding edge computing module in the edge computing subsystem according to the operation collection source at a preset time interval t, that is, the multi-dimensional operation data of each generator i1 is sent to the corresponding edge computing module in the edge computing subsystem according to the operation collection source. Send it to the power generation edge computing module; send the multi-dimensional operation data of each transformer i2 to the power transmission and transformation edge computing module; send the multi-dimensional operation data of each distribution box i3 to the distribution edge computing module.

[0015] The edge computing subsystem performs linear function normalization and feature extraction on the data collected by the information capture subsystem to obtain the characteristic parameters corresponding to each generator i1, transformer i2, and distribution box i3, specifically:

[0016] The following operations are performed every preset statistical period T0:

[0017] As a preferred method of the present invention, the same operation data generated at each time interval t within a statistical period T0 is Record as an operation data set, where p = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10; where k = 1, 2, 3. Record about the operation data There are 30 data sets in total.

[0018] As a preferred method of the present invention, the current time is obtained and set as the data recording time t', and the maximum value of the running data in each data group is extracted. and minimum value Where T0 is a positive integer multiple of the preset time t, that is, T0=Kt, where K is the preset period base and is a positive integer; through the formula For all running data in each data group Perform linear function normalization and scale it proportionally to the range of 0 to 100 to obtain several pre-processed running data corresponding to each data group By formula Calculate several pre-processing operation data corresponding to each data group The average and variance

[0019] By formula Calculate the characteristic parameter of each generator i1 - E1 (Y i1 ), the characteristic parameters of each transformer i2 - E1 (Y i2 ) and the characteristic parameters of each distribution box - E1(Y i3 ).

[0020] By formula Calculate the characteristic parameter E2(Y i1 ), the characteristic parameters of each transformer i2 are E2 (Y i2 ) and the characteristic parameters of each distribution box E2(Y i3 ), where α1 and α2 are the preset impact factors.

[0021] As a preferred embodiment of the present invention, a data vector is generated by combining the data recording time t', the unique number i=i1, i2, i3 and the characteristic parameters, including the power generation data vector (t', i1, E1 (Y i1 ), E2(Y i1 )), power transmission and transformation data vector (t', i2, E1 (Y i2 ), E2(Y i2 )) and power distribution data vector (t', i3, E1(Y i3 ), E2(Y i3 )).

[0022] The database saves all data vectors sent by the edge computing subsystem, and classifies and saves these data vectors according to the data recording time and unique number. The specific process is as follows:

[0023] Extract the unique number from each data vector, including i1, i2 and i3, and associate the data vectors with the same unique number to generate multiple sets of unique number-data vector pairs. Put all vector pairs in parallel to build a dictionary data structure with the unique number as the key and the data vector as the value.

[0024] For each unique number-data vector pair, the data recording time t' of each data vector is further extracted, and the data is sorted in the order of these times. The principle of sorting is to ensure that the vector with the earliest data recording time is at the beginning of the vector pair, and the latest vector is at the end of the vector pair. As time goes by, when the database receives a new data vector, it first finds the corresponding vector pair according to the unique number in the data vector, and then adds the new vector to the end of the vector pair.

[0025] The operation processing module obtains the vector pair containing the data vector from the database and inputs it into the logic judgment process. The specific process is as follows:

[0026] Whenever a new recorded data vector is added to the end of the corresponding vector pair, it is input into the logic judgment process to obtain criteria A, criteria B and abnormal index C, where criteria A and criteria B will be used as judgment conditions to participate in the logic judgment process of the next data vector. Whether to generate an alarm signal is determined based on the result of the logic judgment.

[0027] As a preferred embodiment of the present invention, the logic judgment process is specifically as follows:

[0028] Get the data vector of the new record, extract the unique number, extract the characteristic parameter 1 and characteristic parameter 2, and extract the criterion A, criterion B and abnormal index C generated by the previous logical judgment;

[0029] First judgment: judge whether the characteristic parameter 1 is greater than the criterion A. If so, increase the value of the abnormal index C by 1; if not, decrease the value of the abnormal parameter C by 1; then enter the second judgment;

[0030] Second judgment: judge whether the characteristic parameter 2 is greater than the criterion B. If so, increase the value of the abnormal index C by 1; if not, decrease the value of the abnormal parameter C by 1; then enter the third judgment;

[0031] The third judgment: judge whether the value of abnormal parameter C is greater than 0. If not, set the value of criterion A equal to characteristic parameter 1, set the value of criterion B equal to characteristic parameter 2, set abnormal parameter C equal to 2, output criterion A, criterion B and abnormal parameter C and end this logic judgment process;

[0032] If so, enter the fourth judgment: determine whether the value of the abnormal parameter C is greater than the preset threshold value CMax. If so, generate the first alarm signal; if not, generate the second alarm signal; then set the value of criterion A equal to the preset initial value A0, set the value of criterion B equal to the preset initial value B0, output criterion A, criterion B, abnormal parameter C and unique number and end this logical judgment process.

[0033] The alarm display module receives the unique number and alarm signal generated by the operation processing module and executes the alarm prompt;

[0034] Generate an equipment form containing various power equipment, namely, various generators i1, transformers i2 and distribution boxes i3, and immediately select the corresponding power equipment on the equipment form after receiving the unique number generated by the operation processing module;

[0035] As a preferred embodiment of the present invention, if the alarm signal 1 is received, it is determined that the corresponding power equipment has a long-term operation data anomaly, and the power equipment corresponding to the alarm signal 1 and its unique number are highlighted in red;

[0036] If the second alarm signal is received, it is determined that the corresponding power equipment has a short-term data anomaly, and the power equipment corresponding to the second alarm signal and its unique number are highlighted in yellow;

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) The present invention reduces the need for standardization of the entire power equipment data acquisition equipment in terms of data format, data fluctuation range, etc. through unified data acquisition and normalization operations; prevents the poor compatibility and interoperability between devices from causing complexity in data analysis and operation and maintenance, and improves the overall efficiency of the system;

[0039] (2) The present invention normalizes the collected power equipment data through the edge computing subsystem, pre-processes the collected data through edge computing before centralized calculation, extracts feature data from it, and then concentrates the feature data in the database and calculation processing module for logical judgment; thus, data security risks caused by data leakage in the data are prevented, and sensitive information of the power system is prevented from being leaked during the calculation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to facilitate the understanding of those skilled in the art, the present invention is further described below with reference to the accompanying drawings:

[0041] Figure 1 is a system block diagram of the present invention;

[0042] Figure 2 It is a schematic diagram of the data structure in the form of a data vector dictionary of the present invention;

[0043] Figure 3 It is a schematic diagram of the working process of the operation processing module of the present invention;

[0044] Figure 4 It is a logic judgment flow chart of the operation processing module of the present invention. DETAILED DESCRIPTION

[0045] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] See also Figure 1 As shown, a power equipment data acquisition device includes an information capture subsystem, an edge computing subsystem, a database, a calculation processing module and an alarm display module. The information capture subsystem includes a power generation link perception module, a power transmission and transformation link perception module and a power distribution link perception module.

[0047] Each sensing module in the information capture subsystem collects the operating data of various power equipment in the power generation link, power transmission and distribution link at a preset time interval t, specifically:

[0048] The power generation link sensing module uniquely numbers each generator, and the unique number is i1, i1 = 1, 2, 3, ..., n1; where n1 is the total number of generators. At every preset time interval t, multiple oscilloscopes and sensors are used to collect multi-dimensional operating data of each generator i1, including: stator current effective value Rotor current effective value Actual output voltage imbalance Rotor current three-phase asymmetry Generator frequency Active Power Reactive power Engine temperature Cooling medium temperature and unit noise

[0049] The sensing module of the power transmission and transformation link uniquely numbers each transformer, and the unique number is i2, i2 = 1, 2, 3, ..., n2; where n2 is the total number of transformers. At every preset time interval t, multiple oscilloscopes and sensors are used to collect multi-dimensional operating data of each transformer i2, including: the average current of the coils on both sides Average voltage of the coils on both sides Insulation resistance of transformer windings and bushings Actual load Power Factor Rated coil voltage Vibration frequency and noise intensity Get the rated current of each transformer i2 coil and coil rated voltage By formula Calculate the current deviation of each transformer i2 By formula Calculate the voltage deviation of each transformer i2

[0050] The distribution link sensing module uniquely numbers each distribution box, and the unique number is i3, i3 = 1, 2, 3, ..., n3. At every preset time interval t, multiple oscilloscopes and sensors are used to collect multi-dimensional operating data of each transformer i2, including: actual output current Actual output voltage Power Factor Current imbalance Busbar temperature Distribution box shell temperature Vibration frequency and harmonic content Get the rated output current of each distribution box i3 and rated output voltage By formula Calculate the current deviation of each distribution box i3 By formula Calculate the voltage deviation of each distribution box i3

[0051] The information capture subsystem sends the acquired multi-dimensional operation data to the corresponding edge computing module in the edge computing subsystem according to the operation collection source at a preset time interval t, that is, the multi-dimensional operation data of each generator i1 is sent to the corresponding edge computing module in the edge computing subsystem according to the operation collection source. Send it to the power generation edge computing module; send the multi-dimensional operation data of each transformer i2 to the power transmission and transformation edge computing module; send the multi-dimensional operation data of each distribution box i3 to the distribution edge computing module.

[0052] The edge computing subsystem performs linear function normalization and feature extraction on the data collected by the information capture subsystem to obtain the characteristic parameters corresponding to each generator i1, transformer i2, and distribution box i3, specifically:

[0053] The following operations are performed every preset statistical period T0:

[0054] The same running data generated at each time interval t within a statistical period T0 Record as an operation data set, where p = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10; where k = 1, 2, 3. Record about the operation data There are 30 data sets in total.

[0055] Get the current time and set it as the data recording time t', extract the maximum value of the running data in each data group and minimum value Where T0 is a positive integer multiple of the preset time t, that is, T0=Kt, where K is the preset period base and is a positive integer; through the formula For all running data in each data group Perform linear function normalization and scale it proportionally to the range of 0 to 100 to obtain several pre-processed running data corresponding to each data group By formula Calculate several pre-processing operation data corresponding to each data group The average and variance

[0056] By formula Calculate the characteristic parameter of each generator i1 - E1 (Y i1 ), the characteristic parameters of each transformer i2 - E1 (Y i2 ) and the characteristic parameters of each distribution box - E1(Y i3 ).

[0057] By formula Calculate the characteristic parameter E2(Y i1 ), the characteristic parameters of each transformer i2 are E2 (Y i2 ) and the characteristic parameters of each distribution box E2(Y i3 ), where α1 and α2 are the preset impact factors.

[0058] Furthermore, a data vector is generated by combining the data recording time t', the unique number i = i1, i2, i3 and the characteristic parameters, including the power generation data vector (t', i1, E1 (Y i1 ), E2(Y i1 )), power transmission and transformation data vector (t', i2, E1 (Y i2 ), E2(Y i2 )) and power distribution data vector (t', i3, E1(Y i3 ), E2(Y i3 )).

[0059] The database saves all data vectors sent by the edge computing subsystem, and classifies and saves these data vectors according to the data recording time and unique number. The specific process is as follows:

[0060] See also Figure 2 As shown, extract the unique number from each data vector, including i1, i2 and i3, and associate the data vectors with the same unique number to generate multiple sets of unique number-data vector pairs. Put all vector pairs in parallel to construct a dictionary data structure with the unique number as the key and the data vector as the value.

[0061] For each unique number-data vector pair, the data recording time t' of each data vector is further extracted, and the data is sorted in the order of these times. The principle of sorting is to ensure that the vector with the earliest data recording time is at the beginning of the vector pair, and the latest vector is at the end of the vector pair. As time goes by, when the database receives a new data vector, it first finds the corresponding vector pair according to the unique number in the data vector, and then adds the new vector to the end of the vector pair.

[0062] The operation processing module obtains the vector pair containing the data vector from the database and inputs it into the logic judgment process. The specific process is as follows:

[0063] See also Figure 3 As shown in the figure, whenever a new recorded data vector is added to the end of the corresponding vector pair, it is input into the logic judgment process to obtain criteria A, criteria B and abnormal index C, where criteria A and criteria B will be used as judgment conditions to participate in the logic judgment process of the next data vector. Whether to generate an alarm signal is determined based on the result of the logic judgment.

[0064] See also Figure 4 As shown, the logic judgment process is specifically as follows:

[0065] Get the data vector of the new record, extract the unique number, extract the characteristic parameter 1 and characteristic parameter 2, and extract the criterion A, criterion B and abnormal index C generated by the previous logical judgment;

[0066] First judgment: judge whether the characteristic parameter 1 is greater than the criterion A. If so, increase the value of the abnormal index C by 1; if not, decrease the value of the abnormal parameter C by 1; then enter the second judgment;

[0067] Second judgment: judge whether the characteristic parameter 2 is greater than the criterion B. If so, increase the value of the abnormal index C by 1; if not, decrease the value of the abnormal parameter C by 1; then enter the third judgment;

[0068] The third judgment: judge whether the value of abnormal parameter C is greater than 0. If not, set the value of criterion A equal to characteristic parameter 1, set the value of criterion B equal to characteristic parameter 2, set abnormal parameter C equal to 2, output criterion A, criterion B and abnormal parameter C and end this logic judgment process;

[0069] If so, enter the fourth judgment: determine whether the value of the abnormal parameter C is greater than the preset threshold value CMax. If so, generate the first alarm signal; if not, generate the second alarm signal; then set the value of criterion A equal to the preset initial value A0, set the value of criterion B equal to the preset initial value B0, output criterion A, criterion B, abnormal parameter C and unique number and end this logical judgment process.

[0070] The alarm display module receives the unique number and alarm signal generated by the operation processing module and executes the alarm prompt;

[0071] Generate an equipment form containing various power equipment, namely, various generators i1, transformers i2 and distribution boxes i3, and immediately select the corresponding power equipment on the equipment form after receiving the unique number generated by the operation processing module;

[0072] Furthermore, if the alarm signal 1 is received, it is determined that the corresponding power equipment has a long-term abnormal operation data, and the power equipment corresponding to the alarm signal 1 and its unique number are highlighted in red;

[0073] If the second alarm signal is received, it is determined that the corresponding power equipment has a short-term data anomaly, and the power equipment corresponding to the second alarm signal and its unique number are highlighted in yellow;

[0074] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0075] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and the claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations;

[0076] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A power equipment data acquisition device, comprising an edge computing subsystem, a database, and a computing processing module, characterized in that; The edge computing subsystem includes a power generation edge module, a power transmission and transformation edge computing module, and a power distribution edge computing module; the operating data collected by the information capture subsystem is subjected to linear function normalization processing and feature extraction to obtain feature parameters 1 and 2 corresponding to each generator i1, transformer i2, and distribution box i3; a data vector is generated by combining data recording time, unique number, feature parameter 1, and feature parameter 2; The database saves the data vectors sent by the edge computing subsystem, and classifies and saves the data vectors according to the data recording time and unique number, generating a dictionary-style data structure; The operation processing module obtains a vector pair containing a data vector from a database and inputs the vector pair into a logic judgment process. If the judgment condition is met, a first alarm signal or a second alarm signal is generated.

2. The power equipment data acquisition device according to claim 1, characterized in that: It also includes an information capture subsystem and an alarm display module; The information capture subsystem includes a power generation link sensing module, a power transmission and transformation link sensing module, and a power distribution link sensing module, which collects the operating data of various power equipment in the power generation link, the power transmission and transformation link, and the power distribution link at preset time intervals t; The alarm display module receives the unique number and alarm signal generated by the operation processing module and executes the alarm prompt; generates an equipment form containing each power device, namely each generator i1, transformer i2 and distribution box i3, and immediately selects the corresponding power device on the equipment form after receiving the unique number generated by the operation processing module; If the alarm signal 1 is received, it is determined that the corresponding power equipment has a long-term abnormal operation data, and the power equipment corresponding to the alarm signal 1 and its unique number are highlighted in red; If the second alarm signal is received, it is determined that the corresponding power equipment has generated a short-period data anomaly, and the power equipment corresponding to the second alarm signal and its unique number are highlighted in yellow.

3. The power equipment data acquisition device according to claim 1, characterized in that: The specific process of performing linear function normalization on the running data is as follows: Every preset statistical period T0, the same operation data generated at each time interval t within a statistical period T0 is Recorded as an operation data group, where p = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10; where k = 1, 2, 3; record the operation data There are 30 data sets in total; Get the current time and set it as the data recording time t', extract the maximum value of the running data in each data group and minimum value Where T0 is a positive integer multiple of the preset time t, that is, T0=Kt, where K is the preset period base and is a positive integer; through the formula For all running data in each data group Perform linear function normalization and scale it proportionally to the range of 0 to 100 to obtain several pre-processed running data corresponding to each data group 4. The power equipment data acquisition device according to claim 1, characterized in that: The specific process of extracting features from running data and generating data vectors is as follows: By formula Calculate several pre-processing operation data corresponding to each data group The average and variance By formula Calculate the characteristic parameter of each generator i1, E1(Y i1 ), characteristic parameters of each transformer i2 - E1 (Y i2 ) and the characteristic parameters of each distribution box - E1(Y i3 ); By formula Calculate the characteristic parameter E2(Y i1 ), the characteristic parameters of each transformer i2 are E2 (Y i2 ) and the characteristic parameters of each distribution box E2(Y i3 ), where α1 and α2 are the preset impact factors; Combine the data recording time t', the unique number i = i1, i2, i3 and the characteristic parameters to generate a data vector, including the power generation data vector (t', i1, E1 (Y i1 ), E2(Y i1 )), power transmission and transformation data vector (t', i2, E1 (Y i2 ), E2(Y i2 )) and power distribution data vector (t', i3, E1(Y i3 ), E2(Y i3 )).

5. The electric power equipment data acquisition device according to claim 1, characterized in that: The specific process of classifying and saving data vectors according to data recording time and unique number is as follows; Extract the unique number from each data vector, including i1, i2 and i3, and associate the data vectors with the same unique number to generate multiple sets of unique number-data vector pairs; put all vector pairs in parallel to construct a dictionary data structure with the unique number as the key and the data vector as the value; For each set of unique number-data vector pairs, the data recording time t' of each data vector is further extracted, and the data is sorted in the order of these times; the principle of sorting is to ensure that the vector with the earliest data recording time is at the beginning of the vector pair, and the latest vector is at the end of the vector pair; as time goes by, when the database receives a new data vector, it first finds the corresponding vector pair according to the unique number in the data vector, and then adds the new vector to the end of the vector pair.

6. The power equipment data acquisition device according to claim 1, characterized in that: The logic judgment process is specifically as follows: Whenever a new recorded data vector is added to the end of the corresponding vector pair, it is input into the logic judgment process to obtain criterion A, criterion B and abnormal index C, where criterion A and criterion B will be used as judgment conditions to participate in the logic judgment process of the next data vector; Determine whether an alarm signal is generated according to the following process: Get the data vector of the new record, extract the unique number, extract the characteristic parameter 1 and characteristic parameter 2, and extract the criterion A, criterion B and abnormal index C generated by the previous logical judgment; First judgment: judge whether the characteristic parameter 1 is greater than the criterion A. If so, increase the value of the abnormal index C by 1; if not, decrease the value of the abnormal parameter C by 1; then enter the second judgment; Second judgment: judge whether the characteristic parameter 2 is greater than the criterion B. If so, increase the value of the abnormal index C by 1; if not, decrease the value of the abnormal parameter C by 1; then enter the third judgment; The third judgment: judge whether the value of abnormal parameter C is greater than 0. If not, set the value of criterion A equal to characteristic parameter 1, set the value of criterion B equal to characteristic parameter 2, set abnormal parameter C equal to 2, output criterion A, criterion B and abnormal parameter C and end this logic judgment process; If so, enter the fourth judgment: determine whether the value of the abnormal parameter C is greater than the preset threshold value CMax. If so, generate the first alarm signal; if not, generate the second alarm signal; then set the value of criterion A equal to the preset initial value A0, set the value of criterion B equal to the preset initial value B0, output criterion A, criterion B, abnormal parameter C and unique number and end this logical judgment process.

7. The electric power equipment data acquisition device according to claim 2, characterized in that: The specific process of collecting the operating data of various power equipment in the power generation link, power transmission and distribution link, is as follows: The power generation link sensing module uniquely numbers each generator, and the unique number is i1, i1 = 1, 2, 3, ..., n1; where n1 is the total number of generators; at every preset time interval t, multiple oscilloscopes and sensors are used to collect multi-dimensional operating data of each generator i1, including: stator current effective value Rotor current effective value Actual output voltage imbalance Rotor current three-phase asymmetry Generator frequency Active Power Reactive power Engine temperature Cooling medium temperature and unit noise The sensing module of the power transmission and transformation link uniquely numbers each transformer, and the unique number is i2, i2=1, 2, 3, ..., n2; where n2 is the total number of transformers; at every preset time interval t, multiple oscilloscopes and sensors are used to collect multi-dimensional operating data of each transformer i2, including: the average current of the coils on both sides Average voltage of the coils on both sides Insulation resistance of transformer windings and bushings Actual load Power Factor Rated coil voltage Vibration frequency and noise intensity Get the rated current of each transformer i2 coil and coil rated voltage By formula Calculate the current deviation of each transformer i2 By formula Calculate the voltage deviation of each transformer i2 The distribution link sensing module uniquely numbers each distribution box, and the unique number is i3, i3 = 1, 2, 3, ..., n3; at every preset time interval t, a variety of oscilloscopes and sensors are used to collect multi-dimensional operating data of each transformer i2, including: actual output current Actual output voltage Power Factor Current imbalance Busbar temperature Distribution box shell temperature Vibration frequency and harmonic content Get the rated output current of each distribution box i3 and rated output voltage By formula Calculate the current deviation of each distribution box i3 By formula Calculate the voltage deviation of each distribution box i3 All collected multi-dimensional operation data are distributed to various modules in the edge computing subsystem.

8. The electric power equipment data acquisition device according to claim 7, characterized in that: The specific process of allocating all collected multi-dimensional operation data to each module in the edge computing subsystem is as follows: At every preset time interval t, the acquired multi-dimensional operation data is sent to the corresponding edge computing module in the edge computing subsystem according to the operation collection source, that is, the multi-dimensional operation data of each generator i1 is sent to the corresponding edge computing module in the edge computing subsystem according to the operation collection source. Send it to the power generation edge computing module; send the multi-dimensional operation data of each transformer i2 to the power transmission and transformation edge computing module; send the multi-dimensional operation data of each distribution box i3 to the distribution edge computing module.

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