A power equipment data acquisition device

Abstract

The present invention relates to the technical field of intelligent power equipment, and specifically relates to a power equipment data acquisition device, which includes an information capture subsystem, an edge computing subsystem, a database, an operation processing module, and an alarm display module; through unified data acquisition and normalization operations, the present invention reduces the degree of standardization requirements of the entire power equipment data acquisition device in terms of data format, data fluctuation range, etc.; improves the overall efficiency of the system; through the edge computing subsystem, the power equipment data collected is normalized, and the collected data is preprocessed through edge computing before centralized operation, feature data is extracted therefrom, and then the feature data is centralized in the database and the operation processing module for logical judgment; prevents data security risks brought by data leakage in the data, and prevents the leakage of sensitive information of the power system during the operation process.

Description

Technical Field

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

[0002] With the development of the electrical Internet of Things, the electrical system has also started to develop towards intelligence, informatization, and automation, and the importance of power equipment management in the electrical system security protection 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 related to national economy and people's livelihood. The application of a large number of power equipment in the power system has promoted the development of the power system towards intelligence and networking on the one hand, but also brought the security risks of the power system caused by network security. With the development of the intelligent power system, embedded networked devices are widespread and widely deployed in the power system, so higher requirements are now put forward for the security protection of power equipment. The safety of power equipment largely affects the overall safety of the power system. Once the power equipment is attacked, it will cause power equipment failures and trigger a chain reaction, threatening the normal operation and stability of a large-scale power grid.

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

[0005] Secondly, the existing power equipment data acquisition methods have great potential data security risks. At present, some intelligent devices have vulnerabilities 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 a 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 art, and to propose a power equipment data acquisition device;

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

[0009] A power equipment data acquisition device includes an information capture subsystem, an edge computing subsystem, a database, an operation processing module, and an alarm display module. Among them, 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 operation data of various power equipment at preset time intervals t in the power generation, power transmission and transformation, and power distribution links respectively. Specifically:

[0011] The sensing module in the power generation link assigns a unique number to each generator, and the unique number is i1, where i1 = 1, 2, 3,..., n1; n1 is the total number of generators. At preset time intervals t, multi-dimensional operation data of each generator i1 is collected through a variety of oscilloscopes and sensors, including: the effective value of the stator current The effective value of the rotor current The actual output voltage unbalance degree The three-phase asymmetry degree of the rotor current The generator frequency The active power The reactive power The engine temperature The temperature of the cooling medium And the unit noise

[0012] The sensing module in the power transmission and transformation link assigns a unique number to each transformer, and the unique number is i2, where i2 = 1, 2, 3,..., n2; n2 is the total number of transformers. At preset time intervals t, multi-dimensional operation data of each transformer i2 is collected through a variety of oscilloscopes and sensors, including: the average current of the two-side coils The average voltage of the two-side coils The insulation resistance of the transformer winding and bushing The actual load The power factor The rated voltage of the coil The vibration frequency And the noise intensity Obtain the rated current of the coil of each transformer i2 And the rated voltage of the coil Through the formula Calculate the current deviation of each transformer i2 Through the formula Calculate the voltage deviation of each transformer i2

[0013] The sensing module in the power distribution link assigns a unique number to each distribution box, and the unique number is i3, where i3 = 1, 2, 3,..., n3. At preset time intervals t, multi-dimensional operation data of each transformer i2 is collected through a variety of oscilloscopes and sensors, including: the actual output current The actual output voltage The power factor The current unbalance degree The bus temperature Temperature of the distribution box housing Vibration frequency Harmonic content Obtain the rated output current of each distribution box i3 And the rated output voltage Through the formula Calculate the current deviation of each distribution box i3 Through the 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 at each preset time interval t according to the operation collection source, that is, the multi-dimensional operation data of each generator i1 Send 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 processing 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] Execute the following operations every preset statistical period T0:[[]]

[0017] As a preferred mode of the present invention, 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 A total of 30 data groups.

[0018] As a preferred mode of the present invention, obtain the current time and make it the data recording time t', and extract the maximum value of the operation data in each data group And the minimum value Where T0 is a positive integer multiple of the preset time t, that is, T0 = Kt, where K is a preset cycle base number and is a positive integer; through the formula For all operation data in each data group Perform linear function normalization processing, and scale it geometrically to the range of 0 to 100 to obtain several preprocessed operation data corresponding to each data group Through the formula Calculate several preprocessed operation data corresponding to each data group The average value of And variance

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

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

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

[0022] The database stores all the data vectors sent by the edge computing subsystem, and classifies and stores these data vectors according to the data recording time and the 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 groups of unique number - data vector pairs. Arrange all the vector pairs side by side to construct a dictionary - type data structure with the unique number as the key and the data vector as the value.

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

[0025] The operation processing module obtains vector pairs containing data vectors from the database and inputs them into the logical judgment process. The specific process is as follows:

[0026] Whenever a data vector of a new record is added to the end of the corresponding vector pair, it is input into the logical judgment process to obtain criterion A, criterion B, and anomaly index C. Among them, criterion A and criterion B will be used as judgment conditions to participate in the logical judgment process of the next data vector. Whether to generate an alarm signal is judged according to the result of the logical judgment.

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

[0028] Obtain the data vector of the new record, extract the unique number therein, extract the first characteristic parameter and the second characteristic parameter therein, and extract criterion A, criterion B, and anomaly index C generated by the previous logical judgment;

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

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

[0031] Third judgment: Judge whether the value of the anomaly parameter C is greater than 0. If not, set the value of criterion A equal to the first characteristic parameter, set the value of criterion B equal to the second characteristic parameter, set the anomaly parameter C equal to 2, output criterion A, criterion B, and the anomaly parameter C, and end this logical judgment process;

[0032] If so, enter the fourth judgment: Judge whether the value of the anomaly parameter C is greater than the preset threshold CMax. If so, generate a first alarm signal; if not, generate a 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, the anomaly parameter C, and the unique number, and end this logical judgment process.

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

[0034] Generate a device form including each power device, that is, each generator i1, transformer i2, and distribution box i3, and immediately select the corresponding power device on the device form when receiving the unique number generated by the operation processing module;

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

[0036] If the warning signal 2 is received, it is determined that the corresponding power equipment has short-cycle data anomalies, and the power equipment corresponding to the warning signal 2 and its unique number are highlighted in yellow;

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] (1) The present invention reduces the degree of standardization requirements 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 complexity of data analysis and operation and maintenance caused by poor compatibility and interoperability between devices, and improves the overall efficiency of the system;

[0039] (2) The present invention performs normalization processing on the collected power equipment data through the edge computing subsystem, preprocesses the collected data through edge computing before centralized operation, extracts feature data from it, and then concentrates the feature data into the database and operation processing module for logical judgment; prevents potential data security hazards caused by data leakage in the data, and prevents the leakage of sensitive information of the power system during the operation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings:

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

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

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

[0044] Figure 4 is the logic judgment flowchart of the operation processing module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the protection scope of the present invention.

[0046] Please refer toFigure 1 As shown in the figure, a power equipment data acquisition device includes an information capture subsystem, an edge computing subsystem, a database, an operation 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 perception module in the information capture subsystem collects the operation data of various power equipment in the power generation link, power transmission and transformation link, and power distribution link at preset time intervals t respectively. Specifically:

[0048] The power generation link perception module assigns a unique number to each generator, and the unique number is i1, where i1 = 1, 2, 3,..., n1; n1 is the total number of generators. At preset time intervals t, multi-dimensional operation data of each generator i1 is collected through various oscilloscopes and sensors, including: the effective value of the stator current the effective value of the rotor current the actual output voltage unbalance degree the three-phase asymmetry degree of the rotor current the generator frequency the active power the reactive power the engine temperature the temperature of the cooling medium and the unit noise

[0049] The power transmission and transformation link perception module assigns a unique number to each transformer, and the unique number is i2, where i2 = 1, 2, 3,..., n2; n2 is the total number of transformers. At preset time intervals t, multi-dimensional operation data of each transformer i2 is collected through various oscilloscopes and sensors, including: the average current of both sides of the coil the average voltage of both sides of the coil the insulation resistance of the transformer winding and bushing the actual load the power factor the rated voltage of the coil the vibration frequency and the noise intensity Obtain the rated current of the coil of each transformer i2 and the rated voltage of the coil Through the formula Calculate the current deviation of each transformer i2 Through the formula Calculate the voltage deviation of each transformer i2

[0050] The perception module in the power distribution link assigns a unique number to each distribution box, and the unique number is i3, where i3 = 1, 2, 3,..., n3. At every preset time interval t, multi-dimensional operation data of each transformer i2 is collected through a variety of oscilloscopes and sensors, including: actual output current Actual output voltage Power factor Current unbalance degree Bus temperature Distribution box shell temperature Vibration frequency Harmonic content Obtain the rated output current of each distribution box i3 And rated output voltage Through the formula Calculate the current deviation of each distribution box i3 Through the formula Calculate the voltage deviation of each distribution box i3

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

[0052] The edge computing subsystem performs linear function normalization processing 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] Execute the following operations every preset statistical period T0:[[]]

[0054] Regard the same operation data generated at each time interval t within a statistical period T0 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 A total of 30 data groups are recorded.

[0055] Obtain the current time and make it the data recording time t', and extract the maximum value of the operation 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 a preset cycle base number and is a positive integer; through the formula For all the operating data in each data group Perform linear function normalization processing on it, and scale it geometrically to the range of 0 to 100 to obtain several preprocessed operating data corresponding to each data group Through the formula Calculate the average value of several preprocessed operating data corresponding to each data group and variance

[0056] Through the formula Calculate the characteristic parameter one E1(Y of each generator i1 i1 )、the characteristic parameter one E1(Y of each transformer i2 i2 ) and the characteristic parameter one E1(Y of each distribution box i3 ).

[0057] Through the formula Calculate the characteristic parameter two E2(Y of each generator i1 i1 ), the characteristic parameter two E2(Y of each transformer i2 i2 ) and the characteristic parameter two E2(Y of each distribution box i3 ), where α1 and α2 are preset influence factors

[0058] Furthermore, combine the data recording time t', the unique number i = i1, i2, i3 and the characteristic parameters to generate data vectors, including the power generation data vector (t', i1, E1(Y i1 ), E2(Y i1 )), the power transmission and transformation data vector (t', i2, E1(Y i2 ), E2(Y i2 )) and the power distribution data vector (t', i3, E1(Y i3 ), E2(Y i3 ))

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

[0060] Figure 2 Please refer to 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 groups of unique number - data vector pairs. Arrange all the vector pairs side by side to construct a dictionary - form data structure with the unique number as the key and the data vector as the value

[0061] ​For each set of unique ID-data vector pairs, further extract the data recording time t' of each data vector, and sort the data in the order of these times. The sorting principle is to ensure that the vector with the earliest data recording time is placed at the beginning of the vector pair, while the latest vector is placed at the end of the vector pair. As time goes by, when the database receives a new data vector, first find the corresponding vector pair according to the unique ID in the data vector, and then add the new vector to the end of the vector pair.

[0062] The operation processing module obtains the vector pairs containing data vectors from the database and inputs them into the logical judgment process. The specific process is as follows:

[0063] Please refer to Figure 3 As shown, whenever a newly recorded data vector is added to the end of the corresponding vector pair, it is input into the logical judgment process to obtain criterion A, criterion B, and anomaly index C. Among them, criterion A and criterion B will be used as judgment conditions to participate in the logical judgment process of the next data vector. Determine whether to generate an alarm signal according to the result of the logical judgment.

[0064] Please refer to Figure 4 As shown, the specific logical judgment process is as follows:

[0065] Obtain the newly recorded data vector, extract the unique ID therein, extract the first characteristic parameter and the second characteristic parameter therein, and extract the criterion A, criterion B, and anomaly index C generated by the previous logical judgment;

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

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

[0068] Third judgment: Judge whether the value of the anomaly parameter C is greater than 0. If not, set the value of criterion A equal to the first characteristic parameter, set the value of criterion B equal to the second characteristic parameter, set the anomaly parameter C equal to 2, output criterion A, criterion B, and the anomaly parameter C, and end this logical judgment process;

[0069] If so, enter the fourth judgment: Judge whether the value of the anomaly parameter C is greater than the preset threshold 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, the anomaly parameter C, and the unique ID, 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 alarm prompts;

[0071] Generate a device form including each power device, that is, each generator i1, transformer i2, and distribution box i3, and immediately select the corresponding power device on the device form when receiving the unique number generated by the operation processing module;

[0072] Further, if alarm signal 1 is received, it is determined that the corresponding power device has generated long-period operation data anomalies, and the corresponding power device and its unique number of alarm signal 1 are highlighted in red;

[0073] If alarm signal 2 is received, it is determined that the corresponding power device has generated short-period data anomalies, and the corresponding power device and its unique number of alarm signal 2 are highlighted in yellow;

[0074] It should be understood that the terms "including" and "comprising" used in the specification and claims of this disclosure indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0075] It should also be understood that the terms used in this disclosure specification are only for the purpose of describing specific embodiments and are not intended to limit this disclosure. As used in this disclosure specification and claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms. It should be further understood that the term "and / or" used in this disclosure specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, 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 present invention to the specific embodiments. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited 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 same running data generated at each time interval t within a statistical period T0 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 By formula Calculate several pre-processing operation data corresponding to each data group The average and variance Where p = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10; where k = 1, 2, 3; When k=1, ik is i1; i1 is a unique number for each generator, i1=1, 2, 3, ..., n1; where n1 is the total number of generators; When k=2, ik is i2; i2 is a unique number for each transformer, i2=1, 2, 3, ..., n2; where n2 is the total number of transformers; When k=3, ik is i3; i3 is a unique number for each distribution box, i3=1, 2, 3, ..., n3; in Run data for preprocessing The number of 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 ), characteristic parameters of each transformer i2 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 )); 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 information capture subsystem and 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 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.

5. The electric 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.

6. The 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 distribution box i3, 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.

7. The electric power equipment data acquisition device according to claim 6, characterized in that: The specific process of allocating all collected multi-dimensional operating data to each module in the edge computing subsystem is as follows: at every preset time interval t, the acquired multi-dimensional operating data is sent to the corresponding edge computing modules in the edge computing subsystem according to the collection source, that is, the multi-dimensional operating data of each generator i1 is sent to the power generation edge computing module; the multi-dimensional operating data of each transformer i2 is sent to the power transmission and transformation edge computing module; the multi-dimensional operating data of each distribution box i3 is sent to the distribution edge computing module.

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