Process layer device commissioning method and apparatus, electronic device, and storage medium

Abstract

The application discloses a process layer device debugging method and device, electronic equipment and a storage medium. The method comprises the following steps: collecting a feedback signal generated by a target process layer device in the process of testing a target test item of the target process layer device; performing signal segment division on the feedback signal to obtain at least two feedback signal segments, and determining a full-time domain significant transmission aggregate representation vector of the at least two feedback signal segments; determining a debugging result of the target process layer device according to the full-time domain significant transmission aggregate representation vector and a pre-determined debugging result determination model. The technical scheme of the embodiment of the application can reduce the influence of human factors on the debugging result, improve the accuracy and reliability of the debugging result, and provide technical support for the safe and stable operation of a power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent substation, and particularly relates to a process layer device debugging method and device, electronic equipment and a storage medium. BACKGROUND

[0002] In the power system, the substation is an important part of the power grid, responsible for voltage conversion and power distribution. With the continuous expansion of the power system scale and the progress of technology, the automation level of the substation is getting higher and higher, especially the secondary system becomes more and more complex. The secondary system mainly includes relay protection devices, automation devices, communication equipment, etc., which work together to ensure the safe and stable operation of the primary system (main circuit system). In the secondary system, the process layer devices (such as circuit breakers, switches, transformers, and transformers) are directly connected to the electrical equipment of the primary system, and their state directly affects the safe operation of the power system, so the reliability and stability of these devices are crucial. The traditional manual debugging method not only consumes time and effort, but also in the process of manual debugging, any small operation error may cause deviation of the debugging result, in addition, the experience, technical level and working state of the debugging personnel will affect the quality of the debugging. SUMMARY

[0003] The present application provides a process layer device debugging method, device, electronic equipment and storage medium, which can reduce the influence of human factors on the debugging result, improve the accuracy and reliability of the debugging result, and provide technical support for the safe and stable operation of the power system.

[0004] According to an aspect of the present application, a process layer device debugging method is provided. The method comprises:

[0005] In the process of testing the target test item of the target process layer device, collecting the feedback signal generated by the target process layer device;

[0006] Segmenting the feedback signal to obtain at least two feedback signal segments, and determining a full-time domain significant transmission aggregate representation vector of the at least two feedback signal segments;

[0007] According to the full-time domain significant transmission aggregate representation vector and a predetermined debugging result determination model, determining the debugging result of the target process layer device.

[0008] According to another aspect of the present application, a process layer device debugging device is provided. The device comprises:

[0009] The feedback signal acquisition module is configured to collect the feedback signal generated by the target process layer device in the process of testing the target test item of the target process layer device;

[0010] a representation vector determination module configured to perform signal segment division on the feedback signal to obtain at least two feedback signal segments, and determine a full-time domain significant transfer aggregated representation vector of the at least two feedback signal segments;

[0011] a debugging result determination module configured to determine a debugging result of the target process layer device according to the full-time domain significant transfer aggregated representation vector and a predetermined debugging result determination model.

[0012] According to another aspect of the present application, an electronic device is provided, which comprises:

[0013] at least one processor; and

[0014] a memory in communication with the at least one processor; wherein

[0015] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the process layer device debugging method according to any one of the embodiments of the present application.

[0016] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the process layer device debugging method according to any one of the embodiments of the present application when executed by the processor.

[0017] In the technical solution of the embodiments of the present application, during the testing of the target test item of the target process layer device, the feedback signal generated by the target process layer device is collected; the feedback signal is divided into at least two feedback signal segments, and a full-time domain significant transfer aggregated representation vector of the at least two feedback signal segments is determined; and the debugging result of the target process layer device is determined according to the full-time domain significant transfer aggregated representation vector and a predetermined debugging result determination model. The technical solution of the embodiments of the present application can reduce the influence of human factors on the debugging result, improve the accuracy and reliability of the debugging result, and provide technical support for the safe and stable operation of the power system.

[0018] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of 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.

[0020] Figure 1 This is a flowchart of a process layer device debugging method provided according to the first embodiment of the present invention;

[0021] Figure 2 This is a flowchart of a process layer device debugging method provided according to the second embodiment of the present invention;

[0022] Figure 3 This is a structural diagram of a process layer device debugging device provided according to a third embodiment of the present invention;

[0023] Figure 4 It is a structural diagram of an electronic device for implementing the process layer device debugging method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Example 1

[0027] Figure 1A flowchart of a process layer device debugging method is provided for the first embodiment of the present application. The embodiment can be applied to the debugging of process layer devices in a substation. The method can be executed by a process layer device debugging apparatus, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. As shown in Figure 1 the method comprises the following steps.

[0028] In S110, feedback signals generated by the target process layer device are collected during the testing of the target test items for the target process layer device.

[0029] The target process layer device comprises at least one of a circuit breaker, a switch, a transformer, a mutual inductor, a sensor, an actuator, a communication device, and a time synchronization device. The circuit breaker is used to open or close a circuit in a power system, and automatically cut off the fault current when a fault occurs in a line or device, to protect the safe and stable operation of the power system. The switch is used for isolating power supply, switching operation, and connecting and disconnecting a small current circuit. The mutual inductor is used to convert high voltage or large current into low voltage or small current, to facilitate the measurement and protection of the access of the device. The actuator is a device used to execute control commands in a power system, such as an electric operating mechanism, a pneumatic operating mechanism, etc. The communication device is a device used to realize information transmission and exchange in a power system, such as an Ethernet switch, a fiber transceiver, etc. The time synchronization device is a device used to realize time synchronization in a power system, such as a GPS clock, an NTP server, etc.

[0030] The target test items are a series of test tasks designed for the target process layer device, aiming to comprehensively evaluate the performance, function, reliability, safety, etc. of the target process layer device. Generally, the target test items can be classified into functional testing, performance testing, compatibility testing, and safety testing, etc. The feedback signals refer to various information and data generated by the target process layer device during the execution of the test tasks, such as voltage, current, frequency, and temperature, etc.

[0031] In the embodiment of the present application, the target process layer device can be tested based on the target test items by an automated debugging device, and the feedback signals generated by the target process layer device when executing the test tasks of the target test items can be collected through various sensors installed on the target process layer device, such as voltage sensors, temperature sensors, etc. It can be understood that the feedback signals can reflect the running state, performance, abnormal conditions, etc. of the target process layer device. By collecting the feedback signals, the behavior data of the target process layer device under specific conditions can be obtained, potential problems and improvement points can be found, and thus the debugging result of the target process layer device can be obtained.

[0032] S120, performing signal segment division on the feedback signal to obtain at least two feedback signal segments, and determining a full-time domain significant transmission aggregated representation vector of the at least two feedback signal segments.

[0033] The full-time domain significant transmission aggregated representation vector refers to a higher-quality feature representation obtained by performing feature time sequence dynamic propagation on corresponding time domain feature information in the at least two feedback signal segments. The processing manner of the feature time sequence dynamic propagation is to capture the trend of the time domain feature information of the feedback signal segments changing over time by combining principal component analysis and gradient attenuation mechanism, and to perform effective information transmission and aggregation by using the trend.

[0034] In the embodiment of the present application, the feedback signal can be first divided into at least two feedback signal segments. By dividing the feedback signal into signal segments, time-frequency analysis such as short-time Fourier transform, wavelet transform, etc. can be performed on the feedback signal to capture the time and frequency information of the feedback signal at the same time. Then, for each feedback signal segment of the at least two feedback signal segments, the corresponding time domain feature information is determined. Finally, since the time domain feature information of each feedback signal segment in the at least two feedback signal segments is different, and different time domain feature information has different importance and contribution to the subsequent debugging result determination task of the target process layer device, in order to extract meaningful key feature information from the time domain feature information corresponding to the at least two feedback signal segments and establish a connection between the multiple time domain feature information, so as to better understand the behavior of the feedback signal and provide a basis for the subsequent debugging result determination task, in the technical solution of the present application, the at least two feedback signal segments need to be further subjected to feature time sequence dynamic propagation to obtain a full-time domain significant transmission aggregated representation of the at least two feedback signal segments.

[0035] S130, determining the debugging result of the target process layer device according to the full-time domain significant transmission aggregated representation vector and a predetermined debugging result determination model.

[0036] The debugging result determination model is a machine learning model based on a classifier. Common machine learning models based on a classifier include logistic regression, decision tree, support vector machine, random forest, etc.

[0037] In the embodiment of the present application, the classifier-based machine learning model can be trained in advance according to a training set composed of historical data to obtain a debugging result determination model, and then the debugging result of the target process layer device can be determined according to the full-time significant transmission aggregated representation vector and the pre-determined debugging result determination model. Specifically, the full-time significant transmission aggregated representation vector can be input into the classifier-based debugging result determination model to obtain the debugging result of the target process layer device. By determining the debugging result of the target process layer device according to the full-time significant transmission aggregated representation vector and the pre-determined performance evaluation model, the influence of human factors on the debugging result can be reduced, the accuracy and reliability of the debugging result can be ensured, and technical support can be provided for the safe and stable operation of the power system.

[0038] In the technical scheme of the embodiment of the present application, the feedback signal generated by the target process layer device is collected during the testing of the target test item of the target process layer device; the feedback signal is divided into at least two feedback signal segments, and the full-time significant transmission aggregated representation vector of the at least two feedback signal segments is determined; and the debugging result of the target process layer device is determined according to the full-time significant transmission aggregated representation vector and the pre-determined debugging result determination model. Through the division of the feedback signal obtained during the debugging of the target process layer device based on the target test item, the full-time significant transmission aggregated representation vector representing the full-time information of the divided feedback signal segments is determined, and the debugging result is determined according to the full-time significant transmission aggregated representation vector and the debugging result determination model, which can reduce the influence of human factors on the debugging result, improve the accuracy and reliability of the debugging result, and provide technical support for the safe and stable operation of the power system.

[0039] Embodiment Two

[0040] Figure 2 A flowchart of a process layer device debugging method provided by the second embodiment of the present application is shown in FIG. 10. The second embodiment of the present application is optimized based on the above-described embodiments, and the schemes not described in detail in the second embodiment of the present application are described in the above-described embodiments. As shown in FIG. 10, the method includes the following steps. Figure 2

[0041] S210, determine a target test item for a target process layer device, and control an automatic debugging device connected to the target process layer device to generate a test signal corresponding to the target test item.

[0042] ​In the embodiment of the present application, before testing the target test item for the target process layer device, the automation debugging device is connected to the target process layer device through the quick connection plug of the automation debugging device, and then the automation debugging device generates the test signal corresponding to the target test item based on the target test item, so as to test the target process layer device by using the generated test signal. Compared with the traditional manual debugging test process, the test efficiency can be improved by generating the test signal by using the automation debugging device.

[0043] Optionally, the target test item for the target process layer device is determined, and the automation debugging device connected to the target process layer device is controlled to generate the test signal corresponding to the target test item, including: determining the device type and the characteristic parameter of the target process layer device according to the information in the standard debugging case library, and determining the target test item for the target process layer device according to the device type and the characteristic parameter; based on the target test item, the analog terminal of the automation debugging device connected to the target process layer device is controlled to generate the test signal corresponding to the target test item.

[0044] In the standard debugging case library, the standard test cases for all process layer devices in the substation are included, and the device type and the characteristic parameter corresponding to the process layer device are included in the standard test case.

[0045] In the embodiment of the present application, the device type and the characteristic parameter of the target process layer device can be determined according to the information in the standard debugging case library, specifically, the device type and the characteristic parameter of the target process layer device can be determined by matching the device identification of the target process layer device with the information in the standard debugging case library. Then, the target test item for the target process layer device is determined according to the device type and the characteristic parameter of the target process layer device. It can be understood that by using the information in the standard debugging case library to determine the target test item for the target process layer device, consistent debugging steps can be performed on the same process layer device by different debugging personnel, and the difference caused by human factors can be reduced. After the target test item for the target process layer device is determined, the analog terminal of the automation debugging device connected to the target process layer device is controlled to generate the test signal corresponding to the target test item.

[0046] S220, the test signal is fed back to the target process layer device to test the target process layer device, and the feedback signal generated by the target process layer device after receiving the test signal is collected.

[0047] In an embodiment of the present invention, after generating a test signal corresponding to a target test item, the test signal can be fed back to a target process layer device to test the target process layer device. At the same time, by collecting a feedback signal generated by the target process layer device after receiving the test signal, it is possible to verify whether the target process layer device responds normally to the test signal, that is, to verify whether the various functions of the target process layer device are working normally, and also to provide data support for determining subsequent debugging results.

[0048] S230: Divide the feedback signal into signal segments to obtain at least two feedback signal segments.

[0049] In this embodiment of the present invention, the feedback signal can be segmented to obtain at least two feedback signal segments. By segmenting the feedback signal, it is easier to perform time-frequency analysis on the feedback signal, such as short-time Fourier transform and wavelet transform, to simultaneously capture the time and frequency information of the feedback signal.

[0050] S240: Determine a feedback signal segment sequence based on at least two feedback signal segments, and determine a time domain coding feature vector sequence corresponding to the feedback signal segment sequence.

[0051] Among them, the feedback signal segment sequence is obtained by arranging at least two feedback signal segments in chronological order, the time domain coding feature vector sequence is obtained by arranging the time domain coding feature vectors extracted from at least two feedback signal segments in chronological order, and the time domain feature coding vector is the vector representation of the time domain feature information extracted from the vector.

[0052] In this embodiment of the present invention, a feedback signal segment sequence may be first determined based on at least two feedback signal segments, and then a time-domain coding feature vector sequence may be obtained based on the determined feedback signal sequence. It is understood that there is a one-to-one correspondence between the feedback signal segments in the feedback signal segment sequence and the time-domain coding feature vectors in the time-domain coding feature vector sequence.

[0053] Optionally, determining the time domain coding feature vector sequence corresponding to the feedback signal segment sequence includes: extracting the local time domain features of each feedback signal segment in the feedback signal segment sequence through a predetermined feature extraction model to obtain at least two local time domain feature information corresponding to the at least two feedback signal segments; determining at least two time domain coding feature vectors corresponding to the at least two local time domain feature information, and determining the time domain coding feature vector sequence corresponding to the feedback signal segment sequence based on the at least two time domain coding feature vectors.

[0054] In the embodiment of the present application, the local time domain features of each feedback signal segment in the feedback signal segment sequence can be mined through a feature extraction model based on a 1D-CNN model to obtain the local time domain feature information of each feedback signal segment. Then, the time domain coding feature vector corresponding to the local time domain feature information of each feedback signal segment can be determined, and the time domain coding feature vector sequence corresponding to the feedback signal segment sequence can be determined according to the determined at least two time domain coding feature vectors.

[0055] In the embodiment of the present application, the feature time sequence dynamic propagation can be performed on the time domain coding feature vector sequence to generate a full time domain significant transmission aggregate representation vector capable of reflecting the dynamic changes of the time domain feature information of the feedback signal in the time sequence, which is used to determine the debugging result of the target process layer device.

[0056] In the embodiment of the present application, the feature time sequence dynamic propagation can be performed on the time domain coding feature vector sequence to generate a full time domain significant transmission aggregate representation vector capable of reflecting the dynamic changes of the time domain feature information of the feedback signal in the time sequence, which is used to determine the debugging result of the target process layer device.

[0057] Optionally, the feature time sequence dynamic propagation on the time domain coding feature vector sequence to obtain the full time domain significant transmission aggregate representation vector of the at least two feedback signal segments includes: for the time domain coding feature vector in the time domain coding feature vector sequence, performing principal component extraction on the current time domain coding feature vector to obtain a current principal component representation; taking the time domain coding feature vectors other than the current time domain coding feature vector in the time domain coding feature vector sequence as reference time domain coding feature vectors; performing principal component extraction on each reference time domain coding feature vector to obtain a reference principal component representation of the reference time domain coding feature vector; and performing time sequence attenuation dynamic aggregation on the current principal component representation and the reference principal component representation to obtain the full time domain significant transmission aggregate representation vector of the at least two feedback signal segments.

[0058] The principal component extraction is a commonly used data dimension reduction technique, which can identify the main change direction in the data set and convert these directions into new orthogonal feature vectors, i.e., principal components. Through principal component extraction, noise can be removed and important information features can be highlighted. The time sequence attenuation dynamic aggregation is a powerful time series data analysis technique, which combines time attenuation and dynamic aggregation as two key elements, and can more effectively extract key features and information in data.

[0059] In the embodiment of the present application, for the time domain coded feature vectors in the sequence of time domain coded feature vectors, a time domain coded feature vector at any time node is selected as a current time domain coded feature vector, and principal component extraction is performed on the current time domain coded feature vector to obtain a current principal component representation vector as a current principal component representation. Then, the time domain coded feature vectors other than the current time domain coded feature vector in the sequence of time domain coded feature vectors are taken as reference time domain coded feature vectors, and principal component extraction is performed on each reference time domain coded feature vector to obtain a reference principal component representation vector of each reference time domain coded feature vector as a reference principal component representation. Finally, the current principal component representation vector and the reference principal component representation vectors are subjected to time sequence attenuation dynamic aggregation to obtain a full-time domain significant transmission aggregation representation vector of the at least two feedback signal segments.

[0060] Optionally, the specific process of performing time sequence attenuation dynamic aggregation on the current principal component representation vector and the reference principal component representation vectors is as follows:

[0061] In the first step, the time sequence propagation attenuation entropy absolute factors of the reference principal component representation vectors relative to the current principal component representation vector are calculated to obtain a sequence of time sequence propagation attenuation entropy absolute factors. By calculating the time sequence propagation attenuation entropy absolute factors, the uncertainty of the random variable can be quantified, and the trend and information loss of the time domain coded feature vectors of the feedback signal segments over time can be captured.

[0062] In the second step, the time sequence propagation attenuation entropy absolute factors in the sequence of time sequence propagation attenuation entropy absolute factors are subjected to time dimension modulation based on the time span between the reference principal component representation vectors and the current principal component representation vector to obtain a sequence of time span modulation propagation attenuation entropy factors. Here, in order to further enhance the understanding ability of the model for the transmission of the time domain coded feature vectors of the feedback signal segments at long time intervals, the time span modulation propagation attenuation entropy factors are introduced to form the sequence of time span modulation propagation attenuation entropy factors. This adjustment enables the model to more reasonably handle the relationship between long-term and short-term memories, and ensures that even large differences in the time domain coded feature vectors between feedback signal segments that are far apart are considered to be within the normal range of fluctuations.

[0063] In the third step, the sequence of time span modulation propagation attenuation entropy factors is input into an information transmission screening module based on a gating function to obtain a sequence of time span modulation propagation attenuation weight. By inputting the sequence subjected to time dimension modulation into an information transmission screening module adopting a gating mechanism, it is determined which time domain coded feature vectors of the feedback signal segments should be continued to participate in the calculation according to the context, so as to identify the most influential propagation path and allocate the corresponding weight accordingly.

[0064] In the fourth step, the time span modulation propagation decay weight sequence is calculated, and the weighted sum of each reference principal component representation vector is calculated to obtain a reference principal component significant propagation aggregated representation vector. This process effectively integrates the long-term trends and short-term fluctuations in the time-domain encoding feature vector of the feedback signal, and provides a solid foundation for the final full-time-domain information propagation aggregated representation.

[0065] In the fifth step, the position-wise sum of the reference principal component significant propagation aggregated representation vector and the current principal component representation vector is calculated to obtain a full-time-domain significant propagation aggregated representation vector. The full-time-domain significant propagation aggregated representation vector not only reflects the state of the target process layer device at the current time point, but also contains the historical trends of the state of the target process layer device over time, which comprehensively reflects the feedback signal and the state transition of the target process layer device within a given time period, and is very helpful for understanding the behavior of the target process layer device.

[0066] Optionally, the determination process of the time sequence propagation decay entropy absolute factor sequence is as follows: in the first step, the position-wise division of each reference principal component representation vector with respect to the corresponding position of the current principal component representation vector is calculated to obtain a time-domain encoding feature principal component decay vector; in the second step, the absolute value of each feature value of the time-domain encoding feature principal component decay vector is calculated to obtain a time-domain encoding feature principal component decay logarithm vector; in the third step, the position-wise point multiplication of each reference principal component representation vector and the time-domain encoding feature principal component decay logarithm vector is calculated, and the position-wise point addition of each position feature value in the obtained point multiplication vector is performed to obtain a time sequence propagation decay value; in the fourth step, the exponential function with the natural constant e as the base and the time sequence propagation decay value as the exponent is calculated to obtain the time sequence propagation decay entropy absolute factor sequence.

[0067] Optionally, the determination process of the time span modulation propagation decay entropy factor sequence is as follows: in the first step, the time stamps of the current principal component representation vector and each reference principal component representation vector are subtracted and then rounded down to obtain a time span value; in the second step, the exponential function with the natural constant e as the base and the time span value as the exponent is calculated to obtain a time span modulation value; in the third step, each reference principal component representation vector corresponding to the time sequence propagation decay entropy absolute factor is divided by the time span modulation value to obtain the time span modulation propagation decay entropy factor sequence.

[0068] Optionally, the determination process of the sequence of the time span modulation propagation attenuation weight is as follows: comparing each time span modulation propagation attenuation entropy factor in the sequence of the time span modulation propagation attenuation entropy factor with a predetermined threshold to obtain the sequence of the time span modulation propagation attenuation weight; wherein, if the time span modulation propagation attenuation entropy factor is greater than the predetermined threshold, inputting the time span modulation propagation attenuation entropy factor greater than the predetermined threshold into a sigmoid function; if the time span modulation propagation attenuation entropy factor is less than or equal to the predetermined threshold, setting the time span modulation propagation attenuation entropy factor less than or equal to the predetermined threshold to zero.

[0069] Optionally, the related formula used in step S250 is as follows:

[0070] X = {x1, x2,..., x i ,..., x p};

[0071] v i = PCA(x i );

[0072]

[0073] V = {v1, v2,..., v i ,..., v p};

[0074]

[0075] wherein, X is a sequence of time domain coding feature vectors, x1, x2,..., x i ,..., x p are the 1st, 2nd,..., i-th,..., p-th time domain coding feature vectors in the sequence of time domain coding feature vectors, pCA(x i ) is the principal component extraction based on eigenvalues of x i , v i is the principal component representation vector of x i , x i T is the transposed vector of x i , n is the number of eigenvalues in the time domain coding feature vector, C i is the covariance matrix corresponding to x, U i is the principal component orthogonal matrix corresponding to x i , U i T is the transposed matrix of U i , v i1 , v i2 ,..., v im are each principal component vector in the sequence of principal component vectors, Λi is x i The corresponding diagonal matrix diag(λ i1 , λ i2 ,..., λ im ) is a diagonal matrix with λ i1 , λ i2 ,..., λ im on the diagonal, λ i1 , λ i2 ,..., λ im are the weight values of the respective principal component vectors, arg max k (·) is the k value corresponding to the maximum value, j is the maximum approximate matching value, V is the sequence of principal component representation vectors, v1, v2,..., v i ,..., v p are the first, second,..., i-th,..., p-th principal component representation vectors in the sequence of principal component representation vectors, v is is the feature value of each position in v i , v ps is the feature value of each position in v p , L is the number of feature values in v i , log represents the logarithmic function value with 2 as the base, exp(·) represents the exponential function with the natural constant e as the base, d i is the time series propagation decay entropy absolute factor between v i and v p , t p and t i represent the time stamps of the p-th and i-th principal component representation vectors, respectively, is the floor operation, e (i→p) is the time span modulation propagation decay entropy factor between v i and v p , mask(·) is the masking processing, sigmoid(·) is the sigmoid function, θ is the predetermined threshold, w i is the time span modulation propagation decay weight, p is the number of principal component representation vectors, V p+1 is the full-time-domain salient transfer aggregated representation vector.

[0076] S260, determining the debugging result of the target process layer device according to the full-time-domain salient transfer aggregated representation vector and the predetermined debugging result.

[0077] The technical scheme of the embodiment of the present application determines a target test item for a target process layer device, controls an automatic commissioning device connected with the target process layer device to generate a test signal corresponding to the target test item, feeds back the test signal to the target process layer device to test the target process layer device, collects a feedback signal generated by the target process layer device after receiving the test signal, performs signal segment division on the feedback signal to obtain at least two feedback signal segments, determines a feedback signal segment sequence based on the at least two feedback signal segments, determines a time domain coding feature vector sequence corresponding to the feedback signal segment sequence, performs feature time sequence dynamic propagation on the time domain coding feature vector sequence to obtain a full-time domain significant transmission aggregate representation vector of the at least two feedback signal segments, and determines a commissioning result of the target process layer device according to the full-time domain significant transmission aggregate representation vector and a pre-determined commissioning result determination model. The technical scheme of the embodiment of the present application can realize an automatic closed-loop process of target process layer device commissioning, reduce risks in a test process, reduce the influence of human factors on a commissioning result, improve the accuracy and reliability of the commissioning result, and provide technical support for the safe and stable operation of a power system.

[0078] Embodiment three

[0079] Figure 3 A structural schematic diagram of a process layer device commissioning apparatus provided by the embodiment three of the present application is shown in FIG. 3. Figure 3 As shown in the figure, the apparatus includes:

[0080] A feedback signal collecting module 310, configured to collect a feedback signal generated by a target process layer device in a process of testing a target test item for the target process layer device;

[0081] A representation vector determining module 320, configured to perform signal segment division on the feedback signal to obtain at least two feedback signal segments, and determine a full-time domain significant transmission aggregate representation vector of the at least two feedback signal segments;

[0082] A commissioning result determining module 330, configured to determine a commissioning result of the target process layer device according to the full-time domain significant transmission aggregate representation vector and a pre-determined commissioning result determination model.

[0083] Optionally, the feedback signal collecting module 310 includes:

[0084] The test signal generation unit is configured to determine a target test item for the target process layer device, and control an automation commissioning device connected to the target process layer device to generate a test signal corresponding to the target test item.

[0085] The feedback signal acquisition unit is configured to feed back the test signal to the target process layer device to test the target process layer device, and acquire a feedback signal generated by the target process layer device after receiving the test signal.

[0086] Optionally, the test signal generation unit comprises:

[0087] The target test item determination subunit is configured to determine a device type and a characteristic parameter of the target process layer device according to information in a standard commissioning case library, and determine a target test item for the target process layer device according to the device type and the characteristic parameter.

[0088] The test signal generation subunit is configured to control an analog terminal of an automation commissioning device connected to the target process layer device to generate a test signal corresponding to the target test item based on the target test item.

[0089] Optionally, the representation vector determination module 320 comprises:

[0090] The vector sequence determination unit is configured to determine a feedback signal segment sequence based on the at least two feedback signal segments, and determine a time-domain coding feature vector sequence corresponding to the feedback signal segment sequence.

[0091] The representation vector determination unit is configured to perform feature time sequence dynamic propagation on the time-domain coding feature vector sequence to obtain a full-time-domain significant transmission aggregated representation vector of the at least two feedback signal segments.

[0092] Optionally, the vector sequence determination unit comprises:

[0093] The feature information determination subunit is configured to extract a local time-domain feature of each feedback signal segment in the feedback signal segment sequence by using a pre-determined feature extraction model to obtain at least two local time-domain feature information corresponding to the at least two feedback signal segments.

[0094] The vector sequence determination subunit is configured to determine at least two time-domain coding feature vectors corresponding to the at least two local time-domain feature information, and determine a time-domain coding feature vector sequence corresponding to the feedback signal segment sequence based on the at least two time-domain coding feature vectors.

[0095] Optionally, the representation vector determination unit comprises:

[0096] The current principal component representation determination subunit is configured to perform principal component extraction on a current time-domain coded feature vector in the sequence of time-domain coded feature vectors to obtain a current principal component representation of the current time-domain coded feature vector.

[0097] The reference feature vector determination subunit is configured to take other time-domain coded feature vectors in the sequence of time-domain coded feature vectors except the current time-domain coded feature vector as reference time-domain coded feature vectors.

[0098] The reference principal component representation determination subunit is configured to perform principal component extraction on each reference time-domain coded feature vector to obtain a reference principal component representation of the reference time-domain coded feature vector.

[0099] The representation vector determination subunit is configured to perform time-series attenuation dynamic aggregation on the current principal component representation and the reference principal component representation to obtain a full-time-domain significant transmission aggregated representation vector of the at least two feedback signal segments.

[0100] Optionally, the target process layer device includes at least one of a circuit breaker, a knife switch, a transformer, a mutual inductor, a sensor, an actuator, a communication device, and a time synchronization device.

[0101] The process layer device debugging apparatus provided by the embodiments of the present application can execute the process layer device debugging method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0102] Embodiment Four

[0103] Figure 4 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0104] As Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0105] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0106] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the process layer device commissioning method.

[0107] In some embodiments, the process layer device commissioning method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the process layer device commissioning method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the process layer device commissioning method by any other appropriate means, such as by means of firmware.

[0108] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0109] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program

[0110] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0111] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0112] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0113] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0114] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the present disclosure are achieved, and the present disclosure is not limited herein.

[0115] The specific embodiments described above are not intended to be limiting, and persons skilled in the art will appreciate that various modifications, combinations, sub-combinations and alternatives can be made to the specific embodiments without departing from the spirit and principles of the disclosure. Accordingly, the disclosure is not limited to the specific embodiments described above, but only by the scope of the appended claims.

Claims

1. A process level device commissioning method, characterized by, The method comprises: acquiring a feedback signal generated by a target process layer device during testing of a target test item of the target process layer device; performing signal segment division on the feedback signal to obtain at least two feedback signal segments, and determining a full-time-domain significant transmission aggregate representation vector of the at least two feedback signal segments; determining a debugging result of the target process layer device according to the full-time-domain significant transmission aggregate representation vector and a predetermined debugging result determination model; wherein the determination of the full-time-domain significant transmission aggregate representation vector of the at least two feedback signal segments comprises: determining a feedback signal segment sequence based on the at least two feedback signal segments, and determining a time-domain coding feature vector sequence corresponding to the feedback signal segment sequence; performing principal component extraction on a current time-domain coding feature vector in the time-domain coding feature vector sequence to obtain a current principal component representation; taking other time-domain coding feature vectors in the time-domain coding feature vector sequence except the current time-domain coding feature vector as reference time-domain coding feature vectors; performing principal component extraction on each reference time-domain coding feature vector to obtain a reference principal component representation of the reference time-domain coding feature vector; performing time-series attenuation dynamic aggregation on the current principal component representation and the reference principal component representation to obtain the full-time-domain significant transmission aggregate representation vector of the at least two feedback signal segments.

2. The method of claim 1, wherein, acquiring a feedback signal generated by a target process layer device during testing of a target test item of the target process layer device, comprising: determining a target test item of the target process layer device, and controlling an automated debugging device connected to the target process layer device to generate a test signal corresponding to the target test item; feeding back the test signal to the target process layer device to test the target process layer device, and acquiring a feedback signal generated by the target process layer device after receiving the test signal.

3. The method of claim 2, wherein, determining a target test item of the target process layer device, and controlling an automated debugging device connected to the target process layer device to generate a test signal corresponding to the target test item, comprising: determining a device type and a characteristic parameter of the target process layer device according to information in a standard debugging case library, and determining a target test item of the target process layer device according to the device type and the characteristic parameter; controlling an analog terminal of the automated debugging device connected to the target process layer device to generate a test signal corresponding to the target test item based on the target test item.

4. The method of claim 1, wherein, determining a time-domain coding feature vector sequence corresponding to the feedback signal segment sequence, comprising: extracting local time-domain features of each feedback signal segment in the feedback signal segment sequence through a predetermined feature extraction model to obtain at least two local time-domain feature information corresponding to the at least two feedback signal segments; determine at least two time-domain coding feature vectors corresponding to the at least two local time-domain feature information, and determine a time-domain coding feature vector sequence corresponding to the feedback signal segment sequence based on the at least two time-domain coding feature vectors.

5. The method of claim 1, wherein, The target process layer device includes at least one of a circuit breaker, a knife switch, a transformer, a mutual inductor, a sensor, an actuator, a communication device, and a time synchronization device.

6. A process level device commissioning apparatus, characterized by, The device comprises: a feedback signal acquisition module, configured to acquire a feedback signal generated by a target process layer device during testing of a target test item of the target process layer device; a representation vector determination module, configured to perform signal segment division on the feedback signal to obtain at least two feedback signal segments, and determine a full-time-domain significant transmission aggregate representation vector of the at least two feedback signal segments; a debugging result determination module, configured to determine a debugging result of the target process layer device according to the full-time-domain significant transmission aggregate representation vector and a pre-determined debugging result determination model; The representation vector determination module comprises: a vector sequence determination unit, configured to determine a feedback signal segment sequence based on the at least two feedback signal segments, and determine a time-domain coding feature vector sequence corresponding to the feedback signal segment sequence; a representation vector determination unit, configured to perform feature time sequence dynamic propagation on the time-domain coding feature vector sequence to obtain the full-time-domain significant transmission aggregate representation vector of the at least two feedback signal segments; The representation vector determination unit comprises: a current principal component representation determination sub-unit, configured to perform principal component extraction on a current time-domain coding feature vector in the time-domain coding feature vector sequence to obtain a current principal component representation; a reference feature vector determination sub-unit, configured to take other time-domain coding feature vectors in the time-domain coding feature vector sequence except the current time-domain coding feature vector as reference time-domain coding feature vectors; a reference principal component representation determination sub-unit, configured to perform principal component extraction on each reference time-domain coding feature vector to obtain a reference principal component representation of the reference time-domain coding feature vector; a representation vector determination sub-unit, configured to perform time sequence attenuation dynamic aggregation on the current principal component representation and the reference principal component representation to obtain the full-time-domain significant transmission aggregate representation vector of the at least two feedback signal segments.

7. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the process layer device debugging method in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to enable the processor to execute the process layer device debugging method in any one of claims 1-5 when executed.

Images