CN116299051A - Overcurrent short circuit detection circuit and detection protection system - Google Patents
Overcurrent short circuit detection circuit and detection protection system Download PDFInfo
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Abstract
The application discloses an overcurrent short circuit detection circuit and a detection protection system. Firstly, arranging a plurality of current values, voltage values and circuit power values at preset time points into a current input vector, a voltage input vector and a power input vector respectively, then, respectively passing through a sequence encoder comprising a first convolution layer and a second convolution layer to obtain a current feature vector, a voltage feature vector and a power feature vector, then, fusing the current feature vector, the voltage feature vector and the power feature vector based on a Gaussian density map to obtain a fusion feature matrix, then, carrying out manifold curved surface optimization on the fusion feature matrix to obtain an optimized fusion feature matrix, and finally, passing the optimized fusion feature matrix through a classifier to obtain a classification result for indicating whether to cut off a power supply. In this way, current signals and over-current events can be intelligently detected.
Description
Technical Field
The present application relates to the field of intelligent detection, and more particularly, to an overcurrent short-circuit detection circuit and a detection protection system.
Background
An overcurrent short-circuit detection circuit is a circuit for monitoring a current signal and an overcurrent event, and is commonly used in a motor and a power supply unit. Its function is to protect components and loads in the circuit from damage or fire caused by overcurrent or short circuit.
In order to ensure the characteristics of high speed, high precision and high reliability of the circuit, the complexity and the cost of the existing overcurrent short-circuit detection circuit are high; on the other hand, the overcurrent short-circuit detection circuit needs to be adapted to different working environments and conditions, such as temperature, humidity, noise, interference and the like. These factors may affect the performance and stability of the circuit, resulting in false positives or false negatives.
Therefore, an optimized overcurrent short-circuit detection circuit and a detection protection system are desired.
Disclosure of Invention
The present application has been made in order to solve the above technical problems. The embodiment of the application provides an overcurrent short-circuit detection circuit and a detection protection system. Firstly, arranging a plurality of current values, voltage values and circuit power values at preset time points into a current input vector, a voltage input vector and a power input vector respectively, then, respectively passing through a sequence encoder comprising a first convolution layer and a second convolution layer to obtain a current feature vector, a voltage feature vector and a power feature vector, then, fusing the current feature vector, the voltage feature vector and the power feature vector based on a Gaussian density map to obtain a fusion feature matrix, then, carrying out manifold curved surface optimization on the fusion feature matrix to obtain an optimized fusion feature matrix, and finally, passing the optimized fusion feature matrix through a classifier to obtain a classification result for indicating whether to cut off a power supply. In this way, current signals and over-current events can be intelligently detected.
According to one aspect of the present application, there is provided a detection protection system of an overcurrent short-circuit detection circuit, including:
the circuit parameter acquisition module is used for acquiring current values, voltage values and circuit power values of a plurality of preset time points in a preset time period of the circuit to be detected;
the data structuring module is used for respectively arranging the current values, the voltage values and the circuit power values of the plurality of preset time points into a current input vector, a voltage input vector and a power input vector according to the time dimension;
the sequence coding module is used for respectively passing the current input vector, the voltage input vector and the power input vector through a sequence coder comprising a first convolution layer and a second convolution layer to obtain a current characteristic vector, a voltage characteristic vector and a power characteristic vector;
the Gaussian fusion module is used for fusing the current characteristic vector, the voltage characteristic vector and the power characteristic vector based on a Gaussian density chart to obtain a fusion characteristic matrix;
the manifold curved surface optimization module is used for performing manifold curved surface optimization on the fusion feature matrix to obtain an optimized fusion feature matrix; and
and the detection result generation module is used for enabling the optimized fusion feature matrix to pass through a classifier to obtain a classification result, and the classification result is used for indicating whether the power supply is cut off or not.
In the above detection protection system of the overcurrent short-circuit detection circuit, the sequence coding module includes:
a first convolution unit, configured to perform one-dimensional convolution encoding on the current input vector, the voltage input vector, and the power input vector with a one-dimensional convolution kernel having a first length by using a first convolution layer of the sequence encoder to obtain a first scale current feature vector, a first scale voltage feature vector, and a first scale power feature vector;
a second convolution unit, configured to perform one-dimensional convolution encoding on the current input vector, the voltage input vector, and the power input vector with a one-dimensional convolution kernel having a second length, using a second convolution layer of the sequence encoder, to obtain a second scale current feature vector, a second scale voltage feature vector, and a second scale power feature vector, where the second length is different from the first length; and
and the fusion unit is used for cascading the first scale current feature vector and the second scale current feature vector to obtain the current feature vector, cascading the first scale voltage feature vector and the second scale voltage feature vector to obtain the voltage feature vector, and cascading the first scale power feature vector and the second scale power feature vector to obtain the power feature vector.
In the above detection protection system for an overcurrent short-circuit detection circuit, the first convolution unit is further configured to:
and respectively carrying out convolution processing, pooling processing and activating processing on input data by using a first convolution layer of the sequence encoder to obtain the first scale current characteristic vector, the first scale voltage characteristic vector and the first scale power characteristic vector by the first convolution layer of the sequence encoder, wherein the input of the first convolution layer of the sequence encoder is the current input vector, the voltage input vector and the power input vector.
In the above detection protection system for an overcurrent short-circuit detection circuit, the second convolution unit is further configured to:
and respectively carrying out convolution processing, pooling processing and activating processing on input data by using a second convolution layer of the sequence encoder to obtain the second scale current characteristic vector, the second scale voltage characteristic vector and the second scale power characteristic vector by the second convolution layer of the sequence encoder, wherein the input of the second convolution layer of the sequence encoder is the current input vector, the voltage input vector and the power input vector.
In the above detection protection system of the overcurrent short-circuit detection circuit, the gaussian fusion module includes:
a fused gaussian density map construction unit for fusing the current feature vector, the voltage feature vector and the power feature vector by using a gaussian density map in the following fusion formula to obtain a fused gaussian density map;
wherein, the fusion formula is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a per-position mean vector between the current feature vector, the voltage feature vector and the power feature vector, and +.>
Representing the variance between the eigenvalues of the respective positions in the current eigenvector, the voltage eigenvector and the power eigenvector; and
and the Gaussian discretization unit is used for discretizing the Gaussian distribution of each position of the fusion Gaussian density map to obtain the fusion feature matrix.
In the above detection protection system of the overcurrent short-circuit detection circuit, the manifold curved surface optimization module is configured to:
performing manifold curved surface optimization on the fusion feature matrix by using the following optimization formula to obtain the optimized fusion feature matrix;
wherein, the optimization formula is:
Wherein, the liquid crystal display device comprises a liquid crystal display device,
is the +.o of the fusion feature matrix>
Characteristic value of the location->
And->
Is the mean and standard deviation of the feature value set of the fusion feature matrix, and +.>
Is the +.f of the optimized fusion feature matrix>
Characteristic values of the location.
In the above detection protection system of the overcurrent short-circuit detection circuit, the detection result generation module includes:
the matrix unfolding unit is used for unfolding the optimized fusion feature matrix into an optimized fusion feature vector according to a row vector or a column vector;
the full-connection coding unit is used for carrying out full-connection coding on the optimized fusion feature vector by using a plurality of full-connection layers of the classifier so as to obtain a coding classification feature vector; and
and the classification unit is used for passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
According to another aspect of the present application, there is provided an overcurrent short-circuit detection circuit, which operates with the detection protection system of any one of the foregoing overcurrent short-circuit detection circuits.
Compared with the prior art, the overcurrent short-circuit detection circuit and the overcurrent short-circuit detection protection system provided by the application are characterized in that firstly, current values, voltage values and circuit power values at a plurality of preset time points are respectively arranged into current input vectors, voltage input vectors and power input vectors, then the current feature vectors, the voltage feature vectors and the power feature vectors are obtained through a sequence encoder comprising a first convolution layer and a second convolution layer, then, the current feature vectors, the voltage feature vectors and the power feature vectors are fused based on a Gaussian density diagram to obtain a fusion feature matrix, then, manifold curved surface optimization is carried out on the fusion feature matrix to obtain an optimized fusion feature matrix, and finally, the optimized fusion feature matrix is subjected to a classifier to obtain a classification result for indicating whether a power supply is cut off. In this way, current signals and over-current events can be intelligently detected.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. The following drawings are not intended to be drawn to scale, with emphasis instead being placed upon illustrating the principles of the present application.
Fig. 1 is an application scenario diagram of a detection protection system of an overcurrent short-circuit detection circuit according to an embodiment of the application.
Fig. 2 is a block diagram of a detection protection system of an overcurrent short-circuit detection circuit according to an embodiment of the application.
Fig. 3 is a schematic block diagram of the sequence encoding module in the detection protection system of the overcurrent short-circuit detection circuit according to the embodiment of the application.
Fig. 4 is a schematic block diagram of the gaussian fusion module in the detection protection system of the overcurrent short-circuit detection circuit according to the embodiment of the present application.
Fig. 5 is a schematic block diagram of the detection result generation module in the detection protection system of the overcurrent short-circuit detection circuit according to the embodiment of the application.
Fig. 6 is a flowchart of a detection protection method of an overcurrent short-circuit detection circuit according to an embodiment of the application.
Fig. 7 is a schematic diagram of a system architecture of a detection protection method of an overcurrent short-circuit detection circuit according to an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some, but not all embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, are also within the scope of the present application.
As used in this application and in the claims, the terms "a," "an," "the," and/or "the" are not specific to the singular, but may include the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
Although the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server. The modules are merely illustrative, and different aspects of the systems and methods may use different modules.
Flowcharts are used in this application to describe the operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Aiming at the technical problems, the technical conception of the method is to utilize deep learning and artificial intelligence technology to intelligently detect current signals and overcurrent events, improve the high speed, high precision and high reliability of a circuit, enhance the anti-interference capability of the circuit and reduce the risk of false alarm or missing alarm.
Specifically, in the technical scheme of the application, first, current values, voltage values and circuit power values of a plurality of preset time points in a preset time period of a circuit to be detected are obtained. Here, by acquiring three different types of data (i.e., a current value, a voltage value, and a power value), the operation state of the circuit to be detected can be analyzed from different angles, and the comprehensiveness and reliability of detection can be improved. For example, if only one type of data (e.g., current value) is acquired, it may not be possible to distinguish whether the overcurrent is due to an excessive load or due to a short circuit. In addition, by acquiring data of a plurality of preset time points, the actual condition of the circuit to be detected in different working states can be reflected more accurately, and erroneous judgment or missed judgment caused by deviation of a single data point is avoided.
Then, the current values, the voltage values, and the circuit power values at the plurality of predetermined time points are arranged into a current input vector, a voltage input vector, and a power input vector, respectively, in a time dimension. Therefore, the state information of the circuit to be detected can be converted into sequence data, so that the subsequent sequence coding module can conveniently extract the characteristics.
And then, respectively passing the current input vector, the voltage input vector and the power input vector through a sequence coding module comprising a first convolution layer and a second convolution layer to obtain a current characteristic vector, a voltage characteristic vector and a power characteristic vector. Here, the sequence encoding module is a network model based on a Recurrent Neural Network (RNN), and may capture timing information in an input vector, reflecting the trend and rule of current, voltage and power changes. The first convolution layer and the second convolution layer respectively use convolution kernels with different scales, so that implicit characteristic distribution information of the current input vector, the voltage input vector and the power input vector under different time spans can be respectively extracted, and the expressive capacity and the robustness of characteristics are enhanced.
Further, the current eigenvector, the voltage eigenvector and the power eigenvector are fused based on a gaussian density map to obtain a fusion eigenvector. Here, the gaussian density map has an advantage that features such as a shape, a center, a degree of dispersion, etc. of data can be reflected without being affected by the dimension and the scale of the data. Specifically, feature vectors of current, voltage and power are fused based on a Gaussian density chart, and the relevance and the difference between the three signals can be effectively extracted, so that a comprehensive and accurate fusion feature matrix is obtained, the state and the behavior of a circuit to be detected can be better described by the fusion feature matrix, and the performance and the stability of an overcurrent short-circuit detection protection system are improved.
And then, the fusion feature matrix is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the power supply is cut off or not. Here, the classifier may learn a classification rule according to the relationship between the feature matrix and the label in the training data, and is used for performing classification prediction on the fused feature matrix input during inference, so as to obtain a classification result. Thus, the rapid identification and judgment of the overcurrent short-circuit event are realized. In the subsequent application, in response to the classification result of "power off", protection measures for power off should be collected in time, so as to avoid further damage to components and loads.
In the technical scheme of the application, when the current feature vector, the voltage feature vector and the power feature vector are fused based on the Gaussian density diagram to obtain the fusion feature matrix, because the current feature vector, the voltage feature vector and the power feature vector respectively express time sequence correlation features of a current value, a voltage value and a circuit power value, due to source data difference, probability density difference exists in class probability expression of a classifier. Although the current feature vector, the voltage feature vector and the power feature vector are fused based on Gaussian probability density through a Gaussian density chart, due to randomness introduced in the Gaussian discretization process, the fused feature matrix obtained through fusion still has inconsistent class probability density caused by probability density differences of the current feature vector, the voltage feature vector and the power feature vector, so that convergence of the fused feature matrix in a class probability density space of a classifier is poor, and accuracy of classification results obtained through the classifier is affected.
Thus, the applicant of the present application targets the fusion feature matrix
The reference line meshing of the manifold curved surface with Gaussian probability density is specifically expressed as:
wherein the method comprises the steps of
And->
Is a feature value set +.>
Mean and standard deviation of (2), and->
Is the +.f of the fusion feature matrix after optimization>
Characteristic values of the location.
Here, the reference of the manifold surface of Gaussian probability density is linearized with the fusion feature matrix
Statistical properties of the high-dimensional feature set, namely mean value and standard deviation, are taken as reference anchor points of probability density measurement, and are subjected to wire netting along the local linear embedding direction of the manifold curved surface to obtain low-dimensional constraint expression of a neighborhood network of local probability density extremum, so that the local distribution of the high-dimensional features is constrained based on neighborhood distribution by reconstructing the probability density expression of the manifold curved surface, and the spatial convergence of the quasi-probability density of the high-dimensional features of the fusion feature matrix is improved, namely, the consistency of the probability density expression of the fusion feature matrix in a probability density space is improvedThe accuracy of the classification result obtained by the fusion feature matrix through the classifier is improved.
The application has the following technical effects:
1. an optimized overcurrent short-circuit detection circuit and a detection protection scheme are provided.
2. The scheme can effectively detect the current signal and the overcurrent event, improves the high speed, the high precision and the high reliability of the circuit, reduces the complexity and the cost of the circuit, enhances the anti-interference capability of the circuit, and reduces the risk of false alarm or missing alarm.
Fig. 1 is an application scenario diagram of a detection protection system of an overcurrent short-circuit detection circuit according to an embodiment of the application. As shown in fig. 1, in this application scenario, first, current values (e.g., D1 shown in fig. 1), voltage values (e.g., D2 shown in fig. 1), and circuit power values (e.g., D3 shown in fig. 1) at a plurality of predetermined time points within a predetermined period of time of a circuit to be detected (e.g., N shown in fig. 1) are acquired, and then the current values, voltage values, and circuit power values at the plurality of predetermined time points are input to a server (e.g., S shown in fig. 1) in which a detection protection algorithm of an overcurrent short-circuit detection circuit is deployed, wherein the server is able to process the current values, voltage values, and circuit power values at the plurality of predetermined time points using the detection protection algorithm of the overcurrent short-circuit detection circuit to obtain a classification result for indicating whether to cut off a power supply.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Fig. 2 is a block diagram of a detection protection system of an overcurrent short-circuit detection circuit according to an embodiment of the application. As shown in fig. 2, a detection protection system 100 of an overcurrent short-circuit detection circuit according to an embodiment of the present application includes: a circuit parameter obtaining module 110, configured to obtain current values, voltage values and circuit power values at a plurality of predetermined time points within a predetermined period of time of the circuit to be detected; a data structuring module 120, configured to arrange the current values, the voltage values, and the circuit power values at the plurality of predetermined time points into a current input vector, a voltage input vector, and a power input vector according to a time dimension, respectively; a sequence encoding module 130, configured to pass the current input vector, the voltage input vector, and the power input vector through a sequence encoder including a first convolution layer and a second convolution layer, respectively, to obtain a current feature vector, a voltage feature vector, and a power feature vector; a gaussian fusion module 140, configured to fuse the current feature vector, the voltage feature vector and the power feature vector based on a gaussian density map to obtain a fusion feature matrix; the manifold curved surface optimizing module 150 is configured to perform manifold curved surface optimization on the fusion feature matrix to obtain an optimized fusion feature matrix; and a detection result generating module 160, configured to pass the optimized fusion feature matrix through a classifier to obtain a classification result, where the classification result is used to indicate whether to cut off the power supply.
More specifically, in the embodiment of the present application, the circuit parameter obtaining module 110 is configured to obtain the current value, the voltage value, and the circuit power value at a plurality of predetermined time points within a predetermined period of time of the circuit to be detected. By acquiring three different types of data (namely, a current value, a voltage value and a power value), the operation state of the circuit to be detected can be analyzed from different angles, and the comprehensiveness and the reliability of detection are improved. For example, if only one type of data (e.g., current value) is acquired, it may not be possible to distinguish whether the overcurrent is due to an excessive load or due to a short circuit. In addition, by acquiring data of a plurality of preset time points, the actual condition of the circuit to be detected in different working states can be reflected more accurately, and erroneous judgment or missed judgment caused by deviation of a single data point is avoided.
More specifically, in the embodiment of the present application, the data structuring module 120 is configured to arrange the current values, the voltage values, and the circuit power values at the plurality of predetermined time points into a current input vector, a voltage input vector, and a power input vector according to a time dimension, respectively. Therefore, the state information of the circuit to be detected can be converted into sequence data, so that the subsequent sequence coding module can conveniently extract the characteristics.
More specifically, in the embodiment of the present application, the sequence encoding module 130 is configured to pass the current input vector, the voltage input vector, and the power input vector through a sequence encoder including a first convolution layer and a second convolution layer, respectively, to obtain a current feature vector, a voltage feature vector, and a power feature vector. The sequence coding module is a network model based on a cyclic neural network (RNN), and can capture time sequence information in an input vector and reflect the change trend and rule of current, voltage and power. The first convolution layer and the second convolution layer respectively use convolution kernels with different scales, so that implicit characteristic distribution information of the current input vector, the voltage input vector and the power input vector under different time spans can be respectively extracted, and the expressive capacity and the robustness of characteristics are enhanced.
Accordingly, in one specific example, as shown in fig. 3, the sequence encoding module 130 includes: a first convolution unit 131, configured to perform one-dimensional convolution encoding on the current input vector, the voltage input vector, and the power input vector with a one-dimensional convolution kernel having a first length by using a first convolution layer of the sequence encoder to obtain a first scale current feature vector, a first scale voltage feature vector, and a first scale power feature vector; a second convolution unit 132, configured to perform one-dimensional convolution encoding on the current input vector, the voltage input vector, and the power input vector with a one-dimensional convolution kernel having a second length, to obtain a second scale current feature vector, a second scale voltage feature vector, and a second scale power feature vector, respectively, using a second convolution layer of the sequence encoder, where the second length is different from the first length; and a fusion unit 133, configured to concatenate the first scale current feature vector and the second scale current feature vector to obtain the current feature vector, concatenate the first scale voltage feature vector and the second scale voltage feature vector to obtain the voltage feature vector, and concatenate the first scale power feature vector and the second scale power feature vector to obtain the power feature vector.
Accordingly, in a specific example, the first convolution unit 131 is further configured to: and respectively carrying out convolution processing, pooling processing and activating processing on input data by using a first convolution layer of the sequence encoder to obtain the first scale current characteristic vector, the first scale voltage characteristic vector and the first scale power characteristic vector by the first convolution layer of the sequence encoder, wherein the input of the first convolution layer of the sequence encoder is the current input vector, the voltage input vector and the power input vector.
Accordingly, in a specific example, the second convolution unit 132 is further configured to: and respectively carrying out convolution processing, pooling processing and activating processing on input data by using a second convolution layer of the sequence encoder to obtain the second scale current characteristic vector, the second scale voltage characteristic vector and the second scale power characteristic vector by the second convolution layer of the sequence encoder, wherein the input of the second convolution layer of the sequence encoder is the current input vector, the voltage input vector and the power input vector.
More specifically, in the embodiment of the present application, the gaussian fusion module 140 is configured to fuse the current feature vector, the voltage feature vector, and the power feature vector based on a gaussian density map to obtain a fusion feature matrix. Here, the gaussian density map has an advantage that features such as a shape, a center, a degree of dispersion, etc. of data can be reflected without being affected by the dimension and the scale of the data. Specifically, feature vectors of current, voltage and power are fused based on a Gaussian density chart, and the relevance and the difference between the three signals can be effectively extracted, so that a comprehensive and accurate fusion feature matrix is obtained, the state and the behavior of a circuit to be detected can be better described by the fusion feature matrix, and the performance and the stability of an overcurrent short-circuit detection protection system are improved.
Accordingly, in one specific example, as shown in fig. 4, the gaussian fusion module 140 includes: a fused gaussian density map construction unit 141 for fusing the current feature vector, the voltage feature vector, and the power feature vector using a gaussian density map in the following fusion formula to obtain a fused gaussian density map; wherein, the fusion formula is:
Wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a per-position mean vector between the current feature vector, the voltage feature vector and the power feature vector, and +.>
Representing the variance between the eigenvalues of the respective positions in the current eigenvector, the voltage eigenvector and the power eigenvector; and a gaussian discretization unit 142, configured to perform discretization processing on the gaussian distribution of each position of the fused gaussian density map to obtain the fused feature matrix.
More specifically, in the embodiment of the present application, the manifold curved surface optimization module 150 is configured to perform manifold curved surface optimization on the fusion feature matrix to obtain an optimized fusion feature matrix. In the technical scheme of the application, when the current feature vector, the voltage feature vector and the power feature vector are fused based on the Gaussian density diagram to obtain the fusion feature matrix, because the current feature vector, the voltage feature vector and the power feature vector respectively express time sequence correlation features of a current value, a voltage value and a circuit power value, due to source data difference, probability density difference exists in class probability expression of a classifier. Although the current feature vector, the voltage feature vector and the power feature vector are fused based on Gaussian probability density through a Gaussian density chart, due to randomness introduced in the Gaussian discretization process, the fused feature matrix obtained through fusion still has inconsistent class probability density caused by probability density differences of the current feature vector, the voltage feature vector and the power feature vector, so that convergence of the fused feature matrix in a class probability density space of a classifier is poor, and accuracy of classification results obtained through the classifier is affected. Therefore, the applicant of the present application performs reference meshing of the manifold curved surface of gaussian probability density on the fusion feature matrix.
Accordingly, in one specific example, the manifold curve optimization module 150 is configured to: performing manifold curved surface optimization on the fusion feature matrix by using the following optimization formula to obtain the optimized fusion feature matrix; wherein, the optimization formula is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the +.o of the fusion feature matrix>
Characteristic value of the location->
And->
Is the mean and standard deviation of the feature value set of the fusion feature matrix, and +.>
Is the +.f of the optimized fusion feature matrix>
Characteristic values of the location.
The standard line meshing of the manifold curved surface with Gaussian probability density takes the statistical characteristics, namely the mean value and standard deviation, of the high-dimensional feature set of the fusion feature matrix as standard anchor points of probability density measurement, and the low-dimensional constraint expression of the neighborhood network of the local probability density extremum is obtained through line meshing along the local linear embedding direction of the manifold curved surface, so that the local distribution based on the neighborhood distribution is constrained based on the reference-based relative spatial position relation of the local distribution of the high-dimensional feature through reconstructing the probability density expression of the manifold curved surface, and therefore, the spatial convergence of the class probability density of the high-dimensional feature of the fusion feature matrix, namely, the consistency of the probability density expression of the fusion feature matrix in the probability density space is improved, and the accuracy of the classification result obtained by the fusion feature matrix through a classifier is improved.
More specifically, in the embodiment of the present application, the detection result generating module 160 is configured to pass the optimized fusion feature matrix through a classifier to obtain a classification result, where the classification result is used to indicate whether to cut off the power supply. Here, the classifier may learn a classification rule according to the relationship between the feature matrix and the label in the training data, and is used for performing classification prediction on the fused feature matrix input during inference, so as to obtain a classification result. Thus, the rapid identification and judgment of the overcurrent short-circuit event are realized. In the subsequent application, in response to the classification result of "power off", protection measures for power off should be collected in time, so as to avoid further damage to components and loads.
That is, in the technical solution of the present application, the tag of the classifier includes a power-off (first tag) and a power-off (second tag), wherein the classifier determines to which classification tag the classification feature vector belongs through a soft maximum function. It should be noted that the first tag p1 and the second tag p2 do not include a manually set concept, and in fact, during the training process, the computer model does not have a concept of "whether to cut off the power supply", which is just two kinds of classification tags, and the probability that the output feature is under the two classification tags, that is, the sum of p1 and p2 is one. Therefore, the classification result of whether to cut off the power supply is actually that the classification label is converted into the classification probability distribution conforming to the natural rule, and the physical meaning of the natural probability distribution of the label is essentially used instead of the language text meaning of whether to cut off the power supply.
It should be appreciated that the role of the classifier is to learn the classification rules and classifier using a given class, known training data, and then classify (or predict) the unknown data. Logistic regression (logistics), SVM, etc. are commonly used to solve the classification problem, and for multi-classification problems (multi-class classification), logistic regression or SVM can be used as well, but multiple bi-classifications are required to compose multiple classifications, but this is error-prone and inefficient, and the commonly used multi-classification method is the Softmax classification function.
Accordingly, in one specific example, as shown in fig. 5, the detection result generating module 160 includes: a matrix expansion unit 161, configured to expand the optimized fusion feature matrix into an optimized fusion feature vector according to a row vector or a column vector; a full-connection encoding unit 162, configured to perform full-connection encoding on the optimized fusion feature vector by using multiple full-connection layers of the classifier to obtain an encoded classification feature vector; and a classification unit 163, configured to pass the encoded classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
In summary, the detection protection system 100 of the overcurrent short-circuit detection circuit according to the embodiment of the application is illustrated, firstly, a plurality of current values, voltage values and circuit power values at predetermined time points are respectively arranged into a current input vector, a voltage input vector and a power input vector, then the current feature vector, the voltage feature vector and the power feature vector are obtained through a sequence encoder comprising a first convolution layer and a second convolution layer respectively, then, the current feature vector, the voltage feature vector and the power feature vector are fused based on a gaussian density diagram to obtain a fusion feature matrix, then, manifold surface optimization is performed on the fusion feature matrix to obtain an optimized fusion feature matrix, and finally, the optimized fusion feature matrix is passed through a classifier to obtain a classification result for indicating whether to cut off a power supply. In this way, current signals and over-current events can be intelligently detected.
As described above, the detection protection system 100 according to the embodiment of the present application based on the overcurrent short-circuit detection circuit of the embodiment of the present application may be implemented in various terminal devices, for example, a server or the like having a detection protection algorithm based on the overcurrent short-circuit detection circuit of the embodiment of the present application. In one example, the detection protection system 100 based on the overcurrent short-circuit detection circuit of the embodiment of the application may be integrated into the terminal device as one software module and/or hardware module. For example, the detection protection system 100 based on the overcurrent short-circuit detection circuit of the embodiment of the application may be a software module in the operating system of the terminal device, or may be an application program developed for the terminal device; of course, the detection protection system 100 based on the overcurrent short-circuit detection circuit according to the embodiment of the application can also be one of numerous hardware modules of the terminal device.
Alternatively, in another example, the protection system 100 for detecting an overcurrent short-circuit according to the embodiment of the application and the terminal device may be separate devices, and the protection system 100 for detecting an overcurrent short-circuit may be connected to the terminal device through a wired and/or wireless network, and transmit the interaction information according to a agreed data format.
Further, in the technical scheme of the application, an overcurrent short-circuit detection circuit is provided, and the overcurrent short-circuit detection circuit runs with the detection protection system of any one of the overcurrent short-circuit detection circuits.
Fig. 6 is a flowchart of a detection protection method of an overcurrent short-circuit detection circuit according to an embodiment of the application. As shown in fig. 6, a detection protection method of an overcurrent short-circuit detection circuit according to an embodiment of the application includes: s110, acquiring current values, voltage values and circuit power values of a plurality of preset time points in a preset time period of a circuit to be detected; s120, arranging the current values, the voltage values and the circuit power values of the plurality of preset time points into a current input vector, a voltage input vector and a power input vector according to time dimensions respectively; s130, respectively passing the current input vector, the voltage input vector and the power input vector through a sequence encoder comprising a first convolution layer and a second convolution layer to obtain a current characteristic vector, a voltage characteristic vector and a power characteristic vector; s140, fusing the current feature vector, the voltage feature vector and the power feature vector based on a Gaussian density map to obtain a fusion feature matrix; s150, manifold curved surface optimization is carried out on the fusion feature matrix so as to obtain an optimized fusion feature matrix; and S160, enabling the optimized fusion feature matrix to pass through a classifier to obtain a classification result, wherein the classification result is used for indicating whether to cut off a power supply.
Fig. 7 is a schematic diagram of a system architecture of a detection protection method of an overcurrent short-circuit detection circuit according to an embodiment of the application. As shown in fig. 7, in the system architecture of the detection protection method of the overcurrent short-circuit detection circuit, first, current values, voltage values and circuit power values at a plurality of predetermined time points within a predetermined period of time of the circuit to be detected are obtained; then, arranging the current values, the voltage values and the circuit power values of the plurality of preset time points into a current input vector, a voltage input vector and a power input vector according to the time dimension respectively; then, the current input vector, the voltage input vector and the power input vector are respectively passed through a sequence encoder comprising a first convolution layer and a second convolution layer to obtain a current feature vector, a voltage feature vector and a power feature vector; then, fusing the current feature vector, the voltage feature vector and the power feature vector based on a Gaussian density map to obtain a fusion feature matrix; then, manifold curved surface optimization is carried out on the fusion feature matrix so as to obtain an optimized fusion feature matrix; and finally, the optimized fusion feature matrix passes through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the power supply is cut off or not.
In a specific example, in the detection protection method of the above-mentioned overcurrent short-circuit detection circuit, the step of passing the current input vector, the voltage input vector and the power input vector through a sequence encoder including a first convolution layer and a second convolution layer to obtain a current feature vector, a voltage feature vector and a power feature vector includes: performing one-dimensional convolution encoding on the current input vector, the voltage input vector and the power input vector by using a first convolution layer of the sequence encoder through a one-dimensional convolution kernel with a first length to obtain a first scale current feature vector, a first scale voltage feature vector and a first scale power feature vector; performing one-dimensional convolution encoding on the current input vector, the voltage input vector and the power input vector with a one-dimensional convolution kernel having a second length using a second convolution layer of the sequence encoder to obtain a second scale current feature vector, a second scale voltage feature vector and a second scale power feature vector, respectively, the second length being different from the first length; and cascading the first scale current feature vector and the second scale current feature vector to obtain the current feature vector, cascading the first scale voltage feature vector and the second scale voltage feature vector to obtain the voltage feature vector, and cascading the first scale power feature vector and the second scale power feature vector to obtain the power feature vector.
In a specific example, in the detection protection method of the above-mentioned overcurrent short-circuit detection circuit, the one-dimensional convolution encoding is performed on the current input vector, the voltage input vector and the power input vector with a one-dimensional convolution kernel having a first length by using a first convolution layer of the sequence encoder to obtain a first scale current feature vector, a first scale voltage feature vector and a first scale power feature vector, and the method further includes: and respectively carrying out convolution processing, pooling processing and activating processing on input data by using a first convolution layer of the sequence encoder to obtain the first scale current characteristic vector, the first scale voltage characteristic vector and the first scale power characteristic vector by the first convolution layer of the sequence encoder, wherein the input of the first convolution layer of the sequence encoder is the current input vector, the voltage input vector and the power input vector.
In a specific example, in the detection protection method of the above-mentioned overcurrent short-circuit detection circuit, the one-dimensional convolution encoding is performed on the current input vector, the voltage input vector and the power input vector with a one-dimensional convolution kernel having a second length by using a second convolution layer of the sequence encoder to obtain a second scale current feature vector, a second scale voltage feature vector and a second scale power feature vector, where the second length is different from the first length, and the method further includes: and respectively carrying out convolution processing, pooling processing and activating processing on input data by using a second convolution layer of the sequence encoder to obtain the second scale current characteristic vector, the second scale voltage characteristic vector and the second scale power characteristic vector by the second convolution layer of the sequence encoder, wherein the input of the second convolution layer of the sequence encoder is the current input vector, the voltage input vector and the power input vector.
In a specific example, in the detection protection method of the above-mentioned overcurrent short-circuit detection circuit, fusing the current feature vector, the voltage feature vector and the power feature vector based on a gaussian density map to obtain a fused feature matrix includes: fusing the current feature vector, the voltage feature vector and the power feature vector by using a Gaussian density map in the following fusion formula to obtain a fused Gaussian density map; wherein, the fusion formula is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a per-position mean vector between the current feature vector, the voltage feature vector and the power feature vector, and +.>
Representing the variance between the eigenvalues of the respective positions in the current eigenvector, the voltage eigenvector and the power eigenvector; and discretizing the Gaussian distribution of each position of the fusion Gaussian density map to obtain the fusion feature matrix.
In a specific example, in the detection protection method of the over-current short-circuit detection circuit, performing manifold curved surface optimization on the fusion feature matrix to obtain an optimized fusion feature matrix, including: performing manifold curved surface optimization on the fusion feature matrix by using the following optimization formula to obtain the optimized fusion feature matrix; wherein, the optimization formula is:
Wherein, the liquid crystal display device comprises a liquid crystal display device,
is the +.o of the fusion feature matrix>
Characteristic value of the location->
And->
Is the mean and standard deviation of the feature value set of the fusion feature matrix, and +.>
Is the +.f of the optimized fusion feature matrix>
Characteristic values of the location.
In a specific example, in the detection protection method of the above-mentioned overcurrent short-circuit detection circuit, the optimized fusion feature matrix is passed through a classifier to obtain a classification result, where the classification result is used to indicate whether to cut off the power supply, and the method includes: expanding the optimized fusion feature matrix into an optimized fusion feature vector according to a row vector or a column vector; performing full-connection coding on the optimized fusion feature vector by using a plurality of full-connection layers of the classifier to obtain a coding classification feature vector; and passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
Here, it will be understood by those skilled in the art that the specific operations of the respective steps in the detection protection method of the above-described overcurrent short-circuit detection circuit have been described in detail in the description of the detection protection system 100 of the overcurrent short-circuit detection circuit with reference to fig. 1 to 5, and thus, repetitive descriptions thereof will be omitted.
According to another aspect of the present application, there is also provided a non-volatile computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a computer, can perform a method as described above.
Program portions of the technology may be considered to be "products" or "articles of manufacture" in the form of executable code and/or associated data, embodied or carried out by a computer readable medium. A tangible, persistent storage medium may include any memory or storage used by a computer, processor, or similar device or related module. Such as various semiconductor memories, tape drives, disk drives, or the like, capable of providing storage functionality for software.
This application uses specific words to describe embodiments of the application. Reference to "a first/second embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present application may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the invention are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof. Although a few exemplary embodiments of this invention have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this invention. Accordingly, all such modifications are intended to be included within the scope of this invention as defined in the following claims. It is to be understood that the foregoing is illustrative of the present invention and is not to be construed as limited to the specific embodiments disclosed, and that modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The invention is defined by the claims and their equivalents.
Claims (8)
1. A detection protection system for an overcurrent short-circuit detection circuit, comprising:
the circuit parameter acquisition module is used for acquiring current values, voltage values and circuit power values of a plurality of preset time points in a preset time period of the circuit to be detected;
the data structuring module is used for respectively arranging the current values, the voltage values and the circuit power values of the plurality of preset time points into a current input vector, a voltage input vector and a power input vector according to the time dimension;
The sequence coding module is used for respectively passing the current input vector, the voltage input vector and the power input vector through a sequence coder comprising a first convolution layer and a second convolution layer to obtain a current characteristic vector, a voltage characteristic vector and a power characteristic vector;
the Gaussian fusion module is used for fusing the current characteristic vector, the voltage characteristic vector and the power characteristic vector based on a Gaussian density chart to obtain a fusion characteristic matrix;
the manifold curved surface optimization module is used for performing manifold curved surface optimization on the fusion feature matrix to obtain an optimized fusion feature matrix; and
and the detection result generation module is used for enabling the optimized fusion feature matrix to pass through a classifier to obtain a classification result, and the classification result is used for indicating whether the power supply is cut off or not.
2. The system for protection of an overcurrent short circuit detection circuit according to claim 1, wherein the sequence encoding module comprises:
a first convolution unit, configured to perform one-dimensional convolution encoding on the current input vector, the voltage input vector, and the power input vector with a one-dimensional convolution kernel having a first length by using a first convolution layer of the sequence encoder to obtain a first scale current feature vector, a first scale voltage feature vector, and a first scale power feature vector;
A second convolution unit, configured to perform one-dimensional convolution encoding on the current input vector, the voltage input vector, and the power input vector with a one-dimensional convolution kernel having a second length, using a second convolution layer of the sequence encoder, to obtain a second scale current feature vector, a second scale voltage feature vector, and a second scale power feature vector, where the second length is different from the first length; and
and the fusion unit is used for cascading the first scale current feature vector and the second scale current feature vector to obtain the current feature vector, cascading the first scale voltage feature vector and the second scale voltage feature vector to obtain the voltage feature vector, and cascading the first scale power feature vector and the second scale power feature vector to obtain the power feature vector.
3. The system for protection of detection of an over-current short-circuit detection circuit of claim 2, wherein the first convolution unit is further configured to:
and respectively carrying out convolution processing, pooling processing and activating processing on input data by using a first convolution layer of the sequence encoder to obtain the first scale current characteristic vector, the first scale voltage characteristic vector and the first scale power characteristic vector by the first convolution layer of the sequence encoder, wherein the input of the first convolution layer of the sequence encoder is the current input vector, the voltage input vector and the power input vector.
4. The system for protection of detection of an over-current short-circuit detection circuit of claim 3, wherein the second convolution unit is further configured to:
and respectively carrying out convolution processing, pooling processing and activating processing on input data by using a second convolution layer of the sequence encoder to obtain the second scale current characteristic vector, the second scale voltage characteristic vector and the second scale power characteristic vector by the second convolution layer of the sequence encoder, wherein the input of the second convolution layer of the sequence encoder is the current input vector, the voltage input vector and the power input vector.
5. The system for protection of an overcurrent short-circuit detection circuit according to claim 4, wherein the gaussian fusion module comprises:
a fused gaussian density map construction unit for fusing the current feature vector, the voltage feature vector and the power feature vector by using a gaussian density map in the following fusion formula to obtain a fused gaussian density map;
wherein, the fusion formula is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a per-position mean vector between the current feature vector, the voltage feature vector and the power feature vector, and +. >
Representing the variance between the eigenvalues of the respective positions in the current eigenvector, the voltage eigenvector and the power eigenvector; and
and the Gaussian discretization unit is used for discretizing the Gaussian distribution of each position of the fusion Gaussian density map to obtain the fusion feature matrix.
6. The system for detecting and protecting an overcurrent short-circuit detection circuit according to claim 5, wherein the manifold curve optimization module is configured to:
performing manifold curved surface optimization on the fusion feature matrix by using the following optimization formula to obtain the optimized fusion feature matrix;
wherein, the optimization formula is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the +.o of the fusion feature matrix>
Characteristic value of the location->
And->
Is the mean and standard deviation of the feature value set of the fusion feature matrix, and +.>
Is the +.f of the optimized fusion feature matrix>
Characteristic values of the location.
7. The system for protecting against detection of an overcurrent short-circuit detection circuit according to claim 6, wherein the detection result generation module comprises:
the matrix unfolding unit is used for unfolding the optimized fusion feature matrix into an optimized fusion feature vector according to a row vector or a column vector;
The full-connection coding unit is used for carrying out full-connection coding on the optimized fusion feature vector by using a plurality of full-connection layers of the classifier so as to obtain a coding classification feature vector; and
and the classification unit is used for passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
8. An overcurrent short-circuit detection circuit, characterized in that the overcurrent short-circuit detection circuit operates with the detection protection system of the overcurrent short-circuit detection circuit according to claim 1.
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