CN115759658A - Enterprise energy consumption data management system suitable for smart city - Google Patents
Enterprise energy consumption data management system suitable for smart city Download PDFInfo
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Abstract
The application relates to the field of smart cities, and particularly discloses an enterprise energy consumption data management system suitable for smart cities, which extracts multi-scale dynamic association characteristics of energy consumption power of each electric device in an enterprise on a time dimension by adopting an artificial intelligence detection algorithm based on deep learning, and takes the energy consumption power between each electric device into consideration, so that a network model with different receptive fields is further utilized to extract the multi-scale association characteristics of energy consumption change characteristics between each electric device, and the energy consumption detection of the enterprise is carried out according to the multi-scale association characteristics. Like this, can accurately carry out intellectual detection system to whether enterprise's energy consumption normally, and then implement corresponding energy consumption management to improve wisdom city energy utilization efficiency.
Description
Technical Field
The application relates to a wisdom city field, and more specifically relates to an enterprise energy consumption data management system suitable for wisdom city.
Background
The construction of smart cities is an important trend of city development in the future and is an inevitable requirement for the sustainable development of cities. Energy consumption data management is carried out to the enterprise and the energy utilization efficiency of the smart city can be improved.
The existing enterprise energy consumption management system is usually simple energy consumption total value monitoring, for example, a preset threshold value is set to judge whether enterprise energy consumption is normal, the enterprise energy consumption management mode is too simple and is not in line with the actual enterprise energy consumption use condition, the reason is that the enterprise energy consumption use amount changes along with time and business conditions, and the enterprise energy consumption management requirement cannot be met by simple threshold value reference.
Therefore, an optimized enterprise energy consumption data management scheme suitable for the smart city is expected.
Disclosure of Invention
The present application is proposed to solve the above-mentioned technical problems. The embodiment of the application provides an enterprise energy consumption data management system suitable for smart city, which extracts multi-scale dynamic association characteristics of energy consumption power of each electric device in an enterprise on a time dimension by adopting an artificial intelligence detection algorithm based on deep learning, and takes the energy consumption power among each electric device into consideration to have an association relation, so that a network model with different receptive fields is further utilized to extract the multi-scale association characteristics of energy consumption change characteristics among each electric device, and the energy consumption detection of the enterprise is carried out. Like this, can accurately carry out intellectual detection system to whether enterprise's energy consumption normally, and then implement corresponding energy consumption management to improve wisdom city energy utilization efficiency.
According to an aspect of the present application, there is provided an enterprise energy consumption data management system suitable for a smart city, comprising:
the energy consumption data acquisition module is used for acquiring energy consumption power values of all electric equipment of an enterprise to be detected at a plurality of preset time points in a preset time period;
the device energy consumption characteristic extraction module is used for respectively arranging energy consumption power values of each electric device at a plurality of preset time points in a preset time period into power input vectors and then obtaining a plurality of power characteristic vectors through the multi-scale neighborhood characteristic extraction module;
the device-to-device correlation feature extraction module is used for performing two-dimensional arrangement on the plurality of power feature vectors to form a global power feature matrix and then obtaining a first scale energy consumption correlation feature map and a second scale energy consumption correlation feature map through a double-current network model comprising a first convolutional neural network and a second convolutional neural network, wherein the first convolutional neural network and the second convolutional neural network have different feature receptive fields;
the characteristic fusion module is used for fusing the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram to obtain a classification characteristic diagram; and
and the management result generation module is used for enabling the classification characteristic graph to pass through a classifier to obtain a classification result, and the classification result is used for indicating whether the energy consumption of the enterprise to be detected is normal or not.
In the above-mentioned enterprise energy consumption data management system who is applicable to wisdom city, equipment energy consumption characteristic extraction module includes: a first scale feature extraction unit, configured to input the power input vector into a first convolution layer of the multi-scale neighborhood feature extraction module to obtain a first neighborhood scale power feature vector, where the first convolution layer has a first one-dimensional convolution kernel with a first length; a second scale feature extraction unit, configured to input the power input vector into a second convolution layer of the multi-scale neighborhood feature extraction module to obtain a second neighborhood scale power feature vector, where the second convolution layer has a second one-dimensional convolution kernel with a second length, and the first length is different from the second length; and the multi-scale cascading unit is used for cascading the first neighborhood scale power characteristic vector and the second neighborhood scale power characteristic vector to obtain a plurality of power characteristic vectors.
In above-mentioned enterprise energy consumption data management system who is applicable to smart city, first scale feature extraction unit is further used for: performing one-dimensional convolution coding on the power input vector by using a first convolution layer of the multi-scale neighborhood characteristic extraction module according to the following formula to obtain a first neighborhood scale power characteristic vector; wherein the formula is:
wherein a is the width of the first convolution kernel in the X direction, F (a) is the first convolution kernel parameter vector, G (X-a) is the local vector matrix operated with the convolution kernel function, w is the size of the first convolution kernel, and X represents the power input vector. A second scale feature extraction unit, further configured to: performing one-dimensional convolutional coding on the power input vector by using a second convolutional layer of the multi-scale neighborhood feature extraction module according to the following formula to obtain a second neighborhood scale power feature vector; wherein the formula is:
wherein b is the width of the second convolution kernel in the X direction, F (b) is a second convolution kernel parameter vector, G (X-b) is a local vector matrix operated with the convolution kernel function, m is the size of the second convolution kernel, and X represents the power input vector.
In the above system for managing enterprise energy consumption data applicable to a smart city, the first convolutional neural network uses a first convolutional kernel having a first scale, and the second convolutional neural network uses a second convolutional kernel having a second scale, wherein the first scale is different from the second scale.
In the above system for managing enterprise energy consumption data applicable to a smart city, the first convolutional neural network uses a first hole convolution kernel having a first hole rate, and the second convolutional neural network uses a second hole convolution kernel having a second hole rate, where the first hole rate is different from the second hole rate, and the first hole convolution kernel and the second hole convolution kernel have the same size.
In the above-mentioned enterprise energy consumption data management system who is applicable to smart city, the feature fusion module is further used for: fusing the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram according to the following formula to obtain the classification characteristic diagram; wherein the formula is:
wherein M is 1i And M 2i Respectively obtaining diagonal matrixes M obtained by linear transformation of ith characteristic matrixes of the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram ci Is the i-th feature matrix of the classification feature map, reLU (-) represents the ReLU activation function,
representing the multiplication of matrices and the division between the numerator matrix and denominator matrix as a division by location of the eigenvalues of the matrices, exp (-) represents the exponential operation of the matrices, which represents the calculation of a natural exponential function value raised to the power of the eigenvalues of each location in the matrix.
In the above enterprise energy consumption data management system suitable for smart city, the management result generation module includes: the expansion unit is used for expanding the classification feature map into a classification feature vector based on a row vector or a column vector; a full-concatenation encoding unit, configured to perform full-concatenation encoding on the classification feature vector using a plurality of full-concatenation layers of the classifier to obtain an encoded classification feature vector; and the classification result generating unit is used for enabling the coded classification feature vector to pass through a Softmax classification function of the classifier to obtain the classification result.
According to another aspect of the application, an enterprise energy consumption data management method applicable to a smart city is provided, and includes:
acquiring energy consumption power values of all electric equipment of an enterprise to be detected at a plurality of preset time points within a preset time period;
respectively arranging energy consumption power values of each electric device at a plurality of preset time points in a preset time period into power input vectors, and then obtaining a plurality of power characteristic vectors through a multi-scale neighborhood characteristic extraction module;
after the plurality of power characteristic vectors are two-dimensionally arranged into a global power characteristic matrix, obtaining a first scale energy consumption associated characteristic diagram and a second scale energy consumption associated characteristic diagram through a double-flow network model comprising a first convolutional neural network and a second convolutional neural network, wherein the first convolutional neural network and the second convolutional neural network have different characteristic receptive fields;
fusing the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram to obtain a classification characteristic diagram; and
and passing the classification characteristic diagram through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the energy consumption of the enterprise to be detected is normal or not.
According to still another aspect of the present application, there is provided an electronic apparatus including: a processor; and a memory in which computer program instructions are stored, which, when executed by the processor, cause the processor to perform the method for managing enterprise energy consumption data applicable to a smart city as described above.
According to yet another aspect of the present application, there is provided a computer readable medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to execute the method for enterprise energy consumption data management for smart cities as described above.
Compared with the prior art, the enterprise energy consumption data management system suitable for the smart city extracts the multi-scale dynamic correlation characteristics of the energy consumption power of each electric device in the enterprise on the time dimension by adopting the artificial intelligence detection algorithm based on deep learning, and takes the energy consumption power among the electric devices into consideration to have the correlation relationship, so that the multi-scale correlation characteristics of the energy consumption change characteristics among the electric devices are further extracted by utilizing network models with different receptive fields, and the energy consumption detection of the enterprise is carried out according to the multi-scale correlation characteristics. Like this, can accurately carry out intellectual detection system to whether enterprise's energy consumption normally, and then implement corresponding energy consumption management to improve wisdom city energy utilization efficiency.
Drawings
The above and other objects, features and advantages of the present application will become more apparent by describing in more detail embodiments of the present application with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings, like reference numbers generally represent like parts or steps.
Fig. 1 is a diagram of an application scenario of an enterprise energy consumption data management system for a smart city according to an embodiment of the application;
FIG. 2 is a block diagram of an enterprise energy consumption data management system for a smart city according to an embodiment of the present application;
FIG. 3 is a system architecture diagram of an enterprise energy consumption data management system for a smart city according to an embodiment of the present application;
FIG. 4 is a block diagram of an equipment energy consumption feature extraction module in an enterprise energy consumption data management system for a smart city according to an embodiment of the present application;
fig. 5 is a block diagram of a management result generation module in the enterprise energy consumption data management system for a smart city according to an embodiment of the application;
FIG. 6 is a flowchart of an enterprise energy consumption data management method for a smart city according to an embodiment of the application;
fig. 7 is a block diagram of an electronic device according to an embodiment of the application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein.
Overview of a scene
As for the background technology, smart city construction is an important trend in future city development and is an inevitable requirement for sustainable development of cities. Energy consumption data management is carried out to the enterprise and the energy utilization efficiency of the smart city can be improved.
The existing enterprise energy consumption management system is usually simple energy consumption total value monitoring, for example, a preset threshold value is set to judge whether enterprise energy consumption is normal, the enterprise energy consumption management mode is too simple and is not in line with the actual enterprise energy consumption use condition, the reason is that the enterprise energy consumption use amount changes along with time and business conditions, and the enterprise energy consumption management requirement cannot be met by simple threshold value reference. Therefore, an optimized enterprise energy consumption data management scheme suitable for the smart city is expected.
At present, deep learning and neural networks have been widely applied in the fields of computer vision, natural language processing, speech signal processing, and the like. In addition, deep learning and neural networks also exhibit a level close to or even exceeding that of humans in the fields of image classification, object detection, semantic segmentation, text translation, and the like.
In recent years, deep learning and the development of neural networks provide new solutions and schemes for enterprise energy consumption data management of smart cities.
Accordingly, because the existing enterprise energy consumption management system generally simply monitors the total energy consumption value to detect whether the enterprise energy consumption is normal, and considering that the usage amount of the enterprise energy consumption changes with time and business conditions, the detection method for the enterprise energy consumption cannot meet the existing management requirements. Based on this, in the technical scheme of the application, an artificial intelligence detection algorithm based on deep learning is adopted to extract multi-scale dynamic association characteristics of the energy consumption power of each electric device in an enterprise in a time dimension, and considering that the energy consumption power of each electric device also has an association relation, therefore, network models with different receptive fields are further utilized to extract the multi-scale association characteristics of the energy consumption change characteristics of each electric device, and the energy consumption detection of the enterprise is carried out accordingly. Like this, can accurately carry out intellectual detection system to whether enterprise's energy consumption normally, and then implement corresponding energy consumption management to improve wisdom city energy utilization efficiency.
Specifically, in the technical scheme of the application, firstly, energy consumption power values of all electric devices of an enterprise to be detected at a plurality of preset time points in a preset time period are obtained. Then, considering that the energy consumption power values of each of the electric devices have different mode change characteristics in different time span periods, after the energy consumption power values of each of the electric devices at a plurality of predetermined time points in a predetermined time period are respectively arranged as power input vectors, feature mining is performed in a multi-scale neighborhood feature extraction module to extract dynamic multi-scale neighborhood correlation features of the energy consumption power of each of the electric devices in different time spans, so as to obtain a plurality of power feature vectors.
Further, when the energy consumption of each of all the electric devices of an enterprise is actually detected, the electric devices cooperate with each other to perform corresponding operations, so that the energy consumption and the power of each electric device have a corresponding correlation relationship. Therefore, in the technical solution of the present application, after the multiple power feature vectors are further two-dimensionally arranged into a global power feature matrix to integrate the dynamic multi-scale neighborhood associated feature information of the energy consumption power of each electric device, feature mining is performed on the multiple power feature vectors through a dual-current network model including a first convolutional neural network and a second convolutional neural network to extract multi-scale associated feature information of the dynamic variation feature of the energy consumption power value of each electric device, that is, associated feature distribution information of the energy consumption power dynamic features of some electric devices in each electric device, so as to obtain a first scale energy consumption associated feature map and a second scale energy consumption associated feature map. In particular, here, the first convolutional neural network and the second convolutional neural network have different characteristic receptive fields. Accordingly, in one particular example of the subject application, the first convolutional neural network can employ a first convolutional kernel having a first scale and the second convolutional neural network employs a second convolutional kernel having a second scale, wherein the first scale is different from the second scale. In another specific example of the present application, the first convolutional neural network may further use a first hole convolution kernel having a first hole rate, and the second convolutional neural network uses a second hole convolution kernel having a second hole rate, where the first hole rate is different from the second hole rate, and the first hole convolution kernel and the second hole convolution kernel have the same size. In this way, the multi-scale characteristic receptive field can be utilized to perform multi-scale associated characteristic extraction on the dynamic characteristics of the energy consumption power of specific electric equipment which are cooperated with each other in the plurality of electric equipment, so that the accuracy of the follow-up enterprise energy consumption detection is improved.
And then, fusing the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram to obtain a classification characteristic diagram, so as to express multi-scale dynamic correlation characteristics of the energy consumption power of each electric device, namely the dynamic characteristic information of the energy consumption power of the whole enterprise to be detected, which contains the correlation of the cooperative work among the electric devices, and then performing classification processing in a classifier so as to obtain a classification result for expressing whether the energy consumption of the enterprise to be detected is normal. Therefore, whether the enterprise energy consumption to be detected is normally intelligently detected or not can be accurately detected, and then corresponding energy consumption management is implemented so as to improve the energy utilization efficiency of the smart city.
Particularly, in the technical solution of the present application, the power feature vectors obtained by the multi-scale neighborhood feature extraction module include multi-scale power correlation features in a time sequence dimension, so that after two-dimensional arrangement is performed as a global power feature matrix, staggered distribution of correlation scales included in the global power feature matrix among the arranged power feature vectors may migrate to different feature distribution directions under feature extraction of different receptive fields of the first convolutional neural network and the second convolutional neural network, so that a negative correlation relationship between corresponding positions exists between feature matrices arranged along a channel dimension of the first scale energy consumption correlation feature map and the second scale energy consumption correlation feature map, thereby affecting a fusion effect of the first scale energy consumption correlation feature map and the second scale energy consumption correlation feature map.
Therefore, the applicant of the present application fuses the corresponding feature matrices of the first scale energy consumption associated feature map and the second scale energy consumption associated feature map by using a full orthographic nonlinear re-weighting method, and the fusion is expressed as follows:
M 1i and M 2i Respectively obtaining diagonal matrixes M through linear transformation of ith characteristic matrixes of the first scale energy consumption associated characteristic diagram and the second scale energy consumption associated characteristic diagram ci Is the ith feature matrix of the classification feature map, and the division between the numerator matrix and the denominator matrix is a division by position of the matrix feature values.
Here, the full forward projection nonlinear re-weighting guarantees full forward of the projection by the ReLU function to avoid aggregating negatively correlated information, and at the same time introduces a nonlinear re-weighting mechanism to aggregate the feature value distributions of the corresponding feature matrices of the first scale energy consumption associated feature map and the second scale energy consumption associated feature map with respect to each other, so that the intrinsic structure of the respective feature matrices of the classification feature maps can penalize distant connections to strengthen local coupling. Therefore, the synergistic effect of the spatial feature transformation corresponding to the full orthographic projection reweighting of the feature matrix corresponding to the first scale energy consumption associated feature map and the second scale energy consumption associated feature map in the high-dimensional feature space is achieved, the fusion effect of the first scale energy consumption associated feature map and the second scale energy consumption associated feature map is improved, and the classification accuracy is further improved. Like this, can accurately intelligently detect the energy consumption of enterprise, and then implement corresponding energy consumption management to the testing result to improve wisdom city energy utilization efficiency.
Based on this, this application has proposed an enterprise energy consumption data management system suitable for smart city, and it includes: the energy consumption data acquisition module is used for acquiring energy consumption power values of all electric equipment of an enterprise to be detected at a plurality of preset time points in a preset time period; the device energy consumption characteristic extraction module is used for respectively arranging energy consumption power values of each electric device at a plurality of preset time points in a preset time period into power input vectors and then obtaining a plurality of power characteristic vectors through the multi-scale neighborhood characteristic extraction module; the device-to-device correlation feature extraction module is used for performing two-dimensional arrangement on the plurality of power feature vectors to form a global power feature matrix and then obtaining a first scale energy consumption correlation feature map and a second scale energy consumption correlation feature map through a double-current network model comprising a first convolutional neural network and a second convolutional neural network, wherein the first convolutional neural network and the second convolutional neural network have different feature receptive fields; the characteristic fusion module is used for fusing the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram to obtain a classification characteristic diagram; and the management result generation module is used for enabling the classification characteristic graph to pass through a classifier to obtain a classification result, and the classification result is used for indicating whether the energy consumption of the enterprise to be detected is normal or not.
Fig. 1 is an application scenario diagram of an enterprise energy consumption data management system suitable for a smart city according to an embodiment of the application. As shown in fig. 1, in this application scenario, the energy consumption power values of all the electric devices of the enterprise to be detected at a plurality of predetermined time points within a predetermined time period are obtained by an energy consumption power meter (e.g., P as illustrated in fig. 1). Then, the data are input into a server (for example, S in fig. 1) deployed with an enterprise energy consumption data management algorithm applicable to the smart city, wherein the server can process the input data by using the enterprise energy consumption data management algorithm applicable to the smart city to generate a classification result indicating whether the energy consumption of the enterprise to be detected is normal or not.
Having described the general principles of the present application, various non-limiting embodiments of the present application will now be described with reference to the accompanying drawings.
Exemplary System
Fig. 2 is a block diagram of an enterprise energy consumption data management system suitable for a smart city according to an embodiment of the application. As shown in fig. 2, an enterprise energy consumption data management system 300 for a smart city according to an embodiment of the present application includes: an energy consumption data acquisition module 310; a device energy consumption feature extraction module 320; an inter-device association feature extraction module 330; a feature fusion module 340; and a management result generation module 350.
The energy consumption data acquisition module 310 is configured to acquire energy consumption power values of all electric devices of an enterprise to be detected at multiple predetermined time points within a predetermined time period; the device energy consumption feature extraction module 320 is configured to arrange energy consumption power values of the electric devices at multiple predetermined time points in a predetermined time period into power input vectors respectively, and then obtain multiple power feature vectors through the multi-scale neighborhood feature extraction module; the inter-device correlation feature extraction module 330 is configured to perform two-dimensional arrangement on the multiple power feature vectors to obtain a first scale energy consumption correlation feature map and a second scale energy consumption correlation feature map through a double-flow network model including a first convolutional neural network and a second convolutional neural network after the multiple power feature vectors are arranged to be a global power feature matrix, where the first convolutional neural network and the second convolutional neural network have different feature receptive fields; the feature fusion module 340 is configured to fuse the first scale energy consumption associated feature map and the second scale energy consumption associated feature map to obtain a classification feature map; and the management result generating module 350 is configured to pass the classification feature map through a classifier to obtain a classification result, where the classification result is used to indicate whether the energy consumption of the enterprise to be detected is normal.
Fig. 3 is a system architecture diagram of an enterprise energy consumption data management system suitable for a smart city according to an embodiment of the present application. As shown in fig. 3, in the system architecture of the enterprise energy consumption data management system 300 suitable for the smart city, firstly, the energy consumption data acquisition module 310 obtains energy consumption power values of all electric devices of an enterprise to be detected at a plurality of predetermined time points within a predetermined time period; the device energy consumption feature extraction module 320 is configured to arrange the energy consumption power values of the electric devices at a plurality of predetermined time points within a predetermined time period, which are obtained by the energy consumption data acquisition module 310, into power input vectors respectively, and then obtain a plurality of power feature vectors through the multi-scale neighborhood feature extraction module; then, the inter-device correlation feature extraction module 330 two-dimensionally arranges the plurality of power feature vectors obtained by the device energy consumption feature extraction module 320 into a global power feature matrix, and then obtains a first scale energy consumption correlation feature map and a second scale energy consumption correlation feature map through a double-flow network model including a first convolutional neural network and a second convolutional neural network, where the first convolutional neural network and the second convolutional neural network have different characteristic receptive fields; then, the feature fusion module 340 fuses the first scale energy consumption associated feature map and the second scale energy consumption associated feature map obtained by the inter-device associated feature extraction module 330 to obtain a classification feature map; further, the management result generating module 350 may pass the classification feature map through a classifier to obtain a classification result, where the classification result is used to indicate whether the energy consumption of the enterprise to be detected is normal.
Specifically, in the operation process of the enterprise energy consumption data management system 300 applicable to the smart city, the energy consumption data acquisition module 310 is configured to acquire energy consumption power values of all electric devices of an enterprise to be detected at a plurality of predetermined time points within a predetermined time period. It should be understood that, in the existing enterprise energy consumption management system, generally, the total energy consumption value is simply monitored to detect whether the enterprise energy consumption is normal, and it is considered that the amount of energy consumption used by the enterprise changes with time and business conditions, therefore, in the technical scheme of the present application, an artificial intelligence detection algorithm based on deep learning is adopted to extract the multi-scale dynamic correlation feature of the energy consumption power of each electric device in the enterprise in the time dimension, and it is considered that the energy consumption power between each electric device also has a correlation relationship, so that a network model with different receptive fields is further utilized to extract the multi-scale correlation feature of the energy consumption change feature between each electric device, and thus the energy consumption detection of the enterprise is performed. In a specific example of the present application, the energy consumption power values of all the electric devices of the enterprise to be detected at a plurality of predetermined time points within a predetermined time period may be obtained by an energy consumption power meter.
Specifically, in the operation process of the enterprise energy consumption data management system 300 suitable for the smart city, the device energy consumption feature extraction module 320 is configured to arrange energy consumption power values of the electrical devices at a plurality of predetermined time points in a predetermined time period into power input vectors respectively and then obtain a plurality of power feature vectors through the multi-scale neighborhood feature extraction module. Considering that the energy consumption power values of each of the electric devices have different mode change characteristics in different time span periods, after the energy consumption power values of each of the electric devices at a plurality of predetermined time points in a predetermined time period are respectively arranged as power input vectors, feature mining is performed in a multi-scale neighborhood feature extraction module to extract dynamic multi-scale neighborhood correlation features of the energy consumption power of each of the electric devices in different time spans, so as to obtain a plurality of power feature vectors.
Fig. 4 is a block diagram of an equipment energy consumption feature extraction module in an enterprise energy consumption data management system for a smart city according to an embodiment of the present application. As shown in fig. 4, the device energy consumption feature extraction module 320 includes: a first scale feature extraction unit 321, configured to input the power input vector into a first convolution layer of the multi-scale neighborhood feature extraction module to obtain a first neighborhood scale power feature vector, where the first convolution layer has a first one-dimensional convolution kernel with a first length; a second scale feature extraction unit 322, configured to input the power input vector into a second convolution layer of the multi-scale neighborhood feature extraction module to obtain a second neighborhood scale power feature vector, where the second convolution layer has a second one-dimensional convolution kernel with a second length, and the first length is different from the second length; and a multi-scale cascading unit 323, configured to cascade the first neighborhood scale power feature vector and the second neighborhood scale power feature vector to obtain the plurality of power feature vectors. Wherein, the inputting the power input vector into the first convolution layer of the multi-scale neighborhood characteristic extraction module to obtain a first neighborhood scale power characteristic vector includes: performing one-dimensional convolution coding on the power input vector by using a first convolution layer of the multi-scale neighborhood characteristic extraction module according to the following formula to obtain a first neighborhood scale power characteristic vector;
wherein the formula is:
wherein a is the width of the first convolution kernel in the X direction, F (a) is the first convolution kernel parameter vector, G (X-a) is the local vector matrix operated with the convolution kernel function, w is the size of the first convolution kernel, and X represents the power input vector. And inputting the power input vector into a second convolution layer of the multi-scale neighborhood feature extraction module to obtain a second neighborhood scale power feature vector, comprising: performing one-dimensional convolutional coding on the power input vector by using a second convolutional layer of the multi-scale neighborhood feature extraction module according to the following formula to obtain a second neighborhood scale power feature vector;
wherein the formula is:
wherein b is the width of the second convolution kernel in the X direction, F (b) is a second convolution kernel parameter vector, G (X-b) is a local vector matrix operated with the convolution kernel function, m is the size of the second convolution kernel, and X represents the power input vector.
Specifically, in the operation process of the enterprise energy consumption data management system 300 applicable to the smart city, the inter-device correlation feature extraction module 330 is configured to perform two-dimensional arrangement on the plurality of power feature vectors to obtain a first scale energy consumption correlation feature map and a second scale energy consumption correlation feature map through a dual-flow network model including a first convolutional neural network and a second convolutional neural network after the two-dimensional arrangement is performed on the power feature vectors to form a global power feature matrix, where the first convolutional neural network and the second convolutional neural network have different characteristic receptive fields. Considering that when the energy consumption of each of all the electric devices of an enterprise is actually detected, the electric devices cooperate with each other to perform corresponding operations, so that the energy consumption and the power of each electric device have a corresponding correlation relationship. Therefore, in the technical solution of the present application, after the multiple power feature vectors are further two-dimensionally arranged into a global power feature matrix to integrate the dynamic multi-scale neighborhood associated feature information of the energy consumption power of each electric device, feature mining is performed on the multiple power feature vectors through a dual-current network model including a first convolutional neural network and a second convolutional neural network to extract multi-scale associated feature information of the dynamic variation feature of the energy consumption power value of each electric device, that is, associated feature distribution information of the energy consumption power dynamic features of some electric devices in each electric device, so as to obtain a first scale energy consumption associated feature map and a second scale energy consumption associated feature map. In particular, here, the first convolutional neural network and the second convolutional neural network have different characteristic receptive fields. Accordingly, in one particular example of the present application, the first convolutional neural network may use a first convolutional kernel having a first scale, and the second convolutional neural network uses a second convolutional kernel having a second scale, wherein the first scale is different from the second scale. In another specific example of the present application, the first convolutional neural network may further use a first hole convolution kernel having a first hole rate, and the second convolutional neural network uses a second hole convolution kernel having a second hole rate, where the first hole rate is different from the second hole rate, and the first hole convolution kernel and the second hole convolution kernel have the same size. In this way, the multi-scale characteristic receptive field can be utilized to perform multi-scale associated characteristic extraction on the dynamic characteristics of the energy consumption power of specific electric equipment which are cooperated with each other in the plurality of electric equipment, so that the accuracy of the follow-up enterprise energy consumption detection is improved. More specifically, after the plurality of power feature vectors are two-dimensionally arranged into a global power feature matrix, a first scale energy consumption associated feature map and a second scale energy consumption associated feature map are obtained through a double-flow network model including a first convolutional neural network and a second convolutional neural network, and the method includes the following steps: performing convolution processing of a first convolution kernel on input data in forward pass of a layer using layers of the first convolution neural network: performing convolution processing on input data to obtain a convolution characteristic diagram; performing pooling processing based on a local feature matrix on the convolution feature map to obtain a pooled feature map; and performing nonlinear activation on the pooled feature map to obtain an activated feature map; wherein, the output of the last layer of the first convolutional neural network is the first scale energy consumption correlation characteristic diagram, and the input of the first layer of the first convolutional neural network is a global power characteristic matrix; and performing convolution processing of a second convolution kernel on the input data in forward pass of the layers using layers of the second convolutional neural network: performing convolution processing on input data to obtain a convolution characteristic diagram; performing pooling processing based on a local feature matrix on the convolution feature map to obtain a pooled feature map; and performing nonlinear activation on the pooled feature map to obtain an activated feature map; and the output of the last layer of the second convolutional neural network is the second scale energy consumption correlation characteristic diagram, and the input of the first layer of the second convolutional neural network is a global power characteristic matrix.
Specifically, in the operation process of the enterprise energy consumption data management system 300 suitable for the smart city, the feature fusion module 340 is configured to fuse the first scale energy consumption associated feature map and the second scale energy consumption associated feature map to obtain a classification feature map. In the technical scheme of the application, the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram are fused to obtain a classification characteristic diagram, so that multi-scale dynamic correlation characteristics of the energy consumption power of each electric device are represented, that is, the dynamic characteristic information of the energy consumption power of the whole enterprise to be detected, which includes the correlation of cooperative work among the electric devices, is included. Particularly, in the technical solution of the present application, the power feature vectors obtained by the multi-scale neighborhood feature extraction module include multi-scale power correlation features in a time sequence dimension, so that after two-dimensional arrangement is performed as a global power feature matrix, staggered distribution of correlation scales included in the global power feature matrix among the arranged power feature vectors may migrate to different feature distribution directions under feature extraction of different receptive fields of the first convolutional neural network and the second convolutional neural network, so that a negative correlation relationship between corresponding positions exists between feature matrices arranged along a channel dimension of the first scale energy consumption correlation feature map and the second scale energy consumption correlation feature map, thereby affecting a fusion effect of the first scale energy consumption correlation feature map and the second scale energy consumption correlation feature map. Therefore, the applicant of the present application fuses the corresponding feature matrices of the first scale energy consumption associated feature map and the second scale energy consumption associated feature map by using a full orthographic non-linear re-weighting method, and the fusion is expressed as:
wherein M is 1i And M 2i Respectively obtaining diagonal matrixes M obtained by linear transformation of ith characteristic matrixes of the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram ci Is the i-th feature matrix of the classification feature map, reLU (-) represents the ReLU activation function,
representing multiplication of matrices and division between numerator and denominator matrices as division by position of eigenvalues of the matrices, exp (-) representing the exponential operation of the matricesThe exponential operation of the matrix means to calculate a natural exponential function value raised to the eigenvalue of each position in the matrix. Here, the full forward projection nonlinear re-weighting guarantees full forward of the projection by the ReLU function to avoid aggregating negatively correlated information, and at the same time introduces a nonlinear re-weighting mechanism to aggregate the feature value distributions of the corresponding feature matrices of the first scale energy consumption associated feature map and the second scale energy consumption associated feature map with respect to each other, so that the intrinsic structure of the respective feature matrices of the classification feature maps can penalize distant connections to strengthen local coupling. Therefore, the synergistic effect of the spatial feature transformation corresponding to the full orthographic projection reweighting of the feature matrix corresponding to the first scale energy consumption associated feature map and the second scale energy consumption associated feature map in the high-dimensional feature space is achieved, the fusion effect of the first scale energy consumption associated feature map and the second scale energy consumption associated feature map is improved, and the classification accuracy is improved. Like this, can accurately intelligently detect the energy consumption of enterprise, and then implement corresponding energy consumption management to the testing result to improve wisdom city energy utilization efficiency.
Specifically, in the operation process of the enterprise energy consumption data management system 300 suitable for the smart city, the management result generating module 350 is configured to pass the classification feature map through a classifier to obtain a classification result, where the classification result is used to indicate whether the energy consumption of the enterprise to be detected is normal. Namely, the classification characteristic diagram is classified in a classifier to obtain a classification result for indicating whether the energy consumption of the enterprise to be detected is normal or not. Therefore, whether the energy consumption of the enterprise to be detected is normally detected or not can be accurately detected, and then corresponding energy consumption management is implemented so as to improve the energy utilization efficiency of the smart city. In particular, the classifier includes a plurality of fully-connected layers and a Softmax layer cascaded with a last fully-connected layer of the plurality of fully-connected layers. In the classification processing of the classifier, the classification feature map is first projected as a vector, for example, in a specific example, the classification feature map is expanded as a classification feature vector along a row vector or a column vector; then, carrying out multiple full-connection coding on the classification characteristic vector by using multiple full-connection layers of the classifier to obtain a coding classification characteristic vector; and then, inputting the encoding classification characteristic vector into a Softmax layer of the classifier, namely, classifying the encoding classification characteristic vector by using the Softmax classification function to obtain a classification result for indicating whether the energy consumption of the enterprise to be detected is normal or not. More specifically, in one specific example of the present application, the classification feature map is processed using the classifier to obtain a classification result according to the following formula:
O＝softmax{(W n ,B n ):…:(W 1 ,B 1 ) L Project (F) }, where Project (F) denotes the projection of the classification feature map as a vector, W 1 To W n As a weight matrix for each fully connected layer, B 1 To B n Representing the bias vectors of the fully connected layers of each layer.
Fig. 5 is a block diagram of a management result generation module in an enterprise energy consumption data management system for a smart city according to an embodiment of the present application. As shown in fig. 5, the management result generating module 350 includes: an unfolding unit 351, configured to unfold the classification feature map into a classification feature vector based on a row vector or a column vector; a full-concatenation encoding unit 352, configured to perform full-concatenation encoding on the classification feature vector using a plurality of full-concatenation layers of the classifier to obtain an encoded classification feature vector; and a classification result generating unit 353, configured to pass the encoded classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
In summary, the enterprise energy consumption data management system 300 applicable to the smart city according to the embodiment of the present application is clarified, and extracts multi-scale dynamic correlation characteristics of energy consumption power of each electrical device in an enterprise in a time dimension by using an artificial intelligence detection algorithm based on deep learning, and further extracts the multi-scale correlation characteristics of energy consumption change characteristics between each electrical device by using network models with different receptive fields in consideration that the energy consumption power between each electrical device also has a correlation relationship, so as to perform energy consumption detection of the enterprise. Like this, can accurately carry out intellectual detection system to whether enterprise's energy consumption normally, and then implement corresponding energy consumption management to improve wisdom city energy utilization efficiency.
Exemplary method
Fig. 6 is a flowchart of an enterprise energy consumption data management method applied to a smart city according to an embodiment of the application. As shown in fig. 6, the method for managing enterprise energy consumption data in a smart city according to the embodiment of the present application includes the steps of: s110, acquiring energy consumption power values of all electric equipment of an enterprise to be detected at a plurality of preset time points within a preset time period; s120, respectively arranging energy consumption power values of each electric device at a plurality of preset time points in a preset time period into power input vectors, and then obtaining a plurality of power characteristic vectors through a multi-scale neighborhood characteristic extraction module; s130, after the plurality of power characteristic vectors are two-dimensionally arranged into a global power characteristic matrix, obtaining a first scale energy consumption associated characteristic diagram and a second scale energy consumption associated characteristic diagram through a double-flow network model comprising a first convolutional neural network and a second convolutional neural network, wherein the first convolutional neural network and the second convolutional neural network have different characteristic receptive fields; s140, fusing the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram to obtain a classification characteristic diagram; and S150, the classification characteristic graph is processed by a classifier to obtain a classification result, and the classification result is used for indicating whether the energy consumption of the enterprise to be detected is normal or not.
In an example, in the above method for managing enterprise energy consumption data applicable to a smart city, the step S120 includes: inputting the power input vector into a first convolution layer of the multi-scale neighborhood region feature extraction module to obtain a first neighborhood region scale power feature vector, wherein the first convolution layer has a first one-dimensional convolution kernel with a first length; inputting the power input vector into a second convolution layer of the multi-scale neighborhood feature extraction module to obtain a second neighborhood scale power feature vector, wherein the second convolution layer has a second one-dimensional convolution kernel of a second length, and the first length is different from the second length; and cascading the first neighborhood scale power feature vector and the second neighborhood scale power feature vector to obtain the plurality of power feature vectors. Wherein the inputting the power input vector into the first convolution layer of the multi-scale neighborhood region feature extraction module to obtain a first neighborhood region scale power feature vector comprises: performing one-dimensional convolution coding on the power input vector by using a first convolution layer of the multi-scale neighborhood characteristic extraction module according to the following formula to obtain a first neighborhood scale power characteristic vector;
wherein the formula is:
wherein a is the width of the first convolution kernel in the X direction, F (a) is the first convolution kernel parameter vector, G (X-a) is the local vector matrix operated with the convolution kernel function, w is the size of the first convolution kernel, and X represents the power input vector. And inputting the power input vector into a second convolution layer of the multi-scale neighborhood feature extraction module to obtain a second neighborhood scale power feature vector, comprising: performing one-dimensional convolutional coding on the power input vector by using a second convolutional layer of the multi-scale neighborhood feature extraction module according to the following formula to obtain a second neighborhood scale power feature vector;
wherein the formula is:
wherein b is the width of the second convolution kernel in the X direction, F (b) is a second convolution kernel parameter vector, G (X-b) is a local vector matrix operated with the convolution kernel function, m is the size of the second convolution kernel, and X represents the power input vector.
In an example, in the above method for managing enterprise energy consumption data applicable to a smart city, the step S130 includes: the first convolutional neural network uses a first convolutional kernel having a first scale, the second convolutional neural network uses a second convolutional kernel having a second scale, wherein the first scale is different from the second scale; and the first convolutional neural network uses a first hole convolution kernel having a first hole rate, the second convolutional neural network uses a second hole convolution kernel having a second hole rate, wherein the first hole rate is different from the second hole rate, and the first hole convolution kernel and the second hole convolution kernel have the same size.
In an example, in the above method for managing enterprise energy consumption data applicable to a smart city, the step S140 includes: fusing the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram according to the following formula to obtain the classification characteristic diagram;
wherein the formula is:
wherein M is 1i And M 2i Respectively obtaining diagonal matrixes M obtained by linear transformation of ith characteristic matrixes of the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram ci Is the i-th feature matrix of the classification feature map, reLU (-) represents the ReLU activation function,
representing the multiplication of matrices and the division between the numerator matrix and denominator matrix as a division by location of the eigenvalues of the matrices, exp (-) represents the exponential operation of the matrices, which represents the calculation of a natural exponential function value raised to the power of the eigenvalues of each location in the matrix.
In an example, in the above method for managing enterprise energy consumption data applicable to a smart city, the step S150 includes: expanding the classification feature map into a classification feature vector based on a row vector or a column vector; performing full-join encoding on the classification feature vectors using a plurality of full-join layers of the classifier to obtain encoded classification feature vectors; and passing the encoding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
In summary, the enterprise energy consumption data management method applicable to the smart city according to the embodiment of the application is clarified, and the artificial intelligence detection algorithm based on deep learning is adopted to extract the multi-scale dynamic correlation characteristics of the energy consumption power of each electric device in the enterprise in the time dimension, and the energy consumption power of each electric device is also considered to have the correlation relationship, so that the network models with different receptive fields are further utilized to extract the multi-scale correlation characteristics of the energy consumption change characteristics of each electric device, and the energy consumption detection of the enterprise is performed accordingly. Like this, can accurately carry out intellectual detection system to whether enterprise's energy consumption normally, and then implement corresponding energy consumption management to improve wisdom city energy utilization efficiency.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 7.
FIG. 7 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
As shown in fig. 7, the electronic device 10 includes one or more processors 11 and memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The input device 13 may include, for example, a keyboard, a mouse, and the like.
The output device 14 can output various information including the classification result to the outside. The output devices 14 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 10 relevant to the present application are shown in fig. 7, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the functions of the enterprise energy consumption data management method for a smart city according to various embodiments of the present application described in the above-mentioned "exemplary systems" section of this specification.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps in the functions of the method for managing enterprise energy consumption data for a smart city according to various embodiments of the present application described in the above section "exemplary system" of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
The basic principles of the present application have been described above with reference to specific embodiments, but it should be noted that advantages, effects, etc. mentioned in the present application are only examples and are not limiting, and the advantages, effects, etc. must not be considered to be possessed by various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is provided for purposes of illustration and understanding only, and is not intended to limit the application to the details which are set forth in order to provide a thorough understanding of the present application.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations should be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (7)
1. The utility model provides an enterprise energy consumption data management system suitable for wisdom city which characterized in that includes:
the energy consumption data acquisition module is used for acquiring energy consumption power values of all electric equipment of an enterprise to be detected at a plurality of preset time points in a preset time period;
the device energy consumption characteristic extraction module is used for respectively arranging energy consumption power values of each electric device at a plurality of preset time points in a preset time period into power input vectors and then obtaining a plurality of power characteristic vectors through the multi-scale neighborhood characteristic extraction module;
the device-to-device correlation feature extraction module is used for performing two-dimensional arrangement on the plurality of power feature vectors to form a global power feature matrix and then obtaining a first scale energy consumption correlation feature map and a second scale energy consumption correlation feature map through a double-current network model comprising a first convolutional neural network and a second convolutional neural network, wherein the first convolutional neural network and the second convolutional neural network have different feature receptive fields;
the characteristic fusion module is used for fusing the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram to obtain a classification characteristic diagram; and
and the management result generation module is used for enabling the classification characteristic graph to pass through a classifier to obtain a classification result, and the classification result is used for indicating whether the energy consumption of the enterprise to be detected is normal or not.
2. The system for managing enterprise energy consumption data of a smart city according to claim 1, wherein the device energy consumption feature extraction module comprises:
a first scale feature extraction unit, configured to input the power input vector into a first convolution layer of the multi-scale neighborhood feature extraction module to obtain a first neighborhood scale power feature vector, where the first convolution layer has a first one-dimensional convolution kernel with a first length;
a second scale feature extraction unit, configured to input the power input vector into a second convolution layer of the multi-scale neighborhood feature extraction module to obtain a second neighborhood scale power feature vector, where the second convolution layer has a second one-dimensional convolution kernel with a second length, and the first length is different from the second length; and
a multi-scale cascade unit, configured to cascade the first neighborhood scale power feature vector and the second neighborhood scale power feature vector to obtain the multiple power feature vectors.
3. The system for managing enterprise energy consumption data in a smart city according to claim 2,
a first scale feature extraction unit, further configured to: performing one-dimensional convolution coding on the power input vector by using a first convolution layer of the multi-scale neighborhood characteristic extraction module according to the following formula to obtain a first neighborhood scale power characteristic vector;
wherein the formula is:
wherein a is the width of the first convolution kernel in the X direction, F (a) is the first convolution kernel parameter vector, G (X-a) is the local vector matrix operated with the convolution kernel function, w is the size of the first convolution kernel, and X represents the power input vector.
A second scale feature extraction unit, further configured to: performing one-dimensional convolutional coding on the power input vector by using a second convolutional layer of the multi-scale neighborhood feature extraction module according to the following formula to obtain a second neighborhood scale power feature vector;
wherein the formula is:
wherein b is the width of the second convolution kernel in the X direction, F (b) is a second convolution kernel parameter vector, G (X-b) is a local vector matrix operated with the convolution kernel function, m is the size of the second convolution kernel, and X represents the power input vector.
4. The system of claim 3, wherein the first convolutional neural network uses a first convolutional kernel having a first scale, and the second convolutional neural network uses a second convolutional kernel having a second scale, wherein the first scale is different from the second scale.
5. The system of claim 3, wherein the first convolutional neural network uses a first hole convolutional kernel having a first hole rate, and the second convolutional neural network uses a second hole convolutional kernel having a second hole rate, wherein the first hole rate is different from the second hole rate, and the first hole convolutional kernel and the second hole convolutional kernel have the same size.
6. The system of claim 5, wherein the feature fusion module is further configured to: fusing the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram according to the following formula to obtain the classification characteristic diagram;
wherein the formula is:
wherein M is 1i And M 2i Respectively obtaining diagonal matrixes M obtained by linear transformation of ith characteristic matrixes of the first scale energy consumption correlation characteristic diagram and the second scale energy consumption correlation characteristic diagram ci Is the i-th feature matrix of the classification feature map, reLU (-) represents the ReLU activation function,
representing the multiplication of matrices and the division between the numerator matrix and denominator matrix as a division by location of the eigenvalues of the matrices, exp (-) represents the exponential operation of the matrices, which represents the calculation of a natural exponential function value raised to the power of the eigenvalues of each location in the matrix.
7. The system for managing enterprise energy consumption data of smart cities of claim 6, wherein the management result generation module comprises:
the expansion unit is used for expanding the classification feature map into a classification feature vector based on a row vector or a column vector;
a full-concatenation encoding unit, configured to perform full-concatenation encoding on the classification feature vector using a plurality of full-concatenation layers of the classifier to obtain an encoded classification feature vector; and
a classification result generating unit, configured to pass the encoded classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
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