Dual-mode communication meter reading anomaly analysis method and related device

Abstract

The invention provides a dual-mode communication meter reading anomaly analysis method and a related device. The method comprises the following steps: acquiring first power data of a target channel region and a target communication state of a dual-mode channel; performing data preprocessing and feature extraction on the first power data to obtain second power data; performing model training according to the second power data to obtain an anomaly detection model; analyzing the meter reading information of the target channel region to obtain a first anomaly analysis result; if the power line channel and / or the wireless channel are / is in the blocked state, determining that the target communication state is a second anomaly analysis result; determining a first target anomaly analysis result according to the first anomaly analysis result and the second anomaly analysis result; and if the power line channel and the wireless channel are both in the unblocked state, carrying out channel transmission test and channel switching test to obtain a third anomaly analysis result, and determining a second target anomaly analysis result according to the first anomaly analysis result and the third anomaly analysis result.

Description

Technical Field

[0001] The present application relates to the technical field of dual-mode communication, and in particular to a dual-mode communication meter reading abnormality analysis method and related devices. Background Art

[0002] In the context of smart grid, the accuracy of electric energy metering is crucial to the economic operation and management of the power system. Dual-mode communication technology effectively improves the success rate of data collection and the stability of the system by combining power line communication and wireless communication. However, in actual application, various abnormal fault conditions may occur, which will affect the stability of the power system.

[0003] Therefore, how to quickly and accurately identify abnormal fault conditions in the dual-mode communication process needs to be solved urgently. Summary of the invention

[0004] The embodiment of the present application provides a dual-mode communication meter reading anomaly analysis method and related devices, which can quickly and accurately identify abnormal fault conditions in the dual-mode communication process by comprehensively considering power data and communication status, thereby improving the stability and reliability of the power system.

[0005] In a first aspect, an embodiment of the present application provides a dual-mode communication meter reading anomaly analysis method, which is applied to an anomaly analysis module of a dual-mode communication system, wherein the dual-mode communication system includes the anomaly analysis module, an electric meter measurement module, and a channel area, where a is an integer greater than 1, and the method includes:

[0006] Acquire first power data of a preset time period of a target channel area and a target communication state of a dual-mode channel; the target channel area is any one of the a channel areas; the dual-mode channel includes a power line channel and a wireless channel; the communication state includes a smooth state and a blocked state;

[0007] Performing data preprocessing and feature extraction on the first power data to obtain second power data;

[0008] Performing model training according to the second power data to obtain an anomaly detection model;

[0009] Acquire meter reading information of the electric meter metering module in the preset time period corresponding to the target channel area;

[0010] Analyze the meter reading information according to the abnormality detection model to obtain a first abnormality analysis result;

[0011] If the communication state of the power line channel and / or the wireless channel in the target communication state is the blocking state, determining the target communication state as a second abnormal analysis result; determining a first target abnormal analysis result according to the first abnormal analysis result and the second abnormal analysis result;

[0012] If the communication states of the power line channel and the wireless channel in the target communication state are both the unblocked state, a channel transmission test is performed on the power line channel and the wireless channel to obtain a first test result; a channel switching test is performed on the power line channel and the wireless channel to obtain a second test result;

[0013] A third abnormality analysis result is determined according to the first test result and the second test result, and a second target abnormality analysis result is determined according to the first abnormality analysis result and the third abnormality analysis result.

[0014] In a second aspect, an embodiment of the present application provides a dual-mode communication meter reading anomaly analysis device, which is applied to an anomaly analysis module of a dual-mode communication system, wherein the dual-mode communication system includes the anomaly analysis module, an electric meter measurement module, and a channel area, where a is an integer greater than 1, and the device includes an acquisition module, a processing module, a training module, an analysis module, a determination module, and a test module, wherein:

[0015] The acquisition module is used to acquire the first power data of the preset time period of the target channel area and the target communication state of the dual-mode channel; the target channel area is any one of the a channel areas; the dual-mode channel includes a power line channel and a wireless channel; the communication state includes a smooth state and a blocked state;

[0016] The processing module is used to perform data preprocessing and feature extraction on the first power data to obtain second power data;

[0017] The training module is used to perform model training according to the second power data to obtain an anomaly detection model;

[0018] The acquisition module is further used to acquire meter reading information of the electric meter measurement module in the preset time period corresponding to the target channel area;

[0019] The analysis module is used to analyze the meter reading information according to the abnormality detection model to obtain a first abnormality analysis result;

[0020] The determination module is configured to determine that the target communication state is a second abnormal analysis result if the communication state of the power line channel and / or the wireless channel in the target communication state is the blocked state; and determine a first target abnormal analysis result according to the first abnormal analysis result and the second abnormal analysis result;

[0021] The test module is configured to, if the communication states of the power line channel and the wireless channel in the target communication state are both in the unblocked state, perform a channel transmission test on the power line channel and the wireless channel to obtain a first test result; and perform a channel switching test on the power line channel and the wireless channel to obtain a second test result;

[0022] The determination module is further used to determine a third abnormality analysis result according to the first test result and the second test result, and to determine a second target abnormality analysis result according to the first abnormality analysis result and the third abnormality analysis result.

[0023] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps of any method in the first aspect of the embodiment of the present application.

[0024] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in any method of the first aspect of the embodiment of the present application.

[0025] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0026] By implementing the embodiments of the present application and comprehensively considering the power data and communication status, abnormal fault conditions in the dual-mode communication process can be quickly and accurately identified, thereby improving the stability and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 is a system architecture diagram of a dual-mode communication system provided in an embodiment of the present application;

[0029] Figure 2 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application;

[0030] Figure 3 It is a flow chart of a dual-mode communication meter reading abnormality analysis method provided in an embodiment of the present application;

[0031] Figure 4 The present invention provides a block diagram of the functional modules of a dual-mode communication meter reading abnormality analysis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0034] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship. The "plurality" appearing in the embodiments of the present application refers to two or more.

[0035] In the embodiments of the present application, "at least one item" or similar expressions refer to any combination of these items, including any combination of single items or plural items, and refer to one or more, and multiple refers to two or more. For example, at least one item of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0036] The "connection" that appears in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0037] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0038] In the context of smart grids, the accuracy of electric energy metering is crucial to the economic operation and management of power systems. Dual-mode communication technology effectively improves the success rate of data acquisition and the stability of the system by combining power line communication and wireless communication. However, in actual applications, various abnormal fault conditions may occur, which will affect the stability of the power system. Therefore, how to quickly and accurately identify abnormal fault conditions in the dual-mode communication process needs to be solved urgently.

[0039] To solve the above problems, an embodiment of the present application provides a dual-mode communication meter reading anomaly analysis method and related devices, which are applied to an anomaly analysis module of a dual-mode communication system, wherein the dual-mode communication system includes the anomaly analysis module, an electric meter measurement module and a channel areas, where a is an integer greater than 1, and obtains first power data of a preset time period of a target channel area and a target communication state of the dual-mode channel; the target channel area is any one of the a channel areas; the dual-mode channel includes a power line channel and a wireless channel; the communication state includes a smooth state and a blocked state; data preprocessing and feature extraction are performed on the first power data to obtain second power data; model training is performed based on the second power data to obtain an anomaly detection model; meter reading information of the electric meter measurement module in the preset time period corresponding to the target channel area is obtained; and the anomaly detection model is obtained based on the anomaly detection model. The model analyzes the meter reading information to obtain a first abnormal analysis result; if the communication state of the power line channel and / or the wireless channel in the target communication state is the blocked state, the target communication state is determined to be the second abnormal analysis result; the first target abnormal analysis result is determined according to the first abnormal analysis result and the second abnormal analysis result; if the communication state of the power line channel and the wireless channel in the target communication state is both the unblocked state, the power line channel and the wireless channel are subjected to a channel transmission test to obtain a first test result; the power line channel and the wireless channel are subjected to a channel switching test to obtain a second test result; the third abnormal analysis result is determined according to the first test result and the second test result, and the second target abnormal analysis result is determined according to the first abnormal analysis result and the third abnormal analysis result. By comprehensively considering the power data and the communication state, the abnormal fault conditions in the dual-mode communication process can be quickly and accurately identified, thereby improving the stability and reliability of the power system.

[0040] Combine the following Figure 1 The system architecture of a dual-mode communication system for executing a dual-mode communication meter reading abnormality analysis method in an embodiment of the present application is described. Figure 1 It is a system architecture diagram of a dual-mode communication system provided in an embodiment of the present application. The dual-mode communication system includes an abnormality analysis module, an electric meter measurement module and a channel areas, where a is an integer greater than 1.

[0041] Among them, the abnormal analysis module can detect the metering module and channel area in the dual-mode communication system. When an abnormality is detected in the communication link of the metering module or the channel area, the abnormal situation will be analyzed. If a fault is confirmed, an early warning signal will be issued to prompt maintenance personnel to check and repair. These faults may include meter data errors, communication interruptions, equipment failures, etc., which are not specifically limited here.

[0042] The dual-mode communication system includes a different channel area, each channel area has a plurality of dual-mode communication modules, and data transmission can be performed through the power line channel or wireless channel in each channel area.

[0043] Among them, the electric meter measurement module is used to collect, process and transmit power data. The electric meter measurement module can collect various metering data of the electric meter in real time, such as voltage, current, power, energy consumption, etc., which are not specifically limited here. In addition, the electric meter measurement module can also work in conjunction with a channel area, so that power data can be smoothly transmitted between different channel areas through dual-mode channels. Among them, the dual-mode channel includes a power line channel and a wireless channel. Each channel area includes multiple dual-mode communication modules for managing data communications within the area, and the electric meter measurement module maintains a stable connection with the communication links of these channel areas to ensure the timeliness and reliability of power data transmission.

[0044] It can be seen that by detecting power data and communication status through the abnormal analysis module, abnormal fault conditions in the dual-mode communication process can be identified quickly and accurately, thereby improving the stability and reliability of the power system.

[0045] Combine the following Figure 2 The electronic device in the embodiment of the present application is described. Figure 2 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 2 As shown, the electronic device includes one or more processors, a memory, a communication interface and one or more programs, and the processor is communicatively connected with the memory and the communication interface via an internal communication bus.

[0046] Among them, the processor is mainly used for:

[0047] Acquire first power data of a preset time period of a target channel area and a target communication state of a dual-mode channel; the target channel area is any one of a channel area; the dual-mode channel includes a power line channel and a wireless channel; the communication state includes a smooth state and a blocked state;

[0048] Performing data preprocessing and feature extraction on the first power data to obtain second power data;

[0049] Performing model training according to the second power data to obtain an anomaly detection model;

[0050] Acquire meter reading information of an electric meter metering module in a preset time period corresponding to a target channel area;

[0051] Analyze the meter reading information according to the anomaly detection model to obtain a first anomaly analysis result;

[0052] If the communication state of the power line channel and / or the wireless channel in the target communication state is a blocked state, determining the target communication state as a second abnormal analysis result; determining the first target abnormal analysis result according to the first abnormal analysis result and the second abnormal analysis result;

[0053] If the communication states of the power line channel and the wireless channel in the target communication state are both unblocked, a channel transmission test is performed on the power line channel and the wireless channel to obtain a first test result; a channel switching test is performed on the power line channel and the wireless channel to obtain a second test result;

[0054] A third abnormality analysis result is determined according to the first test result and the second test result, and a second target abnormality analysis result is determined according to the first abnormality analysis result and the third abnormality analysis result.

[0055] The one or more programs are stored in the above-mentioned memory and are configured to be executed by the above-mentioned processor, and the one or more programs include instructions for executing any step in the above-mentioned method embodiment.

[0056] Among them, the processor can be, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, units and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication unit can be a communication interface, a transceiver, a transceiver circuit, etc., and the storage unit can be a memory.

[0057] The memory may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the nonvolatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus random access memory (DR RAM).

[0058] It is understandable that the electronic device may include more or fewer structural elements than those in the above structural block diagram, for example, including a power module, physical buttons, Wi-Fi module, speaker, Bluetooth module, sensor, display module, etc., which are not limited here. It is understandable that the electronic device may be equipped with Figure 1 The system architecture described.

[0059] After understanding the software and hardware architecture of this application, Figure 3 A dual-mode communication meter reading abnormality analysis method in an embodiment of the present application is described. Figure 3 It is a flow chart of a dual-mode communication meter reading abnormality analysis method provided in an embodiment of the present application, which is applied to an abnormality analysis module of a dual-mode communication system. The dual-mode communication system includes the abnormality analysis module, an electric meter measurement module and a channel area, where a is an integer greater than 1, and specifically includes the following steps:

[0060] Step S301, obtaining first power data of a preset time period in a target channel area and a target communication state of a dual-mode channel.

[0061] Among them, the target channel area is any one of the a channel areas; the dual-mode channel includes a power line channel and a wireless channel; and the communication state includes a smooth state and a blocked state.

[0062] Specifically, the preset time period includes but is not limited to one day, one month, one quarter, and one year, which are not specifically limited here. The first power data of the power system in the target channel area can be obtained, and the communication status of the dual-mode channel in the target channel area can be detected to obtain the target communication status. Among them, the attenuation of the signal during transmission on the power line channel, the packet loss rate and other indicators can be detected. If the packet loss rate is extremely low and the signal attenuation is within the normal range, it can be determined to be a smooth state. On the contrary, if the packet loss is serious and the signal cannot be transmitted normally, it is a blocked state; for wireless channels, you can check their signal strength, connection stability, data transmission success rate, etc. A strong signal and a high transmission success rate are unblocked states, and frequent disconnections and transmission failures are blocked states.

[0063] Step S302: perform data preprocessing and feature extraction on the first power data to obtain second power data.

[0064] The step of performing data preprocessing and feature extraction on the first power data to obtain the second power data specifically includes:

[0065] The first power data is cleaned and standardized to obtain reference first power data; feature extraction is performed on the reference first power data to obtain power feature data; the power feature data includes p feature sub-data; the feature sub-data includes any one of the following: electrical quantity feature, time domain feature, frequency domain feature; p is an integer greater than 1; the p feature sub-data are randomly divided into C random subsets, and C decision trees are generated according to the C random subsets; C is a positive integer less than p; the relative importance of each feature sub-data in the p feature sub-data is calculated according to the C decision trees to obtain p relative importances; the p relative importances are sorted in order from large to small to obtain a first sorting result; the feature sub-data corresponding to the first M relative importances in the first sorting result are obtained to obtain the second power data; M is a positive integer less than p.

[0066] Specifically, first, by performing data cleaning on the first power data, erroneous, repeated and incomplete power data can be removed to ensure the accuracy and completeness of the data. Then the first power data is data standardized, that is, the first power data is scaled to the interval of 0 to 1, so as to obtain the reference first power data. Feature extraction is performed on the reference first power data to obtain power feature data. Among them, the power feature data includes p feature sub-data, and the feature sub-data includes any of the following: electrical quantity features, time domain features, and frequency domain features. It should be noted that the electrical quantity features include but are not limited to the basic electrical quantity parameters such as voltage, current, active power, and reactive power in the reference first power data, which are used to intuitively reflect the basic operating state of the power system; the time domain features include but are not limited to the waveform characteristics, amplitude change trends, and periodic characteristics of the power signal corresponding to the reference first power data over time, which are used to analyze the dynamic changes of the power signal in the time dimension; the frequency domain features include but are not limited to the spectrum distribution and frequency components corresponding to the reference first power data, which are used to reflect the frequency stability of the power system.

[0067] Next, the p feature sub-data are randomly divided into C random subsets, and C decision trees are generated based on the C random subsets, so that the generated C decision trees have diversity and avoid overfitting problems in a single decision tree. Among them, a decision tree corresponding to each random subset can be generated according to a preset decision tree algorithm, thereby obtaining C decision trees. Then, the relative importance of each feature sub-data in the p feature sub-data is calculated based on the C decision trees to obtain p relative importances. The p relative importances are sorted in order from large to small to obtain a first sorting result. Finally, the feature sub-data corresponding to the first M relative importances in the first sorting result are obtained to obtain the second power data.

[0068] It can be seen that by selecting feature sub-data with higher relative importance as the second power data, the feature sub-data can be streamlined, focusing on the most critical features for power system analysis, reducing the data dimension for subsequent processing, improving calculation efficiency, and avoiding unimportant feature data from interfering with subsequent analysis results.

[0069] The step of calculating the relative importance of each of the p feature sub-data according to the C decision trees to obtain p relative importances specifically includes:

[0070] The Gini index value of the reference feature sub-data at the node m of the decision tree i is calculated according to a preset Gini index calculation formula to obtain a reference Gini index value; the reference feature sub-data is any one of the p feature sub-data; the decision tree i is the i-th decision tree among the C decision trees; the decision tree i includes S nodes, and the node m is the m-th node among the S nodes; S is an integer greater than 1;

[0071] The Gini index calculation formula is as follows:

[0072]

[0073] Among them, G ini,i,m represents the reference Gini index value; p m,k represents the ratio between the number of messages of the kth category in the node m of the decision tree i and the number of messages in the node m, each message corresponding to a reference feature sub-data and a message category;

[0074] The reference Gini index value is calculated according to a preset change calculation formula to obtain a reference change of the reference feature sub-data at the node m of the decision tree i;

[0075] The change calculation formula is as follows:

[0076] D vim,β,i,m =G ini,i,m -G imi,i,e -G imi,i,r

[0077] Among them, D vim,β,i,m represents the reference variation; β represents the reference feature sub-data; G imi,i,e represents the Gini index value of the first new node after the branching of node m of the decision tree i; G imi,i,r represents the Gini index value of the second new node after the node m of the decision tree i branches;

[0078] Importance calculation is performed on the reference variation to obtain a reference relative importance; the reference relative importance is the relative importance corresponding to the reference feature sub-data in the p relative importances.

[0079] The step of calculating the importance of the reference variation to obtain the reference relative importance comprises:

[0080] Calculating the reference change amount according to a preset first importance calculation formula to obtain a reference first importance;

[0081] The first importance calculation formula is as follows:

[0082]

[0083] Among them, D vim,β represents the first importance of the reference;

[0084] Calculate the reference first importance according to a preset second importance calculation formula to obtain the reference relative importance;

[0085] The second importance calculation formula is as follows:

[0086]

[0087] in, Indicates the relative importance of the reference; D vim.n Indicates the first importance of the nth reference; represents the sum of the first importance of the p references.

[0088] It can be seen that by calculating the relative importance of different feature data and screening out key feature data with higher relative importance for subsequent processing, it is convenient to improve the performance and accuracy of the anomaly detection model, thereby ensuring the safe and stable operation of the power system.

[0089] Step S303: Perform model training according to the second power data to obtain an anomaly detection model.

[0090] The step of performing model training according to the second power data to obtain an anomaly detection model specifically includes:

[0091] Obtain a preset first model; the first model includes a filtering network and a classification network; obtain x model parameter combinations corresponding to the first model; each model parameter combination includes a filtering network parameter and a classification network parameter; x is an integer greater than 1; configure the first model according to the filtering network parameters and the classification network parameters of each model parameter combination in the x model parameter combinations to obtain x second models; perform feature extraction on the second power data through the filtering network of each second model in the x second models to obtain x target feature data; perform feature classification on the corresponding target feature data in the x target feature data through the classification network of each second model in the x second models to obtain x classification results; evaluate the x classification results according to a preset loss function to obtain x loss function values; each loss function value corresponds to a second model; determine that the second model corresponding to the minimum loss function value in the x loss function values ​​is the abnormality detection model.

[0092] Specifically, first, a preset first model is obtained, which is a one-dimensional convolutional neural network model. The first model includes a filtering network and a classification network, the filtering network includes a convolution layer and a pooling layer, and the classification network includes a fully connected layer and a Softmax layer. Among them, the Softmax layer refers to an activation function layer commonly used in multi-classification tasks, and its main function is to convert the output of the model into a probability distribution, so that the predicted value of each category is between 0 and 1, and the probability sum of all categories is 1. Then, x model parameter combinations corresponding to the first model are obtained, and each model parameter combination includes a filtering network parameter and a classification network parameter. Among them, the filtering network parameters include but are not limited to the convolution kernel size, the number of convolution kernels, and the pooling size, and the classification network parameters include but are not limited to the number of neurons and the activation function, which are not specifically limited here. Then, the first model is configured according to the filtering network parameters and the classification network parameters of each model parameter combination in the x model parameter combinations to obtain x second models. The second power data is feature extracted by the filtering network of each second model to obtain x target feature data, and then the corresponding target feature data is classified by the classification network of each second model to obtain x classification results. The x classification results are evaluated according to the preset loss function to obtain x loss function values. Each loss function value corresponds to a second model, and the loss function includes but is not limited to the mean square error loss function and the cross entropy loss function, which are not specifically limited here. Finally, the second model corresponding to the minimum loss function value among the x loss function values ​​is determined as the anomaly detection model.

[0093] It can be seen that by selecting the second model corresponding to the minimum loss function value as the anomaly detection model, the operating status of the power system can be detected more accurately, and abnormal situations can be discovered in time, providing strong guarantees for the stable operation, fault warning and rapid maintenance of the power system.

[0094] In a possible embodiment, the second power data is input as an input sample into the first model, that is, a one-dimensional convolutional neural network model, wherein the first layer is a convolutional layer, and the one-dimensional convolution operation formula of the convolutional layer is as follows:

[0095]

[0096] in, represents the vector obtained after the j-th convolution calculation of layer l; P represents the number of input feature vectors; represents the d-th input feature vector of layer l; Represents related operations; represents the jth convolution kernel of layer l convolved with the dth input feature vector; Represents the j-th bias vector of layer l.

[0097] In this convolutional layer, the Rectified Linear Unit (ReLU) is used as the activation function. This activation function has no gradient dissipation problem, has a faster convergence speed, and can improve the sparsity of the network and effectively prevent overfitting problems. The definition of this activation function is as follows:

[0098]

[0099] in, represents the e-th output after the j-th convolution operation of layer l; express The activation value of .

[0100] Next, the feature information of the second power data is dimensionally simplified through the pooling layer, while retaining the most representative feature information to reduce the computational cost and prevent overfitting. The pooling operation can be performed by using the maximum value of the local receptive field as the output, or by using the mean pooling operation by using the mean of the local receptive field as the output, which is not specifically limited here. Among them, the l+1th layer is the pooling layer, and the calculation formula corresponding to its maximum pooling is as follows:

[0101]

[0102] in, represents the activation value of the hth neuron of the jth feature vector in layer l; w represents the width of the pooling area; Represents the pooling result corresponding to the g-th neuron of the j-th feature vector in the l+1 layer.

[0103] Among them, the classification network includes a fully connected layer and a Softmax layer. The fully connected layer flattens the output of the pooling layer into a one-dimensional feature vector, and fully connects the head and tail. The ReLU activation function is applied to the output of the fully connected layer, so that the neural network can learn nonlinear relationships and enhance the network's expressive power. The Softmax layer is used for multi-classification problems and can convert the results into interpretable category probabilities. Assuming the category label y∈{1,2,…,R}, the calculation formula for the conditional probability that sample x belongs to category r is as follows:

[0104]

[0105] Among them, p(y=r|x; θ) represents the conditional probability that sample x belongs to category r; θ represents the set of weight vectors, including weight vectors of all categories; r represents category r; θ r Represents the weight vector of category r; θ j Represents the weight vector of category j; Represents the vector θ rThe dot product of the input sample x is the linear discriminant score of the category; Represents the vector θ j The dot product of the input sample x; express The exponential function is used to convert the score into a positive number to avoid negative probability values; express R is the total number of categories.

[0106] Finally, the loss function value of the model is calculated by the cross entropy loss function, which is as follows:

[0107]

[0108] Among them, U represents the total number of samples; I{y u =r} represents the indicator function, when the sample x u The true label y u When I{y u =r}=1, otherwise I{y u =r}=0.

[0109] In order to minimize the loss function value of the model, it is necessary to optimize the weights of the neural network, and the optimizer uses the back propagation algorithm to complete it. The corresponding calculation formula is as follows:

[0110]

[0111] Among them, θ * represents the optimal model parameters corresponding to the second model, that is, the model parameters corresponding to the anomaly detection model, which include filtering network parameters and classification network parameters; L(f(x; θ), y) represents the loss function; f(x; θ) represents the output value of the model; y represents the target value of the model.

[0112] It can be seen that by training and optimizing the anomaly detection model, the accuracy and reliability of anomaly analysis of meter reading information can be improved, which makes it easier to combine historical data and expert knowledge to analyze the causes of anomalies and provide corresponding solutions and suggestions.

[0113] Step S304: acquiring meter reading information of the electric meter measuring module in the preset time period corresponding to the target channel area.

[0114] Specifically, the meter reading information includes, but is not limited to, electricity consumption data, power data, voltage and current data, power factor, and timestamp information, which are not specifically limited here.

[0115] Step S305: Analyze the meter reading information according to the abnormality detection model to obtain a first abnormality analysis result.

[0116] Specifically, the meter reading information can be input into the anomaly detection model, and the data in the meter reading information can be analyzed and detected by the anomaly detection model, so as to obtain the corresponding first anomaly analysis result. If the meter reading information is consistent with the normal mode of power data, the first anomaly analysis result is determined to be that the meter reading information is in a normal state. If the meter reading information does not conform to the normal mode, the first anomaly analysis result is determined to be that the meter reading information is in an abnormal state, and the corresponding anomaly type is determined according to the abnormal state. For example, if the power value in the meter reading information is much higher than the normal power value range, the anomaly type is determined to be a power overload anomaly; if the power value is much lower than the normal power value range, the anomaly type is determined to be a power underload anomaly, wherein the anomaly type includes but is not limited to power anomaly, voltage anomaly or power consumption anomaly, which is not specifically limited here.

[0117] Step S306: if the communication state of the power line channel and / or the wireless channel in the target communication state is the blocking state, determine the target communication state as the second abnormal analysis result; determine the first target abnormal analysis result according to the first abnormal analysis result and the second abnormal analysis result.

[0118] Specifically, if the communication state of the power line channel and / or the wireless channel in the target communication state is a blocked state, the target communication state is determined to be a second abnormal analysis result, and the first abnormal analysis result and the second abnormal analysis result are integrated to obtain a first target abnormal analysis result. The first target abnormal analysis result can provide maintenance personnel with comprehensive abnormal analysis information, which is convenient for quickly judging the complexity and urgency of the abnormality, so as to promptly execute corresponding treatment measures.

[0119] In a possible embodiment, the test signal can be modulated and demodulated to the frequency band corresponding to the power line channel, and then sent to the power line channel, and the meter is read based on the routing table in the metering module. If the meter reading is successful, the communication state of the power line channel is determined to be unblocked, and if the meter reading fails, the communication state of the power line channel is determined to be blocked. The communication channel can also be switched to a wireless channel, and the test signal can be sent through the wireless channel. If the test signal is sent successfully, the communication state of the wireless channel is determined to be unblocked, and if the test signal fails to be sent, the communication state of the wireless channel is determined to be blocked.

[0120] It can be seen that by conducting targeted tests on the power line channel and the wireless channel respectively, the communication status of the dual-mode communication system can be fully and accurately grasped, and the reliability and performance of the dual-mode communication system can be improved.

[0121] Step S307: If the communication states of the power line channel and the wireless channel in the target communication state are both the unblocked states, a channel transmission test is performed on the power line channel and the wireless channel to obtain a first test result; and a channel switching test is performed on the power line channel and the wireless channel to obtain a second test result.

[0122] The target channel area includes b dual-mode communication modules, where b is an integer greater than 1; the first test result includes b reference first test results; each reference first test result corresponds to a dual-mode communication module; the channel transmission test is performed on the power line channel and the wireless channel to obtain the first test result, and the specific steps include:

[0123] The first dual-mode communication module transmits a preset first test message to a target node through the power line channel and the wireless channel respectively, so that the target node receives the first test message through the power line channel and generates a first response message to transmit to the first dual-mode communication module, and receives the first test message through the wireless channel and generates a second response message to transmit to the first dual-mode communication module; the first dual-mode communication module is any one of the b dual-mode communication modules; obtains the sending time of the first test message, the first receiving time of the first response message, and the second receiving time of the second response message; determines the first receiving time and the second receiving time of the first test message. The difference between the sending time is the first transmission duration; the difference between the second receiving time and the sending time is determined to be the second transmission duration; the first test message is compared with the first response message and the second response message respectively to obtain a first bit error rate and a second bit error rate; if the first transmission duration is less than a preset first transmission duration threshold, the second transmission duration is less than a preset second transmission duration threshold, and the first bit error rate and the second bit error rate are both less than a preset bit error rate threshold, then the first state of the first dual-mode communication module is determined to be a normal state; otherwise, the first state is determined to be an abnormal state; the first state is determined to be a reference first test result corresponding to the first dual-mode communication module.

[0124] In a possible embodiment, the transmission duration and bit error rate of the dual-mode communication module in the channel area can be analyzed, and the dual-mode communication module can be classified according to its transmission duration and bit error rate according to a preset cluster analysis algorithm, thereby obtaining multiple module sets. For a module set with a higher bit error rate or a longer transmission duration, the test frequency of the module set can be increased to collect more data samples in order to more accurately evaluate its performance issues; for a module set with a lower bit error rate or a lower transmission duration, the test frequency of the module set can be reduced to save test resources.

[0125] The second test result includes a plurality of reference second test results; each reference second test result corresponds to a dual-mode communication module; the channel switching test is performed on the power line channel and the wireless channel to obtain the second test result, and the specific steps include:

[0126] Determine that the first dual-mode communication module is in the first working mode; the first working mode is that the power line channel is turned on and the wireless channel is turned off; transmit the preset second test message through the power line channel through the first dual-mode communication module, and record the third reception time of the third response message; switch the channel of the first dual-mode communication module so that the first dual-mode communication module is in the second working mode; the second working mode is that the power line channel is turned off and the wireless channel is turned on; transmit the second test message through the wireless channel through the first dual-mode communication module, and record the fourth reception time of the fourth response message; obtain the reference transmission duration of the wireless channel; determine the difference between the fourth reception time and the third reception time as the first transmission duration; determine the difference between the first transmission duration and the reference transmission duration as the first switching delay; if the first switching delay is less than the preset switching delay threshold, determine that the second state of the first dual-mode communication module is a normal state; otherwise, determine that the second state is an abnormal state; determine that the second state is a reference second test result corresponding to the first dual-mode communication module.

[0127] In a possible embodiment, the first dual-mode communication module can also be set to the second working mode, that is, the power line channel is closed and the wireless channel is turned on. Then, the second switching delay of the first dual-mode communication module from the second working mode to the first working mode is calculated. It should be noted that the working mode switching is repeated multiple times on the first dual-communication module to obtain multiple second switching delays, and the average delay is determined according to the average value of the multiple second switching delays, and the channel switching performance of the first dual-mode communication model is evaluated according to the average delay. Among them, the success rate of the first dual-mode communication model in multiple working mode switching processes can also be counted to evaluate the stability and reliability of the first dual-mode communication model.

[0128] In a possible embodiment, each of the b dual-mode communication modules can be detected, for example, by determining whether the dual-mode communication module is online through a preset heartbeat packet mechanism, that is, each dual-mode communication module sends a heartbeat packet to the concentrator or main station of the dual-mode communication system every 1 minute, and the concentrator or main station monitors the reception of the heartbeat packet. If the concentrator or main station does not receive the heartbeat packet of a dual-mode communication module for more than 3 consecutive minutes, it is considered that the dual-mode communication module is not online, that is, the dual-mode communication module is in an abnormal state. The log record of the dual-mode communication module can also be obtained, and the log record is analyzed to obtain the abnormal situation that occurs in the dual-mode communication module during operation, and the log record includes various events such as startup, restart, error, communication abnormality, etc. Among them, the dual-mode communication module can be updated with firmware, the corresponding abnormal problems can be repaired, and the stability and correctness of the dual-mode communication module can be improved.

[0129] It can be seen that detecting and updating the dual-mode communication module can greatly improve the reliability, stability and performance of the dual-mode communication module, ensure the normal communication function of the dual-mode communication system, and reduce various risks and losses caused by dual-mode communication module failures.

[0130] Step S308: determining a third abnormality analysis result according to the first test result and the second test result, and determining a second target abnormality analysis result according to the first abnormality analysis result and the third abnormality analysis result.

[0131] Specifically, the first test result and the second test result are integrated to obtain a third abnormal analysis result. The first test result is used to represent the channel transmission test results of the power line channel and the wireless channel, and the second test result is used to represent the channel switching test results of the power line channel and the wireless channel. The first abnormal analysis result and the third abnormal analysis result are then integrated to obtain a second target abnormal analysis result.

[0132] It can be seen that through the above method, the power data and communication status can be comprehensively considered, and abnormal fault conditions in the dual-mode communication process can be quickly and accurately identified, thereby improving the stability and reliability of the power system.

[0133] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0134] The embodiment of the present application can divide the electronic device into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0135] In the case of dividing each functional module into corresponding functional modules, Figure 4 It is a functional module composition block diagram of a dual-mode communication meter reading anomaly analysis device provided in an embodiment of the present application, which is applied to an anomaly analysis module of a dual-mode communication system. The dual-mode communication system includes the anomaly analysis module, an electric meter measurement module and a channel area, where a is an integer greater than 1. The dual-mode communication meter reading anomaly analysis device 400 includes an acquisition module 410, a processing module 420, a training module 430, an analysis module 440, a determination module 450 and a test module 460, wherein:

[0136] The acquisition module 410 is used to acquire the first power data of the preset time period of the target channel area and the target communication state of the dual-mode channel; the target channel area is any one of the a channel areas; the dual-mode channel includes a power line channel and a wireless channel; the communication state includes a smooth state and a blocked state;

[0137] The processing module 420 is used to perform data preprocessing and feature extraction on the first power data to obtain second power data;

[0138] The training module 430 is used to perform model training according to the second power data to obtain an anomaly detection model;

[0139] The acquisition module 410 is further used to acquire meter reading information of the electric meter measurement module in the preset time period corresponding to the target channel area;

[0140] The analysis module 440 is used to analyze the meter reading information according to the abnormality detection model to obtain a first abnormality analysis result;

[0141] The determination module 450 is configured to determine that the target communication state is a second abnormal analysis result if the communication state of the power line channel and / or the wireless channel in the target communication state is the blocked state; and determine a first target abnormal analysis result according to the first abnormal analysis result and the second abnormal analysis result;

[0142] The test module 460 is configured to perform a channel transmission test on the power line channel and the wireless channel to obtain a first test result if the communication states of the power line channel and the wireless channel in the target communication state are both in the unblocked state; and perform a channel switching test on the power line channel and the wireless channel to obtain a second test result;

[0143] The determination module 450 is further configured to determine a third abnormality analysis result according to the first test result and the second test result, and to determine a second target abnormality analysis result according to the first abnormality analysis result and the third abnormality analysis result.

[0144] Optionally, in the aspect of performing data preprocessing and feature extraction on the first power data to obtain the second power data, the processing module 420 specifically includes:

[0145] performing data cleaning and data standardization on the first power data to obtain reference first power data;

[0146] Extracting features from the reference first power data to obtain power feature data; the power feature data includes p feature sub-data; the feature sub-data includes any one of the following: electrical quantity features, time domain features, and frequency domain features; p is an integer greater than 1;

[0147] The p feature sub-data are randomly divided into C random subsets, and C decision trees are generated according to the C random subsets; C is a positive integer less than p;

[0148] Calculate the relative importance of each feature sub-data in the p feature sub-data according to the C decision trees to obtain p relative importances;

[0149] Sorting the p relative importances in descending order to obtain a first sorting result;

[0150] The characteristic sub-data corresponding to the first M relative importances in the first sorting result are obtained to obtain the second power data; M is a positive integer less than p.

[0151] Optionally, in the aspect of calculating the relative importance of each feature sub-data in the p feature sub-data according to the C decision trees to obtain p relative importances, the processing module 420 specifically further includes:

[0152] The Gini index value of the reference feature sub-data at the node m of the decision tree i is calculated according to a preset Gini index calculation formula to obtain a reference Gini index value; the reference feature sub-data is any one of the p feature sub-data; the decision tree i is the i-th decision tree among the C decision trees; the decision tree i includes S nodes, and the node m is the m-th node among the S nodes; S is an integer greater than 1;

[0153] The Gini index calculation formula is as follows:

[0154]

[0155] Among them, G ini,i,m represents the reference Gini index value; p m,k represents the ratio between the number of messages of the kth category in the node m of the decision tree i and the number of messages in the node m, each message corresponding to a reference feature sub-data and a message category;

[0156] The reference Gini index value is calculated according to a preset change calculation formula to obtain a reference change of the reference feature sub-data at the node m of the decision tree i;

[0157] The change calculation formula is as follows:

[0158] D vim,β,i,m =G ini,i,m -G imi,i,e -G imi,i,r

[0159] Among them, D vim,β,i,m represents the reference variation; β represents the reference feature sub-data; G imi,i,e represents the Gini index value of the first new node after the branching of node m of the decision tree i; G imi,i,r represents the Gini index value of the second new node after the node m of the decision tree i branches;

[0160] Importance calculation is performed on the reference variation to obtain a reference relative importance; the reference relative importance is the relative importance corresponding to the reference feature sub-data in the p relative importances.

[0161] Optionally, in calculating the importance of the reference variation to obtain the reference relative importance, the processing module 420 specifically further includes:

[0162] Calculating the reference change amount according to a preset first importance calculation formula to obtain a reference first importance;

[0163] The first importance calculation formula is as follows:

[0164]

[0165] Among them, D vim,β represents the first importance of the reference;

[0166] Calculate the reference first importance according to a preset second importance calculation formula to obtain the reference relative importance;

[0167] The second importance calculation formula is as follows:

[0168]

[0169] in, Indicates the relative importance of the reference; D vim.n Indicates the first importance of the nth reference; represents the sum of the first importance of the p references.

[0170] Optionally, in the aspect of performing model training according to the second power data to obtain an anomaly detection model, the training module 430 specifically includes:

[0171] Obtaining a preset first model; the first model includes a filtering network and a classification network;

[0172] Obtain x model parameter combinations corresponding to the first model; each model parameter combination includes a filtering network parameter and a classification network parameter; x is an integer greater than 1;

[0173] The first model is configured according to the filtering network parameters and the classification network parameters of each model parameter combination in the x model parameter combinations to obtain x second models;

[0174] Extracting features from the second power data through a filter network of each of the x second models to obtain x target feature data;

[0175] Performing feature classification on corresponding target feature data among the x target feature data by using a classification network of each second model among the x second models to obtain x classification results;

[0176] Evaluate the x classification results according to a preset loss function to obtain x loss function values; each loss function value corresponds to a second model;

[0177] Determine that a second model corresponding to the minimum loss function value among the x loss function values ​​is the anomaly detection model.

[0178] Optionally, the target channel area includes b dual-mode communication modules, where b is an integer greater than 1; the first test result includes b reference first test results; each reference first test result corresponds to a dual-mode communication module; in terms of performing a channel transmission test on the power line channel and the wireless channel to obtain the first test result, the test module 460 specifically includes:

[0179] The preset first test message is transmitted to the target node through the power line channel and the wireless channel respectively through the first dual-mode communication module, so that the target node receives the first test message through the power line channel and generates a first response message to transmit to the first dual-mode communication module, and receives the first test message through the wireless channel and generates a second response message to transmit to the first dual-mode communication module; the first dual-mode communication module is any one of the b dual-mode communication modules;

[0180] Obtaining a sending time of the first test message, a first receiving time of the first response message, and a second receiving time of the second response message;

[0181] Determine a difference between the first receiving time and the sending time as a first transmission duration;

[0182] Determine the difference between the second receiving time and the sending time as a second transmission duration;

[0183] Compare the first test message with the first response message and the second response message respectively to obtain a first bit error rate and a second bit error rate;

[0184] If the first transmission duration is less than a preset first transmission duration threshold, the second transmission duration is less than a preset second transmission duration threshold, and the first bit error rate and the second bit error rate are both less than a preset bit error rate threshold, then the first state of the first dual-mode communication module is determined to be a normal state; otherwise, the first state is determined to be an abnormal state;

[0185] Determine that the first state is a reference first test result corresponding to the first dual-mode communication module.

[0186] Optionally, the second test result includes a plurality of reference second test results; each reference second test result corresponds to a dual-mode communication module; in terms of performing a channel switching test on the power line channel and the wireless channel to obtain the second test result, the test module 460 specifically further includes:

[0187] Determining that the first dual-mode communication module is in a first working mode; the first working mode is that the power line channel is turned on and the wireless channel is turned off;

[0188] Transmitting a preset second test message through the power line channel by means of the first dual-mode communication module, and recording a third receiving time of a third response message;

[0189] Switching the channel of the first dual-mode communication module so that the first dual-mode communication module is in a second working mode; the second working mode is that the power line channel is closed and the wireless channel is opened;

[0190] Transmitting the second test message through the wireless channel by means of the first dual-mode communication module, and recording a fourth receiving time of a fourth response message;

[0191] Acquiring a reference transmission duration of the wireless channel;

[0192] Determine a difference between the fourth receiving time and the third receiving time as a first transmission duration;

[0193] Determine a difference between the first transmission duration and the reference transmission duration as a first switching delay;

[0194] If the first switching delay is less than a preset switching delay threshold, determining that the second state of the first dual-mode communication module is a normal state; otherwise, determining that the second state is an abnormal state;

[0195] Determine that the second state is a reference second test result corresponding to the first dual-mode communication module.

[0196] It can be seen that by comprehensively considering the power data and communication status, abnormal fault conditions in the dual-mode communication process can be quickly and accurately identified, thereby improving the stability and reliability of the power system.

[0197] It should be noted that the specific implementation of each operation can adopt the corresponding description of the method embodiment shown above, and the dual-mode communication meter reading abnormality analysis device 400 can be used to execute the above method embodiment of the present application, which will not be described in detail.

[0198] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes an electronic device.

[0199] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0200] It should be noted that, for the above-mentioned various embodiments, for the sake of simple description, they are all expressed as a series of action combinations. Those skilled in the art should be aware that the present application is not limited by the described order of actions, because some steps in the embodiments of the present application can be performed in other orders or simultaneously. In addition, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions, steps, modules or units involved are not necessarily required by the embodiments of the present application.

[0201] In the above embodiments, the embodiments of the present application have different focuses on the description of each embodiment. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0202] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

[0203] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by executing software instructions by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (electrically EPROM, EEPROM), registers, hard disks, mobile hard disks, read-only compact disks (CD-ROMs) or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also be present in a terminal device or a management device as discrete components.

[0204] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiments of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0205] The modules / units included in the devices and products described in the above embodiments may be software modules / units or hardware modules / units, or may be partially software modules / units and partially hardware modules / units. For example, for the devices and products applied to or integrated in the chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least some of the modules / units may be implemented in the form of software programs, which run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for the devices and products applied to or integrated in the chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or in different components of the chip module, or at least some of the modules / units may be implemented in the form of software programs. The software programs run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits. It is implemented in the form of a software program, which runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or in different components in the terminal equipment, or, at least some modules / units can be implemented in the form of a software program, which runs on a processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in hardware such as circuits.

[0206] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific implementation method of the embodiments of the present application and is not intended to limit the protection scope of the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A dual-mode communication meter reading abnormality analysis method, characterized in that: An abnormality analysis module applied to a dual-mode communication system, the dual-mode communication system comprising the abnormality analysis module, an electric meter measurement module and a channel areas, a being an integer greater than 1, the method comprising: Acquire first power data of a preset time period of a target channel area and a target communication state of a dual-mode channel; the target channel area is any one of the a channel areas; the dual-mode channel includes a power line channel and a wireless channel; the communication state includes a smooth state and a blocked state; Performing data preprocessing and feature extraction on the first power data to obtain second power data; Performing model training according to the second power data to obtain an anomaly detection model; Acquire meter reading information of the electric meter metering module in the preset time period corresponding to the target channel area; Analyze the meter reading information according to the abnormality detection model to obtain a first abnormality analysis result; If the communication state of the power line channel and / or the wireless channel in the target communication state is the blocking state, determining the target communication state as a second abnormal analysis result; determining a first target abnormal analysis result according to the first abnormal analysis result and the second abnormal analysis result; If the communication states of the power line channel and the wireless channel in the target communication state are both the unblocked state, a channel transmission test is performed on the power line channel and the wireless channel to obtain a first test result; a channel switching test is performed on the power line channel and the wireless channel to obtain a second test result; A third abnormality analysis result is determined according to the first test result and the second test result, and a second target abnormality analysis result is determined according to the first abnormality analysis result and the third abnormality analysis result.

2. The method according to claim 1, characterized in that The performing data preprocessing and feature extraction on the first power data to obtain second power data includes: performing data cleaning and data standardization on the first power data to obtain reference first power data; Extracting features from the reference first power data to obtain power feature data; the power feature data includes p feature sub-data; the feature sub-data includes any one of the following: electrical quantity features, time domain features, and frequency domain features; p is an integer greater than 1; The p feature sub-data are randomly divided into C random subsets, and C decision trees are generated according to the C random subsets; C is a positive integer less than p; Calculate the relative importance of each feature sub-data in the p feature sub-data according to the C decision trees to obtain p relative importances; Sorting the p relative importances in descending order to obtain a first sorting result; The characteristic sub-data corresponding to the first M relative importances in the first sorting result are obtained to obtain the second power data; M is a positive integer less than p.

3. The method according to claim 2, characterized in that The step of calculating the relative importance of each of the p feature sub-data according to the C decision trees to obtain p relative importances includes: The Gini index value of the reference feature sub-data at the node m of the decision tree i is calculated according to a preset Gini index calculation formula to obtain a reference Gini index value; the reference feature sub-data is any one of the p feature sub-data; the decision tree i is the i-th decision tree among the C decision trees; the decision tree i includes S nodes, and the node m is the m-th node among the S nodes; S is an integer greater than 1; The Gini index calculation formula is as follows: Among them, G ini,o,m represents the reference Gini index value; p m,k represents the ratio between the number of messages of the kth category in the node m of the decision tree i and the number of messages in the node m, each message corresponding to a reference feature sub-data and a message category; The reference Gini index value is calculated according to a preset change calculation formula to obtain a reference change of the reference feature sub-data at the node m of the decision tree i; The change calculation formula is as follows: D vim,β,i,m =G ini,i,m -G imi,i,e -G imi,i,r Among them, D vim,β,i,m represents the reference variation; β represents the reference feature sub-data; G imi,i,e represents the Gini index value of the first new node after the branching of node m of the decision tree i; G imi,i,r represents the Gini index value of the second new node after the node m of the decision tree i branches; Importance calculation is performed on the reference variation to obtain a reference relative importance; the reference relative importance is the relative importance corresponding to the reference feature sub-data in the p relative importances.

4. The method according to claim 3, characterized in that The calculating the importance of the reference variation to obtain the reference relative importance includes: Calculating the reference change amount according to a preset first importance calculation formula to obtain a reference first importance; The first importance calculation formula is as follows: Among them, D vim,β represents the first importance of the reference; Calculate the reference first importance according to a preset second importance calculation formula to obtain the reference relative importance; The second importance calculation formula is as follows: in, Indicates the relative importance of the reference; D vim.n Indicates the first importance of the nth reference; represents the sum of the first importance of the p references.

5. The method according to any one of claims 1 to 4, characterized in that: The performing model training according to the second power data to obtain an anomaly detection model includes: Obtaining a preset first model; the first model includes a filtering network and a classification network; Obtain x model parameter combinations corresponding to the first model; each model parameter combination includes a filtering network parameter and a classification network parameter; x is an integer greater than 1; The first model is configured according to the filtering network parameters and the classification network parameters of each model parameter combination in the x model parameter combinations to obtain x second models; Extracting features from the second power data through a filter network of each of the x second models to obtain x target feature data; Performing feature classification on corresponding target feature data among the x target feature data by using a classification network of each second model among the x second models to obtain x classification results; Evaluate the x classification results according to a preset loss function to obtain x loss function values; each loss function value corresponds to a second model; Determine that a second model corresponding to the minimum loss function value among the x loss function values ​​is the anomaly detection model.

6. The method according to claim 1, characterized in that The target channel area includes b dual-mode communication modules, where b is an integer greater than 1; the first test result includes b reference first test results; Each reference first test result corresponds to a dual-mode communication module; The performing a channel transmission test on the power line channel and the wireless channel to obtain a first test result includes: The preset first test message is transmitted to the target node through the power line channel and the wireless channel respectively through the first dual-mode communication module, so that the target node receives the first test message through the power line channel and generates a first response message to transmit to the first dual-mode communication module, and receives the first test message through the wireless channel and generates a second response message to transmit to the first dual-mode communication module; the first dual-mode communication module is any one of the b dual-mode communication modules; Obtaining a sending time of the first test message, a first receiving time of the first response message, and a second receiving time of the second response message; Determine the difference between the first receiving time and the sending time as a first transmission duration; Determine the difference between the second receiving time and the sending time as a second transmission duration; Compare the first test message with the first response message and the second response message respectively to obtain a first bit error rate and a second bit error rate; If the first transmission duration is less than a preset first transmission duration threshold, the second transmission duration is less than a preset second transmission duration threshold, and the first bit error rate and the second bit error rate are both less than a preset bit error rate threshold, then the first state of the first dual-mode communication module is determined to be a normal state; otherwise, the first state is determined to be an abnormal state; Determine that the first state is a reference first test result corresponding to the first dual-mode communication module.

7. The method according to claim 6, characterized in that The second test result includes a plurality of reference second test results; Each reference second test result corresponds to a dual-mode communication module; The performing a channel switching test on the power line channel and the wireless channel to obtain a second test result includes: Determining that the first dual-mode communication module is in a first working mode; the first working mode is that the power line channel is turned on and the wireless channel is turned off; Transmitting a preset second test message through the power line channel by means of the first dual-mode communication module, and recording a third receiving time of a third response message; Switching the channel of the first dual-mode communication module so that the first dual-mode communication module is in a second working mode; the second working mode is that the power line channel is closed and the wireless channel is opened; Transmitting the second test message through the wireless channel by means of the first dual-mode communication module, and recording a fourth receiving time of a fourth response message; Acquiring a reference transmission duration of the wireless channel; Determine a difference between the fourth receiving time and the third receiving time as a first transmission duration; Determine a difference between the first transmission duration and the reference transmission duration as a first switching delay; If the first switching delay is less than a preset switching delay threshold, determining that the second state of the first dual-mode communication module is a normal state; otherwise, determining that the second state is an abnormal state; Determine that the second state is a reference second test result corresponding to the first dual-mode communication module.

8. A dual-mode communication meter reading abnormality analysis device, characterized in that: An abnormality analysis module applied to a dual-mode communication system, the dual-mode communication system comprising the abnormality analysis module, an electric meter measurement module and a channel area, a is an integer greater than 1, the device comprises an acquisition module, a processing module, a training module, an analysis module, a determination module and a testing module, wherein: The acquisition module is used to acquire the first power data of the preset time period of the target channel area and the target communication state of the dual-mode channel; the target channel area is any one of the a channel areas; the dual-mode channel includes a power line channel and a wireless channel; the communication state includes a smooth state and a blocked state; The processing module is used to perform data preprocessing and feature extraction on the first power data to obtain second power data; The training module is used to perform model training according to the second power data to obtain an anomaly detection model; The acquisition module is further used to acquire meter reading information of the electric meter measurement module in the preset time period corresponding to the target channel area; The analysis module is used to analyze the meter reading information according to the abnormality detection model to obtain a first abnormality analysis result; The determination module is configured to determine that the target communication state is a second abnormal analysis result if the communication state of the power line channel and / or the wireless channel in the target communication state is the blocked state; and determine a first target abnormal analysis result according to the first abnormal analysis result and the second abnormal analysis result; The test module is configured to, if the communication states of the power line channel and the wireless channel in the target communication state are both in the unblocked state, perform a channel transmission test on the power line channel and the wireless channel to obtain a first test result; and perform a channel switching test on the power line channel and the wireless channel to obtain a second test result; The determination module is further used to determine a third abnormality analysis result according to the first test result and the second test result, and to determine a second target abnormality analysis result according to the first abnormality analysis result and the third abnormality analysis result.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs comprising instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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