Intelligent Anti-Interference Method and System for Power Consumption Collection in Heterogeneous Network Environments

Abstract

The present invention discloses an intelligent anti-interference method for power consumption collection in a heterogeneous network environment, comprising the following steps: S1 Information of power consumption collection nodes is collected through a heterogeneous network to obtain power consumption collection information; S2 A feature data set is constructed and a BP neural network is trained until convergence; S3 Power collection information is obtained in real time, and after data preprocessing, it is sent into the trained network to obtain a decision result; S4 An engine determines whether decision parameters meet current requirements according to the feedback transmission performance, and makes real-time dynamic adjustments according to the judgment result to resist interference. A system for implementing this method is also disclosed, belonging to the technical field of power data collection. After training is completed, the neural network can receive the processed signal in real time and quickly generate a decision result in a complex environment, realizing dynamic optimization adjustment of system parameters to effectively suppress interference, so as to ensure that the system can still operate stably and achieve efficient data transmission in a complex interference environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of power data acquisition, and specifically relates to an intelligent anti-interference method and system for power consumption acquisition in a heterogeneous network environment. Background Art

[0002] With the transformation and upgrade of the smart grid, the power information acquisition system, as an important part of the smart grid, undertakes the important task of collecting and transmitting power information in real time and accurately. However, in a heterogeneous network environment, power information acquisition devices often face various interference problems, such as electromagnetic interference, communication channel congestion, and data loss and error codes caused by complex network environments, which affect the reliability and accuracy of power consumption information acquisition. Therefore, improving the anti-interference ability of the power system is crucial for ensuring power supply quality and stable operation. Summary of the Invention

[0003] Object of the Invention: The present invention makes improvements to the problems existing in the above-mentioned prior art, that is, the present invention discloses an intelligent anti-interference method and system for power consumption acquisition in a heterogeneous network environment, which is used to effectively improve the accuracy and stability of data acquisition in a heterogeneous network environment.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] In a first aspect, an intelligent anti-interference method for power consumption acquisition in a heterogeneous network environment includes the following steps:

[0006] S1 Construct a heterogeneous network through dual-mode communication of broadband power line carrier and micro-power wireless transmission, construct a power consumption network according to historical power consumption information, set power consumption acquisition nodes according to the power consumption network information, and collect information from the power consumption acquisition nodes through the heterogeneous network to obtain power consumption acquisition information;

[0007] S2 Continuously detect the received signal, analyze the signal spectrum to identify the interference type and characteristic parameters, construct a characteristic data set, and train the BP neural network until convergence;

[0008] S3 Real-time obtain power collection information, and send it to the training network after data preprocessing to obtain a decision result;

[0009] S4 The engine judges whether the decision parameters meet the current requirements according to the feedback transmission performance, and makes real-time dynamic adjustments according to the judgment result to resist interference.

[0010] Preferably, in step S1, it specifically includes:

[0011] S11 Construct a heterogeneous network through dual-mode communication of broadband power line carrier and micro-power wireless transmission, and the heterogeneous network includes multiple signal acquisition and transmission channels;

[0012] S12 Obtain historical power consumption information, construct a power consumption collection network based on the historical power consumption information, and determine the power consumption collection nodes in the power consumption collection network;

[0013] S13 According to the connection relationship between the power consumption collection nodes and each channel in the heterogeneous network, label the collection nodes in the heterogeneous network to obtain the labeled collection nodes;

[0014] S14 Obtain the preset collection channels corresponding to the labeled collection nodes in the heterogeneous network, collect power consumption information from the labeled collection nodes, and obtain power consumption collection information.

[0015] Preferably, in the step S2, it specifically includes:

[0016] S21 Detect the received signal multiple times through spectrum analysis and energy detection algorithms, analyze the signal spectrum to identify the interference type and characteristic parameters.

[0017] S22 Use the interference type, signal-to-interference ratio, number of available frequency sub-bands, and interference suppression method as inputs to obtain the system bit error rate and average information rate in different situations, and construct a training sample library;

[0018] S23 Use the sample library to train the BP neural network until convergence.

[0019] Preferably, in the step S3, it specifically includes:

[0020] S31 Through the heterogeneous network, obtain power information in real time and perform normalization processing on it to ensure that the data is within a reasonable range;

[0021] S32 The input layer of the BP neural network receives the preprocessed interference information and power parameters, and the output layer generates a decision result to decide the best system parameter settings for the current environment.

[0022] Preferably, in the step S4, it specifically includes:

[0023] S41 According to the interference type and interference parameters, the network can decide the number of available frequency sub-bands, avoid the interference frequency band, automatically adjust the system parameters, and select a suitable interference suppression method to cope with different interference environments;

[0024] S42 According to the feedback transmission performance, it can also judge whether the current decision meets the requirements. If not, further adjust the anti-interference strategy.

[0025] In a second aspect, an intelligent anti-interference system for power consumption collection in a heterogeneous network environment includes:

[0026] Network construction and acquisition module, configured to: build a heterogeneous network through dual-mode communication of broadband power line carrier and micro-power wireless transmission, build a power consumption network according to historical power consumption information, set power consumption acquisition nodes according to the power consumption network information, and collect power consumption information of the power consumption acquisition nodes through the heterogeneous network to obtain power consumption acquisition information;

[0027] Signal detection and interference analysis module, configured to: detect the received signal multiple times in different scenarios, analyze the signal spectrum characteristics, identify the interference type and characteristic parameters, and build a characteristic data set for training the BP neural network;

[0028] BP neural network training module, configured to: train the BP neural network using the interference characteristic data set until the network converges, design the input layer, hidden layer and output layer of the BP neural network, train the network using the interference characteristic data set, optimize the network weights and biases, and evaluate the network performance through the loss function and accuracy;

[0029] Real-time decision-making and dynamic adjustment module, configured to: obtain power acquisition information in real time, preprocess the data and send it to the trained BP neural network, obtain the decision result, and dynamically adjust the system parameters according to the transmission performance feedback to resist interference.

[0030] Preferably, the network construction and acquisition module includes:

[0031] Heterogeneous network construction module, configured to: build a heterogeneous network through dual-mode communication of broadband power line carrier and micro-power wireless transmission, and the heterogeneous network includes multiple signal acquisition and transmission channels;

[0032] Power consumption network construction module, configured to: obtain historical power consumption information, build a power consumption acquisition network according to the historical power consumption information, and determine the power consumption acquisition nodes in the power consumption acquisition network;

[0033] Node annotation module, configured to: annotate the acquisition nodes in the heterogeneous network according to the connection relationship between the power consumption acquisition nodes and each channel in the heterogeneous network to obtain the acquisition annotation nodes;

[0034] Power consumption acquisition module, configured to: obtain the preset acquisition channels corresponding to the acquisition annotation nodes in the heterogeneous network to collect power consumption information of the acquisition annotation nodes to obtain power consumption acquisition information.

[0035] Preferably, the signal detection and interference analysis module includes:

[0036] Signal detection and spectrum analysis module, configured to: detect the received signal multiple times in different scenarios, analyze the signal spectrum characteristics, identify the interference type, characteristic parameters and power information;

[0037] A feature dataset construction module, configured to: take interference type, signal-to-noise ratio, number of available frequency sub-bands, and interference suppression method as inputs, obtain the system bit error rate and average information rate under different conditions, and construct a training sample library.

[0038] Preferably, the BP neural network training module includes:

[0039] A BP neural network training module, configured to: train a BP neural network using the interference feature dataset until the network converges, design the input layer, hidden layer, and output layer of the BP neural network, use the interference feature dataset to train the network, optimize the network weights and biases, and evaluate the network performance through a loss function and accuracy.

[0040] A signal preprocessing module, configured to: obtain power acquisition information in real time, perform operations such as normalization, filtering, and denoising on the data, and send it to the BP neural network.

[0041] Preferably, the real-time decision-making and dynamic adjustment module includes:

[0042] A real-time decision-making and dynamic adjustment module, configured to: obtain power acquisition information in real time, preprocess the data and send it to the trained BP neural network, obtain a decision result, and dynamically adjust system parameters according to the transmission performance feedback to resist interference.

[0043] Advantageous effects:

[0044] In the present invention, a heterogeneous network of broadband power line carrier and micro-power wireless transmission and an electricity consumption network are constructed, electricity consumption acquisition nodes are set, electricity consumption information of the electricity consumption acquisition nodes is collected through the heterogeneous network, and at the same time, the received signal is detected multiple times in different scenarios, and the signal spectrum characteristics are analyzed to identify the interference type and characteristic parameters, and a feature dataset is constructed for training the BP neural network. Through training, the weights and biases of the neural network are optimized, enabling it to converge quickly and generate the best anti-interference strategy for different interference scenarios. After training, the neural network can receive the processed signal in real time and quickly generate a decision result in a complex environment, realizing dynamic optimization and adjustment of system parameters to effectively suppress interference, thereby ensuring that the system can still operate stably and achieve high-efficiency data transmission in a complex interference environment. Description of the Drawings

[0045] Figure 1 It is a flowchart of the intelligent anti-interference technology for electricity consumption acquisition in the heterogeneous network environment of the present invention;

[0046] Figure 2 It is a real-time decision-making diagram for anti-interference based on the BP neural network of the present invention. Detailed Embodiments

[0047] The following is a detailed description of the specific embodiments of the present invention.

[0048] The "range" disclosed in the present invention is defined in the form of a lower limit and an upper limit. A given range is defined by selecting a lower limit and an upper limit, and the selected lower limit and upper limit define the boundary of a particular range. The ranges defined in this way can include the end values or not include the end values, and can be combined arbitrarily, that is, any lower limit can be combined with any upper limit to form a range. For example, if a range of 10-50 is listed for a specific parameter, ranges of 10-40 and 20-50 are also contemplated. In addition, if the minimum range values 1 and 2 are listed, and if the maximum range values 3, 4, and 5 are listed, then the following ranges are all contemplated: 1-3, 1-4, 1-5, 2-3, 2-4, and 2-5. In this application, unless otherwise specified, the numerical range "a-b" represents an abbreviated representation of any real number combination between a and b, where a and b are both real numbers. For example, the numerical range "0-5" means that all real numbers between "0-5" have been fully listed herein, and "0-5" is only an abbreviated representation of these numerical combinations.

[0049] If there is no special instruction, all embodiments and optional embodiments of this application can be combined with each other to form a new technical solution.

[0050] If there is no special instruction, all technical features and optional technical features of this application can be combined with each other to form a new technical solution.

[0051] If there is no special instruction, all steps of this application can be carried out sequentially or randomly, and preferably sequentially. For example, the method includes steps (a) and (b), which means that the method can include steps (a) and (b) carried out sequentially, or can also include steps (b) and (a) carried out sequentially. For example, it is mentioned that the method may further include step (c), which means that step (c) can be added to the method in any order. For example, the method can include steps (a), (b), and (c), or can also include steps (a), (c), and (b), or can also include steps (c), (a), and (b), etc.

[0052] If there is no special instruction, the "including" and "comprising" mentioned in this application mean open-ended or can also be closed-ended. For example, the "including" and "comprising" can mean that other components not listed can also be included or comprised, or can only include or comprise the listed components.

[0053] If there is no special instruction, the reaction is carried out under normal temperature and normal pressure conditions.

[0054] If there is no special instruction, all parts or percentages are by weight or weight percentage.

[0055] In the present invention, the substances used are all known substances, which can be purchased or synthesized by known methods.

[0056] In the present invention, the devices or equipment used are all conventional devices or equipment known in the field and can be purchased.

[0057] The following further describes the specific implementation manners of the intelligent anti-interference method and system for power consumption acquisition in a heterogeneous network environment of the present invention in conjunction with embodiments. The intelligent anti-interference method and system for power consumption acquisition in a heterogeneous network environment of the present invention are not limited to the descriptions of the following embodiments.

[0058] Embodiment 1:

[0059] The intelligent anti-interference method for power consumption acquisition in a heterogeneous network environment, as Figure 1 shown, includes the following steps:

[0060] Step 1, construct a dual-mode heterogeneous network and obtain power consumption acquisition information.

[0061] Step 1, construct a heterogeneous network through dual-mode communication of broadband power line carrier and micro-power wireless transmission. The heterogeneous network includes multiple signal acquisition and transmission channels;

[0062] Step 2, obtain historical power consumption information, construct a power consumption acquisition network according to the historical power consumption information, and determine the power consumption acquisition nodes in the power consumption acquisition network;

[0063] Step 3, label the acquisition nodes in the heterogeneous network according to the connection relationship between the power consumption acquisition nodes and each channel in the heterogeneous network to obtain acquisition-labeled nodes;

[0064] Step 4, obtain the preset acquisition channels corresponding to the acquisition-labeled nodes in the heterogeneous network to collect power consumption information for the acquisition-labeled nodes to obtain power consumption acquisition information.

[0065] Step 2, detect the received signal multiple times in different scenarios, analyze the signal spectrum to identify the interference type and characteristic parameters, construct a characteristic data set, and train the BP neural network until convergence.

[0066] Step 1, detect the received signal multiple times through spectrum analysis and energy detection algorithms, analyze the signal spectrum to identify the interference type and characteristic parameters (such as interference power, frequency matching degree, frequency band factor, etc.). Using the interference type, signal-to-interference ratio, number of available frequency subbands, and interference suppression method as inputs, obtain the system bit error rate and average information rate in different situations, and construct a training sample library.

[0067] Step 2: Take the samples with the maximum objective function value under different interference types and JNR as training samples. Measure the system performance using the objective function value, including anti-interference performance and transmission efficiency, which is expressed as:

[0068] ;

[0069] They are respectively the minimization of bit error rate normalization and the maximization of average information transmission rate normalization. They are their respective weights. Use the sample library to train the BP neural network until convergence.

[0070] Step 3: Obtain power acquisition information in real time. After data preprocessing, send it into Figure 2 the trained network to get the decision result.

[0071] Step 1: Through the heterogeneous network, obtain power information in real time and normalize it to ensure that the data is within a reasonable range. The formula is as follows:

[0072] ;

[0073] Among them, is the input parameter, and are respectively the maximum and minimum values of this parameter.

[0074] Step 2: The input layer of the BP neural network receives the preprocessed interference information and power parameters, and the output layer generates a decision result, deciding the best system parameter settings suitable for the current environment, such as spectrum resource allocation, interference suppression methods (frequency domain interference suppression, time domain interference suppression, transform domain interference suppression), etc.

[0075] Step 4: Anti-interference decision and dynamic adjustment

[0076] According to the interference type and interference parameters, the network can decide the number of available frequency subbands, avoid the interference frequency band, automatically adjust the system parameters, and select a suitable interference suppression method to cope with different interference environments. According to the feedback transmission performance (such as bit error rate, information transmission rate, etc.), it can also judge whether the current decision meets the requirements. If not, further adjust the anti-interference strategy.

[0077] The intelligent anti-interference system for power consumption acquisition in a heterogeneous network environment includes:

[0078] A heterogeneous network construction module, used to construct a heterogeneous network through broadband power line carrier and micro-power wireless transmission dual-mode communication. The heterogeneous network includes multiple signal acquisition and transmission channels;

[0079] An electricity network construction module, which is used to obtain historical electricity consumption information, construct an electricity consumption collection network according to the historical electricity consumption information, and determine electricity consumption collection nodes in the electricity consumption collection network;

[0080] A node annotation module, which is used to annotate collection nodes in a heterogeneous network according to the connection relationship between the electricity consumption collection nodes and each channel in the heterogeneous network, and obtain collection annotation nodes;

[0081] An electricity consumption collection module, which is used to obtain a preset collection channel in the heterogeneous network for the corresponding collection annotation nodes to collect electricity consumption information of the collection annotation nodes, and obtain electricity consumption collection information.

[0082] A signal detection and spectrum analysis module, which detects received signals multiple times in different scenarios, analyzes the signal spectrum characteristics, and identifies the interference type, characteristic parameters, and power information.

[0083] A feature dataset construction module, which takes the interference type, signal-to-noise ratio, number of available frequency sub-bands, and interference suppression method as inputs, obtains the system bit error rate and average information rate under different conditions, and constructs a training sample library.

[0084] A BP neural network training module, which uses the interference feature dataset to train the BP neural network until the network converges, designs the input layer (interference type, interference parameters, etc.), hidden layer, and output layer (anti-interference strategy) of the BP neural network, uses the interference feature dataset to train the network, optimizes the network weights and biases, and evaluates the network performance through a loss function and accuracy.

[0085] A signal preprocessing module, which obtains electricity collection information in real time, performs operations such as normalization, filtering, and denoising on the data, and sends it to the BP neural network;

[0086] A real-time decision-making and dynamic adjustment module, which obtains electricity collection information in real time, preprocesses the data and then sends it to the trained BP neural network to obtain decision results, and dynamically adjusts system parameters (such as channel selection, spectrum resource allocation, interference suppression method, etc.) according to the transmission performance feedback to resist interference.

[0087] In this application, a dual-mode communication heterogeneous network of broadband power line carrier and micro-power wireless transmission and a power consumption network are constructed, power consumption acquisition nodes are set, power consumption information of the power consumption acquisition nodes is collected through the heterogeneous network, and at the same time, the received signals are detected multiple times in different scenarios, and the signal spectrum characteristics are analyzed to identify the interference type and characteristic parameters, and a characteristic data set is constructed for training a BP neural network. Through training, the weights and biases of the neural network are optimized so that it can converge quickly and generate the best anti-interference strategy for different interference scenarios. After training, the neural network can receive the processed signals in real time and quickly generate decision results in a complex environment, realizing dynamic optimization and adjustment of system parameters to effectively suppress interference, so as to ensure that the system can still operate stably and achieve efficient data transmission in a complex interference environment.

[0088] The above has described the embodiments of the present invention in detail. However, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge scope of those of ordinary skill in the art.

Claims

1. An intelligent anti-interference method for power consumption acquisition in a heterogeneous network environment, characterized in that, Including the following steps: S1 Construct a heterogeneous network through dual-mode communication of broadband power line carrier and micro-power wireless transmission, construct a power consumption network based on historical power consumption information, set power consumption acquisition nodes according to the power consumption network information, and collect information from the power consumption acquisition nodes through the heterogeneous network to obtain power consumption acquisition information; S2 Continuously detect the received signal, analyze the signal spectrum to identify the interference type and interference characteristic parameters, construct a characteristic data set, and train the BP neural network until convergence; S3 Obtain power collection information in real time, preprocess the data and send it to the trained network to obtain a decision result; S4 The engine judges whether the decision parameters meet the current requirements according to the feedback transmission performance, and adjusts dynamically in real time according to the judgment result to resist interference; In the step S2, it specifically includes: S21 Detect the received signal multiple times through spectrum analysis and energy detection algorithms, and analyze the signal spectrum to identify the interference type and interference characteristic parameters; S22 Use the interference type, signal-to-noise ratio, number of available frequency sub-bands, and interference suppression method as inputs to obtain the system bit error rate and average information rate under different conditions, and construct a training sample library; S23 Use the sample library to train the BP neural network until convergence; In the step S3, it specifically includes: S31 Through the heterogeneous network, obtain power information in real time and normalize it to ensure that the data is within a reasonable range; S32 The input layer of the BP neural network receives the preprocessed interference information and power parameters, and the output layer generates a decision result to decide the best system parameter settings suitable for the current environment.

2. The intelligent anti-interference method for power consumption collection in a heterogeneous network environment according to claim 1, wherein In the step S1, it specifically includes: S11 Construct a heterogeneous network through dual-mode communication of broadband power line carrier and micro-power wireless transmission. The heterogeneous network includes multiple signal acquisition and transmission channels; S12 Obtain historical power consumption information, construct a power consumption acquisition network according to the historical power consumption information, and determine the power consumption acquisition nodes in the power consumption acquisition network; S13 According to the connection relationship between the power consumption acquisition nodes and each channel in the heterogeneous network, label the acquisition nodes in the heterogeneous network to obtain labeled acquisition nodes; S14 Obtain the preset acquisition channels corresponding to the labeled acquisition nodes in the heterogeneous network, and collect power consumption information from the labeled acquisition nodes to obtain power consumption acquisition information.

3. The intelligent anti-interference method for power consumption acquisition in a heterogeneous network environment according to claim 1, characterized in that, In the step S4, it specifically includes: S41 According to the interference type and interference characteristic parameters, the network decides the number of available frequency sub-bands, avoids the interference frequency band, automatically adjusts the system parameters, and selects a suitable interference suppression method to cope with different interference environments; S42 According to the feedback transmission performance, judge whether the current decision meets the requirements. If not, further adjust the anti-interference strategy.

4. The intelligent anti-interference system for power consumption collection in a heterogeneous network environment is characterized in that For implementing the method according to any one of claims 1-3, including: A network construction and acquisition module, configured to: construct a heterogeneous network through dual-mode communication of broadband power line carrier and micro-power wireless transmission, construct a power consumption network according to historical power consumption information, set power consumption acquisition nodes according to the power consumption network information, and collect power consumption information from the power consumption acquisition nodes through the heterogeneous network to obtain power consumption acquisition information; A signal detection and interference analysis module, configured to: detect the received signal multiple times in different scenarios, analyze the spectral characteristics of the signal, identify the interference type and interference characteristic parameters, and construct a feature data set for training a BP neural network; A BP neural network training module, configured to: use the feature data set to train the BP neural network until the network converges, design the input layer, hidden layer, and output layer of the BP neural network, use the feature data set to train the network, optimize the network weights and biases, and evaluate the network performance through a loss function and accuracy; A real-time decision-making and dynamic adjustment module, configured to: obtain power collection information in real time, preprocess the data and send it to the trained BP neural network, obtain the decision result, and dynamically adjust the system parameters according to the transmission performance feedback to resist interference.

5. The intelligent anti-interference system for power consumption acquisition in a heterogeneous network environment according to claim 4, wherein The network construction and collection module includes: A heterogeneous network construction module, configured to: construct a heterogeneous network through dual-mode communication of broadband power line carrier and micro-power wireless transmission, and the heterogeneous network includes multiple signal collection and transmission channels; An electricity consumption network construction module, configured to: obtain historical electricity consumption information, construct an electricity consumption collection network according to the historical electricity consumption information, and determine the electricity consumption collection nodes in the electricity consumption collection network; A node annotation module, configured to: annotate the collection nodes in the heterogeneous network according to the connection relationship between the electricity consumption collection nodes and each channel in the heterogeneous network to obtain the collection annotation nodes; An electricity consumption collection module, configured to: obtain the preset collection channels corresponding to the collection annotation nodes in the heterogeneous network to collect electricity consumption information from the collection annotation nodes to obtain electricity consumption collection information.

6. The intelligent anti-interference system for power consumption collection in a heterogeneous network environment according to claim 4, characterized in that The signal detection and interference analysis module includes: A signal detection and spectrum analysis module, configured to: detect the received signal multiple times in different scenarios, analyze the spectral characteristics of the signal, and identify the interference type, interference characteristic parameters, and power information; A feature data set construction module, configured to: use the interference type, signal-to-noise ratio, available frequency subbands, and interference suppression method as inputs to obtain the system bit error rate and average information rate under different conditions, and construct a training sample library.

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