Atmospheric electric field monitoring data anti-interference compensation method and system, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of atmospheric electric field monitoring, and discloses an atmospheric electric field monitoring data anti-interference compensation method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining original data of atmospheric electric field monitoring, and carrying out the preprocessing; based on the processed original data, extracting interference features; constructing an interference signal identification model for the extracted interference features; and performing compensation correction on a result output by the interference signal identification model to obtain final compensated data. According to the invention, through combination of adaptive filtering and deep learning technologies, various types of interference signals can be identified and processed more comprehensively. Meanwhile, the learning ability of a complex interference mode is enhanced; and the method has relatively high robustness and generalization ability, and is suitable for different monitoring environments and application scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric electric field monitoring, and particularly to an anti-interference compensation method, system, electronic device and storage medium for atmospheric electric field monitoring data. Background Art

[0002] Atmospheric electric field monitoring is an important means to study lightning activities, atmospheric electrical properties and meteorological changes. However, in the actual monitoring process, due to the influence of various factors such as environmental noise, electromagnetic interference, and device self-noise, there are large errors and distortion phenomena in the monitoring data. These interferences will directly affect the prediction accuracy of lightning activities and the analysis results of meteorological changes.

[0003] At present, the commonly used anti-interference methods mainly include hardware filtering and software algorithm processing. Although hardware filtering can effectively reduce some high-frequency noises, it cannot completely eliminate low-frequency interferences in complex environments; while traditional software algorithms (such as mean filtering, Kalman filtering, etc.) although improve the data quality to a certain extent, they are still insufficient for complex dynamic interferences. Therefore, there is an urgent need for an anti-interference compensation method for atmospheric electric field monitoring data that can adapt to various interference scenarios. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention aims to provide an efficient and stable anti-interference compensation method for atmospheric electric field monitoring data to solve the problems existing in the prior art.

[0005] To achieve the above object, the present invention provides an anti-interference compensation method for atmospheric electric field monitoring data, the method comprising:

[0006] Obtaining the original data of atmospheric electric field monitoring and performing preprocessing;

[0007] Extracting interference features based on the processed original data;

[0008] Constructing an interference signal recognition model for the extracted interference features;

[0009] Compensating and correcting the result output by the interference signal recognition model to obtain the final compensated data.

[0010] Preferably, the method for performing the preprocessing includes: selecting a frequency range according to the physical characteristics of the atmospheric electric field; selecting a band-pass filter to allow signals within the frequency range to pass through, and passing the original data through the designed band-pass filter to obtain the processed data.

[0011] Preferably, the method for extracting the interference features includes:

[0012] Using wavelet basis functions to decompose the signal into J layers:

[0013]

[0014] Among them, A J [n] represents the approximate component of the j-th layer, representing the low-frequency effective signal; D j [n] represents the detail component of the j-th layer, representing the high-frequency components in different frequency ranges;

[0015] Extract interference features from the detail component D j [n] obtained by decomposition. The steps include:

[0016] Calculate the energy of each detail component as a measure of the interference intensity:

[0017]

[0018] Among them, E j represents the energy of the detail component of the j-th layer; N represents the signal length; n represents the sample point index of the signal;

[0019] Statistically analyze the amplitude distribution of each detail component and extract statistical features:

[0020]

[0021] Among them, Mean j represents the mean of the detail component of the j-th layer; Std j represents the standard deviation of the component of the j-th layer;

[0022] Determine the frequency range and time position where the interference is located according to the extracted features.

[0023] Preferably, the optimization objectives of the constructed interference signal recognition model include:

[0024]

[0025] Among them, w represents the normal vector of the hyperplane; b represents the intercept of the hyperplane; C represents the regularization parameter; ξ i represents the relaxation variable of the i-th sample;

[0026] The constraint conditions include:

[0027] y i (w·x i +b)≥1-ξ i , ξ i ≥0

[0028] Among them, x i represents the feature vector of the i-th sample; y i ∈{-1, +1} is the label.

[0029] Preferably, the method for performing the compensation and correction includes: based on the output result of the interference signal recognition model, attenuating the interfered detail component, and then reconstructing the signal through inverse wavelet transform.

[0030] The present invention also provides an anti-interference compensation system for atmospheric electric field monitoring data. The system is used to implement the above method and includes: a collection module, an extraction module, a construction module, and a compensation module;

[0031] The collection module is used to obtain the original data of atmospheric electric field monitoring and perform preprocessing;

[0032] The extraction module is used to extract interference features based on the processed original data;

[0033] The construction module is used to construct an interference signal recognition model for the extracted interference features;

[0034] The compensation module is used to perform compensation and correction on the result output by the interference signal recognition model to obtain the final compensated data.

[0035] Preferably, the working process of the extraction module includes: decomposing the signal into J layers using a wavelet basis function:

[0036]

[0037] where A J [n] represents the approximate component of the Jth layer, representing the low-frequency effective signal; D j [n] represents the detail component of the jth layer, representing the high-frequency components in different frequency ranges;

[0038] Extracting interference features from the decomposed detail component D j [n], the steps include:

[0039] Calculating the energy of each detail component as a measure of the interference intensity:

[0040]

[0041] where E j represents the energy of the detail component of the jth layer; N represents the signal length; n represents the sample point index of the signal;

[0042] Statistically analyzing the amplitude distribution of each detail component and extracting statistical features:

[0043]

[0044] where Mean j represents the mean of the detail component of the jth layer; Std jrepresents the standard deviation of the components in the j-th layer;

[0045] According to the extracted features, determine the frequency range and time position where the interference is located.

[0046] Preferably, the optimization objectives of the constructed interference signal recognition model include:

[0047]

[0048] where w represents the normal vector of the hyperplane; b represents the intercept of the hyperplane; C represents the regularization parameter; ξ i represents the slack variable of the i-th sample;

[0049] The constraint conditions include:

[0050] y i (w·x i +b)≥1 - ζ i , ξ i ≥0

[0051] where x i represents the feature vector of the i-th sample; y i ∈{-1, +1} is the label.

[0052] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above method is implemented.

[0053] The present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed, the above method is implemented.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] By combining adaptive filtering and deep learning technologies, the present invention can more comprehensively identify and process various types of interference signals. At the same time, the present invention also enhances the learning ability for complex interference patterns; and has high robustness and generalization ability, and is applicable to different monitoring environments and application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1 is a schematic flowchart of the method according to an embodiment of the present invention;

[0058] Figure 2 Schematic diagram of the structure of the electronic device according to an embodiment of the present invention. Description of the drawings:

[0060] 1010. Processor; 1020. Memory; 1030. Input / output interface; 1040. Communication interface; 1050. Bus. Detailed implementation manners

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be the ordinary meanings understood by those of ordinary skill in the art belonging to the field of the present disclosure. The "first", "second" and similar terms used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before the term cover the elements or objects listed after the term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0063] To make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the drawings and specific implementation manners.

[0064] Embodiment 1

[0065] As can be seen from the background art, the commonly used anti-interference methods mainly include hardware filtering and software algorithm processing. Although hardware filtering can effectively reduce some high-frequency noises, it cannot completely eliminate low-frequency interference in a complex environment; while traditional software algorithms (such as mean filtering, Kalman filtering, etc.) although improve the data quality to a certain extent, they are still insufficient for complex dynamic interference.

[0066] The embodiment of the present invention provides an anti-interference compensation method for atmospheric electric field monitoring data, as Figure 1 shown, the steps include:

[0067] S1. Obtain the original data of atmospheric electric field monitoring and preprocess it.

[0068] Collect the background field strength, rate of change, and spatial gradient through sensors, perform multi-dimensional sampling for dynamic scenarios in complex interference environments, and obtain an initial signal set containing interference coupling.

[0069] Collect the atmospheric electric field background field strength, rate of change, and spatial gradient data through a distributed sensor network. In this embodiment, 100 sensor nodes are deployed in a certain plain area, evenly distributed within a range of 10 square kilometers. Each node real-time collects the electric field background field strength, rate of change, and spatial gradient data. The field strength data is in the unit of V / m, the rate of change is recorded as the change amount per second, and the spatial gradient reflects the field strength difference between adjacent nodes. This distributed layout can comprehensively capture the regional electric field characteristics and avoid data deviation of a single node.

[0070] After that, preprocess the collected original data. The steps include: According to the physical characteristics of the atmospheric electric field, select a reasonable frequency range (for example, 0.01Hz to 10Hz). This range should cover the main frequency bands of the atmospheric electric field change and exclude high-frequency noise at the same time. Select a band-pass filter that allows signals from 0.01Hz to 10Hz to pass through and suppress high-frequency noise above 20Hz. In this embodiment, use the filter design tool in MATLAB or Python (such as scipy.signal.butter) to generate a second-order Butterworth band-pass filter. Pass the original data through the designed band-pass filter to obtain the processed data.

[0071] S2. Extract the interference characteristics based on the processed original data.

[0072] In this embodiment, a multi-scale analysis method is used to decompose the signal into approximate components and detail components at different scales, so as to capture the characteristics of the signal in different time scales and frequency ranges. The multi-scale analysis techniques mainly include wavelet transform and multiresolution analysis (MRA).

[0073] In this embodiment, select the wavelet basis function (db4) and decompose the signal into J layers:

[0074]

[0075] Among them, A J [n] represents the approximate component of the Jth layer, representing the low-frequency effective signal; D j [n] represents the detail component of the jth layer, representing the high-frequency components in different frequency ranges.

[0076] Extract interference features from the detail component D j [n] obtained from the decomposition, the steps include:

[0077] (1) Calculate the energy of each detail component as a measure of interference intensity:

[0078]

[0079] where E j represents the energy of the detail component at the j-th layer; N represents the signal length; n represents the sample point index of the signal.

[0080] (2) Statistically analyze the amplitude distribution of each detail component and extract statistical features:

[0081]

[0082] where Mean j represents the mean of the detail component at the j-th layer; Std j represents the standard deviation of the component at the j-th layer.

[0083] Based on the extracted features, determine the frequency range and time position where the interference is located. If the energy of a certain detail component is significantly higher than that of other layers, the frequency range corresponding to this layer may be the interference frequency band. If the amplitude of a certain detail component is abnormally high within a specific time period, this time period may be the time when the interference occurs.

[0084] S3. For the extracted interference features, construct an interference signal recognition model.

[0085] In this embodiment, a support vector machine is used as the interference signal recognition model. The energy (E j ), mean (Mean j ), and standard deviation (Std j ) of the detail components extracted through step S2 are used as feature vectors to input into the model, and the classifier is trained to distinguish normal signals from interference signals.

[0086] The optimization objective of the model is:

[0087]

[0088] where w represents the normal vector of the hyperplane; b represents the intercept of the hyperplane; C represents the regularization parameter; ξ i represents the relaxation variable of the i-th sample.

[0089] The constraint conditions are:

[0090] y i (w·x i +b)≥1-ζ i , ξ i ≥0

[0091] Among them, x i represents the feature vector of the i-th sample; y i ∈{-1, +1} is the label (+1 represents the interference signal, -1 represents the normal signal).

[0092] The model maps the features to a high-dimensional space through a kernel function to achieve non-linear classification. After training, the model outputs the classification result:

[0093] If w·x + b ≥ 0, it is determined as an interference signal;

[0094] Otherwise, it is a normal signal.

[0095] S4. Compensate and correct the result output by the interference signal recognition model to obtain the final compensated data.

[0096] This embodiment adopts a compensation method of wavelet reconstruction and interference component suppression. The steps include: based on the interference recognition result in step S3, attenuate or set to zero the disturbed detail components, and then reconstruct the signal through inverse wavelet transform. The specific steps are as follows:

[0097] (1) Interference component processing:

[0098] Let the j-th layer detail component recognized as interference be D j , apply an attenuation coefficient α (0 ≤ α ≤ 1) to it to obtain the processed detail component D' j :

[0099] D' j = α·D j

[0100] If it is necessary to completely suppress the interference (such as in a strong noise scenario), then set α equal to 0.

[0101] (2) Signal reconstruction:

[0102] Combine the undisturbed approximation component A J (the low-frequency effective signal of the J-th layer) with the processed detail components D' j , and reconstruct the compensated signal through inverse wavelet transform:

[0103]

[0104] (3) Dynamically optimize the attenuation coefficient:

[0105] To improve the compensation accuracy, dynamically adjust α according to the interference intensity:

[0106] α = exp(-λ·E j )

[0107] Among them, λ is a constant used to control the attenuation rate of the weight factor.

[0108] The technical solution of the present invention can more comprehensively identify and process various types of interference signals by combining adaptive filtering and deep learning technologies. At the same time, the present invention also enhances the learning ability for complex interference patterns; and has high robustness and generalization ability, and is applicable to different monitoring environments and application scenarios.

[0109] It should be noted that the method of the present disclosure embodiment can be executed by a single device, such as a computer or a server, etc. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In this case of a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the present disclosure embodiment, and these multiple devices will interact with each other to complete the described method.

[0110] It should be noted that some embodiments of the present disclosure are described above. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the order of the numbers of the steps in the above embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention. The actions or steps recited in the claims can be executed in a different order than in the above embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0111] Embodiment 2

[0112] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides an anti-interference compensation system for atmospheric electric field monitoring data, including: a collection module, an extraction module, a construction module, and a compensation module; the collection module is used to obtain the original data of atmospheric electric field monitoring and perform preprocessing; the extraction module is used to extract interference features based on the processed original data; the construction module is used to construct an interference signal recognition model for the extracted interference features; the compensation module is used to compensate and correct the result output by the interference signal recognition model to obtain the final compensated data.

[0113] Next, in combination with this embodiment, it will be detailed how the present invention solves the technical problems in actual work.

[0114] Use the collection module to obtain the original data of atmospheric electric field monitoring and perform preprocessing.

[0115] The acquisition module includes several sensors, which are used to collect the background field strength, rate of change, and spatial gradient, and perform multi-dimensional sampling on the dynamic scene in a complex interference environment to obtain an initial signal set containing interference coupling.

[0116] Collect the background field strength, rate of change, and spatial gradient data of the atmospheric electric field through a distributed sensor network. In this embodiment, 100 sensor nodes are deployed in a certain plain area, evenly distributed within a range of 10 square kilometers. Each node collects the background field strength, rate of change, and spatial gradient data of the electric field in real time. The field strength data is in the unit of V / m, the rate of change is recorded as the change amount per second, and the spatial gradient reflects the field strength difference between adjacent nodes. This distributed layout can comprehensively capture the regional electric field characteristics and avoid data deviation of a single node.

[0117] After that, preprocess the collected raw data. The steps include: According to the physical characteristics of the atmospheric electric field, select a reasonable frequency range (for example, 0.01 Hz to 10 Hz). This range should cover the main frequency bands of the atmospheric electric field change and exclude high-frequency noise at the same time. Select a band-pass filter that allows signals from 0.01 Hz to 10 Hz to pass through and suppress high-frequency noise above 20 Hz. In this embodiment, use the filter design tool in MATLAB or Python (such as scipy.signal.butter) to generate a second-order Butterworth band-pass filter. Pass the raw data through the designed band-pass filter to obtain the processed data.

[0118] The extraction module extracts the interference characteristics based on the processed raw data.

[0119] In this embodiment, a multi-scale analysis method is used to decompose the signal into approximate components and detail components at different scales, so as to capture the characteristics of the signal in different time scales and frequency ranges. The multi-scale analysis technology mainly includes wavelet transform and multiresolution analysis (MRA).

[0120] In this embodiment, the wavelet basis function (db4) is selected to decompose the signal into J layers:

[0121]

[0122] Among them, A J [n] represents the approximate component of the Jth layer, representing the low-frequency effective signal; D j [n] represents the detail component of the jth layer, representing the high-frequency components in different frequency ranges.

[0123] Extract the interference characteristics from the decomposed detail component D j [n]. The steps include:

[0124] (1) Calculate the energy of each detail component as a measure of interference strength:

[0125]

[0126] Among them, E j represents the energy of the detail component of the jth layer; N represents the signal length; n represents the sample point index of the signal.

[0127] (2) Count the amplitude distribution of each detail component and extract statistical features:

[0128]

[0129] Among them, Mean j Std represents the mean value of the detail component of the jth layer; j represents the standard deviation of the j-th layer component.

[0130] Based on the extracted features, the frequency range and time location of the interference are determined. If the energy of a detail component is significantly higher than that of other layers, the frequency range corresponding to that layer may be an interference band. If the amplitude of a detail component is abnormally high within a specific time period, that time period may be the time when the interference occurred.

[0131] The construction module constructs an interference signal recognition model based on the extracted interference features.

[0132] This embodiment uses support vector machine as the interference signal recognition model. The energy of the detail component (E j ), Mean j ) and standard deviation (Std j ) is input into the model as a feature vector and the classifier is trained to distinguish normal signals from interference signals.

[0133] The optimization goal of the model is:

[0134]

[0135] Where w represents the normal vector of the hyperplane; b represents the intercept of the hyperplane; C represents the regularization parameter; ξ i represents the slack variable of the i-th sample.

[0136] The constraints are:

[0137] y i (w·x i +b)≥1-ζ i ,ζ i ≥0

[0138] Among them, x iDenote the feature vector of the i-th sample; y i ∈{-1, +1} is the label (+1 represents the interference signal, -1 represents the normal signal).

[0139] The model maps the features to a high-dimensional space through a kernel function to achieve non-linear classification. After training, the model outputs the classification result:

[0140] If w·x + b ≥ 0, it is determined as the interference signal;

[0141] Otherwise, it is the normal signal.

[0142] Finally, the compensation module compensates and corrects the result output by the interference signal recognition model to obtain the final compensated data.

[0143] This embodiment adopts a compensation method of wavelet reconstruction and interference component suppression. The steps include: based on the interference recognition result in step S3, attenuating or setting to zero the interfered detail components, and then reconstructing the signal through inverse wavelet transform. The specific steps are as follows:

[0144] (1) Interference component processing:

[0145] Let the j-th layer detail component identified as interference be D j , apply an attenuation coefficient α (0 ≤ α ≤ 1) to it to obtain the processed detail component D′ j :

[0146] D′ j = α·D j

[0147] If it is necessary to completely suppress the interference (such as in a strong noise scenario), then set α equal to 0.

[0148] (2) Signal reconstruction:

[0149] Combine the undisturbed approximation component A J (the low-frequency effective signal of the J-th layer) with the processed detail components D′ j , and reconstruct the compensated signal through inverse wavelet transform:

[0150]

[0151] (3) Dynamically optimize the attenuation coefficient:

[0152] To improve the compensation accuracy, dynamically adjust α according to the interference intensity:

[0153] α = exp(-λ·E j )

[0154] where λ is a constant used to control the attenuation speed of the weight factor.

[0155] The system of the above embodiment is used to implement the corresponding anti-interference compensation method for atmospheric electric field monitoring data in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0156] It should be noted that the above anti-interference compensation system for atmospheric electric field monitoring data is embodied in the form of functional units. The term "module" here can be implemented in the form of software and / or hardware, and no specific limitation is made thereto.

[0157] For example, the "module" can be a software program, a hardware circuit, or a combination of the two that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor, or a group of processors, etc.) for executing one or more software or firmware programs, and a memory, a merged logic circuit, and / or other suitable components that support the described functions.

[0158] Embodiment III

[0159] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the anti-interference compensation method for atmospheric electric field monitoring data described in any of the above embodiments.

[0160] Figure 2 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0161] The processor 1010 can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0162] The memory 1020 can be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and called and executed by the processor 1010.

[0163] The input / output interface 1030 is used to connect to an input / output module to implement information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0164] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to implement communication interaction between this device and other devices. Among them, the communication module can implement communication through a wired method (such as USB (Universal Serial Bus), network cable, etc.) or through a wireless method (such as a mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0165] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0166] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solutions of the embodiments of this specification and do not necessarily include all the components shown in the figure.

[0167] The system of the above embodiment is used to implement any one of the corresponding atmospheric electric field monitoring data anti-interference compensation methods in the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0168] Embodiment Four

[0169] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute an anti-interference compensation method for atmospheric electric field monitoring data as described in any one of the above embodiments.

[0170] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0171] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute an anti-interference compensation method for atmospheric electric field monitoring data as described in any one of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0172] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; under the concept of the present disclosure, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of brevity.

[0173] In addition, for simplicity of explanation and discussion, and so as not to make the embodiments of the present disclosure difficult to understand, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the devices may be shown in block diagram form in order to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that details regarding the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be entirely within the understanding of those skilled in the art). In cases where specific details (such as circuits) are set forth to describe exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure may be practiced without these specific details or with variations of these specific details. Accordingly, these descriptions should be regarded as illustrative rather than restrictive.

[0174] Although the present disclosure has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0175] Therefore, the units of the examples described in the embodiments of this application can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0176] The embodiments of the present disclosure are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. An anti-interference compensation method for atmospheric electric field monitoring data, characterized in that, The method includes: Obtaining the original data of atmospheric electric field monitoring and performing preprocessing; Extracting interference features based on the processed original data; Constructing an interference signal recognition model for the extracted interference features; Performing compensation and correction on the result output by the interference signal recognition model to obtain the final compensated data.

2. The anti-interference compensation method for atmospheric electric field monitoring data according to claim 1, characterized in that The method for performing the preprocessing includes: selecting a frequency range according to the physical characteristics of the atmospheric electric field; selecting a band-pass filter to allow signals within the frequency range to pass through, and passing the original data through the designed band-pass filter to obtain the processed data.

3. The anti-interference compensation method for atmospheric electric field monitoring data according to claim 1, wherein, The method for extracting the interference features includes: Decomposing the signal into J layers using wavelet basis functions; Among them, A J [n] represents the approximation component of the Jth layer, representing the low-frequency effective signal; D j [n] represents the detail component of the jth layer, representing the high-frequency components in different frequency ranges; Extract interference features from the detail component D j [n] obtained from decomposition, and the steps include: Calculating the energy of each detail component as a measure of the interference intensity; Among them, E j represents the energy of the j-th layer of detail components; N represents the signal length; n represents the sample point index of the signal; Statistically analyzing the amplitude distribution of each detail component and extracting statistical features; Among them, Mean j represents the mean of the j-th level detail component; Std j represents the standard deviation of the j-th level component. Determining the frequency range and time position where the interference is located according to the extracted features.

4. The anti-interference compensation method for atmospheric electric field monitoring data according to claim 1, characterized in that The optimization objectives of the constructed interference signal recognition model include: where \(w\) represents the normal vector of the hyperplane; \(b\) represents the intercept of the hyperplane; \(C\) represents the regularization parameter; \(\xi\) i represents the slack variable of the \(i\)-th sample; The constraint conditions include: y i (w·x i +b)≥1 - ξ i ,ξ i ≥0 where, x i represents the feature vector of the i-th sample; y i ∈{-1, +1} is the label.

5. The anti-interference compensation method for atmospheric electric field monitoring data according to claim 1, characterized in that The method for performing the compensation and correction includes: based on the output result of the interference signal recognition model, performing attenuation processing on the interfered detail components, and then reconstructing the signal through inverse wavelet transform.

6. An anti-interference compensation system for atmospheric electric field monitoring data, the system is used to implement the method described in any one of claims 1-5, and is characterized in that It includes: An acquisition module, an extraction module, a construction module, and a compensation module; The acquisition module is used to obtain the original data of atmospheric electric field monitoring and perform preprocessing; The extraction module is used to extract interference features based on the processed original data; The construction module is used to construct an interference signal recognition model for the extracted interference features; The compensation module is used to perform compensation and correction on the result output by the interference signal recognition model to obtain the final compensated data.

7. The anti-interference compensation system for atmospheric electric field monitoring data according to claim 6, wherein, The working process of the extraction module includes: decomposing the signal into J layers using wavelet basis functions; Among them, A J [n] represents the approximation component of the J-th layer, representing the low-frequency effective signal; d j [n] represents the detail component of the j-th layer, representing the high-frequency components in different frequency ranges; Extract interference features from the detail component D j [n] obtained from the decomposition, and the steps include: Calculating the energy of each detail component as a measure of the interference intensity; Among them, E j represents the energy of the j-th layer of detail components; N represents the signal length; n represents the sample point index of the signal; Statistically analyzing the amplitude distribution of each detail component and extracting statistical features; Among them, Mean j represents the mean value of the j-th layer of detail components; Std j represents the standard deviation of the j-th layer of components. Determining the frequency range and time position where the interference is located according to the extracted features.

8. The anti-interference compensation system for atmospheric electric field monitoring data according to claim 6, characterized in that The optimization objectives of the constructed interference signal recognition model include: where \(w\) represents the normal vector of the hyperplane; \(b\) represents the intercept of the hyperplane; \(C\) represents the regularization parameter; \(\xi\) i represents the slack variable of the \(i\)-th sample; The constraint conditions include: y i (w·x i +b)≥1-ξ i ,ξ i ≥0 where, x i represents the feature vector of the i-th sample; y i ∈{-1, +1} is the label.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed, it implements the method according to any one of claims 1 to 5.

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