Energy analysis method and device for full-node environment noise collection and storage medium

Abstract

The invention discloses an energy analysis method and device for full-node collection of environmental noise and a storage medium, and relates to the technical field of geophysical exploration, and the method is characterized in that the method comprises the steps: obtaining full-node collection data; sampling the node data according to preset slicing time to obtain a sampling sequence of the node data; performing discrete Hilbert transformation on the sampling sequence to obtain an analysis signal corresponding to the node data; calculating noise energy according to the analytic signal; calculating noise energy of data of each node in sequence to obtain noise energy of all nodes in the slicing time; and displaying the noise energy of all nodes through a three-dimensional space to obtain an energy time slice of the slice time. Through the analysis method, the environmental noise energy can be accurately calculated, the intensity of the energy of the environmental noise collected at a node at a certain moment can be visually displayed in real time, guidance is provided for blasting at a construction site, and the quality of seismic data collected at all nodes is improved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and in particular to an energy analysis method, device and storage medium for full-node environmental noise collection. Background Art

[0002] This section is intended to provide a background or context to the embodiments recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] Full-node acquisition has the characteristics of "full space, full time". Compared with traditional cable acquisition, it improves environmental adaptability and makes up for the shortcomings of conventional cable acquisition in some areas. It can be used as a supplement to cable acquisition; the characteristics of node equipment being light, efficient, and suitable for high-density deployment are a future development direction for seismic acquisition. The significant difference between seismic data collected by full nodes and traditional acquisition is that a large amount of redundant data can be used before and after full-node acquisition. Studying the law of how the energy of environmental noise in full-node acquisition data changes over time and conducting noise monitoring can provide certain guidance for construction blasting at the acquisition site.

[0004] In the prior art, the calculation and analysis method for signal energy is to use the square of the signal amplitude as the energy of the signal at the current moment. Under this method, when the amplitude of the noise signal is 0, the calculated energy is 0. According to physical knowledge, the signal potential energy is 0 at this time, but the kinetic energy is the largest. Therefore, its energy should not be 0, but should be kinetic energy plus potential energy. Therefore, using this method to calculate the energy of environmental noise has limitations and affects the quality of seismic data. Summary of the invention

[0005] In response to the above technical problems, the present invention proposes an energy analysis method, device and storage medium for collecting environmental noise at all nodes, which can accurately calculate the environmental noise energy, and display the energy strength of the environmental noise collected by the node at a certain moment in real time and intuitively, provide guidance for blasting at the construction site, and improve the quality of seismic data collected by all nodes.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention includes four aspects.

[0007] In a first aspect, a method for analyzing energy of full-node collected environmental noise is provided, comprising the following steps:

[0008] Acquire all-node collection data; the all-node collection data includes several node data;

[0009] Sampling the node data according to a preset slicing time to obtain a sampling sequence of the node data;

[0010] Performing discrete Hilbert transformation on the sampling sequence to obtain an analytical signal corresponding to the node data;

[0011] Calculate the noise energy of the node at the slicing time according to the analytical signal;

[0012] Calculate the noise energy of each node data at the slicing time in turn, and obtain the noise energy of all nodes within the slicing time;

[0013] The noise energy of all nodes is displayed in three-dimensional space to obtain energy time slices of slice time.

[0014] In some embodiments, sampling the node data according to the preset slicing time to obtain a sampling sequence of the node data includes:

[0015] Determine the sampling interval and time window length of each sample point in the data according to the node data, and list each sample point as a time series;

[0016] Determine the preset slicing time, and convert the slicing time into a point number on the node data in combination with the sampling interval;

[0017] The time series is sampled using the point number as the sampling center to obtain the sampling sequence of the node data.

[0018] In some embodiments, the slicing time is recorded as T, the sampling interval time is recorded as Δt; the point number conversion formula is:

[0019]

[0020] The sampling of the time series includes: taking the point number index as the center, taking L / 2 sample points forward, and taking L / 2 sample points backward; wherein L is the time window length.

[0021] In some embodiments, performing a discrete Hilbert transform on the sampling sequence to obtain an analytical signal corresponding to the node data includes:

[0022] Performing discrete Fourier transform on the sampling sequence to obtain a first transformed sequence;

[0023] Reorganizing the first transformation sequence to obtain a reorganized sequence;

[0024] Performing a discrete inverse Fourier transform on the recombined sequence to obtain a second transformed sequence;

[0025] The imaginary part of the second transformed sequence is taken, and the analytical signal of the channel number is calculated according to the imaginary part and the sampling sequence.

[0026] In some embodiments, the formula for performing discrete Fourier transform on the sampling sequence is:

[0027]

[0028] Where L is the time window length, x1(l) is the sampling sequence of the first node data, e is the Euler formula, j is the complex number symbol, k represents the kth data of the sampling sequence, and N is the number of sample points.

[0029] In some embodiments, the calculation formula of the analytical signal for calculating the channel number based on the imaginary part and the sampling sequence is:

[0030]

[0031] Where x1(l) is the sampling sequence of the first node data; is the imaginary part of the second transformed sequence; L is the time window length.

[0032] In some embodiments, calculating the noise energy of the node at the slicing time according to the analytical signal includes:

[0033] Determine a signal energy representation formula according to the analytical signal;

[0034] According to the signal energy expression formula, the noise energy of each sample point within the time window length is calculated in sequence;

[0035] The root mean square value of multiple noise energies is calculated to obtain the noise energy of the node within the slice time.

[0036] In some embodiments, the signal energy expression formula is:

[0037]

[0038] Where x1(l) is the sampling sequence of the first node data; is the imaginary part of the second transformed sequence; L is the time window length;

[0039] The calculation formula for calculating the root mean square value of multiple noise energies is:

[0040]

[0041] Where E1(T) represents the energy of the first node at the slicing time.

[0042] In a second aspect, an energy analysis device for collecting environmental noise at all nodes is provided, comprising:

[0043] Collection module, used to obtain all node collection data;

[0044] A data processing unit connected to the acquisition module, the data processing unit comprising a sampling module, a transformation module connected to the sampling module, and a calculation module connected to the transformation module; the sampling module is used to sample the node data according to a preset slicing time to obtain a sampling sequence of the node data; the transformation module is used to perform a discrete Hilbert transform on the sampling sequence to obtain an analytical signal corresponding to the node data; the calculation module is used to calculate the noise energy of the node at the slicing time according to the analytical signal; wherein the data processing unit sequentially calculates the noise energy of each node data at the slicing time to obtain the noise energy of all nodes within the slicing time;

[0045] And a display module connected to the data processing unit is used to display the noise energy of all nodes through three-dimensional space to obtain energy time slices of slice time.

[0046] According to a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the aforementioned energy analysis method when executing the computer program.

[0047] In a fourth aspect, a computer-readable storage medium is provided, and when the computer program is executed by a processor, the steps of the energy analysis method as described above are implemented.

[0048] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0049] The present application provides an energy analysis method, device and storage medium for collecting environmental noise at all nodes. The energy analysis method includes: acquiring all-node collection data; the all-node collection data includes a plurality of node data; sampling the node data according to a preset slice time to obtain a sampling sequence of the node data; performing a discrete Hilbert transform on the sampling sequence to obtain an analytical signal corresponding to the node data; calculating the noise energy of the node at the slice time according to the analytical signal; calculating the noise energy of each node data at the slice time in turn to obtain the noise energy of all nodes within the slice time; displaying the noise energy of all nodes through three-dimensional space to obtain an energy time slice of the slice time. Through this energy analysis method, the energy of environmental noise can be calculated based on discrete Hilbert transform. Compared with the traditional method of using the square of the amplitude to represent the energy size, the calculation result is more accurate and can better reflect the energy strength of the actual signal. Therefore, by making energy time slices, the energy strength of the environmental noise collected at a node at a certain moment can be intuitively displayed, and real-time noise monitoring can be carried out on the work area to monitor the construction conditions, and provide guidance on whether and when to blast at the construction site, thereby reducing the impact of noise on the actual seismic data acquisition from the source and improving the quality of seismic data collected at all nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Hereinafter, the present application will be described in more detail based on embodiments and with reference to the accompanying drawings;

[0051] Figure 1 An exemplary flow chart of an energy analysis method for collecting environmental noise at all nodes provided in Embodiment 1 of the present invention;

[0052] Figure 2 In the embodiment of the present invention, Figure 1 An exemplary flow chart of step S2 shown in;

[0053] Figure 3 In the embodiment of the present invention, Figure 1 An exemplary flow chart of step S3 shown in FIG.

[0054] Figure 4 In the embodiment of the present invention, Figure 1 An exemplary flow chart of step S4 shown in;

[0055] Figure 5 This is an exemplary three-dimensional diagram of energy time slices produced when the slice time is 3S in an embodiment of the present invention;

[0056] Figure 6 An exemplary three-dimensional diagram of energy time slices produced when the slice time is 1S, 3S and 7S in an embodiment of the present invention;

[0057] Figure 7 This is a schematic block diagram of an energy analysis device for collecting environmental noise at all nodes provided in Embodiment 2 of the present invention;

[0058] Figure 8 A schematic block diagram of an electronic device provided in Embodiment 3 of the present invention;

[0059] Fig. 9 This is a schematic diagram of a computer-readable storage medium provided in Embodiment 4 of the present invention.

[0060] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0061] The present disclosure is further described below in conjunction with the embodiments shown in the accompanying drawings.

[0062] Embodiment 1:

[0063] In the prior art, the calculation and analysis method for signal energy is to use the square of the signal amplitude as the energy of the signal at the current moment. Under this method, when the amplitude of the noise signal is 0, the calculated energy is 0. According to physical knowledge, the signal potential energy is 0 at this time, but the kinetic energy is the largest. Therefore, its energy should not be 0, but should be kinetic energy plus potential energy. Therefore, using this method to calculate the energy of environmental noise has limitations and affects the quality of seismic data.

[0064] The present application embodiment discloses a method for analyzing the energy of all-node environmental noise collection, such as Figure 1 As shown, the method comprises the following steps: obtaining full-node acquisition data; the full-node acquisition data includes several node data; sampling the node data according to a preset slice time to obtain a sampling sequence of the node data; performing a discrete Hilbert transform on the sampling sequence to obtain an analytical signal corresponding to the node data; calculating the noise energy of the node at the slice time according to the analytical signal; sequentially calculating the noise energy of each node data at the slice time to obtain the noise energy of all nodes within the slice time; displaying the noise energy of all nodes through a three-dimensional space to obtain an energy time slice of the slice time. Through the energy analysis method, the energy of environmental noise can be calculated based on discrete Hilbert transform. Compared with the traditional method of using the square of the amplitude to represent the energy size, the calculation result is more accurate and can better reflect the energy strength of the actual signal. Therefore, by making energy time slices, the energy strength of the environmental noise collected by the node at a certain moment can be intuitively displayed, and the work area can be monitored in real time for noise, thereby monitoring the construction conditions, providing guidance on whether to blast and when to blast at the construction site, reducing the impact of noise on the actual seismic data acquisition from the source, and improving the quality of seismic data collected by the full node.

[0065] A method for analyzing the energy of environmental noise collected by all nodes provided by at least one embodiment of the present disclosure can be implemented in the form of software, hardware, firmware or any combination thereof, and loaded and executed by a processor in a device such as a mobile phone, tablet computer, laptop computer, desktop computer, network server, etc., so as to accurately calculate the environmental noise energy and intuitively display the intensity of the environmental noise energy at a certain moment, facilitate monitoring of construction conditions, reduce the impact of noise on actual seismic data collection from the source, and improve the quality of seismic data collected by all nodes.

[0066] Reference below Figure 1 As shown, an energy analysis method for collecting environmental noise at all nodes provided by at least one embodiment of the present disclosure is described, and the energy analysis method includes steps S1 to S6.

[0067] S1. Obtaining all-node collection data; the all-node collection data includes several node data.

[0068] In some embodiments, a single shot acquisition data contains a total of several data, that is, several node data, which can be set as P nodes. Each node data contains N sample points. The sampling interval between each sample point is Δt (us). The noise time window length in each node data is L, so as to facilitate the calculation of noise energy based on the node data.

[0069] S2. Sampling the node data according to a preset slicing time to obtain a sampling sequence of the node data.

[0070] In some embodiments, step S2, such as Figure 2 As shown, including S21-S23, the details are as follows:

[0071] S21. Determine the sampling interval and time window length of each sample point in the data according to the node data, and list each sample point as a time series.

[0072] In some embodiments, the sampling interval is recorded as Δt (us), the time window length is L, and the time series can be listed as x i (n), i = 1, ..., P; n = 1, ..., N, where P is the Pth node and N is the number of samples in the node data, thus facilitating the subsequent noise energy calculation through the time series.

[0073] S22. Determine a preset slicing time, and convert the slicing time into a point number on the node data in combination with a sampling interval.

[0074] In some embodiments, the slicing time is recorded as T (ms), so the formula for converting the slicing time into the point number on the node data is:

[0075]

[0076] In the formula, T is the slicing time and Δt is the sampling interval time, which determines the point number on the node data.

[0077] S23. Sample the time series using the point number as the sampling center to obtain a sampling sequence of the node data.

[0078] In some embodiments, sampling the time series includes: taking L / 2 sample points forward and L / 2 sample points backward with the point number index as the center; wherein L is the time window length. Taking the first node data (first node data) as an example, the obtained sampling sequence is recorded as x1(l), l=1,…,L+1, so as to calculate its noise energy value based on the node data as an example, and calculate each node data in turn according to the calculation process, so as to accurately obtain the noise energy distribution in the full node acquisition data, so as to provide guidance for blasting construction.

[0079] S3. Perform discrete Hilbert transform on the sampling sequence to obtain an analytical signal corresponding to the node data.

[0080] In some embodiments, step S3, such as Figure 3 As shown, including S31-S34, the details are as follows:

[0081] S31. Perform discrete Fourier transform on the sampling sequence to obtain a first transformed sequence.

[0082] In some embodiments, the formula for performing discrete Fourier transform on the sampling sequence is:

[0083]

[0084] Where L is the time window length, x1(l) is the sampling sequence of the first node data, e is the Euler formula, j is the complex number symbol, k represents the kth data of the sampling sequence, and N is the number of sample points.

[0085] After discrete Fourier transform, the first transform sequence is X1(k).

[0086] S32: reorganize the first transformation sequence to obtain a reorganized sequence.

[0087] In some embodiments, X1(k) is recombined to obtain Z1(k):

[0088]

[0089] Wherein, k represents the kth data of the sequence; L is the time window length.

[0090] S33, performing discrete inverse Fourier transform on the recombined sequence to obtain a second transformed sequence.

[0091] In some embodiments, a discrete inverse Fourier transform is performed on Z1(k) to obtain z1(l):

[0092]

[0093] In the formula, L is the time window length, k is the kth data in the sequence, and e is the Euler formula.

[0094] S34, extracting the imaginary part of the second transformed sequence, and calculating the analytical signal of the channel number according to the imaginary part and the sampling sequence.

[0095] In some embodiments, take the imaginary part of z1(l) Get the analytical signal, the specific formula is as follows:

[0096]

[0097] Where x1(l) is the sampling sequence of the first node data; i is the node number; is the imaginary part of the second transformed sequence; L is the time window length.

[0098] S4. Calculate the noise energy of the node at the slicing time according to the analytical signal.

[0099] In some embodiments, step S4, such as Figure 4 As shown, including:

[0100] S41, determining a signal energy representation formula according to the analytical signal;

[0101] S42, calculating the noise energy of each sample point within the time window length in sequence according to the signal energy expression formula;

[0102] S43. Calculate the root mean square value of multiple noise energies to obtain the noise energy of the node within the slicing time.

[0103] In some embodiments, the signal energy is calculated as:

[0104]

[0105] Where x1(l) is the sampling sequence of the first node data; is the imaginary part of the second transformed sequence; L is the time window length;

[0106] Repeat step S4 to calculate the noise energy of each sample point in the node data, that is, to obtain e1(1), e1(2)...e1(L+1) respectively;

[0107] The calculation formula for calculating the RMS value of multiple noise energies is:

[0108]

[0109] Where E1(T) represents the energy of the first node at the slicing time.

[0110] S5. Calculate the noise energy of each node data at the slicing time in turn to obtain the noise energy of all nodes within the slicing time.

[0111] In some embodiments, step S5 is to repeat steps S2-S4 for each node data, so as to calculate the noise energy of each node data at the slicing time, thereby calculating the environmental noise energy of all node data within the slicing time.

[0112] S6. Display the noise energy of all nodes in three-dimensional space to obtain energy time slices of slice time.

[0113] In some embodiments, the 8s environmental noise data actually collected in the work area contains 240 channels (nodes), each channel contains 8000 sample points, the sampling interval is 1000us, and the time window length is 1000. Using this data, energy time slices are made. For the convenience of three-dimensional display, the 240 nodes are gridded at 20×12. When the slice time is recorded as 3s, the energy time slices obtained are as follows: Figure 5 As shown in the figure, the X-axis represents the line number, the Y-axis represents the channel number (node ​​number), and the Z-axis represents the sample point number; when the slice time is 1S, 3S, and 7S, the calculated environmental noise energy time slice is as follows Figure 6 As shown in the figure, the staff can intuitively display the energy strength of the environmental noise collected at the node at that moment based on the energy time slice diagram, so as to provide guidance for construction blasting and improve the quality of data collected at all nodes.

[0114] The energy analysis method for collecting environmental noise at all nodes provided in the embodiments of the present disclosure can calculate the energy of environmental noise based on discrete Hilbert transform. Compared with the traditional method of using the square of the amplitude to represent the energy size, the calculation result is more accurate and can better reflect the energy strength of the actual signal. Therefore, by making energy time slices, the energy strength of the environmental noise collected by the node at a certain moment can be intuitively displayed, and real-time noise monitoring of the work area can be performed to monitor the construction conditions, and guidance can be given on whether and when to blast at the construction site, thereby reducing the impact of noise on actual seismic data collection from the source and improving the quality of seismic data collected by all nodes.

[0115] Embodiment 2:

[0116] At least one embodiment of the present disclosure further provides an energy analysis device for collecting environmental noise at all nodes. Each module in the device corresponds to the steps in the above method. The effects achieved and the problems solved have been described and will not be repeated here. Figure 7 As shown, including:

[0117] Collection module 11, used to obtain all node collection data;

[0118] A data processing unit 12 connected to the acquisition module 11, the data processing unit includes a sampling module, a transformation module connected to the sampling module, and a calculation module connected to the transformation module; the sampling module is used to sample the node data according to a preset slicing time to obtain a sampling sequence of the node data; the transformation module is used to perform a discrete Hilbert transform on the sampling sequence to obtain an analytical signal corresponding to the node data; the calculation module is used to calculate the noise energy of the node at the slicing time according to the analytical signal; wherein the data processing unit sequentially calculates the noise energy of each node data at the slicing time to obtain the noise energy of all nodes within the slicing time;

[0119] And a display module 13 connected to the data processing unit 12 is used to display the noise energy of all nodes through three-dimensional space to obtain energy time slices of slice time.

[0120] In some embodiments, the output end of the acquisition module 11 is connected to the input end of the data processing unit 12, and the output end of the data processing unit 12 is connected to the input end of the display module 13, so as to execute the steps of the energy analysis method of embodiment one, so as to accurately calculate the energy size of the environmental noise, and use energy time slices to display the energy intensity distribution at a certain moment in real time, so as to carry out real-time noise monitoring of the work area, thereby monitoring the construction conditions, and giving guidance on whether and when to blast at the construction site, thereby reducing the impact of noise on the actual seismic data acquisition from the source and improving the quality of seismic data collected by all nodes.

[0121] In some embodiments, the device may be a part of the software, or may be implemented in combination with corresponding hardware, which will not be described in detail here.

[0122] Embodiment three:

[0123] At least some embodiments of the present disclosure also provide an electronic device, such as Figure 8 As shown, the electronic device includes a memory 21 and a processor 22, wherein the memory 21 stores a computer program, and when the computer program is executed by the processor, the steps of the energy analysis method provided in any embodiment of the present disclosure are performed.

[0124] In some embodiments, the processor 22 is used to execute all or part of the steps in the energy analysis method of any embodiment of the present disclosure. The memory 21 is used to store various types of data, which may include instructions of any application or method in the electronic device, and data related to the application.

[0125] The processor 22 can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the energy analysis method in the above-mentioned embodiment 1.

[0126] The memory 21 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0127] Embodiment 4:

[0128] At least some embodiments of the present disclosure also provide a computer-readable storage medium, such as Fig. 9 As shown, a computer program 31 is stored on the readable storage medium, and when the computer program 31 is executed by a processor, the steps of the energy analysis method provided by any embodiment of the present disclosure are implemented.

[0129] In some embodiments, the storage medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0131] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0132] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0133] Embodiment five:

[0134] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the energy analysis method provided in any embodiment of the present disclosure.

[0135] In summary, the energy analysis method, device and storage medium for full-node environmental noise collection provided by the present application can calculate the energy of environmental noise based on discrete Hilbert transform. Compared with the traditional method of using the square of the amplitude to represent the energy size, the calculation result is more accurate and can better reflect the energy strength of the actual signal. Therefore, by making energy time slices, the energy strength of the environmental noise collected by the node at a certain moment can be intuitively displayed, and real-time noise monitoring of the work area can be performed to monitor the construction conditions, and guidance can be given on whether and when to blast at the construction site, thereby reducing the impact of noise on actual seismic data collection from the source and improving the quality of seismic data collected by the full node.

[0136] The various embodiments in the present disclosure are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0137] The protection scope of the present disclosure is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and their equivalents, the intention of the present disclosure also includes these changes and modifications.

Claims

1. A method for energy analysis of full-node environmental noise collection, characterized in that: The following steps are involved: Get full node collection data; The full node collection data includes several node data; Sampling the node data according to a preset slicing time to obtain a sampling sequence of the node data; Performing discrete Hilbert transformation on the sampling sequence to obtain an analytical signal corresponding to the node data; Calculate the noise energy of the node at the slicing time according to the analytical signal; Calculate the noise energy of each node data at the slicing time in turn, and obtain the noise energy of all nodes within the slicing time; The noise energy of all nodes is displayed in three-dimensional space to obtain energy time slices of slice time.

2. According to claim 1, a method for energy analysis of full-node environmental noise collection is characterized in that: The step of sampling the node data according to the preset slicing time to obtain a sampling sequence of the node data includes: Determine the sampling interval and time window length of each sample point in the data according to the node data, and list each sample point as a time series; Determine the preset slicing time, and convert the slicing time into a point number on the node data in combination with the sampling interval; The time series is sampled using the point number as the sampling center to obtain the sampling sequence of the node data.

3. The energy analysis method for collecting environmental noise by all nodes according to claim 1 is characterized in that: The slicing time is recorded as T, and the sampling interval time is recorded as Δt; the point number conversion formula is: The sampling of the time series includes: taking the point number index as the center, taking L / 2 sample points forward, and taking L / 2 sample points backward; wherein L is the time window length.

4. The energy analysis method for collecting environmental noise by all nodes according to claim 1 is characterized in that: The step of performing discrete Hilbert transformation on the sampling sequence to obtain an analytical signal corresponding to the node data includes: Performing discrete Fourier transform on the sampling sequence to obtain a first transformed sequence; Reorganizing the first transformation sequence to obtain a reorganized sequence; Performing a discrete inverse Fourier transform on the recombined sequence to obtain a second transformed sequence; The imaginary part of the second transformed sequence is taken, and the analytical signal of the channel number is calculated according to the imaginary part and the sampling sequence.

5. The energy analysis method for collecting environmental noise by all nodes according to claim 4 is characterized in that: The formula for performing discrete Fourier transform on the sampling sequence is: Where L is the time window length, x1(l) is the sampling sequence of the first node data, e is the Euler formula, j is the complex number symbol, k represents the kth data of the sampling sequence, and N is the number of sample points.

6. The energy analysis method for collecting environmental noise at all nodes according to claim 1 is characterized in that: The calculation formula of the analytical signal for calculating the number of channels based on the imaginary part and the sampling sequence is: Where x1(l) is the sampling sequence of the first node data; is the imaginary part of the second transformed sequence.

7. The energy analysis method for collecting environmental noise by all nodes according to claim 1 is characterized in that: The calculating the noise energy of the node at the slicing time according to the analytical signal includes: Determine a signal energy representation formula according to the analytical signal; According to the signal energy expression formula, the noise energy of each sample point within the time window length is calculated in sequence; The root mean square value of multiple noise energies is calculated to obtain the noise energy of the node within the slice time.

8. The energy analysis method for collecting environmental noise by all nodes according to claim 7 is characterized in that: The signal energy expression formula is: Where x1(l) is the sampling sequence of the first node data; is the imaginary part of the second transformed sequence; L is the time window length; The calculation formula for calculating the root mean square value of multiple noise energies is: Where E1(T) represents the energy of the first node at the slicing time.

9. An energy analysis device for collecting environmental noise at all nodes, characterized in that: include: Collection module, used to obtain all node collection data; A data processing unit connected to the acquisition module, the data processing unit comprising a sampling module, a transformation module connected to the sampling module, and a calculation module connected to the transformation module; the sampling module is used to sample the node data according to a preset slicing time to obtain a sampling sequence of the node data; the transformation module is used to perform a discrete Hilbert transform on the sampling sequence to obtain an analytical signal corresponding to the node data; the calculation module is used to calculate the noise energy of the node at the slicing time according to the analytical signal; wherein the data processing unit sequentially calculates the noise energy of each node data at the slicing time to obtain the noise energy of all nodes within the slicing time; And a display module connected to the data processing unit is used to display the noise energy of all nodes through three-dimensional space to obtain energy time slices of slice time.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the energy analysis method according to any one of claims 1 to 8 when executing the computer program.

11. A computer-readable storage medium, characterized in that: When the computer program is executed by a processor, the steps of the energy analysis method according to any one of claims 1 to 8 are implemented.

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