A pipeline leakage monitoring and early warning system
By collecting audio signals in the pipeline leakage monitoring system and matching them with the case library, and generating time-frequency graph data, the problem of inaccurate judgment of the leakage position of the pipeline network is solved, and more efficient leakage monitoring and early warning is achieved to adapt to complex environmental conditions.
Patent Information
- Application Number
- CN202411484372.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-23
- Publication Date
- 2025-01-03
- Estimated Expiration
- 2044-10-23
AI Technical Summary
The traditional feature value analysis method cannot meet the accurate positioning of the leakage position of the pipeline network, and the sound and vibration signals of the pipeline network leakage in complex urban environments are easily disturbed, resulting in inaccurate judgment of the leakage position. The existing platforms lack data analysis and prediction capabilities and cannot Dig deep into the value of data.
By collecting audio signals, calculating the number of sample points and characteristic values, generating time domain waveforms, spectrum diagrams, and time frequency diagram data, and matching them with the altitude position case library, feature parameter case library, leaked audio case library and noise audio case library to create a Updated leak signal warning model to improve monitoring accuracy.
It significantly improves the early warning accuracy and monitoring accuracy of pipeline leakage monitoring, and can accurately judge and quickly locate the leakage position of the pipeline network to adapt to different environmental conditions.
Smart Images
Figure CN118998642B_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of pipeline network leakage monitoring, in particular to a pipeline leakage monitoring and early warning system. Background Art
[0002] The urban water supply network is the "lifeline" of the city and an important part of the urban infrastructure. With the continuous expansion of the number and size of cities, the monitoring and control of pipeline leakage has become a major engineering and technical challenge faced by both China and abroad due to planning, design, management and technology. Systematic, comprehensive and efficient leakage monitoring of urban water supply networks is the most direct and effective measure for rapid and accurate leakage search, location and immediate repair and maintenance.
[0003] The traditional eigenvalue analysis method can no longer meet the requirements of accurately locating the location of pipe network leakage in various environments, and it is urgent to achieve efficient, systematic and scientific management. In the face of these problems, in addition to continuously improving water supply facilities, increasing the intensity of pipe network transformation, and improving technical levels, there is an urgent need to establish a complete pipe network leakage monitoring system to accurately locate the location through online analysis to guide daily pipe network operation and maintenance management, shorten the accident handling response time, and reduce daily operating energy consumption and leakage. At present, in complex urban environments, the sound and vibration signals of pipe network leakage are easily disturbed by the surrounding environmental conditions, resulting in inaccurate judgment of the leakage location. The platform lacks data analysis and prediction capabilities and cannot deeply explore the value of data. Summary of the invention
[0004] In view of the above problems, a pipeline leakage monitoring and early warning system is provided. The present invention calculates the number of sampling points N through the collected audio sampling rate and the collection time, calculates the level value, Fourier transform coefficient, center of gravity frequency and pipeline leakage characteristic value of the signal through the feature recognition algorithm, and generates time domain waveform, spectrum diagram, time-frequency diagram data at the same time. Through the matching and comparison analysis of pipeline leakage characteristic value, associated data and altitude position case library, characteristic parameter case library, leakage audio case library and noise audio case library, it can accurately determine whether there is a pipeline leakage. By continuously updating the altitude position case library, characteristic parameter case library, leakage audio case library and noise audio case library, a pipeline leakage signal early warning model that can be suitable for different temperatures, different altitudes, different regions, equipment voltage, temperature and environment is established, and the leakage signal early warning model is updated, which can significantly improve the early warning accuracy of pipeline leakage monitoring.
[0005] To achieve the above object, the technical solution adopted by the present invention is.
[0006] A pipeline leakage monitoring and early warning system comprises the following steps: Step 1: acquiring raw data through an acquisition module, wherein the raw data comprises a leakage audio signal of a tiny vibration generated by real-time monitoring of pipeline leakage through the acquisition module; ambient temperature; altitude; ambient humidity; temperature, voltage, sampling rate, sampling duration and location information of the acquisition module;
[0007] Step 2: The algorithm analysis module calculates the pipeline leakage characteristic value, time domain waveform, spectrum diagram and time-frequency diagram data from the original data obtained in step 1 through the feature recognition algorithm, and preliminarily determines whether there is a suspected pipeline leakage based on the comparison between the pipeline leakage characteristic value and the set threshold, and associates the pipeline leakage characteristic value with the original data;
[0008] Step 3: The data storage module stores the pipeline leakage characteristic value, original data, time domain waveform, spectrum diagram and time-frequency diagram after association in step 2; the visualization analysis module presents the associated data stored in the data storage module in the form of a scatter map, and presents the suspected pipeline leakage signal preliminarily determined by the algorithm analysis module on the scatter map in the form of scatter points of different colors;
[0009] Step 4: The alarm processing module matches and compares the original data and characteristic values associated in step 3 with the altitude position case library, the characteristic parameter case library, the leakage audio case library and the noise audio case library respectively, and determines whether the suspected pipeline leakage signal has a pipeline leakage; if so, a leakage alarm is issued, and the suspected pipeline leakage signal is output to the leakage audio case library of the data storage module for storage; if not, no alarm is issued, and the suspected pipeline leakage signal is output to the noise audio case library of the data storage module for storage;
[0010] Step 5: Repeat steps 1 to 4, and update the set threshold by continuously updating the altitude position case library, the characteristic parameter case library, the leakage audio case library, and the noise audio case library to improve the accuracy of pipeline leakage monitoring and early warning.
[0011] Preferably, the steps of the feature recognition algorithm in step 2 are as follows:
[0012] Step S1: the algorithm analysis module calculates the number of sampling points N according to the sampling rate and sampling duration;
[0013] Step S2: Calculate the level value of the leakage audio signal x(i):
[0014]
[0015] Where N represents the number of sampling points, and i represents the i-th sampling point;
[0016] Step S3: Perform Fourier transform on the leakage audio signal x(i) to obtain the Fourier transform coefficient X(f i ):
[0017]
[0018] Among them, f i represents the i-th discrete frequency point, N represents the number of sampling points, and j represents the imaginary unit;
[0019] Step S4: Calculate the centroid frequency f of the leakage audio signal c :
[0020]
[0021] Among them, X(f i ) is the Fourier transform coefficient, N represents the total number of sampling points, f i represents the i-th discrete frequency point;
[0022] Step S5: Calculate pipeline leakage characteristic value P
[0023] P=log(level*f c / 2000).
[0024] Preferably, the threshold value set in step 2 is the pipeline leakage characteristic value ≥ 30.
[0025] Preferably, the scatter map format in step 3 is based on a world map, the location information of the sampling module is used as the scattered location of the pipeline in the scatter map, and the original data other than the location information, the pipeline leakage characteristic value, the time domain waveform, the spectrum diagram and the time-frequency diagram data are used as the associated data at each sampling module position.
[0026] Preferably, the different color scatter point marking form uses green scatter points to mark the sampling module positions without suspected pipeline leakage signals; and uses red scatter points to mark the sampling module positions with pipeline leakage signals.
[0027] Preferably, the altitude position case library in step 4 uses the position signal and altitude in the original data as the horizontal axis and the vertical axis respectively, and constructs a characteristic parameter case library under different temperatures and position signals in addition to the original data of the position signal and altitude, the pipeline leakage characteristic value, the time domain waveform, the spectrum diagram and the time-frequency diagram data as characteristic parameters.
[0028] Preferably, the acquisition module includes an optical fiber sensor for collecting pipeline leakage audio signals, a thermistor module for obtaining ambient temperature, a GPS module for obtaining location information and altitude of the acquisition module, an SHT31 module for obtaining temperature and humidity of the acquisition module, a voltage sensor for obtaining voltage of the acquisition module, and a 4G network module for uploading data.
[0029] Due to the adoption of the above technical solution, the present invention has the following beneficial effects.
[0030] (1) The present invention updates the set threshold by continuously updating the altitude position case library, the characteristic parameter case library, the leakage audio case library and the noise audio case library, thereby improving the accuracy of the algorithm analysis module in determining the suspected pipeline leakage signal, and further improving the accurate labeling and display of the suspected pipeline leakage signal by the visualization analysis module, thereby improving the accuracy of pipeline leakage monitoring and early warning.
[0031] (2) The present invention uses a visualization analysis module to present the associated data stored in the data storage module in the form of a scatter map, and presents the suspected pipeline leakage signal preliminarily determined by the algorithm analysis module in the form of scattered dots of different colors on the scatter map, so that the pipeline leakage monitoring and early warning and acquisition module positioning are more intuitive, and the user can conveniently view the original data, pipeline leakage characteristic values, time domain waveforms, spectrum diagrams, time-frequency diagram data and other information of each acquisition module on the scatter map, so that the display of pipeline leakage monitoring information is more convenient and efficient.
[0032] (3) This paper establishes a leakage signal warning model that can be used for different temperatures, different altitudes, different regions, equipment voltages, temperatures, and environmental conditions by continuously updating the altitude position case library, characteristic parameter case library, leakage audio case library, and noise audio case library. Updating the leakage signal warning model can significantly improve the warning accuracy of pipeline leakage monitoring.
[0033] (4) The present invention calculates the number of sampling points N through the collected audio sampling rate and the collection time, and calculates the level value, Fourier transform coefficient, center of gravity frequency and pipeline leakage characteristic value of the signal through the feature recognition algorithm. At the same time, it generates time domain waveform, spectrum diagram and time-frequency diagram data. Through the matching and comparison analysis of pipeline leakage characteristic value, associated data and altitude position case library, characteristic parameter case library, leakage audio case library and noise audio case library, it can accurately judge whether there is pipeline leakage, and significantly improve the monitoring accuracy of pipeline leakage monitoring and early warning.
[0034] (5) The present invention solves the problem that the sound and vibration signals of pipe network leakage are easily disturbed in complex urban environments, resulting in inaccurate judgment of the leakage location, and the technical problem that the existing platform lacks data analysis and prediction capabilities and cannot deeply mine the value of data through the pipe network leakage monitoring method and platform based on the Internet of Things architecture. It can more accurately judge whether a pipe network leak occurs, and can quickly locate the pipe network leakage location according to different environmental conditions such as altitude, temperature, region, and equipment voltage. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] The following is a detailed discussion of the making and application of the preferred embodiments of the present invention. However, it should be understood that the present invention provides many applicable inventive concepts that can be embodied in various specific environments. The specific embodiments discussed are only for the purpose of illustrating the specific ways of making and using the present invention, and do not limit the scope of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative work.
[0036] Figure 1 This is a pipeline leakage monitoring flow chart of the present invention.
[0037] Figure 2 Schematic diagram of the altitude position case library of the present invention.
[0038] Figure 3 Schematic diagram of the characteristic parameter case library of the present invention.
[0039] Among them, 1-acquisition module; 2-algorithm analysis module; 3-data storage module; 4-visualization analysis module; 5-alarm processing module; 51-location information; 52-altitude; 53-characteristic parameters; 531-pipeline leakage characteristic value; 532-sampling module voltage; 533-sampling module temperature; 534-ambient temperature; 535-ambient humidity; 536-time domain waveform; 537-spectrum diagram; 538-time-frequency diagram. DETAILED DESCRIPTION
[0040] The following is a detailed discussion of the making and application of the preferred embodiments of the present invention. However, it should be understood that the present invention provides many applicable inventive concepts that can be embodied in various specific environments. The specific embodiments discussed are only intended to illustrate specific ways to make and use the present invention and do not limit the scope of the present invention.
[0041] The present invention updates the set threshold by continuously updating the altitude position case library, the characteristic parameter case library, the leakage audio case library and the noise audio case library, thereby improving the judgment accuracy of the algorithm analysis module 2 on the suspected pipeline leakage signal, and then improving the accurate labeling and display of the suspected pipeline leakage signal by the visualization analysis module 4, thereby improving the accuracy of pipeline leakage monitoring and early warning.
[0042] The present invention uses a visualization analysis module 4 to present the associated data stored in the data storage module in the form of a scatter map, and presents the suspected pipeline leakage signal preliminarily determined by the algorithm analysis module 2 in the form of scattered dots of different colors on the scatter map, so that the pipeline leakage monitoring and early warning and the positioning of the acquisition module 1 are more intuitive, and the user can conveniently view the original data of each acquisition module 1, the pipeline leakage characteristic value 531, the time domain waveform 536, the spectrum diagram 537 and the time-frequency diagram 538 data and other information from the scatter map, so that the display of pipeline leakage monitoring information is more convenient and efficient.
[0043] The following is combined with Figure 1-3 Further elaboration.
[0044] like Figure 1 A pipeline leakage monitoring and early warning system shown includes the following steps.
[0045] Step 1: Obtain raw data through acquisition module 1, the raw data includes leakage audio signal of tiny vibration generated by real-time monitoring of pipeline leakage through acquisition module 1; ambient temperature 534; altitude 52; ambient humidity 535; temperature 533, voltage 532, sampling rate, sampling duration and location information 51 of acquisition module 1. Wherein, the acquisition module 1 includes an optical fiber sensor for collecting pipeline leakage audio signal, a thermistor module for obtaining ambient temperature 534, a GPS module for obtaining location information 51 and altitude 52 of acquisition module 1, an SHT31 module for obtaining temperature 533 and humidity of acquisition module, a voltage sensor for obtaining voltage 532 of acquisition module and a 4G network module for uploading data.
[0046] Step 2: The algorithm analysis module 2 calculates the pipeline leakage characteristic value 531, the time domain waveform 536, the spectrum diagram 537 and the time-frequency diagram 538 data from the raw data obtained in step 1 through the feature recognition algorithm, and preliminarily determines whether there is a suspected pipeline leakage based on the comparison between the pipeline leakage characteristic value 531 and the set threshold, and associates the pipeline leakage characteristic value 531 with the raw data. The set threshold is that the pipeline leakage characteristic value is ≥30.
[0047] The steps of the feature recognition algorithm are as follows: Step S1: The algorithm analysis module 2 calculates the number of sampling points N according to the sampling rate and sampling duration.
[0048] Step S2: Calculate the level value of the leakage audio signal x(i):
[0049]
[0050] Where N represents the number of sampling points, and i represents the i-th sampling point.
[0051] Step S3: Perform Fourier transform on the leakage audio signal x(i) to obtain the Fourier transform coefficient X(f i ):
[0052]
[0053] Among them, f i represents the i-th discrete frequency point, N represents the number of sampling points, and j represents the imaginary unit.
[0054] Step S4: Calculate the centroid frequency f of the leakage audio signalc :
[0055]
[0056] Among them, X(f i ) is the Fourier transform coefficient, N represents the total number of sampling points, f i Represents the i-th discrete frequency point.
[0057] Step S5: Calculate pipeline leakage characteristic value P
[0058] P=log(level*f c / 2000).
[0059] Step 3: The data storage module 3 stores the pipeline leakage characteristic value 531, the original data, the time domain waveform 536, the spectrum diagram 537 and the time-frequency diagram 538 after association in step 2; the visualization analysis module 4 presents the associated data stored in the data storage module in the form of a scatter map, and displays the suspected pipeline leakage signal preliminarily determined by the algorithm analysis module 2 on the scatter map in the form of scatter dots of different colors.
[0060] The scatter map format is based on the world map, the location information 51 of the sampling module is used as the scatter location of the pipeline in the scatter map, and the original data other than the location information 51, the pipeline leakage characteristic value 531, the time domain waveform 536, the spectrum diagram 537 and the time-frequency diagram 538 data are used as the associated data at each sampling module position. The different color scatter mark format uses green scatter points to mark the sampling module positions without suspected pipeline leakage signals; and uses red scatter points to mark the sampling module positions with pipeline leakage signals.
[0061] Step 4: The alarm processing module 5 matches and compares the original data and characteristic values associated in step 3 with the altitude position case library, characteristic parameter case library, leakage audio case library and noise audio case library respectively, and determines whether the suspected pipeline leakage signal exists a pipeline leakage; if so, a leakage alarm is issued, and the suspected pipeline leakage signal is output to the leakage audio case library of the data storage module 3 for storage; if not, no alarm is issued, and the suspected pipeline leakage signal is output to the noise audio case library of the data storage module 3 for storage.
[0062] like Figure 2 As shown, the altitude position case library uses the position signal and altitude 52 in the original data as the horizontal axis and the vertical axis respectively, and constructs a characteristic parameter case library under different temperatures and position signals by using the original data of the position signal and altitude 52, the pipeline leakage characteristic value 531, the time domain waveform 536, the spectrum diagram 537 and the time-frequency diagram 538 as the characteristic parameters 53, such as Figure 3 shown.
[0063] Step 5: Repeat steps 1 to 4, and update the set threshold by continuously updating the altitude position case library, the characteristic parameter case library, the leakage audio case library, and the noise audio case library to improve the accuracy of pipeline leakage monitoring and early warning.
[0064] This paper establishes a leakage signal early warning model that can target different temperatures, different altitudes 52, different regions, acquisition module voltage 532, temperature 533, and environmental conditions by continuously updating the altitude position case library, characteristic parameter case library, leakage audio case library, and noise audio case library. The leakage signal early warning model is updated, which can significantly improve the early warning accuracy of pipeline leakage monitoring.
[0065] Specifically, the acquisition module 1 collects audio by an optical fiber sensor, collects ambient temperature 534 by a thermistor module, collects device location and altitude 52 by a GPS module, obtains acquisition module temperature 533 and humidity by an SHT31 module, obtains acquisition module voltage 532 by a voltage sensor, and then uploads this information by a 4G network module.
[0066] The algorithm analysis module 2 calculates the number of sampling points N according to the audio sampling rate and acquisition time reported by the acquisition; then calculates the pipeline leakage characteristic value 531 through the pipeline leakage characteristic value 531 (generally, the characteristic value ≥30 is a suspected pipeline leakage signal), and at the same time calculates the time domain waveform 536, the spectrum diagram 537, and the time-frequency diagram 538 data, and associates the calculated pipeline leakage characteristic value 531, time domain waveform 536, spectrum diagram 537, and time-frequency diagram 538 data with the original data obtained and uploaded by the acquisition module 1, and stores them in the data storage module 3.
[0067] The calculation steps of the pipeline leakage characteristic value 531 are as follows: first calculate the level value of the signal x(i)
[0068] Where N represents the total number of sampling points, and i represents the i-th sampling point.
[0069] Then perform Fourier transform on the signal x(i) to obtain the Fourier transform coefficient X(f i ):
[0070]
[0071] Among them, f i represents the i-th discrete frequency point, N represents the total number of sampling points, and j represents the imaginary unit.
[0072] Then calculate the centroid frequency f of the signal c :
[0073]
[0074] Among them, X(f i ) is the Fourier transform coefficient, N represents the number of sampling points;
[0075] Finally, the pipeline leakage characteristic value P can be calculated
[0076] P=log(level*f c / 2000).
[0077] The data storage module 3 stores raw data, altitude position case library, characteristic parameter case library, leakage audio case library and noise audio case library respectively. The visual analysis module 4 presents the above information in the form of a map to preliminarily determine whether it is a false alarm caused by construction or other reasons.
[0078] The present invention calculates the number of sampling points N through the collected audio sampling rate and the collection time, calculates the level value, Fourier transform coefficient, center of gravity frequency and pipeline leakage characteristic value 531 of the signal through the feature recognition algorithm, and at the same time, generates time domain waveform 536, spectrum diagram 537, and time-frequency diagram 538 data. Through the matching and comparison analysis of the pipeline leakage characteristic value 531, the associated data and the altitude position case library, the characteristic parameter case library, the leakage audio case library and the noise audio case library, it is accurately determined whether there is a pipeline leakage, thereby significantly improving the monitoring accuracy of pipeline leakage monitoring and early warning.
[0079] The present invention solves the problem that the sound and vibration signals of pipe network leakage are easily disturbed in complex urban environments, resulting in inaccurate judgment of the leakage location, and the technical problem that the existing platform lacks data analysis and prediction capabilities and cannot deeply mine the value of data through a pipe network leakage monitoring method and platform based on the Internet of Things architecture. It can more accurately judge whether a pipe network leak occurs, and can quickly locate the pipe network leakage location according to different environmental conditions such as altitude, temperature, region, and acquisition module voltage 532.
[0080] Although the specification has been described in detail, it should be understood that various changes, substitutions and modifications may be made without departing from the spirit and scope of the invention as defined by the appended claims. In addition, the specific embodiments described are not intended to limit the scope of the invention, and those of ordinary skill in the art can easily understand based on the present invention that currently existing or later to be developed processes, machines, manufactures, material compositions, means, methods, or steps can perform substantially the same functions as the embodiments of the present invention or obtain substantially the same results. Therefore, the appended claims are intended to include such processes, machines, manufactures, material compositions, means, methods or steps within their scope.
Claims
1. A pipeline leakage monitoring and early warning system, characterized by: The following steps are involved: Step 1: Acquire raw data through the acquisition module, the raw data including the leakage audio signal generated by the real-time monitoring of pipeline leakage by the acquisition module; ambient temperature; altitude; ambient humidity; temperature, voltage, sampling rate, sampling duration and location information of the acquisition module; Step 2: The algorithm analysis module calculates the pipeline leakage characteristic value, time domain waveform, spectrum diagram and time-frequency diagram data from the original data obtained in step 1 through the feature recognition algorithm, and preliminarily determines whether there is a suspected pipeline leakage based on the comparison between the pipeline leakage characteristic value and the set threshold, and associates the pipeline leakage characteristic value with the original data; the steps of the feature recognition algorithm are as follows: Step S1: the algorithm analysis module calculates the number of sampling points N according to the sampling rate and sampling duration; Step S2: Calculate the level value of the leakage audio signal x(i): Where N represents the number of sampling points, and i represents the i-th sampling point; Step S3: Perform Fourier transform on the leakage audio signal x(i) to obtain the Fourier transform coefficient X(f i ): Among them, f i represents the i-th discrete frequency point, N represents the number of sampling points, and j represents the imaginary unit; Step S4: Calculate the centroid frequency f of the leakage audio signal c : Among them, X(f i ) is the Fourier transform coefficient, N represents the total number of sampling points, f i represents the i-th discrete frequency point; Step S5: Calculate pipeline leakage characteristic value P P = log (level * fC / 2000) Step 3: The data storage module stores the pipeline leakage characteristic value, original data, time domain waveform, spectrum diagram and time-frequency diagram after association in step 2; the visualization analysis module presents the associated data stored in the data storage module in the form of a scatter map, and presents the suspected pipeline leakage signal preliminarily determined by the algorithm analysis module on the scatter map in the form of scatter points of different colors; Step 4: The alarm processing module matches and compares the raw data and characteristic values associated in step 3 with the altitude position case library, the characteristic parameter case library, the leakage audio case library and the noise audio case library respectively, and determines whether the suspected pipeline leakage signal has a pipeline leakage; if so, a leakage alarm is issued, and the suspected pipeline leakage signal is output to the leakage audio case library of the data storage module for storage; if not, no alarm is issued, and the suspected pipeline leakage signal is output to the noise audio case library of the data storage module for storage; Step 5: Repeat steps 1 to 4, and update the set threshold by continuously updating the altitude position case library, the characteristic parameter case library, the leakage audio case library, and the noise audio case library to improve the accuracy of pipeline leakage monitoring and early warning.
2. A pipeline leakage monitoring and early warning system as claimed in claim 1, characterized in that: In step 2, the threshold is set to be the pipeline leakage characteristic value ≥ 30.
3. A pipeline leakage monitoring and early warning system according to claim 1, characterized in that: The scatter map format in step 3 is based on the world map, and the location information of the sampling module is used as the scattered location of the pipeline in the scatter map. In addition to the location information, the original data, pipeline leakage characteristic values, time domain waveforms, spectrum diagrams and time-frequency diagram data are used as associated data at each sampling module position.
4. A pipeline leakage monitoring and early warning system according to claim 1, characterized in that: The different color scatter point marking form uses green scatter points to mark the sampling module positions without suspected pipeline leakage signals; and uses red scatter points to mark the sampling module positions with pipeline leakage signals.
5. A pipeline leakage monitoring and early warning system according to claim 1, characterized in that: In step 4, the altitude position case library uses the position signal and altitude in the original data as the horizontal axis and the vertical axis respectively, and uses the original data of the position signal and altitude, the pipeline leakage characteristic value, the time domain waveform, the spectrum diagram and the time-frequency diagram data as characteristic parameters to construct a characteristic parameter case library under different temperatures and position signals.
6. A pipeline leakage monitoring and early warning system according to claim 1, characterized in that: The acquisition module includes an optical fiber sensor for collecting pipeline leakage audio signals, a thermistor module for obtaining ambient temperature, a GPS module for obtaining location information and altitude of the acquisition module, an SHT31 module for obtaining temperature and humidity of the acquisition module, a voltage sensor for obtaining voltage of the acquisition module, and a 4G network module for uploading data.